Lead-acid batteries have been the most widely used energy storage units in
stand-alone photovoltaic (PV) applications. To make a full use of those
batteries and to improve their lifecycle, high performance charger is often
required. The implementation of an advanced charger needs accurate
information on the batteries internal parameters. In this work, we selected
CIEMAT model because of its good performance to deal with the widest
range of lead acid batteries. The performance evaluation of this model is
based on the co-simulation LabVIEW/Multisim. With the intention of
determining the impact of the charging process on batteries, the behaviour of
different internal parameters of the batteries was simulated. During the
charging mode, the value of the current must decrease when the batteries’
state of charge is close to be fully charged.
A robust state of charge estimation for multiple models of lead acid battery ...journalBEEI
An accurate estimation technique of the state of charge (SOC) of batteries is an essential task of the battery management system. The adaptive Kalman filter (AEKF) has been used as an obsever to investigate the SOC estimation effectiveness. Therefore, The SOC is a reflexion of the chemistry of the cell which it is the key parameter for the battery management system. It is very complex to monitor the SOC and control the internal states of the cell. Three battery models are proposed and their state space models have been established, their parameters were identified by applying the least square method. However, the SOC estimation accuracy of the battery depends on the model and the efficiency of the algorithm. In this paper, AEKF technique is presented to estimate the SOC of Lead acid battery. The experimental data is used to identify the parameters of the three models and used to build different open circuit voltage–state of charge (OCV-SOC) functions relationship. The results shows that the SOC estimation based-model which has been built by hight order RC model can effectively limit the error, hence guaranty the accuracy and robustness.
Comparison of one and two time constant models for lithium ion battery IJECEIAES
The fast and accurate modeling topologies are very much essential for power train electrification. The importance of thermal effect is very important in any electrochemical systems and must be considered in battery models because temperature factor has highest importance in transport phenomena and chemical kinetics. The dynamic performance of the lithium ion battery is discussed here and a suitable electrical equivalent circuit is developed to study its response for sudden changes in the output. An effective lithium cell simulation model with thermal dependence is presented in this paper. One series resistor, one voltage source and a single RC block form the proposed equivalent circuit model. The 1 RC and 2 RC Lithium ion battery models are commonly used in the literature are studied and compared. The simulation of Lithium-ion battery 1RC and 2 RC Models are performed by using Matlab/Simulink Software. The simulation results in his paper shows that Lithium-ion battery 1 RC model has more maximum output error of 0.42% than 2 RC Lithium-ion battery model in constant current condition and the maximum output error of 1 RC Lithium-ion battery model is 0.18% more than 2 RC Lithium-ion battery model in UDDS Cycle condition. The simulation results also show that in both simple and complex discharging modes, the error in output is much improved in 2 RC lithium ion battery model when compared to 1 RC Lithium-ion battery model. Thus the paper shows for general applications like in portable electronic design like laptops, Lithium-ion battery 1 RC model is the preferred choice and for automotive and space design applications, Lithium-ion 2 RC model is the preferred choice. In this paper, these simulation results for 1 RC and 2 RC Lithium-ion battery models will be very much useful in the application of practical Lithium-ion battery management systems for electric vehicle applications.
A Nonlinear TSNN Based Model of a Lead Acid BatteryjournalBEEI
The paper studies a nonlinear model based on time series neural network system (TSNN) to improve the highly nonlinear dynamic model of an automotive lead acid cell battery. Artificial neural network (ANN) take into consideration the dynamic behavior of both input-output variables of the battery charge-discharge processes. The ANN works as a benchmark, its inputs include delays and charging/discharging current values. To train our neural network, we performed a pulse discharge on a lead acid battery to collect experimental data. Results are presented and compared with a nonlinear Hammerstein-Wiener model. The ANN and nonlinear autoregressive exogenous model (NARX) models achieved satisfying results.
One of the common methods that developed to predict state of charge is open circuit voltage (OCV) method. The problem which commonly occurs is to find the correction parameter between open circuit voltage and loaded voltage of the battery. In this research, correlation between state of charge measurement at loaded condition of a Panasonic LC-VA1212NA1, which is a valve-regulated lead acid (VRLA) battery, and open circuit voltage had been analyzed. Based on the results of research, correlation between battery’s measured voltage under loaded condition and open circuit voltage could be approached by two linearization area. It caused by K v ’s values tend to increase when measured voltage under loaded condition V M < 11.64 volt. However, K v values would be relatively stable for every V M ≥ 11.64 volts. Therefore, estimated state of charge value, in respect to loaded battery voltage, would increase slower on V M < 11.64 volts and faster on other range.
Electrical battery modeling for applications in wireless sensor networks and ...journalBEEI
Modeling the behavior of the battery is non-trivial. Nevertheless, an accurate battery model is required in the design and testing of systems such wireless sensor network (WSN) and internet of things (IoT). This paper presents the one resistive-capacitance (1RC) battery model with simple parameterization technique for nickel metal hydride (NiMH). This model offers a good trade-off between accuracy and parameterization effort. The model’s parameters are extracted through the pulse measurement technique and implemented in a physical and dynamic simulator. Finally, the performance of the model is validated with the real-life NiMH battery by applying current pulses and real wireless sensor node current profiles. The results of the voltage response obtained from both the model and experiments showed excellent accuracy, with difference of less than 2%.
A robust state of charge estimation for multiple models of lead acid battery ...journalBEEI
An accurate estimation technique of the state of charge (SOC) of batteries is an essential task of the battery management system. The adaptive Kalman filter (AEKF) has been used as an obsever to investigate the SOC estimation effectiveness. Therefore, The SOC is a reflexion of the chemistry of the cell which it is the key parameter for the battery management system. It is very complex to monitor the SOC and control the internal states of the cell. Three battery models are proposed and their state space models have been established, their parameters were identified by applying the least square method. However, the SOC estimation accuracy of the battery depends on the model and the efficiency of the algorithm. In this paper, AEKF technique is presented to estimate the SOC of Lead acid battery. The experimental data is used to identify the parameters of the three models and used to build different open circuit voltage–state of charge (OCV-SOC) functions relationship. The results shows that the SOC estimation based-model which has been built by hight order RC model can effectively limit the error, hence guaranty the accuracy and robustness.
Comparison of one and two time constant models for lithium ion battery IJECEIAES
The fast and accurate modeling topologies are very much essential for power train electrification. The importance of thermal effect is very important in any electrochemical systems and must be considered in battery models because temperature factor has highest importance in transport phenomena and chemical kinetics. The dynamic performance of the lithium ion battery is discussed here and a suitable electrical equivalent circuit is developed to study its response for sudden changes in the output. An effective lithium cell simulation model with thermal dependence is presented in this paper. One series resistor, one voltage source and a single RC block form the proposed equivalent circuit model. The 1 RC and 2 RC Lithium ion battery models are commonly used in the literature are studied and compared. The simulation of Lithium-ion battery 1RC and 2 RC Models are performed by using Matlab/Simulink Software. The simulation results in his paper shows that Lithium-ion battery 1 RC model has more maximum output error of 0.42% than 2 RC Lithium-ion battery model in constant current condition and the maximum output error of 1 RC Lithium-ion battery model is 0.18% more than 2 RC Lithium-ion battery model in UDDS Cycle condition. The simulation results also show that in both simple and complex discharging modes, the error in output is much improved in 2 RC lithium ion battery model when compared to 1 RC Lithium-ion battery model. Thus the paper shows for general applications like in portable electronic design like laptops, Lithium-ion battery 1 RC model is the preferred choice and for automotive and space design applications, Lithium-ion 2 RC model is the preferred choice. In this paper, these simulation results for 1 RC and 2 RC Lithium-ion battery models will be very much useful in the application of practical Lithium-ion battery management systems for electric vehicle applications.
A Nonlinear TSNN Based Model of a Lead Acid BatteryjournalBEEI
The paper studies a nonlinear model based on time series neural network system (TSNN) to improve the highly nonlinear dynamic model of an automotive lead acid cell battery. Artificial neural network (ANN) take into consideration the dynamic behavior of both input-output variables of the battery charge-discharge processes. The ANN works as a benchmark, its inputs include delays and charging/discharging current values. To train our neural network, we performed a pulse discharge on a lead acid battery to collect experimental data. Results are presented and compared with a nonlinear Hammerstein-Wiener model. The ANN and nonlinear autoregressive exogenous model (NARX) models achieved satisfying results.
One of the common methods that developed to predict state of charge is open circuit voltage (OCV) method. The problem which commonly occurs is to find the correction parameter between open circuit voltage and loaded voltage of the battery. In this research, correlation between state of charge measurement at loaded condition of a Panasonic LC-VA1212NA1, which is a valve-regulated lead acid (VRLA) battery, and open circuit voltage had been analyzed. Based on the results of research, correlation between battery’s measured voltage under loaded condition and open circuit voltage could be approached by two linearization area. It caused by K v ’s values tend to increase when measured voltage under loaded condition V M < 11.64 volt. However, K v values would be relatively stable for every V M ≥ 11.64 volts. Therefore, estimated state of charge value, in respect to loaded battery voltage, would increase slower on V M < 11.64 volts and faster on other range.
Electrical battery modeling for applications in wireless sensor networks and ...journalBEEI
Modeling the behavior of the battery is non-trivial. Nevertheless, an accurate battery model is required in the design and testing of systems such wireless sensor network (WSN) and internet of things (IoT). This paper presents the one resistive-capacitance (1RC) battery model with simple parameterization technique for nickel metal hydride (NiMH). This model offers a good trade-off between accuracy and parameterization effort. The model’s parameters are extracted through the pulse measurement technique and implemented in a physical and dynamic simulator. Finally, the performance of the model is validated with the real-life NiMH battery by applying current pulses and real wireless sensor node current profiles. The results of the voltage response obtained from both the model and experiments showed excellent accuracy, with difference of less than 2%.
Prediction of li ion battery discharge characteristics at different temperatu...eSAT Journals
Abstract State of charge (SOC) is an important battery parameter which provides a good indication of the useful capacity that can be derived out of a battery system at any given point of time. Li-ion has become state of the art technology for commercial and aerospace applications due to the various advantages that they offer. For spacecrafts requiring long lifetime, SOC estimation is crucial for on-orbit as well as offline data analysis. On-orbit estimation of SOC should be carefully addressed, as this provides information on survivability of battery and also serves as input to Battery Management System (BMS). In addition, detailed offline data analysis of battery electrical characteristics, which indicate the SOC-Voltage relationship is important to assess the performance of the battery under various mission scenarios at both Beginning of life (BOL) and End of Life (EOL) of a spacecraft system. In this work, a hybrid SOC estimation method, incorporating coulomb counting and Unscented Kalman Filter (UKF) is used, to predict the BOL discharge behaviour of an 18650 commercial Li-ion cell at different temperatures and discharge rates. The experimental results are encouraging and the approach gives a prediction error of less than 10%. The study will serve as basis for life assessment of Li-ion cells and batteries used for GEO and LEO missions. Key Words: Li-ion, State of Charge, Unscented Kalman Filter etc…
Solar Module Modeling, Simulation And Validation Under Matlab / SimulinkIJERA Editor
Solar modules are systems which convert sunlight into electricity using the physics of semiconductors. Mathematical modeling of these systems uses weather data such as irradiance and temperature as inputs. It provides the current, voltage or power as outputs, which allows plot the characteristic giving the intensity I as a function of voltage V for photovoltaic cells. In this work, we have developed a model for a diode of a Photovoltaic module under the Matlab / Simulink environment. From this model, we have plotted the characteristic curves I-V and P-V of solar cell for different values of temperature and sunlight. The validation has been done by comparing the experimental curve with power from a solar panel HORONYA 20W type with that obtained by the model.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An electric circuit model for a lithium-ion battery cell based on automotive ...IJECEIAES
The on-board energy storage system plays a key role in electric vehicles since it directly affects their performance and autonomy. The lithium-ion battery offers satisfactory characteristics that make electric vehicles competitive with conventional ones. This article focuses on modeling and estimating the parameters of the lithium-ion battery cell when used in different electric vehicle drive cycles and styles. The model consists of an equivalent electrical circuit based on a second-order Thevenin model. To identify the parameters of the model, two algorithms were tested: TrustRegion-Reflective and Levenberg-Marquardt. To account for the dynamic behavior of the battery cell in an electric vehicle, this identification is based on measurement data that represents the actual use of the battery in different conditions and driving styles. Finally, the model is validated by comparing simulation results to measurements using the mean square error (MSE) as model performance criteria for the driving cycles (UDDS, LA-92, US06, neural network (NN), and HWFET). The results demonstrate interesting performance mostly for the driving cycles (UDDS and LA-92). This confirms that the model developed is the best solution to be integrated in a battery management system of an electric vehicle.
