# A New Update Rule of ADFF-RLS & EKF based Joint-estimation Filters for Real-
time SOC/SOH Identification

Kwangrae Kim, Minho Kim, Suwon Kang, Jungwook Yu, Jungsoo Kim, Huiyong Chun,
and Soohee Han Kwangrae Kim (e-mail<EMAIL_ADDRESS>Minho Kim (e-mail:
<EMAIL_ADDRESS>Jungwook Yu (e-mail<EMAIL_ADDRESS>Jungsoo Kim
(e-mail<EMAIL_ADDRESS>Huiyong Chun (e-mail:
ymir1994@postech.ac.kr)Corresponding author: Soohee Han (e-mail:
<EMAIL_ADDRESS>
###### Abstract

In order to accurately estimate the SOC and SOH of a lithium-ion battery used
in an electric vehicle (EV), we propose an Adaptive Diagonal Forgetting Factor
Recursive Least Square (ADFF-RLS) for accurate battery parameter estimation.
ADFF-RLS includes two new proposals in the existing DFF-RLS; The first is an
excitation tag that changes the behavior of the DFF-RLS and the EKF according
to the dynamics of the input data. The second is auto-tuning that
automatically finds the optimal value of RLS forgetting factor based on
condition number (CN). Based on this, we proposed a joint estimation algorithm
of ADFF-RLS and Extended Kalman Filter (EKF). To verify the accuracy of the
proposed algorithm, we used experimental data of hybrid pattern battery cells
mixed with dynamic and static patterns. In addition, we added a current
measurement error that occurs when measuring at EV, and realized data that is
closer to actual environment. This data was applied to two conventional
estimation algorithms (Coulomb counting, Single EKF), two joint estimation
algorithms (RLS & EKF, DFF-RLS & EKF) and ADFF-RLS & EKF. As a result, the
proposed algorithm showed higher SOC and SOH estimation accuracy in various
driving patterns and actual EV driving environment than previous studies.

###### Index Terms:

Electric Vehicle (EV), State of Charge (SOC), Extended Kalman Filter (EKF),
Joint Estimation, Excitation tag, Multiple Forgetting Factor Recursive Least
Square (MFF-RLS), Adaptive Multiple Forgetting Factor Recursive Least Square
(AMFF-RLS)

##  I Introduction

In response to the demand for reducing pollution, Electric vehicles (EVs) are
becoming increasingly common throughout the world. In general, EVs use Li-ion
batteries with a variety of advantages over other batteries as energy storage
devices. [1] At this time, a battery management system (BMS) that controls the
battery is important for increasing the life of the battery and maintaining
safety.

To control the battery efficiently, BMS needs to know the current internal
state of the battery, the two most important states being state-of-charge
(SOC) indicating current charge of battery and state-of-charge (SOH). The SOC
basically represents the distance the vehicle can travel, and SOH represents
the current and remaining service life of the EV battery. Therefore, real-time
estimation of SOC and SOH is important for improving the efficiency of the
driving cost, driving convenience and economy of the vehicle.

As a representative method for estimating SOC and SOH, there is a model based
method in which a system model in which the input of the battery is used as
the current and the output is the voltage is established and the internal
state is estimated based on the system model. In general, the commercial BMS
of the EV is an Equivalent Circuit Model (ECM), which is easy to apply and has
low computational complexity. [2]

Various SOC/SOH estimation algorithms based on ECM have been studied. Among
them, filter based method and observer based method are mainly used in
commercial BMS. [3, 4, 5, 6, 7]. However, when the existing algorithms are
used solely to estimate the state of the EV, it is generally possible to
estimate only the SOC alone and does not adequately cope with changes in SOH
and characteristic of the battery which changes in real time. Therefore,
algorithms that can estimate the SOH by simultaneously improving the real -
time estimation performance of the SOC by separating the battery parameter
estimation method and the state estimation method have been proposed. This
method is called joint-estimation or dual-estimation. [8, 9]In recent papers,
joint-estimation is commonly used, so in this paper we will refer to it as
follows. [9, 10, 11]

