State of charge estimation based on a modified extended Kalman filter

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 5, October 2023, pp. 5054~5065
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i5.pp5054-5065  5054
Journal homepage: http://ijece.iaescore.com
State of charge estimation based on a modified extended
Kalman filter
Koto Omiloli1
, Ayokunle Awelewa1
, Isaac Samuel1
, Oghorchukwuyem Obiazi1
, James Katende2
1
Department of Electrical and Information Engineering, Covenant University, Ota, Nigeria
2
Department of Electrical and Computer Engineering, Namibia University of Science and Technology, Windhoek, Namibia
Article Info ABSTRACT
Article history:
Received Jun 27, 2022
Revised Dec 13, 2022
Accepted Mar 28, 2023
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.
Keywords:
Extended Kalman filter
Gain
Lithium-ion battery
Noise
State estimate
State of charge
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ayokunle Awelewa
Department of Electrical and Information Engineering, Covenant University
KM 10, Idrioko, Ota, Ogun State, Nigeria
Email: ayokunle.awelewa@covenantuniversity.edu.ng
1. INTRODUCTION
In recent years there has been an increasing application of lithium-ion batteries (LiBs) in on-board
energy storage systems, power backups and electronics [1] due to their inherent properties of superior energy
density, lower self-discharge, and higher cycle lifetime [2] vis-a-vis conventional lead-acid and other
rechargeable batteries. LiBs serve as the primary energy storage system for electrical vehicles [3] as well as
smart grids systems; hence the need for a battery management system (BMS). The BMS closely monitors the
battery state which includes the state of charge (SOC), state of health (SOH) and cell capacity for the purpose
of ensuring a safe and efficient operation [4], [5].
The SOC of a LiB is defined as the percentage of the remaining capacity left in its maximum
available capacity [6], [7]. The SOC is one of the core components that need to be properly managed by the
BMS since it uses the residual capacity function to qualify the performance of LiBs [8]. However, the SOC
cannot be directly measured, and this implies that models that accurately represent the battery dynamics are
required to develop optimum observers for gaining insight into in the internal state of the LiB based on
information from measurable quantities.

Int J Elec & Comp Eng ISSN: 2088-8708 
State of charge estimation based on modified extended Kalman filter (Koto Omiloli)
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The models used in describing LiBs are the electrochemical models (EMs), data-driven models
(DDMs) and equivalent circuit models (ECMs). The EM model uses complex partial derivative equations
and boundary conditions in describing the electrochemical process that takes place within the cell by giving a
detailed insight on the chemical reaction responsible for the internal parameter generation and dynamic
behavior during different operating conditions [9]. These models are known to be highly accurate [10], but
for them to be used efficiently, thorough knowledge of the battery chemicals structure as well as
characteristics must be precisely determined [11]. In DDMs, the battery model is derived by training large set
of measured data without requiring information on the cell characteristics [12]. Equivalent circuit models
consist largely of voltage sources, resistors and capacitors that are used for describing the behavior of the LiB
[13]. Mohammed [9] argued, though ECMs do not provide information into the electrochemical reactions
that takes place within the battery, they should be adopted due to easy implementation, low number of
parameters to tune, and low complexity in setting up their state equations [14], [15].
The extended Kalman filter (EKF) is a well-known and established optimum state observer applied
in nonlinear dynamic systems and has certain merits. In Odry et al. [16], a key merit of the EKF identified
was its variants nature-able to be augmented with stochastic and deterministic approaches tested to be
reliable in the robust estimation of mobile robot’s attitudes. Due to characteristics of having a second order
information embedded in its covariance matrix, the EKF can achieve a faster convergence rate in applications
such as wind speed predictions [17], vehicle position tracking [18] and LiB capacity estimation [19]. In
addition, because of having its posterior and a prior probability estimated, systems with multi-objectives
requirements can be easily designed with higher accuracy than other benchmark functions having similar
requirements [20].
