BY Muhammad Umair Ali-February 2020
Department of Electrical and Computer Engineering
The Graduate School
Pusan National University
ABSTRACT
Due to the escalation in environmental pollution and energy prices, electric vehicles
(EVs) have widely explored in the past few years. Battery electric vehicles (BEVs), plug-in
hybrid electric vehicles (PHEVs), and fuel cell electric vehicles (FCEVs) are the different
variants of EVs. These EVs consist of energy storage and the motor system as the auxiliary
or primary energy source (FCEVs and PHEVs) or the sole energy source (BEVs). The
lithium-ion (Li-ion) batteries are preferred as an energy storage system because of its longlife
cycle, reliability, high energy density, low toxicity, low self-discharge rate, high power
density, and high efficiency. In EVs, a smart battery management system (BMS) is one of
the essential components; it not only measures the states of battery accurately but also
ensures safe charging/discharging operation and prolongs the battery life. The issues of
accurate estimation of the state of charge (SOC), remaining useful life prediction (RUL),
and reduction in charging time of the Li-ion battery is still a bottleneck for the
commercialization of EVs because the Li-ion battery is a highly time-variant, non-linear,
and complex electrochemical system. In this dissertation, a novel fuzzy logic and
temperature feedback-based method, Lagrange multiplier approach, and partial discharge
data (PDD) based support vector machine (SVM) model are presented for reduction of the
charging time, SOC estimation, and RUL prediction of the Li-ion battery, respectively.
This dissertation comprises of four studies, each of which constitutes a step towards a
smarter BMS for EV applications. The first study proposes an efficient, real-time, fastcharging
methodology of Li-ion batteries. Fuzzy logic was adopted to drive the charging
current trajectory for series-connected Li-ion batteries. The voltage and temperature of the
cells were fed to the controller to find the optimal charge current value within the safe
temperature limit. A temperature control unit was also implemented to evade the effects of
fast charging on the aging mechanism. The proposed method of charging also protects the
battery from overvoltage and overheating. Extensive testing and comprehensive analysis
were conducted to examine the proposed charging scheme. The results show that the
proposed charging strategy favors a full battery recharging in 9.76% less time than the
conventional constant-current–constant-voltage (CC/CV) method. The methodology
charges the battery at a 99.26% SOC without significant degradation. The entire scheme
was implemented in real-time, using Arduino interfaced with MATLABTM Simulink. This
decrease in charging time assists in the fast charging of cell phones and notebooks and the
large-scale deployment of EVs.
The second work presents a new online data-driven battery model identification
method, where the battery parameters are updated using the Lagrange multiplier method. A
battery model with unknown battery parameters was formulated in such a way that the
terminal voltage at an instant time step is a linear combination of the voltages and load
current. A cost function was defined to determine the optimal values of the unknown
parameters with different data points measured experimentally. The constraints were added
in the modified cost function using the Lagrange multiplier method, and the optimal value
of the update vector was determined using the gradient approach. An adaptive open-circuit
voltage (OCV) and SOC estimator was designed for the Li-ion battery. The experimental
results showed that the proposed estimator is highly accurate and robust. The proposed
method effectively tracks the time-varying parameters of a battery with high accuracy.
During the SOC estimation, the maximum noted error was 1.28%. The convergence speed
of the proposed method was only 81 s with a deliberate 100% initial error. Owing to the
high accuracy and robustness, the proposed method can be used in the design of a smarter
BMS for real-time applications.
In the third work, the sensitivity analysis is performed for the first and second-order
RC autoregressive exogenous (ARX) battery model to check the influence of voltage and
current transducer measurement uncertainty. The sensitivity analysis is performed under
the following conditions: Current sensor precision of ±5 mA, ±50 mA, ±100 mA, and ±500
mA and voltage sensor precision of ±1 mV, ±2.5 mV, ±5 mV, and ±10mV. The comparative
analysis of both models under the perturbed environment has been carried out. The effects
of the sensor’s sensitivity on the different battery structures and complexity are also
analyzed. Results show that the voltage and current sensor sensitivity has a significant
influence on SOC estimation. This research outcome assists the researcher in selecting the
optimal value of sensor accuracy to accurately estimate the SOC of the Li-ion battery for a
smarter BMS.
In the last work, a novel partial discharge data (PDD) based support vector machine
(SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical
features from the voltage and temperature of PDD to train the SVM models. The
classification and regression attributes of SVM are utilized to classify and predict accurate
RUL. The different ranges of PDD were analyzed to find the optimal range for training the
SVM model. The SVM model trained with optimal PDD features classifies the RUL into
six different classes for gross estimation, and the support vector regression is used to
estimate the accurate value of the last class. The classification and predictive performance
of SVM model trained using the full discharge data and PDD are compared. Results show
that the SVM classification and regression model trained with PDD features can accurately
predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be
utilized for online RUL estimation in Li-ion battery BMS.
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