A brief review on key technologies in the battery management system of electric vehicles

Abstract

Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

Full text

REVIEW ARTICLE
Kailong LIU, Kang LI, Qiao PENG, Cheng ZHANG
A brief review on key technologies in the battery
management system of electric vehicles
© The Author(s) 2018. This article is published with open access at link.springer.com and journal.hep.com.cn
Abstract Batteries have been widely applied in many
high-power applications, such as electric vehicles (EVs)
and hybrid electric vehicles, where a suitable battery
management system (BMS) is vital in ensuring safe and
reliable operation of batteries. This paper aims to give a
brief review on several key technologies of BMS,
including battery modelling, state estimation and battery
charging. First, popular battery types used in EVs are
surveyed, followed by the introduction of key technologies
used in BMS. Various battery models, including the
electric model, thermal model and coupled electro-thermal
model are reviewed. Then, battery state estimations for the
state of charge, state of health and internal temperature are
comprehensively surveyed. Finally, several key and
traditional battery charging approaches with associated
optimization methods are discussed.
Keywords battery management system, battery model-
ling, battery state estimation, battery charging
1 Introduction
Electric vehicles (EVs) and hybrid electric vehicles
(HEVs) have been widely regarded as the most promising
solutions to replace the conventional internal combustion
(IC) engine-based vehicles, and the recent years have seen
a rapid development of EV and HEV technologies.
Batteries have been widely applied as the power supply
for EVs and HEVs due to the advantages such as high
energy density, low environmental pollution and long
cycle life. On the other hand, batteries require particular
care in the EV applications. Improper operations such as
over-current, over-voltage or over-charging/discharging
will cause significant safety issue to the batteries,
noticeably accelerate the aging process, and even cause
fire or explosion [1]. Therefore, the battery management
system (BMS) plays a vital role in ensuring safety and
performance of batteries.
Key technologies in the BMS of EVs include the battery
modelling, internal state estimation and battery charging.
An effective battery model is crucial in battery behaviour
analysis, battery state monitoring, real-time controller
design, thermal management and fault diagnosis. Besides,
some battery internal states, such as state of charge (SOC),
state of health (SOH) and internal temperature, cannot be
measured directly, while these states play important roles
in managing the operation of batteries, and thus need to be
monitored using proper estimation methods. Further,
battery charging is also of great importance in BMS due
to its direct impact on the operation safety and service
availability of battery. A well-designed charging strategy
will protect batteries against damage, limit temperature
variations as well as improve efficiency of energy
conversion. Slow charging has negative effect on the
availability of EV usage, but charging too fast may
adversely lead to large energy loss and temperature rise [2].
Large temperature variation further leads to rapid battery
aging and even causes overheating or supercooling, which
will eventually shorten the battery service life [3].
This paper aims to give a brief review of the key
technologies especially for battery modelling, state
estimation and battery charging in the BMS of EVs.
Recent approaches to tackle the problem of battery
modelling of the electric and thermal characteristics are
surveyed first. These established battery models are used to
capture battery electric and thermal behaviours. Then the
Received September 30, 2017; accepted January 9, 2018
Kailong LIU, Kang LI (✉)
School of Electronics, Electrical Engineering and Computer Science,
Queen’s University Belfast, BT9 5AH Belfast, UK
E-mail: kliu02@qub.ac.uk; k.li@qub.ac.uk
Qiao PENG
School of Physics and Optoelectronic Engineering, Nanjing University
of Information Science and Technology, Nanjing 210044, China
Cheng ZHANG
IDL, Warwick Manufacturing Group, University of Warwick, CV4 7AL
Coventry, UK
Front. Mech. Eng.
https://doi.org/10.1007/s11465-018-0516-8

corresponding independent or joint state estimation
methods of battery SOC and internal temperature are
also reviewed. On the basis of battery models and
estimated internal states, battery charging approaches are
discussed, together with optimization algorithms for
improving the performance of these charging approaches.
The remainder of this paper is organized as follows.
Section 2 discusses some typical battery types used in EVs
and key technologies for BMS. Then battery electric
models, together with thermal models as well as coupled
electro-thermal models are reviewed in Section 3. Section
4 focuses on a comprehensive review of the battery SOC
estimation, SOH estimation, internal temperature estima-
tion and joint state estimation. Then several traditional
charging approaches and their optimization methods are
discussed in Section 5. Finally, Section 6 concludes this
paper.
2 Battery types and key technologies for
BMS
In EV applications, many types of battery can be adopted
as the power supply. There are a number of functional
modules in the BMS. Some popular battery types and key
technologies for BMS are analysed and summarized in this
paper.
2.1 Battery types in EVs
Batteries are generally grouped into two categories based
on the ability of recharging: Primary and secondary
battery. The primary battery can be just used once after
being fully discharged, and the secondary battery is
capable of being recharged after discharging process. For
the applications of EVs and HEVs, the secondary battery
with long cycle life, small energy loss, high power density
and enough safety level is indispensably required. Some
popular types of batteries used in EVs include lithium-ion
(Li-ion), lead acid, nickel-cadmium (NiCd) and nickel-
metal hydride (NiMH), etc. Table 1 illustrates some key
characteristics for these popular battery types. It is clearly
shown that Li-ion battery is significantly better than other
types of battery, especially in terms of large cycle life
which is key to long service of EVs (e.g., 6–10 years’
service life). Besides, Li-ion battery is also composed of
eco-friendly materials without toxic gassing problem and
has high safety level. Therefore, Li-ion battery becomes a
most popular power supply for EVs.
2.2 Key technologies for BMS
On the other hand, battery needs particular care in the EV
applications. Incorrect operations such as too high or too
low temperature, over charging or discharging will speed
up the degradation process of battery dramatically.
Besides, battery pack in EVs is generally composed of
hundreds of battery cells connected in series or parallel
configuration to satisfy the high power and high voltage
requirement for the vehicles. Particular care also needs to
be taken to operate such a complicated battery pack.
Therefore, a proper BMS is crucial in protecting batteries
from damages, which needs be carefully designed [4,5]. In
this paper, some key technologies including battery
modelling, battery state estimation and battery charging
which are required in designing an effective BMS in EVs
will be surveyed. The relationship of these key technolo-
gies is illustrated in Fig. 1. In the applications of EVs,
battery current and voltage can be detected by on-board
current sensor and voltage sensor directly, and surface
temperature of battery pack can be also detected by
temperature sensor or thermocouple conveniently. Then
the well-trained battery models together with suitable
estimation methods can be adopted to achieve independent
or joint state estimations of battery SOC or internal
temperature. After capturing battery electric and thermal
behaviours, battery charging approaches can be optimized
by proper optimization algorithms, and further to charge
battery from initial state to final target with the equilibra-
tion of various charging objectives such as fast charging,
high efficiency of energy conversion and low temperature
rise. If any abnormal situations of battery states occur in
the operation process, the alarm module and safety control
module will work to record or eliminate these cases
accordingly. Therefore, battery modelling, state estimation
and battery control are vital technologies in the BMS, and
Table 1 Popular types of battery in EVs
Battery
type
Service life
/cycle
Nominal
voltage/V
Energy density
/(W$h$kg
–1
)
Power density
/(W$kg
–1
)
Charging
efficiency/%
Self-discharge rate
/(%$month
–1
)
Charging
temperature/
o
C
Discharging
temperature/
o
C
Li-ion
battery
600–3000 3.2–3.7 100–270 250–680 80–90 3–10 0 to 45 –20 to 60
Lead acid
battery
200–300 2.0 30–50 180 50–95 5 –20 to 50 –20 to 50
NiCd
battery
1000 1.2 50–80 150 70–90 20 0 to 45 –20 to 65
NiMH
battery
300–600 1.2 60–120 250–1000 65 30 0 to 45 –20 to 65
2 Front. Mech. Eng.