Much research has been carried out on the SOC estimation subject. Dong et al. [2] proposed an on-
board SOC estimation method based on Kalman filter (KF). The maximum absolute estimation errors of
estimated SOC under four experiment scenarios, namely, open-circuit voltage (OCV), constant power,
maximum discharge capability and dynamic current tests fell in the range of 4 to 5%. However, the
algorithms for SOC were often too heavy to be used for on-board implementation of systems such as a micro-
controller. In [12], an EKF for SOC estimation of a LiB with hysteresis was implemented. Experimental
results derived from 90 cells connected in series and partitioned into four modules showed the method
achieved a maximum error of 1% and good stability in contrast to a roughly tuned EKF which had a
maximum error of 4% and poor stability. The drawback was the method was highly sensitive to the process
noise, and hence extreme care was required for tuning the EKF parameters. Improved extended Kalman filter
(IEKF) was proposed for SOC estimation in [4] by incorporating noise adaptation, fading filter and a linear-
nonlinear filtering based on the traditional EKF method. Results obtained revealed the IEKF maximum error
was roughly 3% under dynamic stress (DST) conditions and 1% under a subjected temperature disturbance,
giving the method a superior advantage over a coulomb counting (CC) method whose results were above
16% for each case. However, the proposed modeling framework and experiment was carried out only on a
single cell battery; hence the accuracy of the method may not be guaranteed for more than one cell.
Due to inaccuracies associated with the EKF when applied in large scale battery energy storage
systems (BESS) with high process and measurement noise, Peng et al. [14] developed an adaptive unscented
Kalman filter (AUKF) having the functionality of noise statistics estimation for accurate estimation of SOC.
The AUKF had the least root-mean-square-error (RMSE), mean absolute error (MAE) and faster SOC
convergence of about 20 s. However, the proposed work did not consider the influence of cell inconsistency
on the variation of battery system model parameters which could affect SOC estimation accuracy. Further
work by [7] looked at combining dual Kalman filter (DKF) and dual extended Kalman filter (DEKF)
algorithms for the purpose of improving the SOC estimation. In the presence of system errors, the DKF was
able to correct SOC automatically and error results were significantly less than single DKF and DEKF
algorithms. However, the predictability of the battery dynamics in both time and frequency over the entire
operating range of the LiB battery was not considered.
The aim of this paper is to design an extended Kalman filter for determining accurate estimation of
the state of charge of a lithium-ion battery useful in providing the available energy left that is not only
suitable for electrical vehicles energy indication but would also have further applications in electronics, grid
power infrastructures as well as drone system’s performance. The proposed method can provide three (3)
decimal places of SOC accuracy even in presence of measurement and process noise based on model
accuracy and assignment of noise co-variance matrices. Furthermore, a contribution was made by improving
the performance of the existing estimator by proposing better priori estimate as well as modification of the
Kalman gain. This study focuses on state of charge of a single cell lithium-ion battery with improvements
that can ensure multi cell level SOC estimation accuracy.
Following this introductory section, this paper is structured as: description of the secondary data is
presented in section 2. The battery model in form of state space equations is derived in section 3. Section 4
explains the offline model parameter identification method used for estimating LiB internal parameters. In

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section 5, the EKF algorithm implemented for the SOC estimation is given. Section 6 presents the
contribution of an improved EKF estimator. The various results as well as interpretations are presented and
compared in section 7. This paper concludes with section 8 which summarizes the work done and establishes
future research direction.
2. DATA COLLECTION
The data set used in this research is a five (5) pulse hybrid pulse power characterization (HPPC)
discharge. The results consist of voltage, current, temperature and ampere-hour readings of a 2.9 Ah
Panasonic 18650PF lithium-ion cell obtained from tests carried out at the university of Wisconsin-Madison
and was provided as a reference for researchers to use to compare their algorithms and model response for
the Kalman filter and neural network state of charge algorithms [21]. The battery cell was tested in an eight
(8) cubic feet thermal chamber having a 25 A, 18 V Digatron firing circuits universal battery tester channel at
five different temperature conditions, 0 o
C, -10 o
C, 10 o
C, -20 o
C, and 25 o
C. The cell parameter and test
equipment specifications are presented in Tables 1 and 2, respectively.
