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.
State of charge estimation of lithium-ion batteries using fractional order sl...ISA Interchange
This paper presents a state of charge (SOC) estimation method based on fractional order sliding mode observer (SMO) for lithium-ion batteries. A fractional order RC equivalent circuit model (FORCECM) is firstly constructed to describe the charging and discharging dynamic characteristics of the battery. Then, based on the differential equations of the FORCECM, fractional order SMOs for SOC, polarization voltage and terminal voltage estimation are designed. After that, convergence of the proposed observers is analyzed by Lyapunov’s stability theory method. The framework of the designed observer system is simple and easy to implement. The SMOs can overcome the uncertainties of parameters, modeling and measurement errors, and present good robustness. Simulation results show that the presented estima- tion method is effective, and the designed observers have good performance.
This presentation explains the resent methodologies utilized to estimate the Battery State of Charge with the help of a Data-Driven and Filter-Based approach for enhancing the performance parameters of a Battery Management System.
The effect of load modelling on phase balancing in distribution networks usin...IJECEIAES
Due to the unequal loads in phases and different customer consumption, the distribution network is unbalanced. Unbalancing in the distribution network, in addition to increasing power losses, causes unbalancing in voltages and increases operating costs. To reduce this unbalancing, various methods and algorithms have been presented. In most studies and even practical projects due to lack of information about the network loads, load models such as constant power model, constant current or constant impedance are used to model the loads. Due to the changing and nonlinear behaviours of today's loads, these models cannot show results in accordance with reality. This paper while introducing an optimal phase-balancing method, discusses the effect of load modelling on phase balancing studies. In this process the re-phasing method for balancing the network and the harmony search algorithm for optimizing the phase displacement process have been used. The simulation was carried out on an unbalanced distribution network of 25 buses. The results show well the effect of this comprehensive modelling on phase balancing studies. It also shows that in the re-phasing method for balancing the network and in the absence of a real load model, the use of which model offers the closest answer to optimal solutions.
Most importantly one can identify locations of inputs and outputs of the portions of a model and specify the operating conditions about which the model is linearized for further analysis. Other important feature of Simulink is a Linear-Quadratic-Gaussian LQG control technique which is used to design optimal dynamic regulators, Kalman estimators and filters.
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
We follow "Rigorous Publication" model - means that all articles appear on IJERD after full appraisal, effectiveness, legitimacy and reliability of research content. International Journal of Engineering Research and Development publishes papers online as well as provide hard copy of Journal to authors after publication of paper. It is intended to serve as a forum for researchers, practitioners and developers to exchange ideas and results for the advancement of Engineering & Technology.
Deep segmentation of the liver and the hepatic tumors from abdomen tomography...IJECEIAES
A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two outputclassified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver tumor segmentation (LITS) challenge, result in a dice similarity coefficient (DSC) of 93.5% for the segmentation of the liver and of 74.40% for the segmentation of the lesion, using a 5-fold cross-validation scheme. Comparative results with the state-of-the-art techniques on the same data show the competing performance of the proposed framework for simultaneous liver and tumor segmentation.
State of charge estimation of lithium-ion batteries using fractional order sl...ISA Interchange
This paper presents a state of charge (SOC) estimation method based on fractional order sliding mode observer (SMO) for lithium-ion batteries. A fractional order RC equivalent circuit model (FORCECM) is firstly constructed to describe the charging and discharging dynamic characteristics of the battery. Then, based on the differential equations of the FORCECM, fractional order SMOs for SOC, polarization voltage and terminal voltage estimation are designed. After that, convergence of the proposed observers is analyzed by Lyapunov’s stability theory method. The framework of the designed observer system is simple and easy to implement. The SMOs can overcome the uncertainties of parameters, modeling and measurement errors, and present good robustness. Simulation results show that the presented estima- tion method is effective, and the designed observers have good performance.
This presentation explains the resent methodologies utilized to estimate the Battery State of Charge with the help of a Data-Driven and Filter-Based approach for enhancing the performance parameters of a Battery Management System.
The effect of load modelling on phase balancing in distribution networks usin...IJECEIAES
Due to the unequal loads in phases and different customer consumption, the distribution network is unbalanced. Unbalancing in the distribution network, in addition to increasing power losses, causes unbalancing in voltages and increases operating costs. To reduce this unbalancing, various methods and algorithms have been presented. In most studies and even practical projects due to lack of information about the network loads, load models such as constant power model, constant current or constant impedance are used to model the loads. Due to the changing and nonlinear behaviours of today's loads, these models cannot show results in accordance with reality. This paper while introducing an optimal phase-balancing method, discusses the effect of load modelling on phase balancing studies. In this process the re-phasing method for balancing the network and the harmony search algorithm for optimizing the phase displacement process have been used. The simulation was carried out on an unbalanced distribution network of 25 buses. The results show well the effect of this comprehensive modelling on phase balancing studies. It also shows that in the re-phasing method for balancing the network and in the absence of a real load model, the use of which model offers the closest answer to optimal solutions.
Most importantly one can identify locations of inputs and outputs of the portions of a model and specify the operating conditions about which the model is linearized for further analysis. Other important feature of Simulink is a Linear-Quadratic-Gaussian LQG control technique which is used to design optimal dynamic regulators, Kalman estimators and filters.
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
We follow "Rigorous Publication" model - means that all articles appear on IJERD after full appraisal, effectiveness, legitimacy and reliability of research content. International Journal of Engineering Research and Development publishes papers online as well as provide hard copy of Journal to authors after publication of paper. It is intended to serve as a forum for researchers, practitioners and developers to exchange ideas and results for the advancement of Engineering & Technology.
Deep segmentation of the liver and the hepatic tumors from abdomen tomography...IJECEIAES
A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two outputclassified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver tumor segmentation (LITS) challenge, result in a dice similarity coefficient (DSC) of 93.5% for the segmentation of the liver and of 74.40% for the segmentation of the lesion, using a 5-fold cross-validation scheme. Comparative results with the state-of-the-art techniques on the same data show the competing performance of the proposed framework for simultaneous liver and tumor segmentation.
Evaluation of lightweight battery management system with field test of elect...IJECEIAES
A battery management system is a crucial part of a battery-powered electric vehicle, which functions as a monitoring system, state estimation, and protection for the vehicle. Among these functions, the state estimation, i.e., state of charge and remaining battery life estimation, is widely researched in order to find an accuracy estimation methodology. Most of the recent researches are based on the study of the battery cell level and the complex algorithm. In practice, there is a statement that the method should be simple and robust. Therefore, this research work is focused on the study of lightweight methodology for state estimation based on the battery pack. The discrete Coulomb counting method and the data-driven approach, based on the Palmgren-Miner method, are proposed for the estimation of the state of charge and remaining battery life, respectively. The proposed methods are evaluated through a battery-powered electric bus under real scenario-based circumstances in the campus transit system. In addition, the battery life-cycle cost analysis is also investigated. The tested bus has currently been in operation in the transit system for more than one year.
Experimental Evaluation of Torque Performance of Voltage and Current Models u...IJPEDS-IAES
In this paper, two kinds of observers are proposed to investigate torque estimation. The first one is based on a voltage model represented with a low- pass filter (LPF); which is normally used as a replacement for a pure integrator to avoid integration drift problem due to dc offset or measurement error. The second estimator used is an extended Kalman filter (EKF) as a current model, which puts into account all noise problems. Both estimation algorithms are investigated during the steady and transient states, tested under light load, and then compared with the measured mechanical torque. In all conditions, it will be shown that the torque estimation error for EKF has remained within narrower error band and yielded minimum torque ripples when compared to LPF estimation. This motivates the use of EKF observer in high performance control drives of induction machines for achieving improved torque response.