The most common structure of joint estimation is to combine two different
algorithms to estimate parameters and states, respectively. Furthermore,
recently, the most studied combinations are using RLS for parameter estimation
and KF for state estimation. [12] The advantage of this combination is that it
has high battery parameter estimation performance and high SOC/SOH estimation
performance while having a level of computation applicable to BMS. These
methods commonly use conventional RLS to apply a single forgetting factor
value (FV) to RLS. When these methods are applied to the actual driving
pattern of the EV, the RLS parameter estimation performance decreases
significantly in a certain period.

To solve this problem, a new SOC & SOH estimation algorithm is proposed based
on the following new techniques. First, we propose a joint estimation
algorithm of the Extended Kalman Filter and the Diagonal Forgetting Factor RLS
(DFF-RLS) that estimates the SOC and SOH based on the real-time parameter
estimation in the BMS of the EV. Second, we propose the auto-tuning method of
DFF-RLS using condition number to improve ’wind-up problem’ which is weakness
of existing RLS-based technique and to estimate more accurate parameter.
Third, we proposed an auto-tuning method to apply the optimal forgetting
factor of DFF-RLS in real time using condition number.

This paper is organized as follows. Section 2 describes the equivalent circuit
model (ECM) and the conventional algorithms (RLS, DMFF-RLS, EKF). Section 3
describes the proposed adaptive multiple forgetting factor recursive least
square (ADFF-RLS). Section 4 introduces Experimental result, which verifies
the performance of the proposed method using real data. Finally, Section 5
describes the overall summary and future work.

##  II Joint estimation using RLS & EKF

###  II-A Equivalent Circuit Model

There are various models representing the battery. Among them, the Equivalent
Circuit Model (ECM) model, which has low complexity and high model reliability
and accuracy, is easy to apply to actual applications. In general, it is known
that the 1RC model can express the dynamics of a battery relatively accurately
with less computational burden for a Li-NMC battery. [2, 13] For this reason,
it is widely used in BMS of commercial EV, and this paper also used 1RC model.

Figure 1: 1RC Equivalent circuit model

The 1RC model consists of one voltage source, one series resistor, and one
series RC branch. Where $V_{L}$is the terminal voltage of battery, $V_{OCV}$
is the open circuit voltage, $I_{L}$ is the terminal current, The branch
consist of $R_{1},\;C_{1}$ is equivalent polarization resistance and
capacitance represent slow response, $R_{0}$ is equivalent serial ohmic
resistance which represents instantaneous response.

###  II-B Joint estimation using DFF-RLS & EKF

Because the characteristics of the battery change in real time, it is
necessary to update the model parameters. Recently, ‘Joint estimation’ which
combines parameter update algorithm and state estimation algorithm has been
studied extensively. By using this method, we can obtain high state estimation
performance while compensating for the disadvantages of each algorithm. [14,
15]

Figure 2: Structure of Joint estimation

In general, EKF and RLS are the most commonly used joint-estimation algorithms
for SOC/SOH estimation. EKF is most common algorithm for estimating the state
of a system, and RLS is widely used for estimating and tracking time-varying
parameters. [16, 17]

In this paper, Joint-estimation type SOC & SOH real-time estimation algorithm
combining DFF-RLS and EKF is proposed. DFF-RLS is an algorithm that can adapt
forgetting to the characteristics of parameters that estimate FV as n-dim
diagonal matrix. It is highly effective when the sensitivity of parameters
such as battery is considerably different.

###  II-C Extended Kalman Filter (EKF)

Extended kalman filter (EKF) is optimal state observer for nonlinear system
which estimates true state recursively. EKF estimates system state from system
model and measurement. [18] At this time, EKF is linearized by finding
jacobian of the nonlinear model equation f(x). This method has the advantage
of easily applying KF to nonlinear models.