Table 1. Panasonic 18650 cell parameters
Manufacturer part number NCR18650PF
Nominal/Min./Max. voltage 3.6/2.5/4.2 V
Nominal/Typical capacity 2.9 Ah
Mass 48 g
Energy storage 9.9 Wh
Charging temp. 10 o
C
Cycles to 80% capacity 500
Dimensions 18.6×65.2 mm
Table 2. Test equipment specifications
Name of equipment Diagatron firing circuit
Channel 25 A, 0.18 V
Current accuracy ±0.1%
Data acquisition frequency 10 Hz
Heat chamber Cincinatti sub-zero ZP
Measurement accuracy ±0.5%
3. BATTERY MODELLING
Due to reasons of balancing complexity and accuracy the ECMs are applied in modelling the LiB
[22]. ECMs consist of internal resistance, resistor-capacitor (RC) pairs also known as polarization
parameters, OCV and in some conditions a hysteresis element [12]. To determine the order of the ECM a
curve fitting tool in MATLAB has been used to fit an N number of exponentials (where N corresponds to the
order of ECM) to the voltage measurement relaxation profile as shown in Figure 1 for the case of 25 °C. To
avoid over parameterization while maintaining reasonable level of fidelity a 2nd
order ECM (R-RC-RC)
model has been chosen for the LiB modeling task.
3.1. Equivalent circuit model
A second order ECM is shown in Figure 2. It consists of an internal resistance Ro which accounts for
the drop and rise behavior of the pulse profile and RC pairs which describes the transient and dynamic
characteristics [23] of the LiB. Applying Kirchhoff’s current law (KCL) at nodes A, B, C gives:
𝑉̇1 = −
1
𝑅1𝐶1
𝑉1 +
1
𝐶1
𝑖𝐾 (1)
𝑉̇2 = −
1
𝑅2𝐶2
𝑉2 +
1
𝐶2
𝑖𝑘 (2)
Taking Kirchhoff’s voltage law across the full loop gives:
𝑉𝑡 = OCV(𝑍𝑡) − 𝑉1 − 𝑉2 − 𝑉0 (3)
where V1, V2, V0 are the voltages across R1 C1 pair, R2 C2 pair polarization elements and R0 (Ω), respectively,
with:

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𝑍𝑡 = 𝑍0 −
𝜂𝑘
𝑄𝑏𝑎𝑡𝑡
∫ 𝑖𝑑𝑡
𝑡
0
. (4)
The (4) represents the SOC equation with the following sign convention for the current.
𝑠𝑔𝑛(𝑖𝑡) = {
+, for discharging
−, for charging
. (5)
The coulombic efﬁciency, describes the efﬁciency of a charging process due to number of electrons
transferred into the cell. It can be estimated from OCV charge and discharge curves [24] and has the
following expression.
𝜂𝑘 = {
1, 𝑖𝑘 > 0 𝑓𝑜𝑟 𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑖𝑛𝑔
𝜂∗
, 𝑖𝑘 < 0 𝑓𝑜𝑟 𝑐ℎ𝑎𝑟𝑔𝑖𝑛𝑔
. (6)
Discretizing (1), (2), (3), (4) using t as sample time, k as present value and k-1 as previous value, the battery
model is derived in the state space form below [25].