Kineto-Elasto Dynamic Analysis of Robot Manipulator Puma-560IOSR Journals
Current industrial robots are made very heavy to achieve high Stiffness
which increases the accuracy of their motion. However this heaviness limits the robot speed and in masses the
required energy to move the system. The requirement for higher speed and better system performance makes it
necessary to consider a new generation of light weight manipulators as an alternative to today's massive
inefficient ones. Light weight manipulators require Less energy to move and they have larger payload abilities
and more maneuverability. However due to the dynamic effects of structural flexibility, their control is much
more difficult. Therefore, there is a need to develop accurate dynamic models for design and control of such
systems.This project presents the flexibility and Kineto - Elasto dynamic analysis of robot manipulator
considering deflection. Based on the distributed parameter method, the generalized motion equations of robot
manipulator with flexible links are derived. The final formulation of the motion equations is used to model
general complex elastic manipulators with nonlinear rigid-body and elastic motion in dynamics and it can be
used in the flexibility analysis of robot manipulators and spatial mechanisms. Manipulator end-effector path
trajectory, velocity and accelerations are plotted. Joint torques is to be determined for each joint trajectory
(Dynamics) .Using joint torques, static loading due to link’s masses, masses at joints, and payload, the robot
arms elastic deformations are to be found by using ANSYS-12.0 software package. Elastic compensation is
inserted in coordinates of robotic programming to get exact end-effectors path. A comparison of paths
trajectory of the end-effector is to be plotted. Also variation of torques is plotted after considering elastic
compensation. These torque variations are included in the robotic programming for getting the accurate endeffect
or’s path trajectory
A Drift-Diffusion Model to Simulate Current for Avalanche Photo DetectorIJERA Editor
In this research, a Drift-Diffusion model is carried out to calculate includes impact ionization mechanism and can calculate dark current and photocurrent of avalanche photo diode. Poisson equation, electron and hole density continuity equations and electron and hole current equations have been solved simultaneously using Gummel method. Consideration of impact ionization enables the model to completely simulate the carriers flow in high electrical field. The simulation has been done using MATLAB and the results are compared with other reliable results obtained by researchers. Our results show despite of hydrodynamics and Monte Carlo methods which are very complicated we can get the current characteristics of photo detector easily with acceptable accuracy. In addition we can use this method to calculate currents of device in high fields.
The article gives the experimental results of the processes occurring in the combined system of traction and magnetic suspension, which was implemented on the basis of the linear switched reluctance motor. The goal of the research is to examine the possibility to combine the levitation and traction functions within one unit. The full- function physical model of the transport system with the magnetic suspension has been produced for experimental verification of the development concept for the combined system of traction and magnetic suspension. The research tests have been performed at the track structure with the limited length in order to study the processes, occurring in the most complicated start-up mode, when the discrete behavior of current in windings has the disturbance effect on the object levitation. The oscillograms of electromechanical transition processes, showing the mutual influence of traction subsystems and a suspension, are provided. The results of researches have illustrated dramatically that the development concept of the combined system of traction and magnetic suspension, based on the linear switched reluctance motor, is absolutely real. Further researches should be aimed at improving the system characteristics by reducing the mutual influence of levitation and traction processes.
Motorcycle Movement Model Based on Markov Chain Process in Mixed TrafficIJECEIAES
Mixed traffic systems are dynamically complex since there are many parameters and variables that influence the interactions between the different kinds of vehicles. Modeling the behavior of vehicles, especially motorcycle which has erratic behavior is still being developed continuously, especially in developing countries which have heterogeneous traffic. To get a better understanding of motorcycle behavior, one can look at maneuvers performed by drivers. In this research, we tried to build a model of motorcycle movement which only focused on maneuver action to avoid the obstacle along with the trajectories using a Markov Chain approach. In Markov Chain, the maneuver of motorcycle will described by state transition. The state transition model is depend on probability function which will use for determine what action will be executed next. The maneuver of motorcycle using Markov Chain model was validated by comparing the analytical result with the naturalistic data, with similarity is calculated using MSE. In order to know how good our proposed method can describe the maneuver of motorcycle, we try to compare the MSE of the trajectory based on Markov Chain model with those using polynomial approach. The MSE results showed the performance of Markov Chain Model give the smallest MSE which 0.7666 about 0.24 better than 4 order polynomial.
Abstract: In this paper three phase load flow analysis on four bus system using Mi Power software is reformed. As power system never operates under steady state condition therefore single phase load flow analysis doesn’t provide accurate results. Hence three phase load flow analysis which can be performed under different contingencies, provide data when system is unbalanced. The system is analysing on the basis of parameter values in MW & MVAR for transmission line and generator buses. Harmonic values of resistance, reactance, and susceptance can predict the condition of small and large kind of system network. This type of analysis is useful for solving the power flow problem in different power systems which will useful to calculate the unknown parameter.
Efficient robotic path planning algorithm based on artificial potential field IJECEIAES
Path planning is crucial for a robot to be able to reach a target point safely to accomplish a given mission. In path planning, three essential criteria have to be considered namely path length, computational complexity and completeness. Among established path planning methods are voronoi diagram (VD), cell decomposition (CD), probability roadmap (PRM), visibility graph (VG) and potential field (PF). The above-mentioned methods could not fulfill all three criteria simultaneously which limits their application in optimal and real-time path planning. This paper proposes a path PF-based planning algorithm called dynamic artificial PF (DAPF). The proposed algorithm is capable of eliminating the local minima that frequently occurs in the conventional PF while fulfilling the criterion of path planning. DAPF also integrates path pruning to shorten the planned path. In order to evaluate its performance, DAPF has been simulated and compared with VG in terms of path length and computational complexity. It is found that DAPF is consistent in generating paths with low computation time in obstacle-rich environments compared to VG. The paths produced also are nearly optimal with respect to VG.
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%.
Reduce state of charge estimation errors with an extended Kalman filter algor...IJECEIAES
Li-ion batteries (LiBs) are accurately estimated under varying operating conditions and external influences using extended Kalman filtering (EKF). Estimating the state of charge (SOC) is essential for enhancing battery efficiency, though complexities and unpredictability present obstacles. To address this issue, the paper proposes a second-order resistance-capacitance (RC) battery model and derives the EKF algorithm from it. The EKF approach is chosen for its ability to handle complex battery behaviors. Through extensive evaluation using a Simulink MATLAB program, the proposed EKF algorithm demonstrates remarkable accuracy and robustness in SOC estimation. The root mean square error (RMSE) analysis shows that SOC estimation errors range from only 0.30% to 2.47%, indicating substantial improvement over conventional methods. These results demonstrate the effectiveness of an EKF-based approach in overcoming external influences and providing precise SOC estimations to optimize battery management. In addition to enhancing battery performance, the results of the study may lead to the development of more reliable energy storage systems in the future. This will contribute to the wider adoption of LiBs in various applications.
Performance Evaluation of Modelling and Simulation of Lead Acid Batteries for...IJPEDS-IAES
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.
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…
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.