Optimization of EDM Process Parameters using Response Surface Methodology for...ijtsrd
The present work demonstrates the optimization process of material removal rate MRR of electrical discharge machining EDM by RSM Response Surface Methodology . The work piece material was EN31 tool steel. The pulse on time, pulse off time, pulse current and voltage were the control parameters of EDM. RSM method was used to design the experiment using rotatable central composite design as this is the most widely used experimental design for modeling a second–order response surface. The process has been successfully modeled using response surface methodology RSM and model adequacy checking is also carried out using Minitab software. The second order response models have been validated with analysis of variance. Finally, an attempt has been made to estimate the optimum machining conditions to produce the best possible responses within the experimental constraints. Dr. N. Mahesh Kumar | Mr. P. Chinna Rao ""Optimization of EDM Process Parameters using Response Surface Methodology for AISI D3 Steel"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23535.pdf
Paper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/23535/optimization-of-edm-process-parameters-using-response-surface-methodology-for-aisi-d3-steel/dr-n-mahesh-kumar
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Microcontroller based bidirectional buck–boost converter for photo-voltaic po...Springer
A common configuration for a stand-alone PV power system may consist of three converters: a buck converter for the PV panel
to charge the battery, a boost converter for the battery to discharge to the load and one for the load voltage regulation. Such a system
requires a coordinated control scheme for three converters which can be complicated. A simple structure for a stand-alone PV plant
consists of a PV array, a battery unit, and its associated bidirectional converter which is a combination of a buck and boost converter.
When controlled properly the system can provide uninterrupted power to the load, despite the intermittent availability of sunlight. In
this paper complete design of the converter is carried out and the simulation has been performed using Psim. From the simulation,
the graphs are presented to show the converter working in buck mode and boost mode. Controller is designed to take care of mode
transition, buck to boost and boost to buck mode automatically based on source voltage. Hardware implementation has been done
using microcontroller (8051).
Photovoltaic System with SEPIC Converter Controlled by the Fuzzy LogicIAES-IJPEDS
In this work, a fuzzy logic controller is used to control the output voltage of a
photovoltaic system with a DC-DC converter; type Single Ended Primary
Inductor Converter (SEPIC). The system is designed for 210 W solar
photovoltaic (SCHOTT 210) panel and to feed an average demand of 78 W.
This system includes solar panels, SEPIC converter and fuzzy logic
controller. The SEPIC converter provides a constant DC bus voltage and its
duty cycle controlled by the fuzzy logic controller which is needed to
improve PV panel’s utilization efficiency. A fuzzy logic controller (FLC) is
also used to generate the PWM signal for the SEPIC converter.
This paper proposes a non-isolated three port SEPIC converter for stand-alone photovoltaic applications. The proposed topology uses the Single Input Multi Output (SIMO) structure. This topology consists of a single photovoltaic source as input and it is a unidirectional power converter. Mathematical analysis for the proposed system is performed and simulations are carried out using MATLAB/Simulink. The design parameters of capacitors and inductors are calculated from small ripple analysis. The simulation analysis for the proposed open loop topology is verified using a real time hardware setup.The entire process is carried out in Continuous Current Mode (CCM) of operation. The experimental results for hardware are verified with simulations and compared.
Modeling and validation of lithium-ion battery with initial state of charge ...nooriasukmaningtyas
The modeling of lithium-ion battery is an important element to the management of batteries in industrial applications. Various models have been studied and investigated, ranging from simple to complex. The second-order equivalent circuit model was studied and investigated since the dynamic behavior of the battery is fully characterized. The simulation model was built in Matlab Simulink using the Kirchhoff Laws principle in mathematical equations, while the battery's internal parameters were identified by using the BTS4000 (battery tester) device. To estimate the full state of charge (SOC), the initial state of charge (SOC0) must be identified or measured. Hence, this paper seeks for the SOC estimation by using experimental terminal voltage data and SOC with Matlab lookup table. Then, the simulated terminal voltage, as well as the SOC of the battery are compared and validated against measured data. The maximum relative error of 0.015V and 2% for terminal voltage and SOC respectively shows that the proposed model is accurate and relevant based on the error analysis.
Prediction of li ion battery discharge characteristics at different temperatu...eSAT Journals
Abstract State of charge (SOC) is an important battery parameter which provides a good indication of the useful capacity that can be derived out of a battery system at any given point of time. Li-ion has become state of the art technology for commercial and aerospace applications due to the various advantages that they offer. For spacecrafts requiring long lifetime, SOC estimation is crucial for on-orbit as well as offline data analysis. On-orbit estimation of SOC should be carefully addressed, as this provides information on survivability of battery and also serves as input to Battery Management System (BMS). In addition, detailed offline data analysis of battery electrical characteristics, which indicate the SOC-Voltage relationship is important to assess the performance of the battery under various mission scenarios at both Beginning of life (BOL) and End of Life (EOL) of a spacecraft system. In this work, a hybrid SOC estimation method, incorporating coulomb counting and Unscented Kalman Filter (UKF) is used, to predict the BOL discharge behaviour of an 18650 commercial Li-ion cell at different temperatures and discharge rates. The experimental results are encouraging and the approach gives a prediction error of less than 10%. The study will serve as basis for life assessment of Li-ion cells and batteries used for GEO and LEO missions. Key Words: Li-ion, State of Charge, Unscented Kalman Filter etc…
Solar Module Modeling, Simulation And Validation Under Matlab / SimulinkIJERA Editor
Solar modules are systems which convert sunlight into electricity using the physics of semiconductors. Mathematical modeling of these systems uses weather data such as irradiance and temperature as inputs. It provides the current, voltage or power as outputs, which allows plot the characteristic giving the intensity I as a function of voltage V for photovoltaic cells. In this work, we have developed a model for a diode of a Photovoltaic module under the Matlab / Simulink environment. From this model, we have plotted the characteristic curves I-V and P-V of solar cell for different values of temperature and sunlight. The validation has been done by comparing the experimental curve with power from a solar panel HORONYA 20W type with that obtained by the model.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An electric circuit model for a lithium-ion battery cell based on automotive ...IJECEIAES
The on-board energy storage system plays a key role in electric vehicles since it directly affects their performance and autonomy. The lithium-ion battery offers satisfactory characteristics that make electric vehicles competitive with conventional ones. This article focuses on modeling and estimating the parameters of the lithium-ion battery cell when used in different electric vehicle drive cycles and styles. The model consists of an equivalent electrical circuit based on a second-order Thevenin model. To identify the parameters of the model, two algorithms were tested: TrustRegion-Reflective and Levenberg-Marquardt. To account for the dynamic behavior of the battery cell in an electric vehicle, this identification is based on measurement data that represents the actual use of the battery in different conditions and driving styles. Finally, the model is validated by comparing simulation results to measurements using the mean square error (MSE) as model performance criteria for the driving cycles (UDDS, LA-92, US06, neural network (NN), and HWFET). The results demonstrate interesting performance mostly for the driving cycles (UDDS and LA-92). This confirms that the model developed is the best solution to be integrated in a battery management system of an electric vehicle.
Optimization of EDM Process Parameters using Response Surface Methodology for...ijtsrd
The present work demonstrates the optimization process of material removal rate MRR of electrical discharge machining EDM by RSM Response Surface Methodology . The work piece material was EN31 tool steel. The pulse on time, pulse off time, pulse current and voltage were the control parameters of EDM. RSM method was used to design the experiment using rotatable central composite design as this is the most widely used experimental design for modeling a second–order response surface. The process has been successfully modeled using response surface methodology RSM and model adequacy checking is also carried out using Minitab software. The second order response models have been validated with analysis of variance. Finally, an attempt has been made to estimate the optimum machining conditions to produce the best possible responses within the experimental constraints. Dr. N. Mahesh Kumar | Mr. P. Chinna Rao ""Optimization of EDM Process Parameters using Response Surface Methodology for AISI D3 Steel"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23535.pdf
Paper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/23535/optimization-of-edm-process-parameters-using-response-surface-methodology-for-aisi-d3-steel/dr-n-mahesh-kumar
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Microcontroller based bidirectional buck–boost converter for photo-voltaic po...Springer
A common configuration for a stand-alone PV power system may consist of three converters: a buck converter for the PV panel
to charge the battery, a boost converter for the battery to discharge to the load and one for the load voltage regulation. Such a system
requires a coordinated control scheme for three converters which can be complicated. A simple structure for a stand-alone PV plant
consists of a PV array, a battery unit, and its associated bidirectional converter which is a combination of a buck and boost converter.
When controlled properly the system can provide uninterrupted power to the load, despite the intermittent availability of sunlight. In
this paper complete design of the converter is carried out and the simulation has been performed using Psim. From the simulation,
the graphs are presented to show the converter working in buck mode and boost mode. Controller is designed to take care of mode
transition, buck to boost and boost to buck mode automatically based on source voltage. Hardware implementation has been done
using microcontroller (8051).
Photovoltaic System with SEPIC Converter Controlled by the Fuzzy LogicIAES-IJPEDS
In this work, a fuzzy logic controller is used to control the output voltage of a
photovoltaic system with a DC-DC converter; type Single Ended Primary
Inductor Converter (SEPIC). The system is designed for 210 W solar
photovoltaic (SCHOTT 210) panel and to feed an average demand of 78 W.
This system includes solar panels, SEPIC converter and fuzzy logic
controller. The SEPIC converter provides a constant DC bus voltage and its
duty cycle controlled by the fuzzy logic controller which is needed to
improve PV panel’s utilization efficiency. A fuzzy logic controller (FLC) is
also used to generate the PWM signal for the SEPIC converter.
This paper proposes a non-isolated three port SEPIC converter for stand-alone photovoltaic applications. The proposed topology uses the Single Input Multi Output (SIMO) structure. This topology consists of a single photovoltaic source as input and it is a unidirectional power converter. Mathematical analysis for the proposed system is performed and simulations are carried out using MATLAB/Simulink. The design parameters of capacitors and inductors are calculated from small ripple analysis. The simulation analysis for the proposed open loop topology is verified using a real time hardware setup.The entire process is carried out in Continuous Current Mode (CCM) of operation. The experimental results for hardware are verified with simulations and compared.
Modeling and validation of lithium-ion battery with initial state of charge ...nooriasukmaningtyas
The modeling of lithium-ion battery is an important element to the management of batteries in industrial applications. Various models have been studied and investigated, ranging from simple to complex. The second-order equivalent circuit model was studied and investigated since the dynamic behavior of the battery is fully characterized. The simulation model was built in Matlab Simulink using the Kirchhoff Laws principle in mathematical equations, while the battery's internal parameters were identified by using the BTS4000 (battery tester) device. To estimate the full state of charge (SOC), the initial state of charge (SOC0) must be identified or measured. Hence, this paper seeks for the SOC estimation by using experimental terminal voltage data and SOC with Matlab lookup table. Then, the simulated terminal voltage, as well as the SOC of the battery are compared and validated against measured data. The maximum relative error of 0.015V and 2% for terminal voltage and SOC respectively shows that the proposed model is accurate and relevant based on the error analysis.