This algorithm performs a ‘primary estimate’ to estimate the state of the past
data, and then performs a ‘posteriori-estimate’ to correct the value obtained
from the primary using the sensor value. Fig. 2.5 conceptually illustrates the
operation of the EKF when system state equation.Equations (2.1) and (2.2)

$\displaystyle\begin{array}[]{@{}l}X(k+1)\quad=\quad AX(k)+Bu(k)+Q\end{array}$
(2.1)

$\displaystyle\begin{array}[]{@{}l}Y(k+1)\quad=\quad HX(k+1)+R\end{array}$
(2.2)

Figure 3: Diagram of overall EKF algorithm

Where ${\widehat{x}}_{k-1},\;{\widehat{x}}_{k}$ are primary estimated state
and posteriori estimated state, A, B, H are system model, Q, R are noise
covariance, P, K are updating covariance and Kalman gain.

$\displaystyle\begin{array}[]{@{}l}\begin{bmatrix}SOC_{k+1}\\\
I_{k+1}\end{bmatrix}=\begin{bmatrix}1&0\\\
0&A_{RC}\end{bmatrix}\begin{bmatrix}SOC_{k}\\\
I_{k}\end{bmatrix}+\begin{bmatrix}\displaystyle\frac{-\eta[k]\Delta t}{Q}&0\\\
B_{RC}&0\end{bmatrix}\begin{bmatrix}I_{k}\\\ 0\end{bmatrix}\end{array}$ (2.3)

We use the 1RC model which is widely used in real BMS’s in the equation of
state equation of EKF. Generally, it is known that the model accuracy
increases as the number of RC branches increases in the nRC model. However,
there is a disadvantage that the computational burden increases due to the
increase of the number of parameters to be estimated and the nonlinearity. [2,
13]

###  II-D Diagonal Forgetting factor Recursive Least Square (DFF-RLS)

When parameters are estimated using a conventional RLS, there is a phenomenon
in which the estimation accuracy is degraded in an area where data excitation
is insufficient. This is called the ‘estimator wind-up’ or ’covariance wind-
up’. One reason for this problem in conventional RLS is that it applies the
same covariance matrix and FV to battery parameters with different changes.
When applying the same error covariance and FV for parameters that show
different changes, errors due to all parameters are lumped into one single
scalar term. As a result, drift occurs in one parameter estimation process,
and the influence of the drift affects all parameter estimates, thereby
reducing the overall estimation performance. It is also difficult to design a
FV optimized for the variation characteristics of each parameter. To solve
this problem, it is necessary to apply covariance matrix and FV for each
parameter.

To solve this problem, we used diagonal forgetting factor -RLS (DFF-RLS) as a
parameter estimation algorithm in this paper. This algorithm can apply
forgetting to the characteristics of the parameters estimating the FV as an
n-dim diagonal matrix. It is highly effective when the sensitivity of
parameters such as battery is considerably different.

In this algorithm, when given data matrix $\phi$ and diagonal forgetting
factor matrix $\Lambda$

$\displaystyle\begin{array}[]{@{}l}\phi(t)=\begin{bmatrix}\begin{array}[]{c}1\\\
v(t-1)\\\ I(t)\\\ I(t-1)\end{array}\end{bmatrix}\end{array}$ (2.2)

$\displaystyle\begin{array}[]{@{}l}\Lambda=\begin{bmatrix}\begin{array}[]{ccc}\lambda_{1}&0&0\\\
0&\ddots&0\\\ 0&0&\lambda_{N}\end{array}\end{bmatrix}\end{array}$ (2.3)

First, we need to initialize parameter $\theta(0)$ and error covariance
$P_{0}$ .