Evaluation of lightweight battery management system with field test of elect...IJECEIAES
A battery management system is a crucial part of a battery-powered electric vehicle, which functions as a monitoring system, state estimation, and protection for the vehicle. Among these functions, the state estimation, i.e., state of charge and remaining battery life estimation, is widely researched in order to find an accuracy estimation methodology. Most of the recent researches are based on the study of the battery cell level and the complex algorithm. In practice, there is a statement that the method should be simple and robust. Therefore, this research work is focused on the study of lightweight methodology for state estimation based on the battery pack. The discrete Coulomb counting method and the data-driven approach, based on the Palmgren-Miner method, are proposed for the estimation of the state of charge and remaining battery life, respectively. The proposed methods are evaluated through a battery-powered electric bus under real scenario-based circumstances in the campus transit system. In addition, the battery life-cycle cost analysis is also investigated. The tested bus has currently been in operation in the transit system for more than one year.
Experimental Evaluation of Torque Performance of Voltage and Current Models u...IJPEDS-IAES
In this paper, two kinds of observers are proposed to investigate torque estimation. The first one is based on a voltage model represented with a low- pass filter (LPF); which is normally used as a replacement for a pure integrator to avoid integration drift problem due to dc offset or measurement error. The second estimator used is an extended Kalman filter (EKF) as a current model, which puts into account all noise problems. Both estimation algorithms are investigated during the steady and transient states, tested under light load, and then compared with the measured mechanical torque. In all conditions, it will be shown that the torque estimation error for EKF has remained within narrower error band and yielded minimum torque ripples when compared to LPF estimation. This motivates the use of EKF observer in high performance control drives of induction machines for achieving improved torque response.
Kineto-Elasto Dynamic Analysis of Robot Manipulator Puma-560IOSR Journals
Current industrial robots are made very heavy to achieve high Stiffness
which increases the accuracy of their motion. However this heaviness limits the robot speed and in masses the
required energy to move the system. The requirement for higher speed and better system performance makes it
necessary to consider a new generation of light weight manipulators as an alternative to today's massive
inefficient ones. Light weight manipulators require Less energy to move and they have larger payload abilities
and more maneuverability. However due to the dynamic effects of structural flexibility, their control is much
more difficult. Therefore, there is a need to develop accurate dynamic models for design and control of such
systems.This project presents the flexibility and Kineto - Elasto dynamic analysis of robot manipulator
considering deflection. Based on the distributed parameter method, the generalized motion equations of robot
manipulator with flexible links are derived. The final formulation of the motion equations is used to model
general complex elastic manipulators with nonlinear rigid-body and elastic motion in dynamics and it can be
used in the flexibility analysis of robot manipulators and spatial mechanisms. Manipulator end-effector path
trajectory, velocity and accelerations are plotted. Joint torques is to be determined for each joint trajectory
(Dynamics) .Using joint torques, static loading due to link’s masses, masses at joints, and payload, the robot
arms elastic deformations are to be found by using ANSYS-12.0 software package. Elastic compensation is
inserted in coordinates of robotic programming to get exact end-effectors path. A comparison of paths
trajectory of the end-effector is to be plotted. Also variation of torques is plotted after considering elastic
compensation. These torque variations are included in the robotic programming for getting the accurate endeffect
or’s path trajectory
A Drift-Diffusion Model to Simulate Current for Avalanche Photo DetectorIJERA Editor
In this research, a Drift-Diffusion model is carried out to calculate includes impact ionization mechanism and can calculate dark current and photocurrent of avalanche photo diode. Poisson equation, electron and hole density continuity equations and electron and hole current equations have been solved simultaneously using Gummel method. Consideration of impact ionization enables the model to completely simulate the carriers flow in high electrical field. The simulation has been done using MATLAB and the results are compared with other reliable results obtained by researchers. Our results show despite of hydrodynamics and Monte Carlo methods which are very complicated we can get the current characteristics of photo detector easily with acceptable accuracy. In addition we can use this method to calculate currents of device in high fields.
The article gives the experimental results of the processes occurring in the combined system of traction and magnetic suspension, which was implemented on the basis of the linear switched reluctance motor. The goal of the research is to examine the possibility to combine the levitation and traction functions within one unit. The full- function physical model of the transport system with the magnetic suspension has been produced for experimental verification of the development concept for the combined system of traction and magnetic suspension. The research tests have been performed at the track structure with the limited length in order to study the processes, occurring in the most complicated start-up mode, when the discrete behavior of current in windings has the disturbance effect on the object levitation. The oscillograms of electromechanical transition processes, showing the mutual influence of traction subsystems and a suspension, are provided. The results of researches have illustrated dramatically that the development concept of the combined system of traction and magnetic suspension, based on the linear switched reluctance motor, is absolutely real. Further researches should be aimed at improving the system characteristics by reducing the mutual influence of levitation and traction processes.
Motorcycle Movement Model Based on Markov Chain Process in Mixed TrafficIJECEIAES
Mixed traffic systems are dynamically complex since there are many parameters and variables that influence the interactions between the different kinds of vehicles. Modeling the behavior of vehicles, especially motorcycle which has erratic behavior is still being developed continuously, especially in developing countries which have heterogeneous traffic. To get a better understanding of motorcycle behavior, one can look at maneuvers performed by drivers. In this research, we tried to build a model of motorcycle movement which only focused on maneuver action to avoid the obstacle along with the trajectories using a Markov Chain approach. In Markov Chain, the maneuver of motorcycle will described by state transition. The state transition model is depend on probability function which will use for determine what action will be executed next. The maneuver of motorcycle using Markov Chain model was validated by comparing the analytical result with the naturalistic data, with similarity is calculated using MSE. In order to know how good our proposed method can describe the maneuver of motorcycle, we try to compare the MSE of the trajectory based on Markov Chain model with those using polynomial approach. The MSE results showed the performance of Markov Chain Model give the smallest MSE which 0.7666 about 0.24 better than 4 order polynomial.
Abstract: In this paper three phase load flow analysis on four bus system using Mi Power software is reformed. As power system never operates under steady state condition therefore single phase load flow analysis doesn’t provide accurate results. Hence three phase load flow analysis which can be performed under different contingencies, provide data when system is unbalanced. The system is analysing on the basis of parameter values in MW & MVAR for transmission line and generator buses. Harmonic values of resistance, reactance, and susceptance can predict the condition of small and large kind of system network. This type of analysis is useful for solving the power flow problem in different power systems which will useful to calculate the unknown parameter.
Efficient robotic path planning algorithm based on artificial potential field IJECEIAES
Path planning is crucial for a robot to be able to reach a target point safely to accomplish a given mission. In path planning, three essential criteria have to be considered namely path length, computational complexity and completeness. Among established path planning methods are voronoi diagram (VD), cell decomposition (CD), probability roadmap (PRM), visibility graph (VG) and potential field (PF). The above-mentioned methods could not fulfill all three criteria simultaneously which limits their application in optimal and real-time path planning. This paper proposes a path PF-based planning algorithm called dynamic artificial PF (DAPF). The proposed algorithm is capable of eliminating the local minima that frequently occurs in the conventional PF while fulfilling the criterion of path planning. DAPF also integrates path pruning to shorten the planned path. In order to evaluate its performance, DAPF has been simulated and compared with VG in terms of path length and computational complexity. It is found that DAPF is consistent in generating paths with low computation time in obstacle-rich environments compared to VG. The paths produced also are nearly optimal with respect to VG.
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%.
Reduce state of charge estimation errors with an extended Kalman filter algor...IJECEIAES
Li-ion batteries (LiBs) are accurately estimated under varying operating conditions and external influences using extended Kalman filtering (EKF). Estimating the state of charge (SOC) is essential for enhancing battery efficiency, though complexities and unpredictability present obstacles. To address this issue, the paper proposes a second-order resistance-capacitance (RC) battery model and derives the EKF algorithm from it. The EKF approach is chosen for its ability to handle complex battery behaviors. Through extensive evaluation using a Simulink MATLAB program, the proposed EKF algorithm demonstrates remarkable accuracy and robustness in SOC estimation. The root mean square error (RMSE) analysis shows that SOC estimation errors range from only 0.30% to 2.47%, indicating substantial improvement over conventional methods. These results demonstrate the effectiveness of an EKF-based approach in overcoming external influences and providing precise SOC estimations to optimize battery management. In addition to enhancing battery performance, the results of the study may lead to the development of more reliable energy storage systems in the future. This will contribute to the wider adoption of LiBs in various applications.
