IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 37, NO. 7, JULY 2022
8513
Battery Thermal Runaway Fault Prognosis in Electric
Vehicles Based on Abnormal Heat Generation and
Deep Learning Algorithms
Da Li, Peng Liu , Zhaosheng Zhang, Lei Zhang , Junjun Deng , Zhenpo Wang , David G. Dorrell ,
Weihan Li , and Dirk Uwe Sauer
Abstract—Efficient battery thermal runaway prognosis is of
great importance for ensuring safe operation of electric vehicles
(EVs). This presents formidable challenges under widely varied and
ever-changing driving conditions in real-world vehicular operations. In this article, an enabling thermal runaway prognosis model
based on abnormal heat generation (AHG) is proposed by combining the long short-term memory neural network (LSTM) and
the convolutional neural network (CNN). The memory cell of the
LSTM is modified and the resultant modified LSTM-CNN serves
to provide accurate battery temperature prediction. The principal
component analysis is used to optimize the model input factors to
improve prediction accuracy and to reduce computing time. A random adjacent optimization method is employed to automatically
optimize the hyperparameters. Finally, a model-based scheme is
presented to achieve AHG-based thermal runaway prognosis. Realworld EV operating data are used to verify the effectiveness and
robustness of the proposed scheme. The verification results indicate
that the presented scheme exhibits accurate 48-time-step battery
temperature prediction with a mean-relative-error of 0.28% and
can realize 27-min-ahead thermal runaway prognosis.
Index Terms—Convolutional neural network (CNN), electric
vehicles (EVs), fault prognosis, lithium-ion batteries, long shortterm memory neural network (LSTM), thermal runaway.
Manuscript received July 23, 2021; revised November 25, 2021; accepted
January 29, 2022. Date of publication February 10, 2022; date of current version
March 24, 2022. This work was supported in part by the Ministry of Science and
Technology of the People’s Republic of China under Grant 2019YFE0107900
and in part by the National Natural Science Foundation of China under Grants
U21A20170 and 52072040. Recommended for publication by Associate Editor
S. Williamson. (Corresponding authors: Peng Liu; Zhaosheng Zhang; Lei
Zhang.)
Da Li, Peng Liu, Zhaosheng Zhang, Lei Zhang, Junjun Deng, and Zhenpo
Wang are with the Collaboration Innovation Center for Electric Vehicles
in Beijing, Beijing 100081, China, and the National Engineering Research Center for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China (e-mail: 3220180229@bit.edu.cn; bitliupeng@bit.edu.cn;
zhaoshengzhang@bit.edu.cn; lei_zhang@bit.edu.cn; dengjunjun@bit.edu.cn;
wangzhenpo@bit.edu.cn).
David G. Dorrell is with the University of the Witwatersrand, Johannesburg
2000, South Africa (e-mail: david.dorrell@wits.ac.za).
Weihan Li and Dirk Uwe Sauer are with RWTH Aachen University, 52062 Aachen, Germany (e-mail: weihan.li@isea.rwth-aachen.de;
dirkuwe.sauer@isea.rwth-aachen.de).
Color versions of one or more figures in this article are available at
https://doi.org/10.1109/TPEL.2022.3150026.
Digital Object Identifier 10.1109/TPEL.2022.3150026
I. INTRODUCTION
A. Motivations
HE development and mass-adoption of electric vehicles
(EVs) are increasingly recognized as a viable means to
combating fossil oil depletion and greenhouse gas emissions
[1]. An integral component of an EV is the battery system and it
plays a pivotal role in determining vehicle performance, safety,
and cost-effectiveness [2]. Currently, lithium-ion batteries are
prevalent in EV applications. They have superior energy and
power density, long cycle life, and declining cost compared to
other battery chemistries. In an EV, numerous battery cells are
connected in series and/or parallel configurations to meet the
voltage and capacity requirements [3], [4]. This necessitates
robust structural enclosing, appropriate electric circuitry and
enabling management algorithms to form a complete battery
system. Substantial improvements have been made with respect
to energy density and service lifespan. However, battery safety
still remains a focus of intensive research [5]. Battery system
failure is by no means a trivial issue and is strongly related to
multifaceted internal and external factors [6]. Unattended faults
including internal and external short-circuit [7], overcharging
[8], overheating [9], [10], and the forth may eventually lead
to battery thermal runaway. Conventional battery management
systems can constantly monitor the external parameters of a
battery system and detect fault occurrence based on rule-based
methods. But it is difficult to prognose a latent thermal runaway
when the monitored parameters are within the preset safety
ranges. Usually, abnormal heat generation (AHG) inside batteries emerges prior to thermal runaway occurrence and this can
be used a viable means for thermal runaway prognosis.