After that, DFF-RLS calculates updating gain(K) :

$\displaystyle\begin{array}[]{@{}l}K\left(t\right)=\displaystyle\frac{P\left(t-1\right)\mathrm{\Lambda}^{-1}\varphi(t)}{I+\varphi\left(t\right)^{T}P(t-1)\mathrm{\Lambda}^{-1}\varphi\left(t\right)}\end{array}$
(2.4)

and estimation error ($\alpha$) :

$\displaystyle\begin{array}[]{@{}l}\alpha\left(t\right)=y\left(t\right)-\varphi\left(t\right)^{T}\theta(t-1)\end{array}$
(2.5)

for each time and updates them. Using this values, we can estimate the model
parareter ($\theta$) and error covariance (P)

$\displaystyle\begin{array}[]{@{}l}\theta\left(t\right)=\theta\left(t-1\right)+K\left(t\right)\alpha(t)\end{array}$
(2.6)

where $\theta$ is estimated parameter, $\varphi$ is sensor input matrix
composed of voltage and current.

##  III Adaptive Diagonal Forgetting Factor Recursive Least Square (ADFF-RLS)

###  III-A Real EV’s driving pattern

The driving pattern of actual EVs is a hybrid profile with a mixture of static
profile (rest and CC-CV charging) and dynamic profile (driving). Therefore,
the algorithm to be mounted on the BMS of the actual EV should show high
estimation performance in the hybrid profile. However, most of the existing
studies have only verified the performance using only the dynamic (driving)
profile represented by UDDS [19] and NEDC [20]. Therefore, when estimating
parameters in real time in a hybrid profile, the estimation accuracy is
significantly reduced. In addition, normal estimation is not performed in the
static profile section. Therefore, the algorithms currently being studied may
show lower performance than the simulation results when applied to the BMS of
the actual EV. In general, even if the algorithm has high estimation accuracy
in the dynamic profile, when the parameter is estimated in real time in the
hybrid profile, the estimation accuracy decreases considerably. In particular,
normal estimation is not made in the static profile section in the middle of
the entire profile. In the case of RLS, covariance wind-up phenomenon occurs
in the static profile section, and normal parameter estimation is not
performed.

In conclusion, the algorithm for the actual BMS needs to verify the estimation
performance of the hybrid profile. In this paper, we focus on this part and
verify the algorithm performance.

###  III-B Covariance (Estimator) wind-up problem

If there is insufficient excitation of the current input data and dynamic
information sufficient to estimate the parameters of the battery is not input
to the estimation algorithm, existing data is continuously forgotten. This
phenomenon leads to exponential growth of the RLS covariance, which is another
cause of the ’wind-up’ problem. [21] Therefore, the parameter estimation
performance of the RLS is drastically reduced in the interval where the
excitation is relatively insufficient among the driving patterns of the EV.
This disadvantage causes a reduction in the overall parameter estimation
performance in the actual EV driving pattern that repeats the patterns of
driving (dynamic pattern), charging and rest (static pattern).

Figure 4: Hybrid profile

###  III-C Condition number (CN) and Auto-tuning method

When estimating the battery parameters in the EV driving cycle using RLS, the
accuracy of parameter estimation fluctuates greatly depending on the FV. For
example, if a FV corresponding to a rapidly varying parameter has a small
value (typically less than 0.8), the estimation accuracy of the parameter
decreases. Therefore, setting this value is very important. At this time, the
optimal value of FF greatly changes according to the driving pattern of the
EV, the characteristics of the battery, and the change pattern of the
corresponding parameter.

Previous studies using joint estimation used a forgetting factor as a fixed
value. There are papers on how to set this value, [22, 23] but they do not
adequately reflect the characteristics of the system used. In actual
commercial BMS, it is generally known to use the best result obtained by
preprocessing that performs fine-tuning through several trials and errors on
specific data. However, in an actual EV environment where driving patterns
change every time and patterns cannot be predicted, this approach is not
optimal.

Therefore, in this paper, we applied an auto-tuning method that can
automatically estimate the optimal FV in real time in real environment. [24]
Through this technique, the SOC/SOH estimation accuracy is not significantly
affected by the value of the initial FV. It also has high estimation accuracy
for various driving patterns. In this study, auto-tuning is performed for the
first value which has the greatest influence on the estimation result among
four FV to reduce the computation amount.