[
𝑉1,𝑘
𝑉2,𝑘
𝑍𝑘
] = [
𝑒
−Δ𝑡
𝜏1 0 0
0 𝑒
−Δ𝑡
𝜏2 0
0 0 1
] [
𝑉1,𝑘−1
𝑉2,𝑘−1
𝑍𝑘−1
] +
[
1 − 𝑒
−Δ𝑡
𝜏1
1 − 𝑒
−Δ𝑡
𝜏2
−𝜂𝑘
Δ𝑡
𝑄𝑏𝑎𝑡𝑡]
𝑖𝑘 (7)
𝑉𝑘 = OCV(𝑍𝑘) − 𝑉1,𝑘 − 𝑉2,k − 𝑉0 (8)
Figure 1. Voltage relaxation test profile
Figure 2. 2nd
order ECM model

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4. MODEL PARAMETER IDENTIFICATION
A global pattern search algorithm (GPSA) is used to estimate values of the LiB model internal
parameters, {𝑂𝐶𝑉, 𝑅0, 𝑅1, 𝑅2, 𝜏1, 𝜏2}. To generate the parameters, sequence of points is computed that best
minimizes the objective function ∑ (𝑉
̂𝑘 − 𝑉𝑘)
𝑛
𝑘 [9]. The algorithm works by generating vectors, vi used to find
which points to search at an iteration count. The search is carried out at set of points around the current point-
which is defined as the previous point that had the best objective function value. The set of points is called a
mesh (m) and is obtained by adding a set of vectors di to the current point. The set of vectors is generated by
taking the scalar multiplication of the mesh size, Δ𝑚
and the pattern vector, vi. Finally, a process called
polling is carried out by the algorithm such that the improved objective function value is computed based on
when complete poll option is set off or on.
4.1. Parameter initialization
The accuracy and convergent speed of the estimation process relies on good parameter initialization.
To obtain the initial parameters, this work compared the fitting function described by the transient shown by
the voltage relaxation profile to the transient terminal voltage of the LiB expressed as (9).
𝑉𝐿 = OCV − 𝑅0𝐼𝑡 − 𝑅1𝐼𝑡𝑒
−𝑡
𝜏1 − 𝑅2𝐼𝑡𝑒
−𝑡
𝜏2 (9)
The (9) is derived from (3) by obtaining the transient solutions to (1) (2). The fitting function describing
Figure 1 is (10).
𝑓(𝑡) = 3.41 − 0.39𝑒−0.24𝑡
− 0.97𝑒−0.96𝑡
(10)
Comparing (9) and (10), the initial parameters is computed from the following.
𝑅
̂1 =
0.39
𝐼𝑡
𝑅
̂2 =
0.97
𝐼𝑡
𝐶
̂1 =
1
𝑅
̂1 × 0.238
𝐶
̂2 =
1
𝑅
̂2 × 0.96
4.2. Look-up tables-OCV curve
After the optimization of the battery parameters is carried out, a look-up table can be created. The
look-up table determines the value of a parameter at different SOC which is crucial for a successful EKF
implementation from which an OCV curve is implemented as shown in Figure 3 using piecewise linear fitting
function [26]. The OCV discharge measurement is carried out by pulse discharging the battery from a fully
charged condition (SOC=100%) in small amperage steps and then allowed to rest for some time called the
resting phase [12]. At this phase, the voltage can be measured to obtain the OCV (SOC) relationship. The
process is continued till the battery is fully discharged and afterwards the steps are performed similarly for a
charging process to yield the charge OCV curve.