State of charge estimation for lithium-ion batteries connected in series usi...IJECEIAES
This paper proposes a method to estimate state of charge (SoC) for Lithiumion battery pack (LIB) with 𝑁 series-connected cells. The cell’s model is represented by a second-order equivalent circuit model taking into account the measurement disturbances and the current sensor bias. By using two sigma point Kalman filters (SPKF), the SoC of cells in the pack is calculated by the sum of the pack’s average SoC estimated by the first SPKF and SoC differences estimated by the second SPKF. The advantage of this method is the SoC estimation algorithm performed only two times instead of 𝑁 times in each sampling time interval, so the computational burden is reduced. The test of the proposed SoC estimation algorithm for 7 samsung ICR18650 Lithium-ion battery cells connected in series is implemented in the continuous charge and discharge scenario in one hour time. The estimated SoCs of the cells in the pack are quite accurate, the 3-sigma criterion of estimated SoC error distributions is 0.5%.
Machine Learning Systems Based on xgBoost and MLP Neural Network Applied in S...aciijournal
In this work, the internal impedance of the lithium-ion battery pack (important measure
of the degradation level of the batteries) is estimated by means of machine learning systems
based on supervised learning techniques MLP - Multi Layer Perceptron - neural network and xgBoost
- Gradient Tree Boosting. Therefore, characteristics of the electric power system, in which
the battery pack is inserted, are extracted and used in the construction of supervised models
through the application of two different techniques based on Gradient Tree Boosting and Multi
Layer Perceptron neural network. Finally, with the application of statistical validation techniques,
the accuracy of both models are calculated and used for the comparison between them and the
feasibility analysis regarding the use of such models in real systems.
Machine learning systems based on xgBoost and MLP neural network applied in s...aciijournal
In this work, the internal impedance of the lithium-ion battery pack (important measure of the degradation level of the batteries) is estimated by means of machine learning systems based on supervised learning techniques MLP - Multi Layer Perceptron - neural network and xgBoost - Gradient Tree Boosting. Therefore, characteristics of the electric power system, in which the battery pack is inserted, are extracted and used in the construction of supervised models through the application of two different techniques based on Gradient Tree Boosting and Multi Layer Perceptron neural network. Finally, with the application of statistical validation techniques, the accuracy of both models are calculated and used for the comparison between them and the feasibility analysis regarding the use of such models in real systems.
Machine Learning Systems Based on xgBoost and MLP Neural Network Applied in S...aciijournal
In this work, the internal impedance of the lithium-ion battery pack (important measure of the degradation level of the batteries) is estimated by means of machine learning systems based on supervised learning techniques MLP - Multi Layer Perceptron - neural network and xgBoost - Gradient Tree Boosting. Therefore, characteristics of the electric power system, in which the battery pack is inserted, are extracted and used in the construction of supervised models through the application of two different techniques based on Gradient Tree Boosting and Multi Layer Perceptron neural network. Finally, with the application of statistical validation techniques, the accuracy of both models are calculated and used for the comparison between them and the feasibility analysis regarding the use of such models in real systems
Machine learning systems based on xgBoost and MLP neural network applied in s...aciijournal
. In this work, the internal impedance of the lithium-ion battery pack (important measure of the degradation level of the batteries) is estimated by means of machine learning systems
based on supervised learning techniques MLP - Multi Layer Perceptron - neural network and xgBoost - Gradient Tree Boosting. Therefore, characteristics of the electric power system, in which
the battery pack is inserted, are extracted and used in the construction of supervised models
through the application of two different techniques based on Gradient Tree Boosting and Multi
Layer Perceptron neural network. Finally, with the application of statistical validation techniques,
the accuracy of both models are calculated and used for the comparison between them and the feasibility analysis regarding the use of such models in real systems.
Machine Learning Systems Based on xgBoost and MLP Neural Network Applied in S...aciijournal
In this work, the internal impedance of the lithium-ion battery pack (important measure of the degradation level of the batteries) is estimated by means of machine learning systems
based on supervised learning techniques MLP - Multi Layer Perceptron - neural network and xgBoost - Gradient Tree Boosting. Therefore, characteristics of the electric power system, in which
the battery pack is inserted, are extracted and used in the construction of supervised models
through the application of two different techniques based on Gradient Tree Boosting and Multi
Layer Perceptron neural network. Finally, with the application of statistical validation techniques,
the accuracy of both models are calculated and used for the comparison between them and the
feasibility analysis regarding the use of such models in real systems.
A Review of Hybrid Battery Management System (H-BMS) for EVTELKOMNIKA JOURNAL
Significant to a major pollution contributor in passenger vehicles, electric vehicles are more
acceptable to use on the road. Electric Vehicles (EVs) burn energy based on the usage of the battery. The
usage of the battery in EVs is monitored and controlled by Battery Management System (BMS). A few
factors monitor and control Battery Management System (BMS). This paper reviewed the battery charging
technology and Remote Terminal Unit (RTU) development as a Hybrid Battery Management System (HBMS)
for Electric Vehicle (EV).
Modeling and Simulation of Solar Photovoltaic module using Matlab/SimulinkIOSR Journals
Abstract: This paper presents the circuit model of photovoltaic (PV) module. Simulation and modeling is done
using MATLAB/ Simulink software package. The proposed model is user friendly and can be used as a common
platform for both applied physics scientist and power electronics engineers. A detailed modeling procedure is
presented. The designed model is verified by using STP255-20/Wd PV module. The IV and PV characteristics
are simulated at different temperature and irradiance conditions and the results are verified. The proposed
model is very simple fast and accurate. The designed model can be used for analysis of PV characteristics and
for simulation of maximum power point tracking algorithms
This paper demonstrates a mathematical representation of Photovoltaic (PV) solar cells and hence panels performance. One-diode solar cell model is implemented to simulate the cell and extract the performance indications. The tested PV modules are BP Solar (60 Watt) and Synthesis Power (50 Watts), which are operating in a PV generation system in the University of Anbar - Iraq, College of Applied Sciences. The math model demonstrates Power versus Voltage (P-V) characteristic curves to depict and study various parameters with affecting variations in the PV array performance. The parameters include ambient and cell temperature degrees and solar irradiance (G) level which are the main elements to dictate the productivity of a solar system. G is represented by sun unit (1 sun=1 kW/m2). The outcomes of the simulation model characteristics curves have been compared with curves provided by the tested modules data sheets. MATLAB software has been used to simulate the model and extract the results. This paper also investigated photovoltaic simulation with maximum power point tracking (MPPT) converter to evaluate hence predict the behaviors of the whole photovoltaic DC current generation using PSIM Power Electronics program. The model focuses on the basic components in PV systems; The panel and the DC-DC converter. The modeling outcome data will be used as a reference verifying the performance of the tested modules during the year seasons under the dominating dusty hot weather in western Iraq.
Electric Vehicles Battery Charging by Estimating SOC using Modified Coulomb C...ijtsrd
Quick and effective battery charging is critical for battery powered vehicles. This paper describes a multilevel charging technique for Li ion batteries used in electric vehicle applications. Instead of a single constant current level, five constant current levels are used to quickly charge the battery. A DC DC converter is used as a current source in the charging circuit for safe and efficient charging. The precise calculation of state of charge SoC is used as an input to enforce the above optimal battery charging technique. The SoC is calculated using a hybrid method that incorporates both the Open Circuit Voltage OCV and Coulomb integral methods. To estimate battery parameters, the Simulink Design Optimization SDO tool is used. The simulations are performed using MATLAB. The difference between the inbuilt battery SoC estimation method and the updated coulomb counting system in terms of SoC estimation is less than 2 . A 3.7 V, 1.1 Ah Li ion battery was used for all of the tests. A. Srilatha | A. Pandian "Electric Vehicles Battery Charging by Estimating SOC using Modified Coulomb Counting" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49153.pdf Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/49153/electric-vehicles-battery-charging-by-estimating-soc-using-modified-coulomb-counting/a-srilatha
Inter-Area Oscillation Damping using an STATCOM Based Hybrid Shunt Compensati...IJPEDS-IAES
FACTS devices are one of the latest technologies which have been used to
improve power system dynamic and stability during recent years. However,
widespread adoption of this technology has been hampered by high cost
and reliability concerns. In this paper an economical phase imbalanced shunt
reactive compensation concept has been introduced and its ability for power
system dynamic enhancement and inter-area oscillation damping are
investigated. A hybrid phase imbalanced scheme is a shunt capacitive
compensation scheme, where two phases are compensated by fixed shunt
capacitor (C) and the third phase is compensated by a Static Synchronous
Compensator (STATCOM) in shunt with a fixed capacitor (CC). The power
system dynamic stability enhancement would be achieved by adding
a conventional Wide Area Damping Controller (WADC) to the main control
loop of the single phase STATCOM. Two different control methodologies
are proposed: a non-optimized conventional damping controller
and a conventional damping controller with optomised parameters that are
added to the main control loop of the unbalanced compensator in order to
damp the inter area oscillations. The proposed arrangement would, certainly,
be economically attractive when compared with a full three-phase
STATCOM. The proposed scheme is prosperously applied in a 13-bus
six-machine test system and various case studies are conducted to
demonstrate its ability in damping inter-area oscillations and power system
dynamic enhancement.
Fuzzy Gain-Scheduling Proportional–Integral Control for Improving the Speed B...IJPEDS-IAES
In this article, we have set up a vector control law of induction machine
where we tried different type of speed controllers. Our control strategy is of
type Field Orientated Control (FOC). In this structure we designed a Fuzzy
Gain-Scheduling Proportional–Integral (Pi) controller to obtain best result
regarding the speed of induction machine. At the beginning we designed a Pi
controller with fixed parameters. We came up to these parameters by
identifying the transfer function of this controller to that of Broïda (second
order transfer function). Then we designed a fuzzy logic (FL) controller.
Based on simulation results, we highlight the performances of each
controller. To improve the speed behaviour of the induction machine, we
have designend a controller called “Fuzzy Gain-Scheduling Proportional–
Integral controller” (FGS-PI controller) which inherited the pros of the
aforementioned controllers. The simulation result of this controller will
strengthen its performances.
Advance Technology in Application of Four Leg Inverters to UPQCIJPEDS-IAES
This article presents a novel application of four leg inverter with
conventional Sinusoidal Pulse Width Modulation (SPWM) Scheme to
Unified Power Quality Conditioner (UPQC). The Power Quality problem
became burning issues since the starting of high voltage AC transmission
system. Hence, in this article it has been discussed to mitigate the PQ issues
in high voltage AC systems through a three phase Unified Power Quality
Conditioner (UPQC) under various conditions, such as harmonic mitigation
scheme, non linear loads, sag and swell conditions as well. Also, it proposes
to control harmoincs with various artificial intelligent techniques. Thus
application of these control technique such as Neural Networks (ANN)
Fuzzy Logic makes the system performance in par with the standards
and also compared with existing system. The simulation results based on
MATLAB/Simulink are discussed in detail to support the concept developed
in the paper.
Modified SVPWM Algorithm for 3-Level Inverter Fed DTC Induction Motor DriveIJPEDS-IAES
In this paper, a modified space vector pulse width modulation (MSVPWM)
algorithm is developed for 3-level inverter fed direct torque controlled
induction motor drive (DTC-IMD). MSVPWM algorithm simplifies
conventional space vector pulse width modulation (CSVPWM) algorithm for
multilevel inverter (MLI), whose complexity lies in sector/subsector/subsubsector
identification; which will commensurate with number of levels. In
the proposed algorithm sectors are identified as in two level inverter
and subsectors/sub-subsectors are identified by shifting the original reference
vector to sector 1 (S1). This is valid due to the fact that a three level space
vector plane is a composition of six two level space planes, and are
symmetrical with reference to six pivot states. Switching state/sequence
selection is also very important while dealing with SVPWM strategy for
MLI. In the proposed algorithm out of 27 available switching states apt
switching state is selected based on sector and subsector number, such that
voltage ripple is considerably less. To validate the proposed algorithm, it is
tested on a three level neutral point clamped (NPC) inverter fed DTC-IMD.
The performance of the MSVPWM algorithm is analyzed by comparing no
load stator current ripple of the three level DTC-IMD with two level
DTC-IMD. Significant reduction in steady state torque and flux ripple is
observed. Hence, reduced acoustic noise is a distinctive facet of the proposed
method.