Performance Evaluation of Modelling and Simulation of Lead Acid Batteries for...IJPEDS-IAES
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.
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…
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.
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.
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).
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
DATA DRIVEN ANALYSIS OF ENERGY MANAGEMENT IN ELECTRIC VEHICLESvivatechijri
Inevitably, there has been a concerted policy push at the national level to promote electric vehicles. In electric vehicles, the progress stands and falls with the performance of the battery. Lithium-ion batteries are considered in this research project, as they are the most crucial component in the electric vehicle power system and require accurate monitoring and control. Proper battery optimization in electric vehicles requires a meticulous energy management system. The energy management system is bound for estimating the battery state of charge, state of health, various distinct factors in the system, and subsystems in real-time. The state of charge estimation accounts for the prevention of over-charge and over-discharge of batteries and provides cell balancing. Traditional SOC estimation approaches, such as open-circuit voltage (OCV) measurement and current integration (coulomb counting), are relatively accurate in some cases. However, estimating the SOC for Li-ion chemistries requires a modified approach. This project presents the Kalman filtering algorithm for the state of charge estimation that provides precise results for a fair computational effort.
State of charge estimation based on a modified extended Kalman filter IJECEIAES
The global transition from fossil-based automobile systems to their electric-driven counterparts has made the use of a storage device inevitable. Owing to its high energy density, lower self-discharge, and higher cycle lifetime the lithium-ion battery is of significant consideration and usage in electric vehicles. Nevertheless, the state of charge (SOC) of the battery, which cannot be measured directly, must be calculated using an estimator. This paper proposes, by means of a modified priori estimate and a compensating proportional gain, an improved extended Kalman filter (IEKF) for the estimation task due to its nonlinear application and adaptiveness to noise. The improvement was achieved by incorporating the residuals of the previous state matrices to the current state predictor and introducing an attenuating factor in the Kalman gain, which was chosen to counteract the effect of the measurement and process noise resulting in better accuracy performance than the conventional SOC curve fitting-based estimation and ampere hour methods. Simulation results show that the standard EKF estimator results in performance with an error bound of 12.9% due to an unstable start, while the modified EKF reduces the maximum error to within 2.05% demonstrating the quality of the estimator.
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.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
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.
A Battery Power Scheduling Policy with Hardware Support In Mobile Devices graphhoc
A major issue in the ad hoc networks with energy constraints is to find ways that increase their lifetime. The use of multihop radio relaying requires a sufficient number of relaying nodes to maintainnetwork connectivity. Hence, battery power is a precious resource that must be used efficiently in order to avoid early termination of any node. In this paper, a new battery power scheduling policy based on dynamic programming is proposed for mobile devices.This policy makes use of the state information of each cell provided by the smart battery package and uses the strategy of dynamic programming to optimally satisfy a request for power. Using extensive simulation it is proved that dynamic programming based schedulingpolicyimproves the lifetime of the mobile nodes.Also a hardware support is proposed to succeeds in distinguishing between real-time and non-real-time traffic and provides the appropriate grade of service, to meet the time constraints associated with real time traffic.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
"Protectable subject matters, Protection in biotechnology, Protection of othe...
Machine learning systems based on xgBoost and MLP neural network applied in satellite lithium-ion battery sets impedance estimation
1. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
Machine learning systems based on xgBoost
and MLP neural network applied in satellite
lithium-ion battery sets impedance estimation
Thiago H. R. Donato and Marcos G. Quiles
Department of Applied Computation, National Space Research Institute,
Sao Jose dos Campos, P.0 Box 12227-010, Brazil
Institute of Science and Technology, Federal University of Sao Paulo,
Sao Jose dos Campos, P.0 Box 12231-280, Brazil
Abstract. In this work, the internal impedance of the lithium-ion battery pack (important mea-
sure 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 xg-
Boost - 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.
Keywords: Lithium-ion battery, Internal impedance, State of charge, Multi Layer Perceptron,
Gradient Tree Boosting, xgBoost
1 Introduction
The choice for the technology to be applied in the electrical power system (EPS) is
important to the success of a satellite mission since it represents around 20 to 30
percent of a satellite total mass.
For outer space applications, lithium-ion batteries have less than one half of the
mass of nickel hydrogen batteries for the same stored energy [6] and, for this reason,
can reduce the system weight of a spacecraft, thus improving the load efficiency of
satellites. As a result, lithium-ion batteries are highly adopted in the satellites of
the United States and European Space Agency (ESA) [17]. In addition, lithium-ion
batteries are expected to become the third generation of satellite power storage
batteries for Chinas future space platform instead of NiMH batteries and NiCd
batteries.
The reliability of Li-ion batteries is an important issues due to the fact that
failures of Li-ion battery not only result in serious inconvenience and enormous re-
placement/repair costs, but also can cause overheating and short circuiting which
DOI: 10.5121/acii.2018.5101 1
2. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
can lead to catastrophic consequences, such as explosion. In order to prevent se-
vere failures from occurring, and to optimize Li-ion battery maintenance schedules,
breakthroughs in prognostics and health monitoring of Li-ion batteries, with an
emphasis on fault detection, correction and remaining-useful-life prediction, must
be achieved [20].
Battery PHM has a wide variety of meaning, ranging from irregular manual
measurements of voltage and electrolyte specific characteristics to fully automated
online observation of various measured and estimated battery parameters. An im-
portant battery PHM analysis consists of the measurement of its state of charge
(SOC). The state of charge (SOC) of Li-ion battery sets is a function of its inter-
nal impedance. Therefore, the state of charge (SOC) can be estimated through the
methods described on Section 2 or determined by the battery internal impedance
measurement or estimation along the operating cycles, as proposed in this study.
2 Li-ion battery state of charge (SOC) estimation
State of charge estimation has always been a big concern for all battery driven
devices but its definition presents many different issues [3]. In general, the SOC
of a battery is defined as the ratio of its current capacity (Qt) to the nominal
capacity (Qn). The nominal capacity is given by the manufacturer and represents
the maximum amount of charge that can be stored in the battery. The SOC can
be defined as follows:
SOCt =
Qt
Qn
. (1)
Therefore, once the nominal capacity is already defined, the methods described
below intend to estimate the actual battery capacity after charge/discharge cycles.
The various mathematical methods of estimation are classified according to
methodology. The classification of these SOC estimation methods is different in the
various literatures. One approach is according to the following categories [18]:
2.1 Direct measurements
Direct measurement methods refer to some physical battery properties such as
the terminal voltage and impedance. Many different direct methods have been
employed: open circuit voltage method, terminal voltage method, impedance mea-
surement method, and impedance spectroscopy method.
Open circuit voltage method There is approximately a linear relationship be-
tween the SOC of the lead-acid battery and its open circuit voltage (OCV ) given
by:
2
3. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
OCV (t) = a1 ∗ SOC(t) + a0. (2)
Where SOC(t) is the SOC of the battery at t, a0 is the battery terminal voltage
when SOC = 0, and a1 is obtained from knowing the value of a0 and OCV (t) at
SOC = 1. Unlike the lead-acid battery, the Li-ion battery does not have a linear
relationship between the OCV and SOC [4].
