This document discusses various methods for estimating the state of charge (SOC) of lithium-ion batteries used in electric vehicles. It first provides background on the importance of accurately estimating SOC and challenges in doing so. It then reviews different modeling approaches (physical models, data-driven models, equivalent circuit models) and filtering techniques like the Kalman filter and extended Kalman filter (EKF) that are often used with equivalent circuit models. Several research papers applying the EKF to SOC estimation are summarized. The document concludes the EKF is the best method for its ability to handle noise and provide accurate SOC estimation with low complexity.

1. Implementation and Calculation of State of Charge for Electric Vehicles
G Loka Priya1, Prerana Athreya R2, S V Thanmayi 3, Shashank H P 4,
Dr. Chandrashekar M Patil5
1Department of Electronics and Communication Engineering, Vidyavardhaka College of Engineering, Mysuru,
India
5Professor and Head, Department of Electronics and Communication Engineering, Vidyavardhaka College of
Engineering Mysuru, India
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Abstract—Lithium-Ion batteries are the wellspring of
energy for every cell phone and electric vehicle. That is
the reason the client ought to exposed as a main
priority about the condition of charge (SOC) and
furthermore the condition of wellbeing (SOH) of the
battery. There are numerous précised strategies
introduced inside the logical papers however typically
these are scientifically serious, so running their sign
handling calculations assimilates a significant measure
of battery power. We want to track down exactness for
energy proficiency by assessing these boundaries
dissecting the (current) venture reaction of the
battery. Battery the executives framework (BMS)
requires an exact expectation of the excess home of
charge of the cell or battery pack. Besides in electric
vehicles, batteries experience a unique functional
climate where the calculations utilized inside the
versatile gadgets to anticipate SOC, similar to coulomb
counting are inadequate for this reason. Taking into
account this, to ascertain State of Charge, expanded
Kalman sifting technique is utilized for two distinct
lithium-particle batteries. For example, the impact of
math of the battery on worth of those boundaries is
talked about. At long last during this venture, we
separate between Coulomb's counting and Extended
Kalman channel strategy and talk about why EKF is
ideal.
Keywords— Li-ion batteries, SOC(state of charge),
SOH(state of health), BMS(Battery management
system), Coulomb counting, Extended Kalman Filter.
I. INTRODUCTION
Conventional partners like wide, e-bicycles and e-bikes
can go farther than normal bicycles with little exertion. E-
bicycles are not difficult to explore! They can be
immediately re-energized any placewe can track down
power, or low-end batteries can be immediately supplanted
with completely energized batteries. As innovation propels,
more electric vehicle batteries currently should be
associated with different pieces of the vehicle, for example,
the vehicle's regulator to speed up. A precise estimation of
the power accessible inside the battery is fundamental for
the productive activity of a strong train and to forestall
traveler drenching. Ultimately, realizing the leftover power
likewise forestalls cheating and unreasonable release of
batteries, which is fundamental for the protected use and
long existence of lithium-particle batteries and has turned
into the favored battery for mixtures and electric vehicles,
yet additionally for electric bikes and bike applications.
Drivers are significant for their particular strength, power
thickness, cycle/schedule wellbeing and their decreased
requirement for care contrasted with lead corrosive
batteries loaded up with water. One of their couple of issues
is the trouble of assessing how much energy left. Lithium-
particle (LIB) batteries have shown extraordinary potential,
for example, energy move innovation, convenient
hardware, and lattice adjustment, dissimilar to the gas tank,
the leftover battery power can't be estimated
straightforwardly. Subsequently, the interior condition of
the battery ought to be taken from the estimating values
that are open utilizing numerical models. These incorporate
versatile models, information driven models utilizing AI,
and equal circuit models (ECM). Versatile models utilize
various scales to depict the electrochemical cycles inside a
phone, these are perplexing and require point by point data
about cell structure to make an exact boundary, however
give a definite portrayal of the cycles inside the phone
during different working circumstances. In information
driven models, AI calculations are utilized to show the
model straightforwardly from the deliberate information
without earlier cell information. Nonetheless, AI
calculations require enormous informational indexes to
prepare the model appropriately. Comparative circuit
models (ECM) depict how batteries work utilizing power
sources and inactive electrical apparatuses, generally
resistors and capacitors. Model still up in the air by
embedding the model expectation into the deliberate
reactions. Contrasted with versatile models, they don't give
point by point comprehension of interior cycles, yet they
are less complicated so they are more straightforward to
recognize. ECMs give a decent harmony among precision
and intricacy, so they are utilized in numerous applications.