these technologies become the thriving areas of research in
the applications of BMS/EVs.
3 Battery modelling
Building a proper model is usually the starting point for
BMS design, control and optimization. Over the years,
numerous battery models with various levels of accuracy
and complexity have been developed. These models can be
primarily categorized as the battery electric model, battery
thermal model, and battery coupled model, which are
detailed in Fig. 2. Other model types such as battery kinetic
models that are far less used in BMS are not covered in this
paper.
3.1 Battery electric model
Battery electric models mainly include electrochemical
model [6–9], reduced-order model [10–13], equivalent
circuit model [14–17] and data-driven model [18–20]. For
electrochemical model, Rahman et al. [6] claimed that the
battery electrochemical model should have the abilities to
capture the spatiotemporal dynamics of battery concentra-
tion, the electrode potential for each phase and the Bulter-
Volmer kinetic to control intercalation reaction. Then an
electrochemical model is established to describe battery
electrochemical behaviours by using particle swarm
optimization (PSO) method to optimize critical model
parameters. Sung and Shin [7] showed that the electro-
chemical model presented a highly accurate prediction
performance but required significant computation effort in
model simulation. Then a model implementation scheme
was developed to embed electrochemical model into the
BMS. The main advantage of using electrochemical model
is that a highly accurate description of electrochemical
processes within the battery can be obtained. However,
many parameters relating to the battery electrochemistry
such as chemical compositions need to be identified, which
is practically difficult to achieve in real-time applications.
Besides, these electrochemical models usually involve
many partial differential equations which need to be
solved, resulting in large computational overheads. By
making suitable assumptions, the full-order electrochemi-
cal models can be approximated by reduced-order models.
For example, Han et al. [10] proposed an approximate
method to capture the solid phase diffusion and electrolyte
concentration distribution of battery, then a simplified
physics-based electrochemical model is developed to
estimate Li-ion battery SOC. Zou et al. [11] proposed a
reduced-order electrochemical model for LiFePO
4
battery
to predict discharging capacity under various conditions,
then robust SOC estimation was achieved based on this
reduced-order battery model. Although this approach leads
to some information loss in the simplified reduced-order
models, they are more desirable for real-time applications
of batteries. The computational overheads become much
lower for reduced-order models, and the corresponding
parameters can be identified by the measured current and
voltage signals. For equivalent circuit models, battery
electric behaviours have been captured by a combination
of circuit components, such as resistances, capacities,
Fig. 1 The relation of key technologies in the BMS
Kailong LIU et al. Key technologies in the battery management system of electric vehicles 3

voltage sources. Because of simple model structure and
relatively small number of model parameters, equivalent
circuit models have been widely adopted in battery real-
time applications. A typical framework of battery equiva-
lent circuit model is illustrated in Fig. 3. The resistor-
capacitor (RC) networks in equivalent circuit model are
related to the battery electric behaviours such as charge
transfer or diffusion processes. The number of RC
networks is treated as the model order, and need be
carefully selected. It has been shown that the first and
second order models are more popular, and higher order
models in many cases are not necessary [14]. Nejad et al.
[15] presented a critical review for commonly used battery
lumped-parameter equivalent circuit model. Comparison
results show that the RC network models have better
dynamic performance especially for SOC and power
predictions. Data-driven models try to capture the relation
between input and output signals of batteries. Various data-
driven models such as neural networks [18,19] and support
vector machine (SVM) [20] have been adopted to describe
battery electric behaviours without the prior knowledge.
The performance of battery data-driven model is highly
dependent on the test data as well as training approaches.
To achieve acceptable model accuracy as well as good
generalization ability, test data should cover enough
battery operation ranges, and the parameters in the training
approaches need to be effectively tuned. Besides, the
adaptive data-driven approaches [21,22] can be used to
achieve better battery modelling results.
3.2 Battery thermal model
Thermal behaviour such as temperature is also a key aspect
in the BMS of EVs because temperature plays a vital role
in battery performance and lifetime [23]. Various models
such as heat generation model, heat transfer model,
reduced-order thermal model and data-driven model have
been developed to capture the thermal behaviours of
batteries. For the heat generation model, a number of
methods are introduced to describe the heat generation in
battery, such as activation, concentration and ohmic losses,
which distribute non-uniform inside the battery. Three
popular approaches to assess the heat generation in the
batteries are illustrated in Eq. (1), which have been widely
applied in real-time applications [24–27].
Fig. 2 Three classifications of battery modelling
Fig. 3 Typical framework of a battery equivalent circuit model
4 Front. Mech. Eng.