Modelling of a 3-Phase Induction Motor under Open-Phase Fault Using Matlab/Si...IJPEDS-IAES
The d-q model of Induction Motors (IMs) has been effectively used as an
efficient method to analyze the performance of the induction machines. This
study presents a step by step Matlab/Simulink implementation
of a star-connected 3-phase IM under open-phase fault (faulty 3-phase IM)
using d-q model. The presented technique in this paper can be simply
implemented in one block and can be made available for control purposes.
The simulated results provide to show the behavior of the star-connected 3-phase IM under open-phase fault condition.
Performance Characteristics of Induction Motor with FielIJPEDS-IAES
With development of power electronics and control Theories, the AC motor
control becomes easier. So the AC motors are used instead of the DC motor
in the drive applications. With this development, a several methods of control
are invented. The field oriented control and direct torque control are from the
best methods to control the drive systems. This paper is compared between
the field oriented control and direct torque control to show the advantages
and disadvantages of these methods of controls. This study discussed the
effects of these methods of control on the total harmonic distortion of the
current and torque ripples. This occurs through study the performance
characteristics of the AC motor. The motor used in this study is an induction
motor. This study is simulated through the MATLAB program.
A Novel Modified Turn-on Angle Control Scheme for Torque- Ripple Reduction in...IJPEDS-IAES
In recent years, Switched Reluctance Motors (SRM) have been dramatically
considered with both researchers and industries. SRMs not only have a
simple and reliable structure, but also have low cost production process.
However, discrete torque production of SRM along with intensive magnetic
saturation in stator and rotor cores are the major drawbacks of utilizing in
variety of industrial applications and also causes the inappropriate torque
ripples. In this paper, a modified logical-rule-based Torque Sharing Function
(TSF) method is proposed considering turn-on angle control. The optimized
turn-on angle for conducting each phase is achieved by estimating the
inductance curve in the vicinity of unaligned position and based on an
analytical solution for each phase voltage equation. Simulation results on a
four-phase switched reluctance motor and comparison with the conventional
methods validates the effectiveness of the proposed method.
Modeling and Simulation of Induction Motor based on Finite Element AnalysisIJPEDS-IAES
This paper presents the development of a co-simulation platform of induction
motor (IM). For the simulation, a coupled model is introduced which
contains the control, the power electronics and also the induction machine.
Each of these components is simulated in different software environments.
So, this study provides an advanced modeling and simulation tools for IM
which integrate all the components into one common simulation platform
environment. In this work, the IM is created using Ansys-Maxwell based on
Finite Element Analysis (FEA), whereas the power electronic converter is
developed in Ansys-Simplorer and the control scheme is build in MATLABSimulink
environment. Such structure can be useful for accurate design
and allows coupling analysis for more realistic simulation. This platform is
exploited to analyze the system models with faults caused by failures of
different drive’s components. Here, two studies cases are presented: the first
is the effects of a faulty device of the PWM inverter, and the second case is
the influence of the short circuit of two stator phases. In order to study the
performance of the control drive of the IM under fault conditions,
a co-simulation of the global dynamic model has been proposed to analyze
the IM behavior and control drives. In this work, the co-simulation has been
performed; furthermore the simulation results of scalar control allowed
verifying the precision of the proposed FEM platform.
Comparative Performance Study for Closed Loop Operation of an Adjustable Spee...IJPEDS-IAES
In this paper an extensive comparative study is carried out between PI
and PID controlled closed loop model of an adjustable speed Permanent
Magnet Synchronous Motor (PMSM) drive. The incorporation of Sinusoidal
Pulse Width Modulation (SPWM) strategy establishes near sinusoidal
armature phase currents and comparatively less torque ripples without
sacrificing torque/weight ratio. In this closed loop model of PMSM drive, the
information about reference speed is provided to a speed controller, to ensure
that actual drive speed tracks the reference speed with ideally zero steady
state speed error. The entire model of PMSM closed loop drive is divided
into two loops, inner loop current and outer loop speed. By taking the
different combinations of two classical controllers (PI & PID) related with
two loop control structure, different approximations are carried out. Hence a
typical comparative study is introduced to familiar with the different
performance indices of the system corresponding to time domain and
frequency domain specifications. Therefore overall performance of closed
loop PMSM drive is tested and effectiveness of controllers will be
determined for different combinations.
Novel Discrete Components Based Speed Controller for Induction MotorIJPEDS-IAES
This paper presents an electronic design based on general purpose discrete
components for speed control of a single phase induction motor drive. The
MOSFETs inverter switching is controlled using Sampled Sinusoidal Pulse
Width Modulation (SPWM) techniques with V/F method based on Voltage
Controlled Oscillator (VCO). The load power is also controlled by a novel
design to produce a suitable SPWM pulse. The proposed electronic system
has ability to control the output frequency with flexible setting of lower limit
to less than 1 Hz and to higher frequency limits to 55 Hz. Moreover, the
proposed controller able to control the value of load voltage to frequency
ratio, which plays a major parameter in the function of IM speed control.
Furthermore, the designed system is characterized by easy manufacturing
and maintenance, high speed response, low cost, and does not need to
program steps as compared to other systems based on Microcontroller
and digital signal processor (DSP) units. The complete proposed electronic
design is made by the software of NI Multisim version 11.0 and all the
internal sub-designs are shown in this paper. Simulation results show the
effectiveness of electronic design for a promising of a high performance IM
PWM drive.
Sensorless Control of a Fault Tolerant PMSM Drives in Case of Single-Phase Op...IJPEDS-IAES
This paper introduces a sensorless-speed-controlled PMSM motor fed by a
four-leg inverter in case of a single phase open circuit fault regardless in
which phase is the fault. To minimize the system performance degradation
due to a single phase open circuit fault, a fault tolerant control strategy that
includes taking appropriate actions to control the two remaining healthy
currents is used in addition to use the fourth leg of the inverter. Tracking the
saliency is done through measuring the dynamic current responses of the
healthy phases of the PMSM motor due the IGBT switching actions using the
fundamental PWM method without introducing any modification to the
operation of the fourth leg of the inverter. Simulation results are provided to
verify the effectiveness of the proposed strategy for sensorless controlling of
a PMSM motor driven by a fault-tolerant four-phase inverter over a wide
speed ranges under the case of a single phase open circuit.
Improved Stator Flux Estimation for Direct Torque Control of Induction Motor ...IJPEDS-IAES
Stator flux estimation using voltage model is basically the integration of the
induced stator back electromotive force (emf) signal. In practical
implementation the pure integration is replaced by a low pass filter to avoid
the DC drift and saturation problems at the integrator output because of the
initial condition error and the inevitable DC components in the back emf
signal. However, the low pass filter introduces errors in the estimated stator
flux which are significant at frequencies near or lower than the cutoff
frequency. Also the DC components in the back emf signal are amplified at
the low pass filter output by a factor equals to . Therefore, different
integration algorithms have been proposed to improve the stator flux
estimation at steady state and transient conditions. In this paper a new
algorithm for stator flux estimation is proposed for direct torque control
(DTC) of induction motor drives. The proposed algorithm is composed of a
second order high pass filter and an integrator which can effectively
eliminates the effect of the error initial condition and the DC components.
The amplitude and phase errors compensation algorithm is selected such that
the steady state frequency response amplitude and phase angle are equivalent
to that of the pure integrator and the multiplication and division by stator
frequency are avoided. Also the cutoff frequency selection is improved; even
small value can filter out the DC components in the back emf signal. The
simulation results show the improved performance of the induction motor
direct torque control drive with the proposed stator flux estimation algorithm.
The simulation results are verified by the experimental results.
Minimization of Starting Energy Loss of Three Phase Induction Motors Based on...IJPEDS-IAES
The purpose of this paper is to minimize energy losses consumed by three
phase induction motors during starting with wide range of load torque from
no load to full load. This will limit the temperature rise and allows for more
numbers of starting during a definite time. Starting energy losses
minimization is achieved by controlling the rate of increasing voltage
and frequency to start induction motor under certain load torque within a
definite starting time. Optimal voltage and frequency are obtained by particle
swarm optimization (PSO) tool according to load torque. Then, outputs of the
PSO are used to design a neuro-fuzzy controller to control the output voltage
and frequency of the inverter during starting for each load torque. The
starting characteristics using proposed method are compared to that of direct
on line and V/F methods. A complete model of the system is developed using
SIMULINK/MATLAB.
Hardware Implementation of Solar Based Boost to SEPIC Converter Fed Nine Leve...IJPEDS-IAES
Multi level inverters are widely used in high power applications because of
low harmonic distortion. This paper deals with the simulation
and implementation of PV based boost to SEPIC converter with multilevel
inverter. The output of PV system is stepped up using boost to sepic
converter and it is converted into AC using a multilevel inverter.
The simulation and experimental results with the R load is presented in this
paper. The FFT analysis is done and the THD values are compared. Boost to
SEPIC converter is proposed to step up the voltage to the required value. The
experimental results are compared with the simulation results. The results
indicate that nine level inverter system has better performance than seven
level inverter system.
Transformer Less Voltage Quadrupler Based DC-DC Converter with Coupled Induct...IJPEDS-IAES
In this paper a voltage quadrupler dc-dc converter with coupled inductor
and π filter is presented. The use of the coupled inductor reduces the high
leakage inductance which is present in a transformer enabled converter.
The output ripples in the converter is reduced by providing a π filter.
The interleaved voltage quadrupler is used in this system in order to boost the
output voltage. The voltage multiplier improves the output voltage gain.
The main advantage of this system is more voltage gain when compared with
the transformer eneabled circuit and the overall efficiency of the system is
improved. The circuit is simple to control. As a final point of this research,
the simulation and the hardware investigational results are presented to
demonstrate the effectiveness of this proposed converter.
IRAMY Inverter Control for Solar Electric VehicleIJPEDS-IAES
Solar Electric Vehicles (SEV) are considered the future vehicles to solve the issues of air pollution, global warming, and the rapid decreases of the petroleum resources facing the current transportation technology. However, SEV are still facing important technical obstacles to overcome. They include batteries energy storage capacity, charging times, efficiency of the solar panels and electrical propulsion systems. Solving any of those problems and electric vehicles will compete-complement the internal combustion engines vehicles. In the present work, we propose an electrical propulsion system based on three phase induction motor in order to obtain the desired speed and torque with less power loss. Because of the need to lightweight nature, small volume, low cost, less maintenance and high efficiency system, a three phase squirrel cage induction motor (IM) is selected in the electrical propulsion system. The IM is fed from three phase inverter operated by a constant V/F control method and Space Vector Pulse Width Modulation (SVPWM) algorithm. The proposed control strategy has been implemented on the texas instruments TM320F2812 Digital Signal Processor (DSP) to generate SVPWM signal needed to trigger the gates of IGBT based inverter. The inverter used in this work is a three phase inverter IRAMY20UP60B type. The experimental results show the ability of the proposed control strategy to generate a three-phase sine wave signal with desired frequency. The proposed control strategy is experimented on a locally manufactured EV prototype. The results show that the EV prototype can be propelled to speed up to 60km/h under different road conditions.
Design and Implementation of Single Phase AC-DC Buck-Boost Converter for Powe...IJPEDS-IAES
This paper discusses the Power Factor Correction (PFC) for single phase AC-DC Buck-Boost Converter (BBC) operated in Continuous Conduction Mode (CCM) using inductor average current mode control. The proposed control technique employs Proportional-Integral (PI) controller in the outer voltage loop and the Inductor Average Current Mode Control (IACMC) in the inner current loop for PFC BBC. The IACMC has advantages such as robustness when there are large variations in line voltage and output load. The PI controller is developed by using state space average model of BBC. The simulation of the proposed system with its control circuit is implemented in MatLab/Simulink. The simulation results show a nearly unity power factor can be attained and there is almost no change in power factor when the line frequency is at various ranges. Experimental results are provided to show its validity and feasibility.