Figure 3. SOC-OCV curve

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5. THE EXTENDED FILTER ALGORITHM
The OCV in practice varies nonlinearly with SOC, hence, the LiB dynamic state in (7), (8) is
represented in the following discrete state space form,
𝑥𝑘+1 = 𝑓(𝑥𝑘, 𝑢𝑘) + 𝑤𝑘 (11)
where 𝑓(𝑥𝑘, 𝑢𝑘) is the process nonlinear function 𝑔(𝑥𝑘, 𝑢𝑘), is the measurement nonlinear function, yk is the
observable function, wk and vk are the system and measurement noise signals respectively. The system noise
has an error co-variance 𝑄𝑘 = 𝐶𝑜𝑣(𝑥𝑘) = 𝑑𝑖𝑎𝑔(𝜎2
𝑅0
, 𝜎2
𝑅1
, 𝜎2
𝑅2
, 𝜎2
𝜏1
, 𝜎2
𝜏2
) [12] and measurement noise
has an error co-variance 𝑅𝑘 = 𝐶𝑜𝑣(𝑦𝑘) = 𝜎2
𝑣 =
1
𝑛
∑ (𝑦
̂𝑖 − 𝑦𝑖)2
𝑖=𝑛
𝑖=0 [9] which are both assumed as Gaussian
distribution. The EKF algorithm is presented as follows using the hat (^) notation to represent state estimates
Step 1: initialization of the internal state 𝑥𝑘+1[0] = [
1
0
0
] and system state error 𝑃𝑘+1[0] = [0]3×3
Step 2: calculating the priori estimates as extension of the Kalman filter [27].
𝑥
̂𝑘+1|𝑘 = 𝐴𝑘𝑥
̂𝑘|𝑘 + 𝐵𝑘𝑢𝑘
𝑃𝑘+1 = 𝐴𝑘𝑃𝑘|𝑘𝐴𝑘
𝑇
+ 𝑄𝑘 (12)
Step 3: The system model is linearized [28] about 𝑥𝑘+1|𝑘 such that
𝐴𝑘 = (
𝜕𝑓(𝑥𝑘,𝑢𝑘)
𝜕𝑥𝑘
)
𝑥𝑘+1|𝑘,𝑢𝑘
= 𝑑𝑖𝑎𝑔 (1, 𝑒
−Δ𝑡
𝜏1 , 𝑒
−Δ𝑡
𝜏2 ) (13)
𝐵𝑘 =
(
−𝜂𝑘
Δ𝑡
𝑄𝑏𝑎𝑡𝑡
𝑅1 (1 − 𝑒
−Δ𝑡
𝜏1 )
𝑅2 (1 − 𝑒
−Δ𝑡
𝜏2 ))
; 𝐶𝑘 = (
𝜕𝑔(𝑥𝑘,𝑢𝑘)
𝜕𝑥𝑘
)
𝑥𝑘+1|𝑘,𝑢𝑘
= [
𝜕𝑉0
𝜕𝑍𝑘
, −1, −1] (14)
where Ak, Bk, and Ck are Jacobean matrices derived from first order Taylor series approximation [28] of, V0
the OCV, Zk the SOC and the state vector 𝑥𝑘 = [
𝑍𝑘
𝑉1,𝑘
𝑉2,𝑘
].
Step 4: calculation of the Kalman gain 𝐾𝑘+1
𝐾𝑘+1 = 𝑃𝑘+1|𝑘𝐶𝑇
(𝐶𝑃𝑘+1|𝑘𝐶𝑇
+ 𝑅𝑘+1)
−1
(15)
Step 5: calculation of a posteriori [12] estimates
𝑥
̂𝑘+1│k+1 = 𝑥
̂𝑘+1│k + 𝐾𝑘+1(𝑦𝑘 − 𝑦
̂𝑘) (16)
Step 6: calculation of the system state error.
𝑃𝑘+1|𝑘+1 = (1 − 𝐾𝑘+1𝐶𝑘+1)𝑃𝑘+1|𝑘 (17)
6. CONTRIBUTION-AN IMPROVED ESTIMATOR
To improve the performance of the EKF, this research made two crucial changes to the state
dynamic estimate matrices (A, B) which have battery parameters represented as functions of SOC and a
modification of the Kalman gain, K. The state and input matrices are expressed as (18),
𝐴
̂𝑘+1 = 𝐴
̂𝑘 + 𝐴𝑘−1 − 𝐴
̂𝑘−1
𝐵
̂𝑘+1 = 𝐵
̂𝑘 + 𝐵𝑘−1 − 𝐵
̂𝑘−1 (18)

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So that the improved priori state estimate is now.