Q1¼RI2
Q2¼IðV–OCV Þ
Q3¼IðV–OCV ÞþIT dOCV
dT
8
>
>
>
>
<
>
>
>
>
:
, (1)
where Ris the battery internal resistance, Iand Vstand for
the battery current and voltage, respectively, OCV is the
battery open circuit voltage, Q1stands for the battery heat
generation which is primarily caused by the large current
crossing the battery internal resistance, Q2stands for the
battery heat generation caused by the over-potentials
across the RC network, Q3stands for the battery heat
generation caused by both the entropy change and Joule’s
heating.
For battery heat transfer, heat convection, heat conduc-
tion and heat radiation are the three main forms within and
outside the battery. Guo et al. [28] developed a three-
dimensional distributed-parameter heat transfer model for
Li-ion battery to study the geometrical current and heat
distribution inside the battery, which is illustrated as
follows,
∂CpT3C
∂t¼–rðk3CrT3CÞþQ, (2)
it can be also expressed as [29],
∂CpT3C
∂t¼–
∂
∂xkx
∂T3C
∂x

–
∂
∂yky
∂T3C
∂y

–
∂
∂zkz
∂T3C
∂z

þQ, (3)
where stands for the battery density, Cpmeans battery
heat capacity, k3Cis the coefficient of battery thermal
conductivity (along three dimensions: kx,ky,kz), Qmeans
battery heat generation.
Assuming the battery temperature distribution within
each layer plane is uniform, and only one dimension of
battery heat conduction is considered (e.g., xdimension),
then a simplified one-dimension heat conduction model
can be developed [30] as follows,
∂CpT1C
∂t¼–
∂
∂xkx
∂T1C
∂x

þQ, (4)
The three-dimensional heat transfer models are capable
of capturing temperature distribution inside the battery,
which can be applied to detect possible hot spots,
especially in high-heat generation applications. The one-
dimensional heat transfer model can capture the tempera-
ture gradient along one direction. However, the computa-
tional overheads of these heat transfer models are often too
large for real-time applications, and they are mainly used in
offline simulations.
Supposing the heat conduction is the main heat transfer
type, heat generation is evenly distributed within the
battery, and further the temperatures of both battery surface
and interior are uniform, then a popular two-stage thermal
model for battery cell [31–33] can be derived as
Cq1dTin=dt¼k1ðTsh –Tin ÞþQ
Cq2dTsh=dt¼k1ðTin –Tsh Þþk2ðTamb –TshÞ
(, (5)
where Tin and Tsh stand for battery internal and surface
temperature, respectively, Tamb means the ambient tem-
perature around battery, Cq1and Cq2stand for the thermal
capacities of battery interior and surface, respectively, k1
stands for the heat conduction between battery surface and
interior, k2stands for the heat conduction between
ambiance and battery surface.
After defining battery heat generation and transfer parts,
many battery reduced-order thermal models have been also
developed to achieve control purpose for battery thermal
management [34–36]. In Ref. [34], the order of a Li-ion
battery model is reduced by converting the one-dimen-
sional boundary-value problem into a low-order linear
model in the frequency domain. The temperature predic-
tion of the reduced order model matches closely with the
experimental data and a three-dimensional finite-element
simulation. Hu et al. [36] proposed a reduced-order state-
space model of battery pack based on a computational fluid
dynamics (CFD) model using the singular value decom-
position method. The proposed model with much less
computation costs is capable of providing similar results as
those from the CFD model.
3.3 Battery coupled electro-thermal model
There is strong coupling between the battery electric and
thermal behaviours. In order to capture battery electric
behaviours (e.g., current, voltage, SOC) and thermal
behaviours (e.g., surface and internal temperature) simul-
taneously, several coupled electro-thermal models have
been developed in the literature, including both lump-
parameter and distributed-parameter models [37–39].
Further, Goutam et al. [40] proposed a three-dimensional
electro-thermal model to estimate battery SOC and
calculate heat generation. This coupled model consists of
a 2D potential distribution model and a 3D temperature
distribution model. Then the battery SOC and temperature
distribution under both constant and dynamic currents are
effectively calculated based on this coupled model. In Ref.
[41], a reduced low-temperature electro-thermal model
was proposed and validated by batteries with three cathode
materials. This reduced model is accurate enough to
develop fast heating and optimal charging approach under
low temperature condition. Basu et al. [42] presented a
coupled three-dimensional electro-thermal model to ana-
lyse the influences of various battery operations such as
coolant flow-rate and discharge current on the battery
Kailong LIU et al. Key technologies in the battery management system of electric vehicles 5