Improvement of Wind farm with PMSG using STATCOMIJPEDS-IAES
This paper studies about the dynamic performance of the Permanent Magnet Synchronous Generator with Static Synchronous Compensator (STATCOM) for Wind farm integration. A whole dynamic model of wind energy conversion system (WECS) with PMSG and STATCOM are established in a MATLAB environment. With this model the dynamic behaviour of the generator and the overall system has been studied to determine the performance of them with and without STATCOM. Final results portrays that the WECS based PMSG with STATCOM improves the transient response of the wind farm when the system is in fault.
Modeling and Control of a Doubly-Fed Induction Generator for Wind Turbine-Gen...IJPEDS-IAES
This paper presents a vector control direct (FOC) of double fed induction generator intended to control the generated stator powers. This device is intended to be implemented in a variable-speed wind-energy conversion system connected to the grid. In order to control the active and reactive power exchanged between the machine stator and the grid, the rotor is fed by a bi-directional converter. The DFIG is controlled by standard relay controllers. Details of the control strategy and system simulation were performed using Simulink and the results are presented in this here to show the effectiveness of the proposed control strategy.
A Review on Design and Development of high Reliable Hybrid Energy Systems wit...IJPEDS-IAES
Hybrid Energy system is a combination of two or more different types of energy resources. Now a day this hybrid energy system plays key role in various remote area power applications. Hybrid energy system is more reliable than single energy system. This paper deals with high reliable hybrid energy system with solar, wind and micro hydro resources. The proposed hybrid system cable of multi mode operation and high reliable due to non communicated based controllers (Droop Characteristic Control) are used for optimal power sharing. Size of battery can be reduced because hydro used as back up source and Maximum power point Tracking also applied to solar and wind energy systems.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
2. ISSN: 2088-8694
IJPEDS Vol. 7, No. 2, June 2016 : 472 – 480
473
batteries simulation. This model and its implementation which is based on the co-simulation
LabVIEW/Multisim are described in detail. This co-simulation is able to provide accurate simulation results
and a very fast simulation speed. Thereby, it reveals the strong instantaneous relationship between the
internal batteries parameters and the charging current rate.
2. BATTERY MODEL
2.1. Equivalent Circuit
Our study is founded on the model that was established by “Centro de Investigaciones Energéticas,
Medioambientales y Tecnológicas” (CIEMAT) and based on the circuit shown in Figure 1. This equivalent
circuit is composed of a voltage source V1 in series with internal resistance R1 which their characteristics
depends on some parameters such as internal temperature (T) and SOC.
Figure 1. Battery model equivalent circuit
The implementation of the model of ns cells in series implies necessarily assigning dissimilar
expressions to the values of V1 and R1 in each different mode. We limit our study to the two main modes of
operation: charge and discharge. For this reason, mathematical formulations are given by the following
equations:
Charging mode
The electromotive force V1 and the internal resistor R1 are functions of the internal components of
the battery:
1
1 0.86 1.2
10
1 1
(2 0.16 )
6 0.48
0.036 1 0.025
1 (1 )
s
bat
s
bat
bat bat
V n SOC
I
R n T
C I SOC
V V R I
(1)
Where ns is the cells number, T is the variation of the battery internal temperature (Tbat ) , SOC is the battery
state of charge at a given time, Ibat is the instantaneous battery current, Vbat is the instantaneous battery
voltage, and C10 is the rated capacity.
Discharging mode
The mathematical equations are analogous to those found at charging mode with a sort of difference
concerning the values.
1
1 1.3 1.5
10
1 1
2.085 0.12 (1- )
4 0.27
0.02 1 0.007
1
s
bat
s
bat
bat bat
V n SOC
I
R n T
C I SOC
V V R I
(2)
2.2. Capacitor Model
The capacity model is defined by the following equation (3). The value of the internal capacity
(Cbat) is settled from the expression of the current I10, which match up to the operating speed of the rated
capacity of the battery C10.
3. IJPEDS ISSN: 2088-8694
Performance Evaluation of Modelling and Simulation of Lead Acid Batteries for … (Benachir Bouchikhi)
474
0.9
10
10
1.67
1 0.005 (T 25)
1 0.67
bat
bat
bat
C
C I
I
(3)
The instantaneous calculated capacity Cbat is used to estimate the SOC [13], as demonstrated in the equations
below:
( ) ( 1)
0 ( ) 1
bat
bat
I
SOC t SOC t
C
SOC t
(4)
2.3. Thermal Model
Equation (5) evaluates the change in electrolyte temperature, due to the internal resistive losses and
the ambient temperature [9], [14]. The thermal model is defined by a first order differential equation with
parameters for thermal resistance and capacitance. This has been done by the following equation:
0
0
( 1)1
( )
t
bat a
bat s
o o
T T
T t T P d
C R
(5)
Where Tbat is the battery’s temperature in °C; T0 is the battery’s initial temperature in °C, expected to be
equal to the nearby ambient temperature; Ps is the R1Ibat
2
power loss of the internal resistance R1 in Watts; Ro
is the thermal resistance in °C/Watts; Co is the thermal capacitance in Joules/°C; is an integration time
variable; and t is the simulation time in seconds.
Even though the battery module is composed of more than one element, a single temperature for the
electrolyte of the complete module was adopted.
3. BATTERY MODEL SIMULATION STRUCTURE
The National Instruments Community presents the principle of co-simulation using the two
simulators LabVIEW and Multisim [15]. Therefore, the battery modelling and simulation are developed in
the following manner. Firstly, the stage circuitry is designed in Multisim which contains three parts: an
equivalent circuit model, a charge or discharge mode switcher, and a thermal model. Then the LabVIEW
code to monitor the circuit is developed, located inside of a LabVIEW control bloc, and coupled to the
Multisim circuit for co-simulation. The two simulators usually exchange data in a synchronised and variable
time step manner. The flowchart of the proposed LabVIEW/Multisim battery model simulation is shown in
the Figure 2.
Figure 2. Flowchart of the proposed LabVIEW/Multisim battery model simulation
LabVIEW code sends two different types of data to Multisim circuitry. The first type is the static
parameters used to define the thermal model, such as the ambient temperature (T_a), the initial temperature
(T_o), the thermal resistance (R_o) and the thermal capacitance (C_o). The second type is the instantaneous
parameters which evaluate the battery circuit parameters such as the voltage source (V_1), the internal
4. ISSN: 2088-8694
IJPEDS Vol. 7, No. 2, June 2016 : 472 – 480
475
resistance (R_1) and the internal capacitance (C_bat). On the other side, the calculation of those parameters
within LabVIEW code depends on the data sent by Multisim stage such as the instantaneous battery
temperature (T_bat) evaluated by the thermal model bloc and the sensed current (I_sense) through the
equivalent circuit.
3.1. Multisim Stage
The equivalent circuit in Figure 3-a is composed of a voltage controlled source V1 in series with a
voltage controlled resistor R1. The values of V1 and R1 depend on the battery operation mode at a given time
and varied with temperature and SOC. The resistance also varied with the sensed current (I_sense) flowing
through the current probe XCP1.
The charge and discharge mode switcher K1 is shown in Figure 3-b. It offers the possibility to switch
between two modes according to the sent value by mean of the terminal Sw_1. At charging time (Sw_1 = 5
V) the positive battery pin (2) is linked to the controlled current source (I_charge). But at discharging time
(Sw_1 = 0 V) the connection is switched to the voltage controlled resistor (R2) instead.
The thermal Model in Figure 3-c trails the battery’s electrolyte temperature. It is composed of two
voltage controlled sources eq1 and eq2 which evaluate the instantaneous battery temperature (T_bat). L.
Castaner and S. Silvestre present the use of ‘sdt’ PSpice function which is the time integral operation [16].
We use this function to solve the equation (5) as the following:
Voltage controlled source eq1:
_ * _ ,2 _ /a oV R bat pwr abs V I sense V T bat V T V R (6)
Voltage controlled source eq2:
20 /o oV T sdt V V C (7)
The calculation of battery temperature V(T_bat) is based on the internal resistance V(R_bat) and the
instantaneous sensed current through the battery V(I_sense). Moreover, the values of the ambient temperature
V(Ta), the thermal resistance V(Ro), the battery initial temperature V(To) and the thermal capacitance V(Co)
are set at the beginning of the simulation and sent to Multisim by LabVIEW- Multisim terminals such as T_a,
R_o, T_o and C_o. Finally the evaluated temperature V(T_bat) is sent to LabVIEW code by the T_bat
terminal.
Figure 3. a- Equivalent circuit model, b- Charge or discharge mode switcher, c- Thermal model
5. IJPEDS ISSN: 2088-8694
Performance Evaluation of Modelling and Simulation of Lead Acid Batteries for … (Benachir Bouchikhi)
476
3.2. LabVIEW Stage
Equations (1-4) which describe the evolution of internal battery parameters and the state of charge
are implemented as separate collaborating blocks as shown in Figure 4. Therefore, the LabVIEW block
diagram to control the Multisim battery circuitry contains three major parts: “Multisim Battery circuitry”,
“Battery model parameters estimations” and “SOC estimation” block. The last two blocs execute the control
system algorithms which estimate the battery model parameters. After that, the estimated values are sent to
the Multisim circuit.
Figure 4. LabVIEW block diagram of battery model parameters estimation
3.2.1. Battery Model Parameters Estimation Bloc
The “Battery model parameters estimation” bloc is a LabVIEW case structure. It estimates, for
every operation mode (charge or discharge), the instantaneous value of model parameters which are
capacitance C_bat by using equations (1) and (2), internal voltage (V_1) and internal resistor (R_1) by using
equations (3) and (4).
3.2.2. SOC estimation bloc
After the estimation of the internal parameters, the “SOC Estimation” bloc is then able to estimate
the new instantaneous value of SOC by using the equation (4), as described in Figure 5. Hence, the
calculation of SOC is based on the battery current (I_bat), the estimated capacity (C_bat) and the SOC
history that is stored by the “Memory” block. The output of this memory bloc is limited between 0 and 1 by
the “Saturation” bloc which presents the instantaneous estimated value of SOC.
Figure 2. Battery state of charge estimation block
4. RESULTS AND DISCUSSIONS
4.1. Simulation Results
Figure 6 represents the front panel of the proposed LabVIEW graphical user interface (GUI) to
simulate the instantaneous battery model parameters evolution. During the charging and discharging mode, it
guaranties the observation of the behaviour of those internal parameters with the possibility of setting the
IJPEDS ISSN: 2088-8694
Performance Evaluation of Modelling and Simulation of Lead Acid Batteries for … (Benachir Bouchikhi)
476
3.2. LabVIEW Stage
Equations (1-4) which describe the evolution of internal battery parameters and the state of charge
are implemented as separate collaborating blocks as shown in Figure 4. Therefore, the LabVIEW block
diagram to control the Multisim battery circuitry contains three major parts: “Multisim Battery circuitry”,
“Battery model parameters estimations” and “SOC estimation” block. The last two blocs execute the control
system algorithms which estimate the battery model parameters. After that, the estimated values are sent to
the Multisim circuit.
Figure 4. LabVIEW block diagram of battery model parameters estimation
3.2.1. Battery Model Parameters Estimation Bloc
The “Battery model parameters estimation” bloc is a LabVIEW case structure. It estimates, for
every operation mode (charge or discharge), the instantaneous value of model parameters which are
capacitance C_bat by using equations (1) and (2), internal voltage (V_1) and internal resistor (R_1) by using
equations (3) and (4).
3.2.2. SOC estimation bloc
After the estimation of the internal parameters, the “SOC Estimation” bloc is then able to estimate
the new instantaneous value of SOC by using the equation (4), as described in Figure 5. Hence, the
calculation of SOC is based on the battery current (I_bat), the estimated capacity (C_bat) and the SOC
history that is stored by the “Memory” block. The output of this memory bloc is limited between 0 and 1 by
the “Saturation” bloc which presents the instantaneous estimated value of SOC.