Terminal voltage method The terminal voltage method is based on the terminal
voltage drops because of the internal impedances when the battery is discharging,
so the electromotive force (EMF) of battery is proportional to the terminal voltage.
Since the EMF of battery is approximately linear proportional to the SOC, the
terminal voltage of battery is also approximately linear proportional to the SOC
[15]. The terminal voltage method has been employed at different discharge cur-
rents and temperatures but, at the end of battery discharge, the estimated error of
terminal voltage method is large, because the terminal voltage of battery suddenly
drops at the end of discharge.
Once the Li-ion battery does not have a linear relationship between the VOC
and SOC, this method is not indicated due to the estimation error.
Impedance spectroscopy method The impedance spectroscopy method mea-
sures battery impedances over a wide range of ac frequencies at different charge and
discharge currents. The values of the model impedances are found by least-squares
fitting to measured impedance values. SOC may be indirectly inferred by measur-
ing present battery impedances and correlating them with known impedances at
various SOC levels [10].
Coulomb counting method The Coulomb counting method measures the dis-
charging current of a battery and integrates the discharging current over time in
order to estimate SOC. Coulomb counting method is done to estimate the SOC(t)
which is estimated from the discharging current, I(t) and previously estimated
SOC values, SOC(t − 1). SOC is calculated by the following equation:
SOC(t) = SOC(t − 1) +
I(t)
Qn
∗ ∆t. (3)
2.2 Machine learning systems
Machine learning system consists of an approach which uses pattern recognition
and machine learning techniques to detect changes in system states [12]. With this
approach, few information regarding to the analyzed system is necessary in order
to build prognostic models due to the fact that only the monitored data itself is
3
4. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
necessary to elaborate them. For this reason, machine learning systems are ad-
equate to EPS systems which are sufficiently complex such that developing an
accurate physical model is prohibitively expensive. As batteries have been affected
by many chemical factors and have nonlinear SOC, machine learning systems offer
good solution for SOC estimation. However, this approach implies in wider confi-
dence intervals than other approaches and requires a substantial amount of data
for training.
After the data preparation, a machine learning algorithm shall be selected in
order to build the machine learning system. The selection of a proper algorithm
for a specific application is a challenging factor in applying data driven prognostics
methods. Examples of machine learning algorithms applied in machine learning
systems to estimate SOC: back propagation Multi Layer Perceptron neural network
(MLP), gradient boosting (xgBoost), radial basis function (RBF), fuzzy logic
methods, support vector machine (SV M), fuzzy neural network, and Kalman filter
[16].
In this study, two machine learning systems were applied: MLP neural network
(Section 2.2) and gradient boosting (Section 2.3)
Multi Layer Perceptron In order to build an algorithm capable of classifying a
label attribute, the Multi Layer Perceptron (MLP) neural network can be applied.
The MLP consists of a feedforward artificial neural network model that can be
used in classification or prognostic issues.
The Multi Layer Perceptron network contains three or more layers (an input and
an output layer with one or more hidden layers) of nonlinearly-activating neurons.
Each neuron combine the inputs multiplied by their correspondent weights and
apply an activation function which output is delivered as input of neurons of the
following layer [8, Chapter 4].
In many practical applications of artificial neural networks (ANN), there ex-
ist natural constraints on the model such as monotonic relations between inputs
and outputs that are known in advance. It is advantageous to incorporate these
constraints into the ANN structure [19]. The monotonic Multi Layer Perceptron
network (MONMLP) is an approach for multi-dimensional function approximation
ensuring monotonicity for selected input-output relations. Moreover, we determine
the requirements for the network structure regarding universal approximation ca-
pabilities.
2.3 Gradient Tree Boosting
Gradient tree boosting is typically used with decision trees (especially CART trees)
of a fixed size as base learners. For this special case Friedman proposes a modifi-
cation to gradient boosting method which improves the quality of fit of each base
learner.
4
5. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
Generic gradient boosting at the m − th step would fit a decision tree hm(x)
to pseudo-residuals. Let Jm be the number of its leaves. The tree partitions the
input space into Jm disjoint regions R1m, . . . , RJmm and predicts a constant value
in each region. Using the indicator notation, the output of hm(x) for input x can
be written as the sum:
hm(x) =
Jm
j=1
bjmI(x ∈ Rjm), (4)
where bjm is the value predicted in the region Rjm.
Then the coefficients bjm are multiplied by some value γm, chosen using line
search so as to minimize the loss function, and the model is updated as follows:
Fm(x) = Fm−1(x)+γmhm(x), γm = γarg min
n
i=1
L(yi, Fm−1(xi)+γhm(xi)) (5)
Friedman proposes to modify this algorithm so that it chooses a separate optimal
value γjm for each of the tree’s regions, instead of a single γm for the whole tree.
He calls the modified algorithm ”TreeBoost” [7]. The coefficients bjm from the tree-
fitting procedure can be then simply discarded and the model update rule becomes:
Fm(x) = Fm−1(x)+
Jm
j=1
γjmI(x ∈ Rjm), γjm = γarg min
xi∈Rjm
L(yi, Fm−1(xi)+γ)
(6)
Root Mean Square Error Machine learning systems apply machine learning
techniques in a supervised approach. Considering a numeric label attribute in the
estimative of the battery set impedance, each observed value can be compared with
the predicted one. This individual deviation is called a residual and the aggregation
of all the residuals is denominated the Root Mean Square Error (RMSE), obtained
as follows [9]:
RMSE =
n
t=1(ˆyt − yt)2
n
(7)
Where:
– ˆyt: predicted instance
– yt: observed instance
– n: number of instances
5
6. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
2.4 Li-ion battery state of charge (SOC) based on battery internal
impedance
According to the definition of the SOC, the function which provides the battery
state of charge based on battery internal impedance is obtained as follows:
SOCt = SOCt0 +
t
t0
(
η · I
Ct
) · dt (8)
Where:
– SOCt0: estimated SOC at time t0, when the estimation process starts
– SOCt: estimated SOC at time t
– η: current efficiency
– I: current - assumed to be positive when charging
– Cn: capacity of the battery at time t
In this study, the battery impedance is obtained through the machine learning
systems (see Section 2.2) which can be applied in the determination of battery state
of charge (SOC).
Bagging optimization According to bagging optimization method, a training
set Dofsizen is divided into m new training sets Di, eachofsizen, by sampling
from D uniformly and with replacement. Sampling the m new training sets with
replacement, implies that some observations may be repeated in each Di[1].
This kind of sample is also known as bootstrap sample. In order to obtain the
resulting ensemble of models, the m models are fitted using the above m bootstrap
samples. Finally, all the models are applied to the scoring set and the labels are
combined by averaging the output (for regression) or voting (for classification).
3 Machine learning systems for Li-ion battery impedance
estimation
This study applies two machine learning systems (MLP neural network - Section
2.2 and gradient boosting - Section 2.3) in order to estimate the battery internal
impedance and compares the obtained results.
To perform the comparison between the two machine learning systems, a battery
testing database provided by NASA Ames Research Center [14] was used as data
set. The database comprises sensor monitoring data of Li-ion batteries mounted in
batches of 4 and running through 3 different operational profiles (charge, discharge
and impedance) at ambient temperatures of 4, 24 and 44 Celsius degrees (see Section
4).
From the analyzed data set, features were selected to characterize the battery
condition during a certain period of time (see Section 5). After the development
6
7. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
of the data set, machine learning systems are trained (see Section 6), Root Mean
Square Error (RMSE) performance metric is used to evaluate the obtained machine
learning systems (see Section 9) and, if the performance criteria are achieved, they
can be considered feasible solutions to estimate the battery internal impedance in
an online basis.