temperature. Contact resistance is verified to play a vital
role in battery temperature based on the analysis of this
coupled model.
4 Battery state estimation
This section gives a review of battery state estimation,
while focus on battery key states including SOC, SOH,
internal temperature and joint state estimation.
4.1 SOC estimation
SOC stands for the remaining battery capacity as a
percentage to the total at the same situation. 100%stands
for the battery is fully charged to its total capacity, and 0%
stands for battery is fully discharged. Accurate SOC
estimation plays a vital role in monitoring existing capacity
state, to further guarantee the safe and healthy operation of
battery [43]. Generally speaking, two approaches are
developed for SOC estimation, which is categorized as
direct estimation approach and model-based approach. For
the direct estimation approach, based on the direct
measurements of battery current and voltage, SOC is
mainly calculated by two different ways named Ampere-
hour (Ah) or coulomb counting method and open circuit
voltage (OCV) based method. Ah method is a general and
simple method to calculate SOC, which is illustrated as
follows,
SOCðkÞ¼SOCðk0Þþ
!
k
k0
ηIðtÞdt=Cn, (6)
where SOCðk0Þis the known initial SOC, ηstands for the
efficiency of battery charging or discharging, Cnstands for
the battery nominal capacity, IðtÞis the current value
which is positive for charging and negative for dischar-
ging.
Since charging or discharging current can be easily
measured, Ah method becomes a straightforward choice
for SOC estimation. However, Ah method is highly
dependent on the current measurements, error accumula-
tion over the time will significantly affect the estimation
accuracy. Besides, it is difficult to determine the initial
SOC accurately in real-time applications especially when
battery is only charged within a limited range, e.g., 10%–
90%. Calibrations of initial SOC and current become the
challenging issues to adopt Ah method for SOC estima-
tion.
It has been proposed that there exists a one-to-one
nonlinear relation between the battery SOC and OCV.
Therefore, using OCV after enough resting to estimate
battery SOC has become an effective and popular
approach, which has been adopted in many applications
[44,45]. Although high estimation accuracy of battery
SOC can be achieved by the OCV method, the resting time
has become a major limitation for OCV-based SOC
estimation. It generally takes a long time to reach
equilibrium after disconnecting the load current (for
example for LiFePO
4
battery, duration time is always
larger than two hours under low temperature condition).
Further, the relation between OCV and SOC also changes
along with battery aging and temperature changes.
The disadvantages of OCV method limit its wide
applications in EVs. This problem can be addressed if
the OCV can be obtained real-time to allow estimation of
SOC during driving. Thus, the model-based approach has
been developed to calculate OCV to further achieve on-
line estimation of battery SOC. In the model-based
approaches, a suitable battery model needs to be carefully
designed. Battery equivalent circuit model [46] and
electrochemical model [47,48] in the forms of standard
state space are usually selected to estimate battery SOC,
while SOC is one of the state variables in these battery
models. Then various state observers are adopted for on-
line SOC estimation [49–53], such as Kalman filter (KF),
extended Kalman filter (EKF), adaptive Kalman filter
(AKF), unscented Kalman filter (UKF), slide mode
observer and H1filter. The accuracy of these model-
based approaches largely depends on the training of the
battery models, the adopted state observers, and the
parameter tuning such as the key parameters in model
and the noise covariance matrix for KF observers. Besides,
the performance of battery SOC estimation by these
different observers is only validated under limited condi-
tions of the test data, and a reliable confidence-zone is
usually difficult to obtain. Therefore, the estimation
performance under various practical conditions, which
are different from the test conditions, cannot be guaran-
teed.
4.2 SOH estimation
There is no single definition for the battery SOH. A general
description of battery SOH can be given as
SOHðtÞ¼SOH ðt0Þþ
!
t
τ¼t0
δfuncðI,T,SOC,othersÞdτ,(7)
where SOHðt0Þrepresents the initial battery SOH, δfunc is
an aging rate function, which strongly depends on several
factors such as current, temperature, SOC, others repre-
sents some other stress factors such as the mechanical
vibrations and over-potential.
For EV applications, battery aging will result in the
degradation of battery capacity and the increase of battery
internal resistance. Thus, the battery SOH can be estimated
by the internal resistance or usable capacity as a kind of
prediction regime changes in computer science field [54].
Numerous approaches have been proposed to estimate
battery SOH, which are categorized into three groups,
namely, model-free, model-based, and data mining
6 Front. Mech. Eng.

methods.
For model-free method, given that the aged capacity
Caged or the increased internal resistance Rinc, battery SOH
can be simply defined as
SOH ¼Caged=Cn100%
SOH ¼Rinc=Rn100%
(, (8)
where Cnand Rnstand for the nominal capacity and
internal resistance of the new battery without being used.
According to the definition of SOH in Eq. (8), one can
apply the standard capacity test [55] or pulse current test
[56] to measure the battery aged capacity and increased
internal resistance. However, this direct method is
inconvenient and not recommended because fully dis-
charge using the controlled current and temperature will
interrupt the normal EV operations. Compared with the
direct measurements of Caged and Rinc, the battery
electrochemical impedance spectroscopy (EIS) can cer-
tainly offer much more information about the battery
health condition. Therefore, researchers have proposed
using battery EIS for health diagnosis [57–60]. However,
the on-board measurement and application of battery EIS
need specific instruments, which will limit its applicability.
Further, a full EIS test also takes long time.
For model-based method, on the one hand, the battery
capacity or internal resistance is taken as the time-varying
parameters based on the battery equivalent circuit model
[15] and electrochemical model [61]. Then various
observers such as particle filters [62,63], Kalman filters
[64–66] and sliding mode [67–69], are adopted to estimate
the capacity and internal resistance during battery opera-
tions, thus the SOH can be obtained accordingly. On the
other hand, in order to analyse the effects of stress factors
on the battery degradation, some researchers focus on
developing battery cycle-life models to predict battery
capacity degradation. Defining the battery capacity loss
Closs as
Closs ¼ðCn–CagedÞ=Cn100%:(9)
Then Closs can be further expressed as
Closs ¼δfuncðfÞAhz, (10)
where fstands for the stress factors to cause capacity
degradation, Ah means the accumulated current through-
put, zis a power law parameter, δfunc represents the effects
of stress factors on the degraded battery capacity.
Considering battery capacity degradation is mainly
associated with the battery current, temperature, SOC
and charging methods, etc., numerous researches have
been proposed to describe battery aging dynamics. For
example, Wang et al. [70] adopted the power law equation
to establish a generalized cycle-life model for LiFePO
4
cell. The life model was calibrated over wide current and
temperature ranges without considering the depth of
discharge. In Ref. [71], a similar cycle-life model was
validated to predict battery capacity loss with constant
temperature at low SOC level. Omar et al. [72] proposed a
battery aging model to predict the degraded capacity in the
situations of both discharging and fast charging. In Ref.
[73], a cycle-life model was validated for the Li-ion battery
based on the profiles proposed by the VDA (German
association of the automotive industry). Suri and Onori
[74] proposed a control-oriented cycle-life model to
capture the battery capacity loss over data mimicking
actual cycling conditions, the corresponding severity factor
map was established to predict and control battery
degradation. In Ref. [75], according to a mechanistic and
prognostic model, a dynamic cycle-life model was
proposed to capture the capacity degradation dynamics
under the conditions of varying load for large-format Li-
ion batteries. Gao et al. [76] analysed the battery aging
under different charging conditions and concluded that
charging current less than 1 C affects the active material
loss, cut-off voltage less than 4.2 V affects the lithium loss
respectively. Then an experiential life model was proposed
to capture the relations of degraded battery capacity and
charging stresses. These cycle-life models play important
roles in optimizing the real-time operations to prolong the
service life of battery. But current researches mainly focus
on the specific working loads. Their accuracies cannot be
guaranteed under real-time applications. Besides, as
battery SOH changes at a much slower rate compared
with the battery SOC, wider ranges of battery operation
and more test data are required to train the battery cycle-
life model, this inevitably increases the difficulty of
engineering implementation.
Similar to battery SOC estimation, data mining methods
have also been applied for battery SOH estimation [77–
87]. Nuhic et al. [79] proposed a battery health diagnosis
and prognosis method in an alternative power train using
SVM, which relies only on on-board measurable data, e.g.,
battery terminal voltage and current and operating
temperature. Klass et al. [80] also proposed an SVM
method to estimate battery SOH. A new data-processing
method is proposed using load collectives to generate the
input and output vectors of the required SVM training data
set. Hu et al. [81] proposed a data-driven classification
method, i.e., the K-nearest neighbor (KNN) method, for
battery capacity estimation. Again, only the on-board
measurable signals, i.e., voltage and current, are needed.
The characteristic features of the charge curve which are
indicative of the battery SOH are identified, and then the
KNN method is used to ‘learn’from data the relationship
between the battery SOH and these features. In Ref. [82],
according to the historical distributions of sensible data
such as battery current, voltage and temperature, a data
mining approach was proposed to estimate the battery
SOH by using clustering and neural network technologies.
Under practical environment, the average error of
estimated SOH can be within 2.2%. Liu et al. [85]
Kailong LIU et al. Key technologies in the battery management system of electric vehicles 7