Models are generally improved on reality. The model
boundaries are off base, and the sensor values are
boisterous. Therefore, the model expectation contrasts
from the genuine framework reaction. To limit prescient
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1064
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2. blunder, a few measurable filtration techniques have been
presented and the most well-known strategy utilized in the
LIB Extensive Kalman Filter (EKF) frameworks. It is broadly
used to gauge installment status, wellbeing status, and cell
power. It can likewise be utilized to gauge online
boundaries.
II. STATE OF CHARGE
The condition of charge of a battery is characterized as the
level of evaluated limit. That is , SOC is the proportion
between battery capacity limit and absolute battery
duration. Assuming the deliberate volume is to be
addressed in coulombs or Ah, the evaluated limit should be
estimated in similar units. The equilibrium or content of
the appraised power will be addressed in watt hours, the
SOC can be evaluated in light of energy stockpiling limit.
This way battery can be securely charged and released
utilizing SOC to further develop the battery duration and
furthermore it provides us with the present status of the
battery Hence to assess the level of SOC utilizing the inside
and outside charger transmission the standard coulomb
computation techniques are utilized. Thus, For this
situation, we can utilize a Qrated, which isn't an element of
temperature or maturing and furthermore restricted
battery limit. Be that as it may, to meet the variety of SOCs
and maturing, utilizing an alternate name, Qdischarge is
vital. This is known as the greatest measure of force
charging, communicated in Ah the battery can be moved
from completely energized mode (SOC 100 percent) to
completely released mode (0% SOC) as displayed in
Equation underneath.
The SOC can then be expressed using the ratio or
percentage of output extracted compared to Qdiscahrge.
where Ib is the battery current.
There are various methods of estimating SOC of a battery.
1. Electrochemical - Impedance spectroscopy method
2. Model Based -
i. Kalman filter
ii. Extended Kalman filter
3. Data Method -
i. Neural network
ii. Fuzzy logic method
4. Traditional methods -
i. Coulomb counting
ii. Integrated voltage method
The figure1. below shows block diagram of Kalman filter .
Fig.1. Basic flow chart of model-based online SOC
estimation method using KF algorithm
III. LITERATURE REVIEW
Chung-Chun Kung, Member, IEEE, Si-Xun Luo, Sung-Hsun
Liu[1]. This study considers the utilization of Arduino to
accomplish province of charge(SOC) of Li-particle Batteries
by AEKF. Boundaries of the Equivalent circuit model will
be recognized through the planned expt. and approximated
by piecewise straight capacities and afterward will be
incorporated into Arduino. The assessed outcome from
this paper shows that the outright worth of the consistent
state SOC assessment mistake is little.
Adhish Nanda, Ayushi Shrivastava , Animesh Majumder
and Priyamvada Singh[2]. This paper addresses a clever
procedure for SOC assessment of the battery where the
SOC assessed by detecting the voltage is formulated.The
batteries and battery the board framework are the vital
parts of EV's . Here, they will look at four primary kinds of
estimations, to be specific Direct Measurement, Book
Keeping Estimation, Adaptive System, Hybrid Method.
M. Mastalia, J.Vazquez-Arenasb, R.Frasera, M.Fowlerc,
S.Afshara, M.Stevens[3]. In this, Dynamic way of behaving
of the Lithium-particle batteries is noticed and they have
utilized two sorts of Kalman sifting incorporate Extended
and Dual Extended two unique calculations for batteries
are thought of. The SOC of the batteries in a dynamical
climate is precisely are anticipated, new actual experiences
are given through examination of the Kalman channel
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1065
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3. boundaries. It is shown that Kalman sifting can foresee the
SOC with greatest 4% mistake.