At this time, condition number (CN) was introduced as a measure for accurate
parameter estimation. CN is a measure to judge the sensitivity of the
estimated parameter and has a lower value as the estimated parameter is robust
to the change of the input. [25] In this case, we can judge that the CN
parameter has a more accurate RC parameter. [26, 27] Based on this, in the
auto-tuning algorithm, the CN of the parameter estimated by RLS is obtained,
and the FFV is updated in the direction of decreasing CN. By applying the
optimal FFV, SOC & SOH estimation performance and estimation performance can
be shown.

CN = $cond(A)$ represents the sensitivity of solution x according to the
change of A, B in equation $Ax\;=\;B$.
The basic definition of CN is :

$\displaystyle\begin{array}[]{@{}l}\displaystyle\frac{\displaystyle\frac{\left\|\Delta
x\right\|}{\left\|x\right\|}}{\displaystyle\frac{\left\|\Delta
A\right\|}{\left\|A\right\|}+\displaystyle\frac{\left\|\Delta
B\right\|}{\left\|B\right\|}}\quad\leq\quad cond(A)\end{array}$ (3.1)

The two matrices A and B used to estimate the parameters in the DFF-RLS are:

$\displaystyle\begin{array}[]{@{}l}A\quad=\quad\sum_{t=0}^{t}{{\Lambda}^{t-i}{\varphi}_{i}{\varphi}_{i}^{T}}\end{array}$
(3.2)

$\displaystyle\begin{array}[]{@{}l}B\quad=\quad\sum_{t=0}^{t}\Lambda^{t-i}\varphi_{i}y\end{array}$
(3.3)

$\displaystyle\begin{array}[]{@{}l}x=\theta\end{array}$ (3.4)

A and B are matrices of current and voltage, including errors due to
measurement noise, disturbance, and round-off. Therefore, it is possible to
determine the reliability of the parameter estimated by DFF-RLS through the
CN. For example, when $cond(A)$is high, it is known that the estimated
parameter x is sensitive to errors of A and B, so that x is a value far from
the true value. Therefore, the reliability of x at this time can be judged to
be low.

Figure 5: Algorithm Flowchart of auto-tuning method

###  III-D Excitation-tag method

If there is insufficient excitation of the current input data and dynamic
information sufficient to estimate the parameters of the battery is not input
to the estimation algorithm, existing data is continuously forgotten. This
phenomenon leads to exponential growth of the RLS covariance, which is another
cause of the ’wind-up’ problem. Therefore, the parameter estimation
performance of the RLS is drastically reduced in the interval where the
excitation is relatively insufficient among the driving patterns of the EV.
This drawback becomes a problem in the actual EV driving pattern that repeats
these patterns — Driving (dynamic pattern), Charging & Rest (static pattern).

Figure 6: Ys

To solve this problem, a different technique can be applied to each section of
the battery profile with different excitations, so that the information on the
battery dynamics can be concentrated on the sections with sufficient
excitation and the sections with insufficient excitation. To apply other
estimation techniques to Based on this idea, we proposed the following
solution-’Excitation Tag’. The concept is simple. The algorithm first
determines whether excitation is sufficient or not enough, and then tags
accordingly. (For example, enough sections are set to 1 and insufficient
sections are set to 0.) The state estimation algorithm is then changed
according to the tag.

Depending on the excitation tag, the way the DFF-RLS and EKF sections work is
different. When the excitation tag is 1, since the reliability of the ECM
model parameter estimated by the RLS with the present input signal is high,
the parameter is updated to the EKF. In this case, since the prediction value
obtained through the model in the EKF is also highly reliable, the value of Q
corresponding to the model prediction is lowered. Conversely, when the
excitation tag is 0, the reliability of the parameter estimated through the
RLS is low, so the parameter is not updated to the EKF, and the Q value at
this time also increases. When this method is applied, the SOC/SOH estimation
performance in the hybrid driving profile can be significantly improved.