𝑥
̂𝑘+1|𝑘 = 𝐴
̂𝑘+1𝑥
̂𝑘|𝑘 + 𝐵
̂𝑘+1𝑢𝑘 (19)
Next a denominator constant L is introduced in the Kalman gain equation such that 𝐿 ≫ (‖𝑤(𝑥)‖2 +
‖𝑣(𝑥)‖2) where w, v are the process and measurement noise respectively. Hence, the modified Kalman gain
is expressed as (20).
𝐾𝑘+1 =
𝑃𝑘+1|𝑘𝐶𝑇
𝐿(𝐶𝑃𝑘+1|𝑘𝐶𝑇+ 𝑅𝑘+1)
(20)
An overview of the modified EKF implementation procedure is shown in Figure 4.
Figure 4. Modified EKF implementation flowchart
7. RESULTS AND DISCUSSION
7.1. Parameter estimation performance
The LiB parameters are computed via conventional curve fitting estimation and GPSA for current
and voltage data at 25 °C shown in Figures 5 and 6 respectively to compare the performance of both
techniques. The curve fitting estimation follows similar approach in section 4 with a modification of fitting

5061
the transient response equations to each partitioned relaxation voltage profile corresponding to an SOC value
in the range [0.1, 1] from which an SOC based curve fitting estimation is achieved. Figure 7 shows the model
performance of the voltage curve fitting estimation via curve fitting. A tolerance value of 0.001 was set for
the GPSA implemented in Simulink which resulted in optimum estimated parameter values as seen in the
battery’s voltage estimation shown in Figure 8. This reveal estimating the LiB parameters via software offers
better cost function minimization and improved fit than conventional approach.
Figure 5. Current profile at 25 °C Figure 6. Voltage profile at 25 °C
Figure 7. Voltage estimation via curve fitting Figure 8. Voltage estimation via GPSA
7.2. Extended Kalman filter performance
A standard EKF without no state modification and disturbance is first implemented and its response
is shown in Figure 9. Performance response from a commonly used SOC based curve fitting and look up
table approach is further shown in Figure 10. The real SOC used as a benchmark for testing the performance
of estimators is the conventional coulomb counting technique [29]. Comparing both responses, it is clear the
EKF estimation far outperforms the curve fitting technique. However, it is worthy to note, the SOC based
curve fitting technique showed an acceptable performance starting at t=0.4×104
s and reached asymptotic
stability from t=2×104
s.
To test for the filtering properties of the EKF, random white gaussian noise [30] was added to the
measurement and process equations. Figure 11 shows the insensitivity of the estimator to these noises and as
it converges to ideal value at various initial SOCs. Furthermore, test analysis shows the state estimation
performance was not affected by the initial values of the SOC when set within a stable range.
The error response in Figure 12 resulted in estimation errors bound of ±12.9%, ±9.18%, ±22.6%,
±32.9% for initial SOC of 100%, 95%, 85%, and 75% respectively. It is observed that from t=1.4 s the error
bound for the initial SOC of 100% dropped by 5.64% which suggests that at the beginning of estimation, the
fluctuations were caused by the less accurate parameters of the system. Likewise, similar error bound
reductions were observed for other initial states. Overall, the EKF implemented setting the initial SOC to
100% demonstrates higher accurateness as performance metrics in Table 3 reveals. This is because the set
initial SOC reflects the real initial SOC of the battery before being discharged.