Carlo Taborelli, Simona Onori[4].The issue of battery
condition of charge assessment is researched utilizing
model based approach. A tentatively approved model of a
battery pack delivered by "All phone advances" explicitly
for light electric vehicles is utilized and there are two
condition of charge assessments calculation preformed i.e
Extended Kalman filter(EKF) and Adaptive rendition of
Extended Kalman channel (AEKF) Estimation is finished
utilizing model clamor covariance Final outcomes shows
that EKF is more exact than AEKF.
Wang Zhifu[5]. The issue of battery condition of charge
assessment is researched utilizing Thevenin model and
afterward EKF is applied which shows higher accuracy. In
this paper, the ohmic Resistance, polarization opposition
and polarization capacitance are the three boundaries of
the Thevenin model not entirely settled. This model was
assembled utilizing MATLAB/SIMULINK. The EKF
calculation in light of Thevenin model is laid out and gives
SOC precisely.
Maamar Souaihia, Bachir Belmadani[6] .The condition of
charge assessment for Li-particle battery is done in light of
identical circuit model. The battery model has been applied
on the second-request Thevenin model, this utilizations
open circuit voltage(OCV) which must be determined with
the upsides of obstruction, capacitance and temperature
This is applied through MATLAB which gives exact
assessment of condition of charge.
Benedikt Rzepka, Simon Bischof and Thomas Blank[7]. In
this paper we will realize that an anticipated activity
enquires information on the inner battery state,
particularly its soc Comparison is done between actual
models. Information driven models utilizing AI and
identical circuit models. Actual models are perplexing and
require nitty gritty data of cell, information driven models
require huge informational collection to prepare the model
appropriately.
Rami Yamin, Ahmed Rachid[8]. This paper is about SOC
and SOH assessment for a battery the executives
framework. It was applied on 48V lead-corrosive battery
pack of an electric bike. Correlation is done between
Coulomb counting and Kalman channel technique. The
Coulomb counting strategy might come up short in light of
estimation mistake and reasonableness against estimation
clamor. The SOC is assessed internet utilizing Kalman
channel programming and show on a low utilization LCD
screen.
Attila Buchman, Claudiu Lung[9]. There are a few exact
strategies yet they are computational escalated so running
their sign handling calculations consumes a lot of battery
power Goal: precision over energy effectiveness different
techniques are talked about, for example, direct strategy,
accounting and versatile frameworks. The target of this
paper is to track down the connection among SOC and SOH.
Sabeur Jemmali, Asma Mlayah, Bilal Manai, Najoua
Essoukri Ben Amara[10]. A productive SOC assessment
calculation depends on better Coulomb counting strategy
in light of piecewise straight connection among SOC and
open circuit voltage. They attempted to beat downsides to
further develop its exactness It defeats the hindrance of
the vulnerability of the underlying SOC esteem. An
equipment execution of this calculation was performed on
a MCU PIC18F and approved by testing charge and release
of the batteries.
Abarkan Mouna, Byou Abdelilah, NKMSirdi, El Hossain
Abarkan[11]. In this paper two assessment techniques are
considered-Open circuit Voltage, Sliding mode strategy.
Consultant model is utilized in this model to accomplish
the goals. Counselor model is a presentation and monetary
model intended to assess the expense of energy in view of
execution and working expenses. The acquired outcomes
are executed on MATLAB/Arduino. In view of the
execution results it is seen that sliding model gives 2%
lesser exactness that assessed Kalman channel technique.
Prashant Shrivastavaa , Tey Kok Soona, Mohd Yamani Idna
Bin Idrisa , Saad Mekhilef[12]. This paper manages Kalman
Filter calculations for model-based web-based SOC
assessment. Four web-based assessment strategies are
considered-Coulomb Counting technique, open circuit
voltage strategy, model based technique and Machine
learning strategy. Model based technique is more proper
for EV application, due to the capacity to manage obscure
clamor signals, low intricacy, high exactness over the other
3 strategies.