4 Data preparation for Li-ion battery impedance estimation
Battery impedance, which decreases over the working time of a battery, is an im-
portant and direct indicator for estimating battery state of charge (SOC). In online
or in-orbit applications, such as electric vehicles and satellites, the battery inter-
nal impedance measurement or monitoring is difficult [11]. It can be used charge
transfer resistance and electrolyte resistance extracted from EIS to estimate battery
capacity [14]. However, these features can only be obtained via offline tests under
the optimal measuring conditions and by using specialized and expensive equip-
ment for EIS measurements [5]. The results of the aging experiment showed that
increase in battery capacity loss or resistance in a lifetime is related to operating
conditions, such as voltage, current, and temperature. However, in practical appli-
cations, several characteristics, such as current and voltage, are controlled to meet
the load requirements of an associated circuit and thus cannot represent battery
aging [13].
4.1 Li-ion battery testing set information
The following sections apply machine learning techniques in Li-ion battery impedance
estimation using battery data provided by National Aeronautics and Space Admin-
istration (NASA) Ames Prognostics Center of Excellence [14], where 134 recharge-
able lithium-ion batteries were tested.
The laboratory setup and data recording were conducted by National Aero-
nautics and Space Administration (NASA) Ames Prognostics Center of Excellence
[14]. According to NASA experiment, the 134 rechargeable lithium-ion batteries
are organized in 34 battery sets. Each battery set contains the test data organized
according to Figure 2.
4.2 Laboratory setup
The experimental setup primarily consists of a set of Li-ion cells (which may reside
either inside or outside an environmental chamber), chargers, loads, EIS equipment
for battery health monitoring (BHM), a suite of sensors (voltage, current and tem-
perature), some custom switching circuitry, data acquisition system and a computer
for control and analysis. Figure 1 details the assembly of the equipment.
7
8. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
Fig. 1. Laboratory setup [14]
The cells are cycled through charge and discharge cycles under different load
and environmental conditions set by the electronic load and environmental chamber
respectively. Periodically EIS measurements will be taken to monitor the internal
condition of the battery using the BHM module. The DAQ system collects the
externally observable parameters from the sensors. The switching circuitry enables
the cells to be in the charge, discharge or EIS health monitoring state as dictated
by the algorithms running on the control computer [14].
4.3 Li-ion battery testing set information
The Li-ion batteries are organized in batches of 4 are run through 3 different op-
erational profiles (charge, discharge and impedance) at ambient temperatures of 4,
24 and 44 oC [14]:
1. Charge step: charging was carried out in a constant current (CC) mode at 1.5A
until the battery voltage reached 4.2V and then continued in a constant voltage
(CV) mode until the charge current dropped to 20mA
2. Discharge step: discharging was conducted in CC mode until the discharge volt-
age reached a predefined cutoff voltage. Fixed and variable load currents at 1,
2, and 4 Amps were used and the discharge runs were stopped at 2V, 2.2V,
2.5V or 2.7V
3. Impedance measurement: measurement was performed through an electrochem-
ical impedance spectroscopy (EIS) frequency sweep from 0.1 Hz to 5 kHz
Figure 2 details the battery data structure of the operational profiles.
4.4 Li-ion battery impedance measurement rectifier
In order to eliminate the noise generated by time-varying current passing through
an electro-chemical cell or battery due to load fluctuation, a filtering approach or an
8
9. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
Fig. 2. Data structure [14]
electronic cancellation technique shall be applied. In the laboratory setup described
above, it was used an electronic device.
Time-varying current flowing in a circuit which includes the cell/battery is
sensed externally to the cell/battery with a magnetically-coupled ac current probe
thereby producing an induced time-varying signal. This induced signal is amplified
to the level of the original time-varying current and applied to the cell/battery’s
terminals in phase-opposition to the original current. As a result, the component of
time-varying current flowing in the cell/battery’s external leads assumes an alter-
nate path around the cell/battery and is effectively canceled within the cell/battery
itself [2].
5 Features selection for Li-ion battery impedance estimation
In this study, the 134 rechargeable lithium-ion batteries are organized in 34 battery
sets. Each battery set contains the test data according to Figure 2. For each C-D
(charge and discharge) cycle, the following features were extracted [20]:
– F1: during charge cycle, time interval between the nominal voltage and the
cutoff voltage
– F2: during charge cycle, time interval between the nominal current and the
cutoff current
– F3: during discharge cycle, time interval between two predefined voltages
– F4: average temperature during the time interval F1
– F5: average temperature during the time interval F2
– F6: during discharge cycle, cutoff voltage
The historical set applied in the machine learning systems modeling includes
the six features (F1, F2, F3, F4, F5 and F6) and the label attribute which corre-
sponds to the rectified battery impedance. For each machine learning system, two
9
10. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
regression models are built in order to estimate the real and imaginary components
of the rectified impedance. Tables 1 and 2 are extractions of the historical sets
corresponding to the real and imaginary components which contain 20 records (C-D
cycles) for each one of the 34 battery sets (684 records - C-D cycles, in total).
Table 1. Historical set - impedance real component
cycle F1 F2 F3 F4 F5 F6 imp re
2008 4 1 9966.407 6422.609 472.313 27.0074 26.3008 2.4556 0.06175
2008 4 2 10226.375 6627.891 472.125 25.6742 26.5323 2.6321 0.05989
2008 4 3 10635.968 6528.063 472.344 25.6754 26.3325 2.5010 0.05919
Table 2. Historical set - impedance imaginary component
cycle F1 F2 F3 F4 F5 F6 imp img
2008 4 1 9966.407 6422.609 472.313 27.0074 26.3008 2.4556 -0.00096
2008 4 2 10226.375 6627.891 472.125 25.6742 26.5323 2.6321 -0.00112
2008 4 3 10635.968 6528.063 472.344 25.6754 26.3325 2.5010 -0.00105
6 Li-ion battery impedance data modeling
After the data preparation, two machine learning techniques (MLP neural network
- Section 2.2 and gradient boosting - Section 2.3) are applied in order to estimate
the battery internal impedance. For each applied technique, a few training cycles
are executed with the variation of the technique hyperparameters. Therefore, each
training cycle generates a regression model which can be compared with the other
obtained regression models by the application of the RMSE (Root Mean Square
Error) validation technique, according to Section 2.3.
7 MLP neural network with monotonicity constraints modeling
Tables 1 and 2 are used to build two regression models based on MLP neural
network technique (Section 2.2).
Multi Layer Perceptron neural network with monotonicity constraints imple-
ments one hidden-layer Multi Layer Perceptron neural network (MLP) models that
enforces monotonic relations on designated input variables. Each training cycle ap-
plies 10 or 20 ensemble members to fit and 1, 2, 3 or 4 hidden nodes in the hidden
layer.
The ensemble members to fit in each training cycle are obtained according to
Section 2.4. Each ensemble member contains a random subset of 70 percent of the
10
11. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
training set and, after generating the 10 or 20 regression models (depending on
the number of ensemble members to fit), the result corresponds to the mean value
obtained through the application of all regression models.
These training cycles with different combinations result into different MLP neu-
ral networks with monotonicity constraints. These different combinations of the
hyperparameters are described in Table 3.