Figure 2. Battery state of charge estimation block
4. RESULTS AND DISCUSSIONS
4.1. Simulation Results
Figure 6 represents the front panel of the proposed LabVIEW graphical user interface (GUI) to
simulate the instantaneous battery model parameters evolution. During the charging and discharging mode, it
guaranties the observation of the behaviour of those internal parameters with the possibility of setting the
IJPEDS ISSN: 2088-8694
Performance Evaluation of Modelling and Simulation of Lead Acid Batteries for … (Benachir Bouchikhi)
476
3.2. LabVIEW Stage
Equations (1-4) which describe the evolution of internal battery parameters and the state of charge
are implemented as separate collaborating blocks as shown in Figure 4. Therefore, the LabVIEW block
diagram to control the Multisim battery circuitry contains three major parts: “Multisim Battery circuitry”,
“Battery model parameters estimations” and “SOC estimation” block. The last two blocs execute the control
system algorithms which estimate the battery model parameters. After that, the estimated values are sent to
the Multisim circuit.
Figure 4. LabVIEW block diagram of battery model parameters estimation
3.2.1. Battery Model Parameters Estimation Bloc
The “Battery model parameters estimation” bloc is a LabVIEW case structure. It estimates, for
every operation mode (charge or discharge), the instantaneous value of model parameters which are
capacitance C_bat by using equations (1) and (2), internal voltage (V_1) and internal resistor (R_1) by using
equations (3) and (4).
3.2.2. SOC estimation bloc
After the estimation of the internal parameters, the “SOC Estimation” bloc is then able to estimate
the new instantaneous value of SOC by using the equation (4), as described in Figure 5. Hence, the
calculation of SOC is based on the battery current (I_bat), the estimated capacity (C_bat) and the SOC
history that is stored by the “Memory” block. The output of this memory bloc is limited between 0 and 1 by
the “Saturation” bloc which presents the instantaneous estimated value of SOC.
Figure 2. Battery state of charge estimation block
4. RESULTS AND DISCUSSIONS
4.1. Simulation Results
Figure 6 represents the front panel of the proposed LabVIEW graphical user interface (GUI) to
simulate the instantaneous battery model parameters evolution. During the charging and discharging mode, it
guaranties the observation of the behaviour of those internal parameters with the possibility of setting the
6. ISSN: 2088-8694
IJPEDS Vol. 7, No. 2, June 2016 : 472 – 480
477
batteries datasheet parameters values manually. Additionally, we can test the impact of the current rate on
batteries charging or discharging process: During the charging mode the GUI gives the possibility to define
directly the value of the current, but during the discharging mode, this value is defined by varying the load
resistance value. Moreover, the behaviour of each battery can be simulated at any given internal parameters'
initial conditions, such as the internal temperature and the state of charge. In the next lines, the simulation
results obtained during the charging mode will be presented. Firstly, we set battery models parameters
(battery 1) as follow:
Battery Model Parameters: ns = 6 and C10 = 190 Ah;
Thermal Model Parameters: C_o = 15 Wh/°C, R_o = 0.2 °C/W, T_o = 25 °C and T_a = 25 °C.
After that we set the simulation parameters by choosing the battery function mode (Charge) and setting the
initial battery state of charge (SOC_1 = 0.1). Finally, we fix the charging current (I_bat = 1 A).
Figure 3. Lead acid battery parameters simulation GUI under co-simulation LabVIEW/Multisim
4.2. Discussions
To evaluate the performance of the proposed method of the modelling and simulation of lead acid
batteries, a second battery (battery 2) is being simulated with the same thermal parameters and simulation
conditions. The battery model parameters are: ns = 6, C10 = 296 Ah.
Figure 7-a shows the evolution of internal resistance for the two batteries during the charging
regime. The continuous curve refers to battery 1. Its internal resistance jumps from 0.32 Ω (SOC = 0.9) to
4.53 Ω (SOC = 0.99). The dashed curve refers to battery 2. Its internal resistance jumps from 0.22 Ω (SOC
=0.9) to 2.91 Ω (SOC = 0.99). As a result, when batteries are charging, the values of internal resistance are
influenced by the state of charge. Moreover, those values increase rapidly when the batteries approach the
fully charged state.
Figure 7-b shows the evolution of the variation of internal temperature for battery 2 at three
charging current rates. The amount of this variation is governed by both the charging current and the state of
charge. During constant charging current, the temperature variation increases following the SOC. But, when
we approach the fully charged state, the internal temperature value rises quickly with increasing charging
current. Furthermore, the internal temperature influences the internal capacity. As a result, the capacity value
of the battery becomes higher when it is close to be fully charged. This is why batteries require a long period
of time in order to be fully charged.
ISSN: 2088-8694
IJPEDS Vol. 7, No. 2, June 2016 : 472 – 480
477
batteries datasheet parameters values manually. Additionally, we can test the impact of the current rate on
batteries charging or discharging process: During the charging mode the GUI gives the possibility to define
directly the value of the current, but during the discharging mode, this value is defined by varying the load
resistance value. Moreover, the behaviour of each battery can be simulated at any given internal parameters'
initial conditions, such as the internal temperature and the state of charge. In the next lines, the simulation
results obtained during the charging mode will be presented. Firstly, we set battery models parameters
(battery 1) as follow:
Battery Model Parameters: ns = 6 and C10 = 190 Ah;
Thermal Model Parameters: C_o = 15 Wh/°C, R_o = 0.2 °C/W, T_o = 25 °C and T_a = 25 °C.
After that we set the simulation parameters by choosing the battery function mode (Charge) and setting the
initial battery state of charge (SOC_1 = 0.1). Finally, we fix the charging current (I_bat = 1 A).
Figure 3. Lead acid battery parameters simulation GUI under co-simulation LabVIEW/Multisim
4.2. Discussions
To evaluate the performance of the proposed method of the modelling and simulation of lead acid
batteries, a second battery (battery 2) is being simulated with the same thermal parameters and simulation
conditions. The battery model parameters are: ns = 6, C10 = 296 Ah.
Figure 7-a shows the evolution of internal resistance for the two batteries during the charging
regime. The continuous curve refers to battery 1. Its internal resistance jumps from 0.32 Ω (SOC = 0.9) to
4.53 Ω (SOC = 0.99). The dashed curve refers to battery 2. Its internal resistance jumps from 0.22 Ω (SOC
=0.9) to 2.91 Ω (SOC = 0.99). As a result, when batteries are charging, the values of internal resistance are
influenced by the state of charge. Moreover, those values increase rapidly when the batteries approach the
fully charged state.
Figure 7-b shows the evolution of the variation of internal temperature for battery 2 at three
charging current rates. The amount of this variation is governed by both the charging current and the state of
charge. During constant charging current, the temperature variation increases following the SOC. But, when
we approach the fully charged state, the internal temperature value rises quickly with increasing charging
current. Furthermore, the internal temperature influences the internal capacity. As a result, the capacity value
of the battery becomes higher when it is close to be fully charged. This is why batteries require a long period
of time in order to be fully charged.
ISSN: 2088-8694
IJPEDS Vol. 7, No. 2, June 2016 : 472 – 480
477
batteries datasheet parameters values manually. Additionally, we can test the impact of the current rate on
batteries charging or discharging process: During the charging mode the GUI gives the possibility to define
directly the value of the current, but during the discharging mode, this value is defined by varying the load
resistance value. Moreover, the behaviour of each battery can be simulated at any given internal parameters'
initial conditions, such as the internal temperature and the state of charge. In the next lines, the simulation
results obtained during the charging mode will be presented. Firstly, we set battery models parameters
(battery 1) as follow:
Battery Model Parameters: ns = 6 and C10 = 190 Ah;
Thermal Model Parameters: C_o = 15 Wh/°C, R_o = 0.2 °C/W, T_o = 25 °C and T_a = 25 °C.
After that we set the simulation parameters by choosing the battery function mode (Charge) and setting the
initial battery state of charge (SOC_1 = 0.1). Finally, we fix the charging current (I_bat = 1 A).
Figure 3. Lead acid battery parameters simulation GUI under co-simulation LabVIEW/Multisim
4.2. Discussions
To evaluate the performance of the proposed method of the modelling and simulation of lead acid
batteries, a second battery (battery 2) is being simulated with the same thermal parameters and simulation
conditions. The battery model parameters are: ns = 6, C10 = 296 Ah.
Figure 7-a shows the evolution of internal resistance for the two batteries during the charging
regime. The continuous curve refers to battery 1. Its internal resistance jumps from 0.32 Ω (SOC = 0.9) to
4.53 Ω (SOC = 0.99). The dashed curve refers to battery 2. Its internal resistance jumps from 0.22 Ω (SOC
=0.9) to 2.91 Ω (SOC = 0.99). As a result, when batteries are charging, the values of internal resistance are
influenced by the state of charge. Moreover, those values increase rapidly when the batteries approach the
fully charged state.
Figure 7-b shows the evolution of the variation of internal temperature for battery 2 at three
charging current rates. The amount of this variation is governed by both the charging current and the state of
charge. During constant charging current, the temperature variation increases following the SOC. But, when
we approach the fully charged state, the internal temperature value rises quickly with increasing charging
current. Furthermore, the internal temperature influences the internal capacity. As a result, the capacity value
of the battery becomes higher when it is close to be fully charged. This is why batteries require a long period
of time in order to be fully charged.
7. IJPEDS ISSN: 2088-8694
Performance Evaluation of Modelling and Simulation of Lead Acid Batteries for … (Benachir Bouchikhi)
478
(a) (b)
Figure 7. Evolution of internal parameters at charging time, a- Internal resistance, b- Internal temperature.
Traditional charging techniques of lead acid batteries either use constant current or constant voltage
to charge batteries, or combine these two schemes. Constant currant is practical at the beginning of battery
charging while the battery voltage is low. When the battery voltage increases to a predefined value, the
charger shifts to the constant voltage mode. However, when upcoming the fully charged state, the value of
internal resistance increase rapidly which causes an exponential drop of the current. Therefore, full charge
takes a long times which leads to very important change on the battery temperature. This fact has an
important influence on the performance and lifetime of the batteries. To overcome those shortcomings, we
intend to improve the performance of the charging process by an intelligent charging algorithm which can
accurately determine that the charging current is necessary. A fuzzy logic system has been implemented to
control the flow of charging current of Lithium-ion battery [17]. This controller will use two inputs which
are voltage and temperature. The new charger will be based also on the fuzzy controller which takes into
account the battery temperature variations and the SOC value when adjusting the charging current.
5. CONCLUSION
In this work we present a new method of simulation of lead acid batteries parameters. The
implementation of the CIEMAT model was based on the co-simulation LabVIEW-Multisim. The circuitry
stage is designed in Multisim and the code of controlling of this circuitry is developed in LabVIEW. The two
simulators characteristically exchange data in a synchronized and variable time step mode. Simulation results
demonstrate the impact of the charging current on the internal resistance, temperature and the capacitance of
the battery. At charging time, internals resistance and temperature increase rapidly when approaching the
fully charged state. Therefore, to improve the performance of the battery charging process by enhancing the
full charge time, the battery charger control algorithm must take the internal temperature and the SOC
information into account when evaluating the charging current. As a result, the charging current must drop
when internal temperature or SOC rises. In the future work, we intend to implement a fuzzy-control-based
battery charger to cop traditional charger fails. This control is taking the variation of temperature and SOC as
input to adjust the charging current as an output.
REFERENCES
[1] M. Chen and G. A. Rincon-Mora, “Accurate Electrical Battery Model Capable of Predicting Runtime and I–V
Performance”, IEEE Transactions on Energy Conversion, vol. 21, no. 2, pp. 504–511, Jun. 2006.
[2] F. M. González-Longatt, “Circuit Based Battery Models: A Review”, in Proceedings of 2nd Congreso
IberoAmericano De Estudiantes de Ingenieria Electrica, Puerto la Cruz, Venezuela, 2006.
[3] O. Tremblay and L.-A. Dessaint, “Experimental Validation of a Battery Dynamic Model for EV Applications”,
World Electric Vehicle Journal, vol. 3, no. 1, pp. 1–10, 2009.
[4] S. M. Mousavi G. and M. Nikdel, “Various Battery Models for Various Simulation Studies and Applications”,
Renewable and Sustainable Energy Reviews, vol. 32, pp. 477–485, Apr. 2014.