proposed a health indicator (HI) extraction and transfor-
mation framework to estimate the battery remaining useful
life (RUL). The relevance vector machine (RVM) algo-
rithm can achieve the satisfied RUL estimation with the
optimized HI. Other data-driven methods, such as Naive
Bayes [84], Bayesian learning [83,86], Bayesian Monte
Carlo method [87] have also been applied for improved
battery SOH estimation.
4.3 Internal temperature estimation
Battery temperature is another key factor to affect the
battery performance in many ways such as lifetime, energy
conversion efficiency, reliability and safety. Surface
temperature is easy to be measured directly using suitable
thermal sensors or thermocouples. But internal temperature
of battery is an internal state which is difficult to be
measured directly. The difference between battery surface
and internal temperatures would be quite significant (e.g.,
sometimes greater than 12 °C [88] in high-power applica-
tions). Overheated internal temperature will accelerate
battery aging and even lead safety problems such as fires
and explosion [89]. Therefore, measuring battery surface
temperature is insufficient to protect battery. Proper
estimation approaches of internal temperature are capable
of not only preventing battery against damages, but also
allowing BMS to make reasonable strategies to save
energy.
One simple method is to inject the proper micro-
temperature sensors into the battery cell [90,91]. However,
these methods are often of high cost and complexity due to
the accessional manufacturing requirements and instru-
mentation challenges. A number of improved methods for
battery temperature estimation have been proposed,
including thermal model-based approaches which usually
adopt battery distributed battery thermal model [92] or
lumped-parameter battery thermal model [93,94]. The
internal temperature is also selected as one of the state
variables in battery thermal models, and various state
observers are adopted for on-line internal temperature
estimation. By combining the suitable state observers,
these model-based approaches can be easily applied to
estimate battery internal temperature on-line with good
estimation results. Since the battery thermal models are
mainly dependent on the information of heat generating
properties and thermal boundary conditions, these model-
based approaches also have the challenging issues such as
tuning parameters and gaining useful thermal information.
Considering that the battery EIS measurements are also
related to the variation of battery temperature [95],
therefore battery internal temperature can be also estimated
using the EIS measurements. Srinivasan et al. [96]
discovered an intrinsic relationship between a cell’s
internal temperature and a readily measurable electrical
parameter, namely the phase shift between an applied
sinusoidal current and the induced voltage, which enables
an instantaneous measurement of the battery internal
temperature. The electrochemical cause of this observed
relationship is analysed. One advantage of this method is
that this observed relationship is almost completely
independent of battery SOC. Further, the identified optimal
frequency range of the sine excitation signal lies in 40–200
Hz, enabling a fast measurement, which is another merit
for on-board applications. A four-probe measurement
technique is later developed to monitor the dynamic
temperature behaviour of the carbon anode in a Li-ion cell
during charging and discharging operations [97]. Zhu et al.
[98] also proposed estimating battery internal temperature
using the measured EIS data. The effect of battery SOC,
SOH and temperature on the EIS measurements is
analysed, and the excitation signal frequency is selected
so that the measured EIS is dominated by temperature, and
independent of the battery SOC and SOH. Raijmakers
et al. [99] also proposed battery sensorless temperature
estimation methods using EIS measurements, and com-
pared the performance of different EIS-based battery
temperature estimation methods [100] under both thermal
equilibrium states and dynamic load conditions. However,
it is worth noting that those battery internal temperature
estimation methods using the battery impedance measure-
ment at a single frequency can only give an ‘averaged’
battery temperature, rather than the temperature distribu-
tion field or the peak internal temperature, under
inhomogeneous or transient temperature distribution con-
ditions.
Besides, data-driven methods such as neural networks
can be adopted to estimate the battery internal temperature.
Highly nonlinear performance of battery dynamic can be
captured by the data-driven methods which are totally free
of background knowledge. Liu et al. [101] proposed a
hybrid data-driven method based on the linear neural
network model to estimate Li-ion battery internal tem-
perature. After EKF filtering, good estimation accuracy is
achieved and this method can be extended to other types of
batteries with minor modifications.
4.4 Joint state estimation
According to the coupled electro-thermal models which
are capable of simultaneously describing battery electric
and thermal behaviours, the joint state estimation of battery
SOC and internal temperature can be achieved, which
plays an important role in some control and equalization
applications of batteries. The key step to achieve joint state
estimation is to build a simple and accurate battery electro-
thermal model firstly, then suitable estimation methods
such as slide mode observer, Kalman filter observer can be
applied accordingly. For example, Bizeray et al. [102]
proposed a thermal-electrochemical model to achieve a
joint estimation of Li-ion battery. After solving the
partial differential equations of coupled model by
orthogonal collocation approach, battery SOC and internal
8 Front. Mech. Eng.