Table 3. MLP neural network with monotonicity constraints hyperparameters
Hyperparameter Description Possible values
hidden1 number of hidden nodes in the first hidden layer 1, 2, 3, 4
n.ensemble number of ensemble members to fit 10, 20
monotone
column indices of covariates for which the
monotonicity constraint should hold
1
bag
logical variable indicating whether or not
bootstrap aggregation (bagging) should
be used
TRUE
iter.max
maximum number of iterations of
the optimization algorithm
500
7.1 MLP neural network modeling with monotonicity constraints and
10 ensemble members to fit
Estimative of real component of battery set impedance Using the back-
propagation algorithm to update the MLP neural network weights, monotonicity
constraints, 10 ensemble members to fit and using different numbers of hidden nodes
in the hidden layer (1, 2, 3 or 4), the real component of the battery impedance
through the cycles is according the Figure 3.
Estimative of imaginary component of battery set impedance Using the
backpropagation algorithm to update the MLP neural network weights, monotonic-
ity constraints, 10 ensemble members to fit and using different numbers of hidden
nodes in the hidden layer (1, 2, 3 or 4), the imaginary component of the battery
impedance through the cycles is according the Figure 4.
7.2 MLP neural network modeling with monotonicity constraints and
20 ensemble members to fit
Estimative of real component of battery set impedance Using the back-
propagation algorithm to update the MLP neural network weights, monotonicity
constraints, 20 ensemble members to fit and using different numbers of hidden nodes
in the hidden layer (1, 2, 3 or 4), the real component of the battery impedance
through the cycles is according the Figure 5.
11
12. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
Fig. 3. Estimated real component of battery set impedance using MLP neural network modeling
with monotonicity constraints and 10 ensemble members to fit
Fig. 4. Estimated imaginary component of battery set impedance using MLP neural network
modeling with monotonicity constraints and 10 ensemble members to fit
Estimative of imaginary component of battery set impedance Using the
backpropagation algorithm to update the MLP neural network weights, monotonic-
ity constraints, 20 ensemble members to fit and using different numbers of hidden
nodes in the hidden layer (1, 2, 3 or 4), the imaginary component of the battery
impedance through the cycles is according the Figure 6.
7.3 MLP neural network with monotonicity constraints model
validation
Applying the RMSE (Root Mean Square Error) validation technique, according to
Section 2.3, each model developed with the application of Multi Layer Perceptron
12
13. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
Fig. 5. Estimated real component of battery set impedance using MLP neural network modeling
with monotonicity constraints and 20 ensemble members to fit
Fig. 6. Estimated imaginary component of battery set impedance using MLP neural network
modeling with monotonicity constraints and 20 ensemble members to fit
technique in the estimative of real and imaginary components of the battery set
impedance was evaluated and the corresponding RMSE values are presented in
Table 4.
Increasing the number of ensemble members to fit, there was no impact on the
root square mean error for estimating the real component of battery set impedance.
However, a higher number of ensemble members to fit minimized the root square
mean error for estimating the imaginary component of battery set impedance.
According to Table 4, a higher number of hidden nodes on the hidden layer
minimizes the root square mean error for estimating the real and imaginary com-
ponents of battery set impedance.
13
14. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
Table 4. MLP neural network with monotonicity constraints model validation
Battery impedance Ensemble members to fit Hidden nodes on layer RMSE
real 20 1 0.0161220600
real 20 2 0.0124101700
real 20 3 0.0100222800
real 20 4 0.0094165090
imaginary 20 1 0.0011236520
imaginary 20 2 0.0007225610
imaginary 20 3 0.0004410664
imaginary 20 4 0.0003257367
The MLP models with the lower root square mean error in estimating the real
and imaginary components of battery set impedance have the configuration of 20
ensemble members to fit and 4 hidden nodes on the hidden layer and an RMSE
error of 0.0094165090 and 0.0003257367, respectively.
8 Gradient boosting modeling
Tables 1 and 2 are used to build two regression models based on gradient boosting
technique (Section 2.3). The training set is divided into training and validation sets.
The validation set is used to evolve the regression models during each gradient
boosting iteration. Each training cycle applies an ’eta’ equal to 0.20 or 0.60 to
control the learning rate and a subsample ratio of the training instances of 0.80,
0.85, 0.90 or 0.95.
These training cycles with different combinations result into different gradient
boosting models. These different combinations of the hyperparameters are described
in Table 5.
Table 5. Gradient boosting hyperparameters
Hyperparameter Description Possible values
objective objective function reg:linear
max depth maximum depth of a tree 10
eta
control the learning rate: scale
contribution of each tree by a factor
of 0 <eta <1
0.20, 0.60
col sample
subsample ratio of columns
when constructing each tree
0.80
ss sample
subsample ratio of the training instance.
0.5 means that xgboost randomly
collected half of the data to grow trees
and this will prevent overfitting
0.80, 0.85, 0.90, 0.95
eval metric evaluation metric per validation cycle root mean square error
nrounds the max number of validation cycles 200
14
15. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
The hyperparameter ’eta’ has an important role in the gradient boosting mod-
eling since it controls the learning rate. This parameter scales the contribution of
each tree by a factor between 0 and 1 when it is added to the current approximation.
It is used to prevent overfitting by making the boosting process more conservative.
Lower value for ’eta’ implies larger value for ’nrounds’: low ’eta’ value means model
more robust to overfitting but slower to compute.
8.1 Gradient boosting modeling with eta = 0.20
Estimating real component of battery set impedance Building an ensemble
of decision trees in which each decision tree is built in order to minimize the error
of the previous one (gradient boosting method), setting the maximum depth of the
trees to 10, applying the root mean square error method as a metric to evolve the
model with the validation set, controlling the learning rate through ’eta’ of 0.20
and subsetting the training set instances with distinct ratios (0.80, 0.85, 0.90 or
0.95), the real component of the battery impedance through the cycles is according
the Figure 7.
Fig. 7. Estimated real component of battery set impedance using gradient boosting modeling with
eta = 0.20
Estimating imaginary component of battery set impedance Building an
ensemble of decision trees in which each decision tree is built in order to minimize
the error of the previous one (gradient boosting method), setting the maximum
depth of the trees to 10, applying the root mean square error method as a metric
to evolve the model with the validation set, controlling the learning rate through
’eta’ of 0.20 and subsetting the training set instances with distinct ratios (0.80,
15
16. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
0.85, 0.90 or 0.95), the imaginary component of the battery impedance through the
cycles is according the Figure 8.
Fig. 8. Estimated imaginary component of battery set impedance using gradient boosting modeling
with eta = 0.20
8.2 Gradient boosting modeling with eta = 0.60
Estimating real component of battery set impedance Building an ensemble
of decision trees in which each decision tree is built in order to minimize the error
of the previous one (gradient boosting method), setting the maximum depth of the
trees to 10, applying the root mean square error method as a metric to evolve the
model with the validation set, controlling the learning rate through ’eta’ of 0.60
and subsetting the training set instances with distinct ratios (0.80, 0.85, 0.90 or
0.95), the real component of the battery impedance through the cycles is according
the Figure 9.
Estimating imaginary component of battery set impedance Building an
ensemble of decision trees in which each decision tree is built in order to minimize
the error of the previous one (gradient boosting method), setting the maximum
depth of the trees to 10, applying the root mean square error method as a metric
to evolve the model with the validation set, controlling the learning rate through
’eta’ of 0.60 and subsetting the training set instances with distinct ratios (0.80,
0.85, 0.90 or 0.95), the imaginary component of the battery impedance through the
cycles is according the Figure 10.