[5] M. Sarvi and M. Safari, “A Fuzzy Model for Ni-Cd Batteries”, IAES International Journal of Artificial Intelligence
(IJ-AI), vol. 2, no. 2, pp. 81–89, 2013.
[6] F. Jin and H. Yong-ling, “State-Of-Charge Estimation of Li-ion Battery using Extended Kalman Filter”,
TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 11, no. 12, pp. 7707–7714, 2013.
[7] J. B. Copetti, E. Lorenzo, and F. Chenlo, “A General Battery Model for PV System Simulation”, Progress in
Photovoltaics: research and applications, vol. 1, no. 4, pp. 283–292, 1993.
[8] L. Castañer, R. Aloy, and D. Carles, “Photovoltaic System Simulation using a Standard Electronic Circuit
Simulator”, Progress in Photovoltaics: Research and applications, vol. 3, no. 4, pp. 239–252, 1995.
IJPEDS ISSN: 2088-8694
Performance Evaluation of Modelling and Simulation of Lead Acid Batteries for … (Benachir Bouchikhi)
478
(a) (b)
Figure 7. Evolution of internal parameters at charging time, a- Internal resistance, b- Internal temperature.
Traditional charging techniques of lead acid batteries either use constant current or constant voltage
to charge batteries, or combine these two schemes. Constant currant is practical at the beginning of battery
charging while the battery voltage is low. When the battery voltage increases to a predefined value, the
charger shifts to the constant voltage mode. However, when upcoming the fully charged state, the value of
internal resistance increase rapidly which causes an exponential drop of the current. Therefore, full charge
takes a long times which leads to very important change on the battery temperature. This fact has an
important influence on the performance and lifetime of the batteries. To overcome those shortcomings, we
intend to improve the performance of the charging process by an intelligent charging algorithm which can
accurately determine that the charging current is necessary. A fuzzy logic system has been implemented to
control the flow of charging current of Lithium-ion battery [17]. This controller will use two inputs which
are voltage and temperature. The new charger will be based also on the fuzzy controller which takes into
account the battery temperature variations and the SOC value when adjusting the charging current.
5. CONCLUSION
In this work we present a new method of simulation of lead acid batteries parameters. The
implementation of the CIEMAT model was based on the co-simulation LabVIEW-Multisim. The circuitry
stage is designed in Multisim and the code of controlling of this circuitry is developed in LabVIEW. The two
simulators characteristically exchange data in a synchronized and variable time step mode. Simulation results
demonstrate the impact of the charging current on the internal resistance, temperature and the capacitance of
the battery. At charging time, internals resistance and temperature increase rapidly when approaching the
fully charged state. Therefore, to improve the performance of the battery charging process by enhancing the
full charge time, the battery charger control algorithm must take the internal temperature and the SOC
information into account when evaluating the charging current. As a result, the charging current must drop
when internal temperature or SOC rises. In the future work, we intend to implement a fuzzy-control-based
battery charger to cop traditional charger fails. This control is taking the variation of temperature and SOC as
input to adjust the charging current as an output.
REFERENCES
[1] M. Chen and G. A. Rincon-Mora, “Accurate Electrical Battery Model Capable of Predicting Runtime and I–V
Performance”, IEEE Transactions on Energy Conversion, vol. 21, no. 2, pp. 504–511, Jun. 2006.
[2] F. M. González-Longatt, “Circuit Based Battery Models: A Review”, in Proceedings of 2nd Congreso
IberoAmericano De Estudiantes de Ingenieria Electrica, Puerto la Cruz, Venezuela, 2006.
[3] O. Tremblay and L.-A. Dessaint, “Experimental Validation of a Battery Dynamic Model for EV Applications”,
World Electric Vehicle Journal, vol. 3, no. 1, pp. 1–10, 2009.
[4] S. M. Mousavi G. and M. Nikdel, “Various Battery Models for Various Simulation Studies and Applications”,
Renewable and Sustainable Energy Reviews, vol. 32, pp. 477–485, Apr. 2014.
[5] M. Sarvi and M. Safari, “A Fuzzy Model for Ni-Cd Batteries”, IAES International Journal of Artificial Intelligence
(IJ-AI), vol. 2, no. 2, pp. 81–89, 2013.
[6] F. Jin and H. Yong-ling, “State-Of-Charge Estimation of Li-ion Battery using Extended Kalman Filter”,
TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 11, no. 12, pp. 7707–7714, 2013.
[7] J. B. Copetti, E. Lorenzo, and F. Chenlo, “A General Battery Model for PV System Simulation”, Progress in
Photovoltaics: research and applications, vol. 1, no. 4, pp. 283–292, 1993.
[8] L. Castañer, R. Aloy, and D. Carles, “Photovoltaic System Simulation using a Standard Electronic Circuit
Simulator”, Progress in Photovoltaics: Research and applications, vol. 3, no. 4, pp. 239–252, 1995.
IJPEDS ISSN: 2088-8694
Performance Evaluation of Modelling and Simulation of Lead Acid Batteries for … (Benachir Bouchikhi)
478
(a) (b)
Figure 7. Evolution of internal parameters at charging time, a- Internal resistance, b- Internal temperature.
Traditional charging techniques of lead acid batteries either use constant current or constant voltage
to charge batteries, or combine these two schemes. Constant currant is practical at the beginning of battery
charging while the battery voltage is low. When the battery voltage increases to a predefined value, the
charger shifts to the constant voltage mode. However, when upcoming the fully charged state, the value of
internal resistance increase rapidly which causes an exponential drop of the current. Therefore, full charge
takes a long times which leads to very important change on the battery temperature. This fact has an
important influence on the performance and lifetime of the batteries. To overcome those shortcomings, we
intend to improve the performance of the charging process by an intelligent charging algorithm which can
accurately determine that the charging current is necessary. A fuzzy logic system has been implemented to
control the flow of charging current of Lithium-ion battery [17]. This controller will use two inputs which
are voltage and temperature. The new charger will be based also on the fuzzy controller which takes into
account the battery temperature variations and the SOC value when adjusting the charging current.
5. CONCLUSION
In this work we present a new method of simulation of lead acid batteries parameters. The
implementation of the CIEMAT model was based on the co-simulation LabVIEW-Multisim. The circuitry
stage is designed in Multisim and the code of controlling of this circuitry is developed in LabVIEW. The two
simulators characteristically exchange data in a synchronized and variable time step mode. Simulation results
demonstrate the impact of the charging current on the internal resistance, temperature and the capacitance of
the battery. At charging time, internals resistance and temperature increase rapidly when approaching the
fully charged state. Therefore, to improve the performance of the battery charging process by enhancing the
full charge time, the battery charger control algorithm must take the internal temperature and the SOC
information into account when evaluating the charging current. As a result, the charging current must drop
when internal temperature or SOC rises. In the future work, we intend to implement a fuzzy-control-based
battery charger to cop traditional charger fails. This control is taking the variation of temperature and SOC as
input to adjust the charging current as an output.
REFERENCES
[1] M. Chen and G. A. Rincon-Mora, “Accurate Electrical Battery Model Capable of Predicting Runtime and I–V
Performance”, IEEE Transactions on Energy Conversion, vol. 21, no. 2, pp. 504–511, Jun. 2006.
[2] F. M. González-Longatt, “Circuit Based Battery Models: A Review”, in Proceedings of 2nd Congreso
IberoAmericano De Estudiantes de Ingenieria Electrica, Puerto la Cruz, Venezuela, 2006.
[3] O. Tremblay and L.-A. Dessaint, “Experimental Validation of a Battery Dynamic Model for EV Applications”,
World Electric Vehicle Journal, vol. 3, no. 1, pp. 1–10, 2009.
[4] S. M. Mousavi G. and M. Nikdel, “Various Battery Models for Various Simulation Studies and Applications”,
Renewable and Sustainable Energy Reviews, vol. 32, pp. 477–485, Apr. 2014.
[5] M. Sarvi and M. Safari, “A Fuzzy Model for Ni-Cd Batteries”, IAES International Journal of Artificial Intelligence
(IJ-AI), vol. 2, no. 2, pp. 81–89, 2013.
[6] F. Jin and H. Yong-ling, “State-Of-Charge Estimation of Li-ion Battery using Extended Kalman Filter”,
TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 11, no. 12, pp. 7707–7714, 2013.
[7] J. B. Copetti, E. Lorenzo, and F. Chenlo, “A General Battery Model for PV System Simulation”, Progress in
Photovoltaics: research and applications, vol. 1, no. 4, pp. 283–292, 1993.
[8] L. Castañer, R. Aloy, and D. Carles, “Photovoltaic System Simulation using a Standard Electronic Circuit
Simulator”, Progress in Photovoltaics: Research and applications, vol. 3, no. 4, pp. 239–252, 1995.
8. ISSN: 2088-8694
IJPEDS Vol. 7, No. 2, June 2016 : 472 – 480
479
[9] M. Ceraolo, “New dynamical Models of Lead-acid Batteries”, Power Systems, IEEE Transactions on, vol. 15, no.
4, pp. 1184–1190, 2000.
[10] S. Barsali and M. Ceraolo, “Dynamical Models of Lead-acid Batteries: Implementation Issues”, Energy
Conversion, IEEE Transactions on, vol. 17, no. 1, pp. 16–23, 2002.
[11] O. Gergaud, G. Robin, B. Multon, and H. B. Ahmed, “Energy Modeling of a Lead-acid Battery within Hybrid
Wind/Photovoltaic Systems”, in European Power Electronic Conference 2003, 2003, pp. 1–8.
[12] N. Achaibou, M. Haddadi, and A. Malek, “Modeling of Lead-acid Batteries in PV Systems”, Energy Procedia, vol.
18, pp. 538–544, 2012.
[13] S. Cho, H. Jeong, C. Han, S. Jin, J. H. Lim, and J. Oh, “State-of-Charge Estimation for Lithium-ion Batteries Under
Various Operating Conditions using an Equivalent Circuit Model”, Computers & Chemical Engineering, vol. 41,
pp. 1–9, Jun. 2012.
[14] R. A. Jackey, “A Simple, Effective Lead-acid Battery Modeling Process for Electrical System Component
Selection”, SAE Technical Paper, 2007.
[15] “Community: Design Guide to Power Electronics Co-Simulation with Multisim and LabVIEW - National
Instruments”, 01-Nov-2011. [Online]. Available: https://decibel.ni.com/content/docs/DOC-19212.
[16] L. Castaner and S. Silvestre, in Modelling Photovoltaic Systems Using PSpice, 1st ed., Chichester: Wiley, 2002, pp.
120–122.
[17] R. Passarella, A. F. Oklilas, and T. Mathilda, “Lithium-ion Battery Charging System using Constant-Current
Method with Fuzzy Logic Based ATmega16”, Int. J. Power Electron. Drive Syst. IJPEDS, vol. 5, no. 2, pp. 166–
175, 2014.
BIOGRAPHIES OF AUTHORS
A. Selmani is a computer science teacher at qualifying school since 2001. Currently he is a Ph.D.
student at the Electronics Automatic and Biotechnology Laboratory, Faculty of Sciences of Meknes,
University Moulay Ismaïl, Morocco. His research interests include fuzzy logic controller based of
the climate under greenhouse using solar energy equipment.
A. Ed-dahhak received Ph.D. from Faculty of Sciences Meknes in 2009. He is a Professor in the
Department of Electrical Engineering, High School of Technology Meknes, Moulay Ismaïl
University, Morocco. Hi is a member of Laboratory of Electronics, Automatics and Biotechnology
of the Faculty of Sciences, Meknes. His current area of research includes electronics, development
of a system for monitoring the climate and managing the drip fertilizing irrigation in greenhouse.
M. Outanoute is currently a Ph.D. student at the Electronics Automatic and Biotechnology
Laboratory, Moulay Ismaïl University, Faculty of Sciences in Meknes, Morocco. His research
interests include advanced modeling and control strategies of climatic parameters under a solar
greenhouse.