T
B. Research Review
To achieve effective battery thermal runaway prognosis, it
is meaningful to fully exploit onboard-available sensors. These
mainly include voltage, current and temperature sensors [11],
[12]. Transient loading currents are subject to driver behaviors
while temperature is an important indicator for thermal runaway.
Extensive investigations have been carried out for battery
surface temperature monitoring. For instance, Alcock et al. used
fiber optic sensors for battery surface temperature monitoring
[13]. Similarly, a distributed fiber optical sensor was deployed to
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IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 37, NO. 7, JULY 2022
measure the in-plane temperature difference across cell surface
and the movement of the hottest region during operation [14].
Unfortunately, it is difficult to judge whether the temperature is
abnormal under different situations. Aiming to realize accurate
and reliable temperature monitoring, internal temperature monitoring is increasingly used with embedded sensors anchored
inside battery cells to obtain the core temperature. In this regard,
Wahl et al. presented a battery internal temperature monitoring
system using embedded optical fiber sensors [15]. However, the
use of expensive sensors dramatically increases system cost
while robust algorithms are absent for applying them under
practical driving conditions.
Some studies have addressed the temperature characteristics of artificially-induced thermal runaway. For instance, Shironita et al. investigated the temperature characteristics of
lithium-ion batteries during laser-irradiation-triggered thermal
runaway [16]. These publications reported on the external characteristics during thermal runaway but provided limited insights
into the underlying mechanisms [17]. Other studies have been
conducted to explore thermal runaway initiation mechanisms
and propagation paths [18], [19]. For instance, Chen et al. proposed a thermal runaway prediction model based on heat release
rate [20]. Similarly, Lee and Kim established a mechanicalelectrochemical-thermal model to predict battery thermal runaway induced by quasi-static indentation [21]. Nevertheless,
these methods only avail either in certain scenarios or under
manually-defined triggering conditions. But in daily vehicular
operation, the influencing factors of thermal runaway including battery grouping, abuse types, and working environments
are multifacets and coupled, and these would reduce the applicability of laboratory-developed thermal runaway prognosis
methods.
Instead, various temperature-based fault diagnosis approaches have been presented. For instance, Sattarzadeh et al.
optimized temperature sensor locations and designed a filtering
scheme for battery fault detection and localization [22]. But this
would inevitably incur additional costs. Hong et al. presented an
entropy-based method for battery fault diagnosis on the basis of
cell inconsistency state [23]. In terms of temperature prediction,
Chen et al. investigated the temperature evolution characteristics
of a lithium-ion battery during an external short-circuit process
and put forward a scheme for predicting the maximum temperature rise [24]. Sun et al. [25] proposed a Kalman filter-based
method for internal temperature estimation and quantitatively
analyzed the major factors affecting heat generation. These
methods can achieve battery temperature prediction under a
specific laboratory environment and have limited prediction or
prognosis horizons. Again, Hong et al. [26] employed a recurrent
neural network to realize battery temperature prediction [26].
Although the proposed method can achieve multistep temperature prediction, the prediction accuracy decreases significantly
with increased prediction horizon. In addition, a large number
of hyperparameters that need to be manually optimized make
the model less applicable in practice.
In summary, the existing studies are insufficient in the following aspects.
1) The accuracy of battery temperature prediction deteriorates rapidly with increasing prediction horizon.
2) The reported temperature-based diagnosis methods only
avail after severe fault occurrence but fall short of early
prognosis.