IV. CONCLUSION
By concentrating on this multitude of papers we came to
realize that there are a few strategies for the assessment of
SOC of the battery and furthermore there are just couple of
techniques which gives more exact outcome. By
remembering about the ability to manage obscure
commotion signals, low intricacy and high exactness we
are thinking about Kalman family calculation to assess the
SOC of the battery. We have seen that Kalman family
comprises of 2 strategies, in that we have seen by thinking
about this multitude of papers is that drawn out Kalman
channel calculation is best reasonable technique for SOC
computation. So specifically we are utilizing broadened
Kalman channel calculation in our SOC assessment model
and carrying out the outcomes for approval utilizing
MATLAB and followed by executing on a microcontroller to
work out the territory of Charge of the battery to expand
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4. the dependability, to guarantee the protected activity and
to keep the battery from over-charging and over-releasing.
V. REFERENCES
[1] Chung-Chun Kung, Si-Xun Luo, Sung-Hsun Liu
“Implementation of The State of Charge Estimation with
Adaptive Extended Kalman Filter for Lithium-ion Batteries
by Arduino”, IEEE 2018.
[2] Adhish Nanda, Ayushi Shrivastava, Animesh Majumder
and Priyamvada Singh “Estimation of State-of-Charge
(SOC) of Lithium Batteries in Electric Vehicles”, Asian
Journal of Applied Science and Technology (AJAST), 2017.
[3] M. Mastali, J. Vazquez-Arenas, R. Fraser, M. Fowler, S.
Afshar, M. Stevens “Battery state of the charge estimation
using Kalman filtering”, Journal of Power Sources, 2018.
[4] Carlo Taborelli, Simona Onori “State of Charge
Estimation Using Extended Kalman Filters for Battery
Management System”, IEEE 2016.
[5] Li Zhia, Zhang Pengb, Wang Zhifua, Song Qianga, Rong
Yinan “State of charge estimation for Li-ion battery based
on extended Kalman filter”, The 8th International
Conference on Applied Energy – ICAE, 2016.
[6] Maamar Souaihia, Bachir Belmadani, Rachid Taleb,
Kamel Tounsi “State of charge estimation by using
extended Kalman filter based on improved open circuit
voltage model”, International Journal of Applied Power
Engineering (IJAPE), 2017.
[7] Benedikt Rzepka, Simon Bischof and Thomas Blank
“Implementing an Extended Kalman Filter for SoC
Estimation of a Li-Ion Battery with Hysteresis: A Step by
Step Guide”, Institute for Data Processing and Electronics
(IPE), Karlsruhe Institute of Technology (KIT), 2021.
[8] Rami Yamin, Ahmed Rachid “Embedded State of Charge
and State of Health Estimator Based on Kalman Filter for
Electric Scooter Battery Management System”, Laboratoire
des Technologies Innovantes University of Picardie Jules
Verne, 2014.
[9] Attila Buchman, Claudiu Lung “State of charge and state
of health estimation of Lithium-Ion batteries”, IEEE 2018.
[10] Ines Baccouche, Sabeur Jemmali, Asma Mlayah, Bilal
Manai, Najoua Essoukri Ben Amara “Implementation of an
Improved Coulomb-Counting Algorithm Based on a
Piecewise SOC-OCV Relationship for SOC Estimation of Li-
Ion Battery”, INTERNATIONAL JOURNAL of RENEWABLE
ENERGY RESEARCH, 2017.
[11] Abarkan Mouna, Byou Abdelilah, N.K.M’Sirdi, El
Hossain Abarkan “Estimation of the state of charge of the
battery using EKF and Sliding mode Observer in Matlab
Arduino/LabView”, IEEE 2018.
[12] Prashant Shrivastavaa, Tey Kok Soona, Mohd Yamani
Idna Bin Idrisa, Saad Mekhilef “Overview of model-based
online state-of-charge estimation using Kalman filter
family for lithium-ion batteries”, Renewable and
Sustainable Energy, 2019.
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1067
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p-ISSN: 2395-0072