A. Lachhab received Ph.D. from Faculty of Sciences in Rabat in 2000. He is a Professor in High
School of Technology of Meknes, Moulay Ismaïl University, Morocco. He is a Member of
Laboratory of Electronic, Automatic and Biotechnology in Faculty of Sciences in Meknes. His
current area of research includes modelling and automatic control.
ISSN: 2088-8694
IJPEDS Vol. 7, No. 2, June 2016 : 472 – 480
479
[9] M. Ceraolo, “New dynamical Models of Lead-acid Batteries”, Power Systems, IEEE Transactions on, vol. 15, no.
4, pp. 1184–1190, 2000.
[10] S. Barsali and M. Ceraolo, “Dynamical Models of Lead-acid Batteries: Implementation Issues”, Energy
Conversion, IEEE Transactions on, vol. 17, no. 1, pp. 16–23, 2002.
[11] O. Gergaud, G. Robin, B. Multon, and H. B. Ahmed, “Energy Modeling of a Lead-acid Battery within Hybrid
Wind/Photovoltaic Systems”, in European Power Electronic Conference 2003, 2003, pp. 1–8.
[12] N. Achaibou, M. Haddadi, and A. Malek, “Modeling of Lead-acid Batteries in PV Systems”, Energy Procedia, vol.
18, pp. 538–544, 2012.
[13] S. Cho, H. Jeong, C. Han, S. Jin, J. H. Lim, and J. Oh, “State-of-Charge Estimation for Lithium-ion Batteries Under
Various Operating Conditions using an Equivalent Circuit Model”, Computers & Chemical Engineering, vol. 41,
pp. 1–9, Jun. 2012.
[14] R. A. Jackey, “A Simple, Effective Lead-acid Battery Modeling Process for Electrical System Component
Selection”, SAE Technical Paper, 2007.
[15] “Community: Design Guide to Power Electronics Co-Simulation with Multisim and LabVIEW - National
Instruments”, 01-Nov-2011. [Online]. Available: https://decibel.ni.com/content/docs/DOC-19212.
[16] L. Castaner and S. Silvestre, in Modelling Photovoltaic Systems Using PSpice, 1st ed., Chichester: Wiley, 2002, pp.
120–122.
[17] R. Passarella, A. F. Oklilas, and T. Mathilda, “Lithium-ion Battery Charging System using Constant-Current
Method with Fuzzy Logic Based ATmega16”, Int. J. Power Electron. Drive Syst. IJPEDS, vol. 5, no. 2, pp. 166–
175, 2014.
BIOGRAPHIES OF AUTHORS
A. Selmani is a computer science teacher at qualifying school since 2001. Currently he is a Ph.D.
student at the Electronics Automatic and Biotechnology Laboratory, Faculty of Sciences of Meknes,
University Moulay Ismaïl, Morocco. His research interests include fuzzy logic controller based of
the climate under greenhouse using solar energy equipment.
A. Ed-dahhak received Ph.D. from Faculty of Sciences Meknes in 2009. He is a Professor in the
Department of Electrical Engineering, High School of Technology Meknes, Moulay Ismaïl
University, Morocco. Hi is a member of Laboratory of Electronics, Automatics and Biotechnology
of the Faculty of Sciences, Meknes. His current area of research includes electronics, development
of a system for monitoring the climate and managing the drip fertilizing irrigation in greenhouse.
M. Outanoute is currently a Ph.D. student at the Electronics Automatic and Biotechnology
Laboratory, Moulay Ismaïl University, Faculty of Sciences in Meknes, Morocco. His research
interests include advanced modeling and control strategies of climatic parameters under a solar
greenhouse.
A. Lachhab received Ph.D. from Faculty of Sciences in Rabat in 2000. He is a Professor in High
School of Technology of Meknes, Moulay Ismaïl University, Morocco. He is a Member of
Laboratory of Electronic, Automatic and Biotechnology in Faculty of Sciences in Meknes. His
current area of research includes modelling and automatic control.
ISSN: 2088-8694
IJPEDS Vol. 7, No. 2, June 2016 : 472 – 480
479
[9] M. Ceraolo, “New dynamical Models of Lead-acid Batteries”, Power Systems, IEEE Transactions on, vol. 15, no.
4, pp. 1184–1190, 2000.
[10] S. Barsali and M. Ceraolo, “Dynamical Models of Lead-acid Batteries: Implementation Issues”, Energy
Conversion, IEEE Transactions on, vol. 17, no. 1, pp. 16–23, 2002.
[11] O. Gergaud, G. Robin, B. Multon, and H. B. Ahmed, “Energy Modeling of a Lead-acid Battery within Hybrid
Wind/Photovoltaic Systems”, in European Power Electronic Conference 2003, 2003, pp. 1–8.
[12] N. Achaibou, M. Haddadi, and A. Malek, “Modeling of Lead-acid Batteries in PV Systems”, Energy Procedia, vol.
18, pp. 538–544, 2012.
[13] S. Cho, H. Jeong, C. Han, S. Jin, J. H. Lim, and J. Oh, “State-of-Charge Estimation for Lithium-ion Batteries Under
Various Operating Conditions using an Equivalent Circuit Model”, Computers & Chemical Engineering, vol. 41,
pp. 1–9, Jun. 2012.
[14] R. A. Jackey, “A Simple, Effective Lead-acid Battery Modeling Process for Electrical System Component
Selection”, SAE Technical Paper, 2007.
[15] “Community: Design Guide to Power Electronics Co-Simulation with Multisim and LabVIEW - National
Instruments”, 01-Nov-2011. [Online]. Available: https://decibel.ni.com/content/docs/DOC-19212.
[16] L. Castaner and S. Silvestre, in Modelling Photovoltaic Systems Using PSpice, 1st ed., Chichester: Wiley, 2002, pp.
120–122.
[17] R. Passarella, A. F. Oklilas, and T. Mathilda, “Lithium-ion Battery Charging System using Constant-Current
Method with Fuzzy Logic Based ATmega16”, Int. J. Power Electron. Drive Syst. IJPEDS, vol. 5, no. 2, pp. 166–
175, 2014.
BIOGRAPHIES OF AUTHORS
A. Selmani is a computer science teacher at qualifying school since 2001. Currently he is a Ph.D.
student at the Electronics Automatic and Biotechnology Laboratory, Faculty of Sciences of Meknes,
University Moulay Ismaïl, Morocco. His research interests include fuzzy logic controller based of
the climate under greenhouse using solar energy equipment.
A. Ed-dahhak received Ph.D. from Faculty of Sciences Meknes in 2009. He is a Professor in the
Department of Electrical Engineering, High School of Technology Meknes, Moulay Ismaïl
University, Morocco. Hi is a member of Laboratory of Electronics, Automatics and Biotechnology
of the Faculty of Sciences, Meknes. His current area of research includes electronics, development
of a system for monitoring the climate and managing the drip fertilizing irrigation in greenhouse.
M. Outanoute is currently a Ph.D. student at the Electronics Automatic and Biotechnology
Laboratory, Moulay Ismaïl University, Faculty of Sciences in Meknes, Morocco. His research
interests include advanced modeling and control strategies of climatic parameters under a solar
greenhouse.
A. Lachhab received Ph.D. from Faculty of Sciences in Rabat in 2000. He is a Professor in High
School of Technology of Meknes, Moulay Ismaïl University, Morocco. He is a Member of
Laboratory of Electronic, Automatic and Biotechnology in Faculty of Sciences in Meknes. His
current area of research includes modelling and automatic control.
9. IJPEDS ISSN: 2088-8694
Performance Evaluation of Modelling and Simulation of Lead Acid Batteries for … (Benachir Bouchikhi)
480
M. Guerbaoui received Ph.D. from Faculty of Sciences Meknes in 2014. He is a teacher at High
School in Engineering Science since 1996. He is a member of Laboratory of Electronics,
Automatics and Biotechnology of the Faculty of Sciences, Meknes. His research interests include
regulating parameters under greenhouse by Fuzzy logic and use of solar energy equipment in the
greenhouse.
B. Bouchikhi received the Ph.D. degree from the Université de droit, d’Economie et des Sciences
d’Aix Marseille III, in 1982. Benachir Bouchikhi was awarded a Doctor of Sciences degree in 1988
from the University of Nancy I. Dr. Bouchikhi got a position of titular professor at the University of
Moulay Ismaïl, Faculty of Sciences in Meknes, Morocco since 1993. He is the director of the
Laboratory of Electronics, Automatic and Biotechnology. His current research focuses on the
development of electronic nose and electronic tongue devices for food analysis and biomedical
applications and the control of the climate and drip fertirrigation under greenhouse. He is author and
co-author of over 65 papers, published on international journals. During the last 10 years he has
coordinated a dozen national and international projects, in the area of food safety, the control of the
climate and drip fertirrigation under greenhouse. He is member of the H2020-MSCA-RISE-2014
project TROPSENSE: "Development of a non-invasive breath test for early diagnosis of tropical
diseases He is member of the Editorial Board of Journal of Biotechnolgy and Bioengineering.
IJPEDS ISSN: 2088-8694
Performance Evaluation of Modelling and Simulation of Lead Acid Batteries for … (Benachir Bouchikhi)
480
M. Guerbaoui received Ph.D. from Faculty of Sciences Meknes in 2014. He is a teacher at High
School in Engineering Science since 1996. He is a member of Laboratory of Electronics,
Automatics and Biotechnology of the Faculty of Sciences, Meknes. His research interests include
regulating parameters under greenhouse by Fuzzy logic and use of solar energy equipment in the
greenhouse.
B. Bouchikhi received the Ph.D. degree from the Université de droit, d’Economie et des Sciences
d’Aix Marseille III, in 1982. Benachir Bouchikhi was awarded a Doctor of Sciences degree in 1988
from the University of Nancy I. Dr. Bouchikhi got a position of titular professor at the University of
Moulay Ismaïl, Faculty of Sciences in Meknes, Morocco since 1993. He is the director of the
Laboratory of Electronics, Automatic and Biotechnology. His current research focuses on the
development of electronic nose and electronic tongue devices for food analysis and biomedical
applications and the control of the climate and drip fertirrigation under greenhouse. He is author and
co-author of over 65 papers, published on international journals. During the last 10 years he has
coordinated a dozen national and international projects, in the area of food safety, the control of the
climate and drip fertirrigation under greenhouse. He is member of the H2020-MSCA-RISE-2014
project TROPSENSE: "Development of a non-invasive breath test for early diagnosis of tropical
diseases He is member of the Editorial Board of Journal of Biotechnolgy and Bioengineering.
IJPEDS ISSN: 2088-8694
Performance Evaluation of Modelling and Simulation of Lead Acid Batteries for … (Benachir Bouchikhi)
480
M. Guerbaoui received Ph.D. from Faculty of Sciences Meknes in 2014. He is a teacher at High
School in Engineering Science since 1996. He is a member of Laboratory of Electronics,
Automatics and Biotechnology of the Faculty of Sciences, Meknes. His research interests include
regulating parameters under greenhouse by Fuzzy logic and use of solar energy equipment in the
greenhouse.
B. Bouchikhi received the Ph.D. degree from the Université de droit, d’Economie et des Sciences
d’Aix Marseille III, in 1982. Benachir Bouchikhi was awarded a Doctor of Sciences degree in 1988
from the University of Nancy I. Dr. Bouchikhi got a position of titular professor at the University of
Moulay Ismaïl, Faculty of Sciences in Meknes, Morocco since 1993. He is the director of the
Laboratory of Electronics, Automatic and Biotechnology. His current research focuses on the
development of electronic nose and electronic tongue devices for food analysis and biomedical
applications and the control of the climate and drip fertirrigation under greenhouse. He is author and
co-author of over 65 papers, published on international journals. During the last 10 years he has
coordinated a dozen national and international projects, in the area of food safety, the control of the
climate and drip fertirrigation under greenhouse. He is member of the H2020-MSCA-RISE-2014
project TROPSENSE: "Development of a non-invasive breath test for early diagnosis of tropical
diseases He is member of the Editorial Board of Journal of Biotechnolgy and Bioengineering.