3) The influencing factors of battery temperature including
battery grouping, abuse, and working environments are
coupled. Current studies only investigate the influence
of individual factors on temperature (e.g., current rate
in[27]), but provide inadequate analysis on the coupling
effect of these involved factors.
4) The inputs and hyperparameters of a neural network model
need to be selected and optimized manually.
5) The existing methods are confined to being effective for
thermal runaway prognosis in well-defined laboratory
conditions.
C. Contributions
This article makes contributions in the following aspects.
1) A modified long short-term memory neural network
(LSTM) is combined with the convolutional neural network (CNN) to extract temporal and spatial features. The
CNN-LSTM model can realize accurate eight-minuteahead battery temperature prediction for real-world EVs
with a mean-relative-error (MRE) of 0.28%.
2) A model-based scheme (MS) is presented to compute the
heat generation rate (HGR) and an enabling strategy is
presented to realize online AHG diagnosis.
3) The vehicle state-driving behavior-local weather analysis
is carried out to select the temperature-related factors
for the modified CNN-LSTM model. Then, the principal
component analysis (PCA) is used to compress these
factors into fewer independent factors so as to reduce the
computing time.
4) A random adjacent optimization method (RAOM) is proposed to automatically optimize the hyperparameters of
the modified CNN-LSTM model, which can also be used
for other machine learning algorithms.
5) Comprehensive real-world EV operating data are used for
model training and verification regarding battery temperature prediction and thermal runaway prognosis.
D. Organization of This Article
The rest of this article is organized as follows. Section II
briefly introduces the data acquisition procedure. Section III
presents the battery thermal runaway prognosis model comprising the modified CNN-LSTM model, MS and AHG diagnosis.
Section IV elaborates on the data preprocessing and hyperparameter determination of the modified CNN-LSTM model.
Section V discusses the temperature prediction performance of
the modified CNN-LSTM model. Section VI provides detailed
discussions on the thermal runaway prognosis results. Finally,
Section VII concludes this article.
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LI et al.: BATTERY THERMAL RUNAWAY FAULT PROGNOSIS IN EVS BASED ON AHG AND DEEP LEARNING ALGORITHMS
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TABLE I
EXAMPLE DATA FORMAT COLLECTED FROM THE NMMCNEV
II. DATA ACQUISITION
The National Monitoring and Management Center for New
Energy Vehicles in China (NMMCNEV) is responsible for the
administrative supervision and management of new energy vehicles running in China. Time-series operating data collected
under a universal data transmission protocol are used in this
article and the sampling frequency is 0.1 Hz. The data of vehicle
no. 1 are divided into a training and a validation dataset after
ten-fold cross-validation, which account for 90% and 10% of
the total data, respectively. The data of vehicle nos. 2 and 3
are used for model verification. It is worth noted that cell no.
1 in vehicle no. 2 experienced thermal runaway at 01:56:40 on
January 21, 2019.
An example data format is given in Table I for demonstration.
The used batteries in these vehicles are prismatic ternary
lithium-ion batteries with a cathode materials ratio of nickel 8,
cobalt 1, and manganese 1. The installed battery pack comprises
of seven series-connected battery modules while each module
contains twelve series-connected battery cells.
III. REAL-WORLD BATTERY AHG PROGNOSIS MODEL BASED
ON THE PROPOSED CNN-LSTM-MS
Thermal runaway happens when the heat generated by the
exothermic reactions inside battery cells cannot be timely and
efficiently dissipated to the ambient [28]. Once a “critical condition” is reached, thermal runaway is inevitable [29], [30]. Before
the “critical condition,” AHG would occur due to various abuse
and latent defects [31], based on which thermal runaway can be
predicted. In this article, a model is proposed for battery AHG
prognosis during charging by combining the CNN, modified
LSTM and MS. First, the PCA is used for parameter compression. Then the modified LSTM is combined with the CNN to
extract temporal and spatial features and realize accurate battery
temperature prediction. Finally, the MS is further used to achieve
AHG-based thermal runaway prognosis. The flowchart of the
proposed scheme is illustrated in Fig. 1. The procedures can be
described as follows.
1) Compress the historical EV operating data using a trained
PCA, which includes vehicle-state-related, drivingbehavior-related, and weather-related parameters.