temperature can be estimated by a modified EKF. Zhang
et al. [103] proposed a coupled thermoelectric model to
capture battery electric and thermal behaviours. The
interaction between battery resistance and internal tem-
perature is simply described by a non-linear look-up table,
then battery SOC and internal temperature can be
estimated simultaneously.
5 Battery charging approach
When a battery energy source is exhausted or its terminal
voltage drops below the cut-off voltage or SOC declines to
20%or lower, the discharging process should be stopped
and the battery needs to be recharged. The charging
performance for various batteries is shown in Table 2.
Incorrect operations such as over-discharging, over-char-
ging or improper charging will speed up the degradation
process of the battery dramatically. Compared with other
types of battery, the Li-ion battery has fairly stable
performance but less cycle life at high-temperature
conditions, while no permission is allowed for being
charged below freezing. According to the enough accurate
estimations of battery SOC, SOH and temperature, proper
battery charging approaches can be effectively designed,
further to charge battery from initial state to final SOC
target value. Meanwhile, the charging approaches can also
protect batteries from overheating, prolong the service life
and improve the capacity utilization.
5.1 Traditional battery charging approach
There are some traditional but popular charging
approaches to solve battery charging problem with
numerous objectives and termination conditions. Four
traditional charging approaches that have been widely
utilized to charge batteries in EVs are listed in Fig. 4. These
typical approaches can be mainly classified as constant-
current (CC) charging, constant-voltage (CV) charging,
constant-current-constant-voltage (CC-CV) charging and
multi-stage constant-current (MCC) charging. In the
following, a particular emphasis is place up on the CC-
CV charging and MCC charging approaches.
The CC charging is a simple but rough approach which
adopts a small constant current rate to charge battery
during the whole charging process. The CC charging is
terminated when the time-to-charge reaches a predefined
threshold. This charging approach is first introduced to
charge NiCd or NiMH batteries [104], and is also widely
used for Li-ion batteries [105]. However, the behaviours of
batteries are highly dependent on the current rate in CC
charging, hence the main challenge for CC charging
approach is to search a suitable charging current rate which
is capable of equilibrating battery charging speed and
capacity utilization. For large current rate in CC charging,
the charging speed is improved but the battery aging
process will be aggravated accordingly. For small current
rate in CC charging, high capacity utilization is achieved
but too low current rate will slow down the battery
charging speed and further have a negative effect on the
convenience of EV usage.
Another simple conventional charging approach is the
CV charging which totally adopts a predefined constant
voltage to charge batteries. The primary superiority of
using CV charging is to avoid over-voltage and irreversible
side reactions which may occur in the charging process,
further to prolong battery cycle life. When the CV charging
is applied, the charging current will gradually reduce due
to the low acceptance with progressing recharge. This
approach however needs a high current rate in order to
keep constant terminal voltage at the early stage of the
charging process, which is easy to cause the battery lattice
collapse, and battery poles broken. The common problem
of CV charging approach is also to select a proper value for
Table 2 Charging performance for various batteries
Battery type Charging performance
Li-ion 1) High temperature can improve charging speed but damage to battery lifetime;
2) charging is dangerous at pretty low temperature, well below freezing
Lead acid 1) Higher temperature leads to lower V-threshold by 3 mV/°C;
2) charging at 0.3 C or less below freezing
NiMH, NiCd 1) Charging acceptance decreases from 70%at 45 °C to 45%at 60 °C, respectively;
2) 0.1 C charging rate between –17 °C and 0 °C;
3) 0.3 C charging between 0 °C and 6 °C
Fig. 4 Traditional charging approaches for battery in EVs
Kailong LIU et al. Key technologies in the battery management system of electric vehicles 9