16
17. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
Fig. 9. Estimated real component of battery set impedance using gradient boosting modeling with
eta = 0.60
Fig. 10. Estimated imaginary component of battery set impedance using gradient boosting mod-
eling with eta = 0.60
8.3 Gradient boosting model validation
Applying the RMSE (Root Mean Square Error) validation technique, according to
Section 2.3, each model developed with the application of Gradient boosting tech-
nique in the estimating real and imaginary components of the battery set impedance
was evaluated and the corresponding RMSE values are presented in Table 6.
A lower ’eta’ minimizes the root square mean error for estimating the real and
imaginary components of battery set impedance. According to Table 6, increasing
the number of instances to be used in the training process, there was a decrease
on the root square mean error for estimating the real component of battery set
impedance. However, a higher number of instances to be used in the training process
17
18. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
Table 6. Gradient boosting model validation
Battery impedance ’eta’ - Learning rate Subsample ratio RMSE
real 0.20 0.80 0.0057317940
real 0.20 0.85 0.0044927730
real 0.20 0.90 0.0034616720
real 0.20 0.95 0.0028836380
imaginary 0.20 0.80 0.0003310718
imaginary 0.20 0.85 0.0006632989
imaginary 0.20 0.90 0.0005046742
imaginary 0.20 0.95 0.0001962272
maximized the root square mean error for estimating the imaginary component of
battery set impedance.
The Gradient boosting models with the lower root square mean error in es-
timating the real and imaginary components of battery set impedance have the
configuration of ’eta’=0.20 and a subsample ratio of 0.95 and an RMSE error of
0.0028836380 and 0.0001962272, respectively.
9 Li-ion battery impedance model validation
According to RMSE validation technique (see Section 2.3), the Multi Layer Per-
ceptron model with the higher performance (20 ensemble members to fit and 4
hidden nodes on the hidden layer) and the Gradient Tree Boosting model with the
higher performance (’eta’=0.20 and a subsample ratio of 0.95) are compared in 11
regarding the estimation of the real and imaginary components of the battery set
impedance.
Fig. 11. RMSE State of charge (SOC) model validation
18
19. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
10 Conclusion
In this study, two distinct machine learning approaches were applied in order to
estimate the battery impedance of satellite Li-ion battery sets which is used in the
determination of their state of charge (SOC) (see Section 2.4). The obtained RMSE
(Root Mean Square Error) of both approaches has demonstrated the feasibility of
such machine learning systems in estimating Li-ion battery sets battery impedance
in cases where an error below 0.10 is acceptable.
However, once the obtained RMSE (Root Mean Square Error) of the Gradient
Tree Boosting model is lower in estimating both real and imaginary components
of the battery impedance, this approach is recommended over the Multi Layer
Perceptron model.
Acknowledgements
M.G.Q thanks FAPESP (Grant 2011/18496-7) and CNPq (Grant 310908/2015-9).
References
1. J. A. Aslam, R. A. Popa, and R. L. Rivest. On estimating the size and confidence of a
statistical audit. Proceedings of the Electronic Voting Technology Workshop, 7, 2007.
2. K. S. Champlin. Method and apparatus for suppressing time-varying signals in batteries
undergoing charging or discharging, 11 1992.
3. Y. Chang, W. The state of charge estimating methods for battery: A review. International
Scholarly Research Notices Applied Mathematics, 2013:1–7, 2013.
4. M. Coleman, C. K. Lee, C. Zhu, and W. G. Hurley. State of-charge determination from emf
voltage estimation: using impedance, terminal voltage, and current for lead-acid and lithium-
ion batteries. IEEE Transactions on Industrial Electronics, 54:25502557, 2007.
5. M. Dalal, J. Ma, and D. He. Lithium-ion battery life prognostic health management system
using particle filtering framework. Proc. Inst. Mech. Eng. Part O J. Risk, 225:8190, 2011.
6. G. J. Dudley. Lithium-ion batteries for space. Proceedings of the Fifth European Space Power
Conference, page 17, 1998.
7. T. Hastie, R. Tibshirani, and J. H. Friedman. The elements of statistical learning: data mining,
inference, and prediction, volume 02. Springer, USA, 2009.
8. S. Haykin. Neural networks - A comprehensive foundation, volume 02. Cambridge University
Press, USA, 1999.
9. R. J. Hyndman and A. B. Koehler. Another look at measures of forecast accuracy. Interna-
tional Journal of Forecasting, 04:679688, 2006.
10. R. Li, J. F. Wu, H. Y. Wang, and G. C. Li. Prediction of state of charge of lithium-ion
rechargeable battery with electrochemical impedance spectroscopy theory. Proceedings of the
5th IEEE Conference on Industrial Electronics and Applications, page 684688, 2010.
11. D. Liu, H. Wang, Y. Peng, W. Xie, and H. Liao. Satellite lithium-ion battery remaining cycle
life prediction with novel indirect health indicator extraction. Energies, 6:36543668, 2013.
12. J. Liu and G. Wang. A multi-step predictor with a variable input pattern for system state
forecasting. Mechanical Systems and Signal Processing, 23:15861599, 2009.
13. M. Parviz and S. Moin. Boosting approach for score level fusion in multimodal biometrics
based on auc maximization. J. Inf. Hiding Multimed. Signal Process, 2:5159, 2011.
19
20. Advanced Computational Intelligence: An International Journal, Vol.5, No.1, January 2018
14. B. Saha and K. Goebel. Battery data set, 2007. NASA Ames Research Center, Moffett Field,
CA.
15. S. Sato and A. Kawamura. A new estimation method of state of charge using terminal voltage
and internal resistance for lead acid battery. Proceedings of the Power Conversion Conference,
page 565570, 2002.
16. M. Schwabacher. A survey of data-driven prognostics. AIAA Meeting Papers, 2005.
17. D. Wang, G. Li, and Y. Pan. The technology of lithium-ion batteries for spacecraft application.
Aerospace Shanghai, 4:5459, 2000.
18. N. Watrin, B. Blunier, and A. Miraoui. Review of adaptive systems for lithium batteries state-
of-charge and state-of-health estimation. Proceedings of IEEE Transportation Electrification
Conference and Expo, pages 1–6, 2012.
19. H. Zhang and Z. Zhang. Feedforward networks with monotone constraints. International Joint
Conference on Neural Networks, 03:1820–1823, 1999.
20. J. Zhang and J. Lee. A review on prognostics and health monitoring of li-ion battery. Journal
of Power Sources, 196:60076014, 2011.
Authors
Thiago Donato graduated at Electrical Engineering from Fed-
eral University of Itajub - UNIFEI (2006) and has worked dur-
ing five years in private companies ( EMBRAER - aircraft man-
ufacturer company - and TOTVS - software company) develop-
ing solutions which apply machine learning techniques in the
resolution of major issues. Donato is enrolled in master’s at
Computer Science from National Space Research Institute -
INPE (2018). Has experience in Machine Learning, acting on
the following subjects: data and text preparation and mining,
neural network and other machine learning classification tech-
niques.
Marcos G. Quiles is an Associate Professor at the Depart-
ment of Science and Technology, Federal University of So Paulo,
Brazil. He received the BS degree, with honors, in 2003 from the
State University of Londrina, Brazil, and a Ph.D. degree from
the University of So Paulo, Brazil, in 2009, both in Computer
Science. From January to July of 2008, Quiles was a Visiting
Scholar in the Perception and Neurodynamics Lab at The Ohio
State University. From January to December of 2017, Quiles
was an Academic Visitor at the University of York, York-UK.
He was awarded a Brazilian research productivity fellowship
from the Brazilian National Research Council (CNPq). His re-
search interests include nature-inspired computing, machine learning, complex net-
works, and their applications in interdisciplinary problems.
20