2) Feed the modified CNN-LSTM model with compressed
parameters and perform battery temperature prediction for
Fig. 1. Flowchart of the proposed battery thermal runaway prognosis method
based on the CNN-LSTM-MS.
the following time steps using the modified CNN-LSTM
model.
3) Calculate the HGR using the MS.
4) Diagnose the AHG using the proposed diagnosis strategy.
A. Model Training and Temperature Prediction Using the
Modified CNN-LSTM Model
Accurate and timely battery temperature prediction is the
premise of efficient thermal runaway prognosis. In this regard,
the historical EV operating data containing temperature-related
parameters are used as the inputs of the prediction model, which
is given by
⎞
⎛
T1,q F1,1 · · · F1,n
(1)
H k×(n+1) = ⎝ · · · · · · Ft,j · · · ⎠
Tk,q Fk,1 · · · Fk,n
where n and k denote the number of factors and the time step,
respectively; Tt,q (t = 1, 2, …, k) and Ft,j (t = 1, 2, …, k; j = 1,
2, …, n) represent the temperature of battery cell q and the jth
factor at time step t.
The output of the prediction model is given by
⎞
⎛
Tk+1,q
⎠
(2)
B p×1 = ⎝ · · ·
Tk+p,q
where p is the prediction window size (PWS).
This poses challenges for the data-driven-based temperature
prediction: long prediction time steps in the temporal dimension;
multiple temperature influencing factors in the spatial dimension
during real-world operation, including vehicle states, driving
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Fig. 2.
IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 37, NO. 7, JULY 2022
Fig. 3.
Memory cell of the LSTM.
behaviors, and external environmental factors; and high predicting accuracy requirement for thermal runaway prognosis.
Machine learning algorithms including the random forest [32]
and support vector machine [33] are well-suited for classification and one-time-step prediction while the back-propagation
neural network is widely used for classification and regression
problems [34]. Recently, the CNN and LSTM are commonly
employed to process spatial and temporal features. In this article,
the two are combined to extract temporal and spatial features of
historical EV operating data for accurate battery temperature
prediction, which are represented by n and k in (1), respectively.
The CNN is used for the spatial feature extraction in this
article. It is a deep learning algorithm that involves with convolutional computation [35] and is widely used in image denoising
[36], image classification [37], speech emotion recognition [38],
and so on. The superiority of the CNN in spatial feature extraction is indicated in [39] and [40].
For the temporal feature extraction, the LSTM [41], [42] is
utilized, which is a kind of recurrent neural networks [43] and
is widely used in emotion classification [44], natural language
processing [45], and stock price prediction [46]. The memory
cell of the LSTM is shown in Fig. 2, where xt , ct , and ht are
the input, state and output at time step t, respectively. Compared
with recurrent neural networks, there are three added gates in
the memory cell; they are the forget gate ft , the input gate it , and
the output gate ot . Therefore, the LSTM can better handle the
problem of gradient disappearance/explosion and is appropriate
for long short-term time series data prediction.
If the memory cell state is also an input for the three gates,
the important states can be also remembered. In this article, the
memory cell state ct-1 at time step t-1 serves as the input for the
three gates to modify the memory cell of the LSTM, which is
shown in Fig. 3.
The modified LSTM can be described as
Memory cell of the modified LSTM.
where Wf , Wi , Wo , and Wc are the weighting matrices for the
forget gate, input gate, output gate, and input unit; bf , bi , bo , and
bc are the biases for the forget gate, input gate, output gate, and
input unit.
As for real-world implementation, the historical EV operating
data are fed into the modified CNN-LSTM model for training.
The trained CNN-LSTM model can be implemented for realtime prediction. It is worth mentioning that the model can be
continuously trained using the data generated in previous trips.
B. Model-Based Scheme for AHG Diagnosis
During normal charging and discharging operations, the total heat generation consists of reversible and irreversible heat
generation [30]. The irreversible heat generation can be largely
ascribed to the joule effect when currents pass through battery cells, while the reversible heat generation is mainly due
to the polarization. Heat exchange between batteries and the
ambient exists through conduction, convection, and radiation.