constant voltage to obtain a good trade-off among the
charging speed, electrolyte decomposition and capacity
utilization. Reference [106] summarizes the characteristic
of CV charging, and it concludes that CV charging
approach is capable of effectively improving the charging
speed but bringing great damages to the battery capacity.
This is primarily caused by the sharp increase of charging
current when battery is charged from low SOC. The start
current is far larger than the acceptable range of the battery,
leading to the battery lattice frame collapses, and further
aggravating the pulverization of the active substance in
battery pole. But as battery capacity increases, the charging
current will reduce dramatically. The charging speed for
CV approach is relatively fast due to a high average battery
current during the SOC interval from 0.15 to 0.8, and the
charging current will reduce very slightly when SOC
reaches 0.9.
By integrating CC charging and CV charging, a hybrid
charging approach named CC-CV has been proposed, as
shown in Fig. 5. In this approach, a battery is firstly
charged by a predefined constant current in CC phase and
the battery voltage will increase to the maximum safe
threshold. Afterwards, the battery enters into the CV phase
with a predefined constant voltage, entailing the contin-
uous step-down of the charging current. This CV phase
will end until a terminal value of the decreasing current or a
goal capacity is reached. The standard CC-CV approach is
first utilized to charge lead acid battery with the preset
values of constant current as well as constant voltage
which are recommended by battery manufacturers, and is
also extended to charge Li-ion battery with some
modifications. Because of higher terminal voltage and
charging acceptance for Li-ion battery, constant current in
the applications of Li-ion battery CC-CV charging should
be much larger than that of lead acid battery, which is
usually chosen from 0.5 to 3.0 C [107]. In CC-CV
charging process, CC stage and CV stage can be
complementary in some way [108], the capacity loss
caused by the large electrochemical polarization in CC
stage will be effectively compensated by CV stage. Hence
the CC-CV charging approach is superior to the sole CC as
well as sole CV charging in the applications of EVs, and
has been selected as a benchmark to compare with the
performance of other newly developed battery charging
approaches [109,110]. Although standard CC-CV charging
approach is easy to apply, the challenging issue is to set the
appropriate constant current rate at the CC stage and
constant voltage value at the CV stage. Battery charging
speed of CC-CV approach is primarily determined by the
constant current rate, while the capacity utilization of
battery charging is mainly affected by the values of
constant voltage and termination. For constant current rate
in CC-CV, on the one hand, high value of current rate may
cause lithium plating, further to cause low efficiency of
energy conversion, and battery temperature may exceed
permissible levels especially in high power applications.
On the other hand, low charging current may decrease
battery charging speed and affect the convenience of EVs
usage. Therefore, it is vital to design a proper CC-CV
approach to improve the overall charging performance and
guarantee the operation safety of battery.
Another popular traditional charging approach is the
MCC charging, as shown in Fig. 6. This approach has been
successfully developed to charge numerous types of
battery such as lead acid battery [111], NiMH battery
[112] and Li-ion battery [113]. The mainly difference
between MCC charging and CC-CV charging is that in
MCC charging, the multi-stage series of monotonic
charging currents are injected into battery during total
charging process. This series of charging currents should
be gradually reduced as the form of various constant
currents stages (ICC1>ICC2>:::>ICCN). When terminal
voltage goes up to a default voltage threshold by the
constant current in one stage, charging procedure will turn
into another constant current stage and then a new less
constant current rate will be utilized accordingly. This
decrease process of charging current will continue until
battery terminal voltage reaches the last default voltage
threshold under the condition of minimum current. The
charging speed for standard MCC approach will be usually
a bit slower than the standard CC-CV approach with the
same initial current.
Fig. 5 Battery current and voltage of CC-CV charging approach
Fig. 6 Battery current and voltage of MCC charging approach
10 Front. Mech. Eng.

Table 3 gives a brief comparison of the traditional
charging approaches mentioned above, while the advan-
tages, disadvantages and key elements to design these
approaches are summarized. All in all, for rough charging
approaches including sole CC charging and CV charging,
the implementation costs are relatively low with just a few
parameters need to be considered. However, these simple
charging approaches would cause many charging problems
such as battery lattice collapse, and battery poles broken. It
is significantly difficult to equilibrate battery capacity
utilization and charging speed by using just sole CC or CV
charging approach. In order to further improve charging
performance such as avoiding over-voltage, enhancing
capacity utilization and achieving fast charging, some
hybrid charging approaches including CC-CV and MCC
are developed. The open problem for using these hybrid
approaches is to search the proper current and voltage
values to efficiently equilibrate conflicting objectives such
as charging speed, energy loss, temperature variation and
battery lifetime. Besides, the analysis of electrochemical
reaction such as lithium plating during these charging
process are still at its primitive stage and will be a thriving
area of research in the field of EV applications.
5.2 Optimization of battery charging approach
On the basis of the standard traditional charging, many
optimized charging approaches have been developed to
improve the charging performance of batteries in EVs
recently. These optimizations of charging approach can be
categorized as four fields, which are detailed in Fig. 7.
The first field is the optimization of CV charging. Some
approaches have been adopted to enhance the charging
performance of standard CV charging. Objectives such as
charging speed and temperature variation are considered in
these approaches. In Ref. [114], a constant voltage with
various restricting current approach is presented to limit
the variation of battery temperature. Low battery tempera-
ture rise in total charging process is achieved by
modulating the current rate of proposed approach. Lee
and Park [115] proposed a fast charging control scheme in
CV stage based on the battery internal impedance. In
comparison with standard CV charging, battery charging
speed becomes faster by using the developed control
scheme.
Given that CC-CV charging and MCC charging are two
simple and efficient charging approaches, numerous
researches have been developed to improve battery
charging performance on the basis of CC-CV or MCC
approach. The framework to improve the CC-CV/MCC
charging approach can be summarized in Fig. 8.
For CC-CV charging optimization, the key optimal
elements are the current rate in CC phase and constant
voltage value in CV phase. A number of researches to
improve the CC-CV charging approach have been
developed recently. In Ref. [116], a cycle control algorithm
associated with the zero computational method is proposed
to optimize the CC-CV profile of Li-ion battery. This
improved battery charger is validated to drive the CC-CV
process accurately and smoothly. In Ref. [117], a closed-
form approach to search the optimal charging strategy for a
Li-ion battery is proposed. A cost function which considers
the charging time, energy loss and temperature rise is used
to acquire the optimal CC-CV charging profile. Hsieh et al.
[118] designed a controller to improve the performance of
Li-ion battery in CC-CV charging. The general CV mode
is replaced by two modes: Sense and charge. Then the
Table 3 Comparison of traditional battery charging approaches in EVs
Approach Advantages Disadvantages Key elements
CC Easy to implement Capacity utilization is low 1) Charging constant current rate;
2) terminal condition
CV 1) Easy to implement;
2) stable terminal voltage
Easy to cause the lattice collapse of battery 1) Charging constant voltage;
2) terminal condition
CC-CV 1) Capacity utilization is high;
2) stable terminal voltage
Difficult to balance objectives such as
charging speed, energy loss, temperature
variation
1) Constant current rate in CC phase;
2) constant voltage in CV phase;
3) terminal condition
MCC 1) Easy to implement;
2) easy to achieve fast charging
Difficult to balance objectives such as
charging speed, capacity utilization and
battery lifetime
1) The number of CC stages;
2) constant current rates for each stage
Fig. 7 Optimizations of battery charging approach in EVs
Kailong LIU et al. Key technologies in the battery management system of electric vehicles 11