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Figure 9. Standard EKF performance without
disturbance
Figure 10. Conventional SOC based curve fitting
estimation performance
Figure 11. Standard EKF performance at 95%, 85% and 75% SOC initials
Figure 12. Standard EKF error at various initial SOC

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Table 3. EKF performance for various initial SOC
Initial SOC for standard EKF
100% 95% 85%
RMSE (×10-4
) 2.19 2.41 3.89 5.72
MAE (×10-7
) 2.49 2.83 4.92 7.50
The result of this research contribution described in the previous section is shown in Figure 13
compared against the conventional curve fitting, ampere hour SOC estimation techniques. It can be seen the
modification made to the standard EKF algorithm only varied little from the conventional coulomb counting
technique [29] used as the real SOC. Performance metrics in Table 4 shows the proposed algorithm
outperformed the other estimators with lesser RMSE and MAE values, roughly adding two (2) decimal
places of accuracy to the standard EKF. The error bound of the modified EKF lies at ±2.05% which clearly
shows the quality of the estimator. However, the choice of the added denominator gain L could cause
instability if chosen so large in magnitudes above 103
as shown in Figure 14. This is because the Kalman gain
becomes so small that the a priori estimate diverges from the a posteriori estimate. After various tuning, L is
required to lie in the range [400; 1,000] to achieve high estimation accuracy.
Figure 13. Modified EKF performance against conventional methods
Table 4. Performance metrics of estimators
Estimators
Curve fitting Ampere hour Standard EKF Modified EKF
RMSE 5.13×10-2
2.18×10-2
2.19×10-4
3.25×10-6
MAE 5.61×10-4
3.14×10-4
2.49×10-7
5.92×10-9
Figure 14. Modified EKF with unstable gain (L=106
)

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8. CONCLUSION
This paper has covered extensively a single cell LiB SOC estimation process based on the EKF
estimator using a second order ECM. The findings of this work show how significant an added denominator
constant to the Kalman gain and predictor accuracy is essential for improving the estimator’s accuracy and
stability for noisy sensors. Practically, this improvement can be incorporated in existing EKF design
embedded in BMS to offer higher accuracy level of SOC indication for EVs. Further research activity in this
area will consider the impact that robust higher-order sliding mode observers have in reliably counteracting
the effect of large disturbances and unknown noise signals.
ACKNOWLEDGMENT
The authors deeply recognize the assistance given by Covenant University in all aspects of this
research, especially the payment of the publication charges.
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BIOGRAPHIES OF AUTHORS
Koto Omiloli obtained his bachelor’s degree in Electrical and Electronic
Engineering from Niger Delta University. He is currently a master’s student in the department
of Electrical and Information Engineering, Covenant University, Nigeria. His research interest
is on nonlinear control and observer design. He can be contacted at email:
andrew.omiloli@stu.cu.edu.ng.
Ayokunle Awelewa obtained his Ph.D. in Electrical and Electronic Engineering
from Covenant University, Ota, Nigeria, where he is currently an Associate Professor in the
Department of Electrical and Information Engineering. His research areas include modelling
and control of renewable energy systems, power system stabilization and control, and
modelling and simulation of dynamical systems. He can be contacted at email:
ayokunle.awelewa@covenantuniversity.edu.ng.
Isaac Samuel obtained his Ph.D. in Electrical and Electronics Engineering from
Covenant University, Ota, Nigeria, where he is currently an Associate Professor in the
Department of Electrical and Information Engineering. His research areas include power
system voltage stability and operation, reliability, and maintenance. He can be contacted at
email: isaac.samuel@covenantuniversity.edu.ng.
Oghorchukwuyem Obiazi is a software engineer with a bachelor’s degree in
Computer Engineering from the University of Benin, Nigeria. She is currently pursuing a
master’s degree in Covenant University, Nigeria. Her research interests include
energy-efficient load balancing in cloud computing, software engineering and the application
of data analysis in software startup scaling. She can be contacted at email:
oghorchukwuyem.obiazipgs@stu.cu.edu.ng.
James Katende is a Professor of Electrical Engineering and a lecturer in
Department of Electrical and Computer Engineering, Namibia University of Science and
Technology, Windhoek, Namibia. His research areas include Robotics and automation, power
system and control, modelling and simulation of dynamical systems, and renewable energy.
He can be contacted at email: sempa54@yahoo.com.

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