Figure 7: Excitation tag checker

###  III-E Adaptive DFF-RLS (ADFF-RLS)

We proposed Adaptive Diagonal Forgetting Factor Recursive Least Square (ADFF-
RLS) which improved the existing DFF-RLS. The contribution of this proposal is
as follows.

Figure 8: Flowchart of ADFF-RLS

  1. 1.

The covariance wind-up problem, a chronic problem of RLS, was solved by
applying DFF-RLS.

  2. 2.

Combining DFF-RLS and EKF, it was possible to obtain high SOC and SOH
estimation performance compared to other algorithms (Single EKF, MFF-RLS &
EKF) while compensating for the shortcomings of each algorithm.

  3. 3.

By applying the auto-tuning method of the forgetting factor of RLS through the
condition number, it is possible to automatically and adaptively apply the
optimal forgetting factor without fine-tuning the input data used for
estimation.

  4. 4.

Excitation tag that adaptively changes the tuning parameter of EKF according
to the excitation of the input data is newly proposed to improve the
estimation performance more than the existing EKF.

To verify the performance of the proposed algorithm, the results were verified
with the battery data used in the actual EV in the next section.

##  IV Results

This section shows a comparative study between the common SOC/SOH estimation
method and the proposed ADFF-RLS method. Experimental data have been collected
from a Li-ion (NMC) battery pack used in actual EV uses various data with
different temperature from SOH.

###  IV-A Experimental test setup

Experimental data used Li-ion NMC 72Ah battery pack used in actual EV. The
battery was tested with various current input profiles (Static and dynamic
profile) and various SOH (Beginning of life) and EOL (End of life). The
SOC/SOH was estimated using the voltage (V), current (I) and temperature (T)
data obtained from this experiment.

###  IV-B Error comparison result

TABLE I: Driving profile with different SOH and noise  Profile number |  Test profile |  Life status |  Temperature
---|---|---|---
3-1 |  Driving - GB/T |  BOL |  30∘C
3-2 |  Driving - GB/T |  EOL |  30∘C
3-3 |  Driving - US06 |  BOL |  30∘C
3-4 |  Driving - GB/T+US06 |  EOL |  30∘C
TABLE II: Maximum and average absolute estimation error of SOC  Test profile |  Single EKF |  MFF-RLS&EKF |  DFF-RLS&EKF
---|---|---|---
Error [%] |  MAX |  AVG |  MAX |  AVG |  MAX |  AVG
Driving - GB/T (BOL) |  4.211 |  1.805 |  1.650 |  1.315 |  0.060 |  0.048
Driving - GB/T (EOL) |  6.450 |  2.763 |  16.734 |  2.603 |  0.459 |  0.144
Driving - US06 (BOL) |  6.649 |  3.204 |  7.648 |  4.727 |  0.644 |  0.497
Driving - GB/T+US06 (EOL) |  5.355 |  2.665 |  1.552 |  1.152 |  0.017 |  0.013
TABLE III: Maximum and average absolute estimation error of estimated voltage (SOH error)  Test profile |  Single EKF |  MFF-RLS&EKF |  DFF-RLS&EKF
---|---|---|---
Error [mV] |  MAX |  AVG |  MAX |  AVG |  MAX |  AVG
Driving - GB/T (BOL) |  60.600 |  4.620 |  72.913 |  8.792 |  27.854 |  0.337
Driving - GB/T (EOL) |  56.220 |  9.523 |  2916.000 |  18.775 |  49.428 |  4.815
Driving - US06 (BOL) |  110.300 |  7.968 |  126.390 |  22.616 |  208.130 |  10.602
Driving - GB/T+US06 (EOL) |  41.130 |  8.299 |  42.354 |  15.714 |  37.584 |  1.774

Standardized car driving test profile GB / T and US06, and EKF alone (Single
EKF) for battery test data tested with a mixture of two profiles, previously
proposed MFF-RLS + EKF, and proposed in this study Algorithm (ADFF-RLS + EKF)
was applied and the results were compared and verified. From the overall data,
we can see that the proposed algorithm has higher estimation accuracy.