faster charging trajectory can be achieved. In Ref. [119], a
battery charging cost function with three objectives
including charging time, energy loss and temperature rise
especially the battery internal temperature is presented
based on a coupled thermoelectric model. Then the
teaching learning-based optimization (TLBO) method is
applied to balance three conflicting objectives, further to
obtain an optimal CC-CV pattern for Li-ion battery. In Ref.
[120], a model-based strategy was proposed to optimize
the CC-CV charging pattern for Li-ion battery manage-
ment. The desirable trade-offs among charging speed,
energy conversion efficiency and temperature variation can
be achieved based on the multi-objective biogeography-
based optimization (M-BBO) approach. Then the current
regions to efficiently equilibrate these key objectives are
also identified. He et al. [121] presented a user-cell aware
charging strategy to maximize the charged Li-ion battery
capacity. The charging strategy is an extended version of
standard CC-CV which starts with CC charging until the
battery voltage reaches a predefined voltage, and then the
battery will be charged with another different predefined
voltage until the charging current falls to cut-off current.
The phase-locked loop (PLL) control [122] is also adopted
to improve CC-CV charging performance. In Ref. [123], a
current-pumped battery charger (CPBC) based on PLL-
CC-CV is presented to improve the performance of Li-ion
battery charging. Results illustrate that the battery capacity
and efficiency are improved.
For MCC charging optimization, the main and challen-
ging target is to determine the number of current stages in
MCC profile and the corresponding current rates for each
CC stage. One popular approach to improve MCC
charging performance is the fuzzy logic technology. In
Refs. [124,125], the fuzzy logic controller is utilized to
convert the charging quality characteristics (charging time
and normalized discharged capacity) into a single fuzzy
dual-response performance index, and a five-stage MCC
charging pattern is optimized to improve charging
efficiency. In Refs. [126,127], the fuzzy logic control is
adopted to regulate the weights within the fitness function
of Li-ion charging process. Then the optimal MCC
charging patterns can be optimized by the PSO algorithm
Fig. 8 Summary of improvements to the CC-CV/MCC charging approach
12 Front. Mech. Eng.

based on the designed fuzzy-logic fitness function. The
Taguchi-based method is another effective approach to
search the optimal MCC charging pattern. Liu et al. [128]
presented a Taguchi-based approach to accelerate charging
speed and prolong cycle life for a Li-ion battery. A five-
stage MCC charging pattern is optimized by the con-
secutive orthogonal array technique. According to the
Taguchi approach together with the SOC estimation, Vo
et al. [129] proposed a four-stage MCC charging pattern to
equilibrate battery temperature variation, charging speed
and energy conversion efficiency. Besides, some other
technologies such as ant colony system, function or model-
based methods are also applied to improve the MCC
charging performance. Liu et al. [130] proposed an MCC
charging approach with various weights in each stage
based on an internal-DC-resistance (DC: Direct current)
model to balance the conflicts between charging speed and
energy loss during charging process. Khan et al. [131]
presented a unique approach to search the optimal MCC
charging pattern by using equivalent circuit model for
Li-ion battery. The three and five CC stages are both
discussed based on the optimal pattern to improve the
charging speed and efficiency. In summary, charging
objectives such as charging speed, energy loss and capacity
utilization of the total MCC charging process are primarily
determined by the number of CC stages and the current
values in each stage. The implementation cost of MCC
charging is reduced because of no regulations of voltage
are required.
In addition to the optimizations of traditional charging
approaches, a number of other charging approaches,
obtained by using computational intelligent technologies
such as dynamic programming (DP), model predictive
control (MPC), evolutionary algorithms and pseudo-
spectral optimization, have been also reported. The DP
method is generally adopted to optimize battery charging
profiles based on the proper battery models [132,133].
Since DP method is capable of examining sub-problems
and combining the decisions to further obtain the best
solution, both non-linear and time varying parameters in
battery models can be accepted by DP method, thus DP
becomes one of the most flexible methods to search battery
charging profiles. However, often a large quantity of
information needs to be stored by using DP, which leads to
large computation cost especially in high dimension
charging problems. The MPC-based charging approaches
also need to design a suitable battery model firstly [134–
136]. Then the charging behaviours such as current,
voltage, and even temperature can be predicted directly by
the designed electric model or thermal model. Hard battery
constraints in total charging process can be also effectively
solved by MPC. Nevertheless, influences of battery
temperature and aging on the parameters of battery
model need to be further studied to improve prediction
accuracy. For evolutionary algorithm [137,138], it can
effectively search the charging profiles especially in the
situations that some charging problems have no theoretical
foundations. But the computation cost in evolutionary
algorithm is usually too large. Researchers need to select
the suitable evolutionary algorithm and the parameters
empirically. Charging battery based on the pseudo-spectral
optimization is also a popular and effective method in the
real battery charging applications [139,140]. Many
complicated charging conditions can be considered by
pseudo-spectral optimization. However, strong theoretical
foundations and much battery information are required by
using pseudo-spectral optimization, which will bring large
challenges in real-world applications of EVs with growing
requirements for battery charging.
6 Conclusions
Key technologies in the BMS of EVs have been reviewed
in this paper, especially in the fields of battery modelling,
state estimation and battery charging. Battery modelling
together with the estimations of battery internal states and
parameters play a vital role in revealing a hologram of
battery operating status in the applications of EVs firstly.
After capturing these key states, suitable battery charging
approach can be designed to protect battery against
damages, improve efficiency of energy conversion, and
prolong the battery lifetime. However, most of the key
technologies in the BMS are achieved and validated in
specific test conditions. The modelling, estimation and
charging performance in real-world applications that
would be different from the test conditions, or in a
worse-case scenario, is difficult to guarantee. Therefore, to
explore the limitations or to develop a confidence interval
of the presented algorithms and approaches are required to
tackle this challenging issue.
Acknowledgements This work was financially supported by UK EPSRC
under the ‘Intelligent Grid Interfaced Vehicle Eco-charging (iGIVE) project
EP/L001063/1 and NSFC under grants Nos. 61673256, 61533010 and
61640316. Kailong Liu would like to thank the EPSRC for sponsoring his
research.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted use, distribution, and reproduc-
tion in any medium, provided the appropriate credit is given to the original
author(s) and the source, and a link is provided to the Creative Commons
license, indicating if changes were made.
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