##  V Conclusions

In this paper, we proposed a real-time joint estimation (DMFF-RLS & EKF)
algorithm using excitation tag and verified by experiment. The contribution of
this proposal is as follows.

Through the joint estimation of DFF-RLS and EKF, it can adapt to changing
environments in EV real-time driving environment and obtain high SOC / SOH
estimation performance.

  1. 1.

Condition number was introduced to numerically represent the accuracy of the
estimated parameters and auto-tuning was performed based on the condition
number. Therefore, high estimation accuracy can be obtained without fine-
tuning of tuning parameters, and adaptive response can be achieved through
automatic tuning even if the driving pattern changes.

  2. 2.

Through the above proposals, high estimation accuracy can be obtained by
improving ’covariance wind-up problem’ which is a disadvantage of RLS-based
estimation method.

In order to evaluate the accuracy of the SOC/SOH estimation result of this
algorithm, we applied the hybrid driving pattern cell data of real EV’s with
two SOH (BOL & EOL) to the proposed method, and confirmed the SOC estimation
results for each data. The same data were compared with several conventional
algorithms. The proposed algorithm was more accurate than the other
algorithms. These results were common to all battery life types. Therefore, it
is confirmed that the proposed algorithm has higher SOC/SOH estimation
accuracy than the existing algorithm in the environment where the traveling
pattern changes in real time. This result is expected to provide more accurate
information about the driver. Therefore, the driver can manipulate the EV in a
more economical way and obtain an improved driving experience.

## Acknowledgements

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Kwangrae Kim received the B.S. degree inelectrical engineering from Konkuk
University, Seoul, South Korea, in 2015, and the M.S. degree in electrical
engineering from the Pohang Uni-versity of Science and Technology
(POSTECH),Pohang, South Korea, in 2018, where he is cur-rently pursuing the
Ph.D. degree in electrical engineering. His research interests include ithium-
ion battery state estimation, battery man-agement systems, and optimization
applications in battery field.
---
Minho Kim received the B.S. degree in mechan-ical engineering from Korea
University, Seoul,South Korea, in 2015. He is currently pursuingthe Ph.D.
degree with the Department of CreativeIT Engineering, Pohang University of
Science andTechnology (POSTECH), Pohang, South Korea.His main research
interests include energy storagesystems and machine learning technology
---
Suwon Kang received the B.S. degree in electrical engineering from Kyungpook
national university, Daegu, South Korea, in 2015, and the M.S. degree in
electrical engineering from the Pohang University of Science and Technology
(POSTECH), Pohang, South Korea, in 2017. He is currently working in the LG
chem. His research interests include lithium-ion battery state estimation,
functional safety engineering in automotive battery field.
---
Jungwook Yu received the B.S. degree ininformation management and technology
from Syracuse University, NY, USA, in 2012. He iscurrently pursuing the Ph.D.
degree withthe Department of Creative IT Engineering, Pohang University of
Science and Technology(POSTECH), Pohang, South Korea. His research interests
include battery management systems, parameter estimation, and neural networks
---
Jungsoo Kim was born in Seoul, South Korea. He received the B.S. degree in
mechanical engineering from Korea University, Seoul, SouthKorea, in 2013. He
is currently pursuing the Ph.D.degree (M.S–Ph.D. integrated program) with the
Department of Creative IT Engineering (CiTE), Pohang University of Science and
Technology(POSTECH), Pohang, South Korea. His research interests include li-
ion battery state estimation, li-ion battery parameter identification, battery
management systems, and their applications to electric vehicles and
smartgrids.
---
Huiyong Chun was born in Seoul, SouthKorea, in 1994. He received the B.S.
degree in electrical engineering from the Pohang University of Science and
Technology (POSTECH), Pohang,South Korea, in 2017, where he is currently
pursuing the Ph.D. degree with the Department of Creative IT Engineering. His
research inter-ests include lithium-ion battery state and parameter
estimation, battery management systems, and machine learning applications in
lithium-ionbattery
---