The temperature increases alone with accumulated heat inside
battery cells. The heat generation under normal operations can be
described by the Bernardi equation [47], which can be described
as
∂Uoc
I
∂Uoc
I
(Uoc − U ) + T
=
IR + T
(9)
Q=
V
∂T
V
∂T
ft = σ(W f · [ct−1 , ht−1 , xt ] + bf )
(3)
it = σ(W i · [ct−1 , ht−1 , xt ] + bi )
(4)
g2 = tan h(W c · [ct−1 , ht−1 , xt ] + bc )
(5)
ct = ft ct−1 + it g2
(6)
ot = σ(W o · [ct−1 , ht−1 , xt ] + bo )
(7)
where V, Uoc , U, T, R, and I are the volume, open circuit voltage,
terminal voltage, temperature, internal impedance, and charging
current, respectively.
It can be seen that the battery temperature is subject to electrical parameters including internal impedance, charging current,
and open circuit voltage. On top of it, it is meaningful to form
electro-thermal coupling models [48]. Other thermal models
have also been established to compute battery heat generation
for temperature-induced [49] and internal short-circuit-triggered
[50], [51] thermal runaway. In daily vehicular operations, timevarying and random latent defects inside battery cells make
thermal runaway accidents arbitrary and exhibit different characteristics. These make it extremely difficult to establish a unified
heat generation model. To cope with the issue, an MS is proposed
to calculate the battery heat generation based on temperature
prediction by the modified CNN-LSTM model.
According to the energy conservation law, thermal balance
holds thoroughout vehicle operations, which is given by
yt = ht = ot · tan h(ct )
(8)
Qge = Qab + Qdis
(10)
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LI et al.: BATTERY THERMAL RUNAWAY FAULT PROGNOSIS IN EVS BASED ON AHG AND DEEP LEARNING ALGORITHMS
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the convective heat transfer coefficient is set as 5 W/(m2 ·K) as
indicated in [52] and [53].
TABLE II
BATTERY SPECIFICATIONS
C. Battery AHG Diagnosis Strategy
where Qge and Qab are the battery heat generation and absorption
rates and Qdis is the heat dissipation rate between batteries and
the ambient. As Qge is difficult to be uniformly described for
different scenarios in real-world vehicular operation, the right
side of (10) is utilized to calculate Qge using real-time battery
temperature measurement.
The heat absorption rate Qab can be computed by [51]
Cq mq ΔTq
Cq mq ∂Tq
=
(11)
∂t
Δt
where Cq is the specific heat; mq is the battery mass and ΔTq is
the temperature difference during the sampling interval Δt.
For the heat dissipation rate Qdis , there are two major heat
transfer ways, including the heat conduction rate Qcond and heat
convection rate Qconv , which can be given by
Qab =
Qdis = Qcond + Qconv .
(12)
The heat dissipation rate between battery surface and the
ambient can be calculated using Newton’s formula [51], which
is given by
Qconv = αAconv (Tq − Tambient )
λbat
(Tq − Tq−1 )
δ1
+ Acond,bat
(15)
where Qge,real is the heat generation calculated based on the
real-world operation data; Qge,normal is the previously calculated normal heat generation under normal conditions by the
MATLAB/Simulink; and G is the AHG judgment threshold.
It should be mentioned that during real-word vehicular operations, it is difficult to obtain accurate ambient temperature,
and the local temperature can be readily acquired and thus used
in this article. There are multiple solid and gas layers between
battery surface and the environment. According to the Newton’s
formula and the Fourier law [51], the heat transfer rate between
battery surface and the environment can be described by
Qtrans =
Tambient − Tweather
X
Y
δθ
1
+
A θ λθ
A μ αμ
μ=1
θ=1
(16)
where Tweather is the local temperature; X and Y are the numbers
of the solid and gas layers.
Therefore, for steady-state heat transfer, the relationship between the local and the battery ambient temperature can be
described by
Tambient = Tweather + D
(17)
where D is a constant with
X
D = Qtrans
λbat
(Tq − Tq+1 )
δ1
λsink
(Tq − Tsink )
δ2
Qge,real − Qge,normal > G
(13)
where α is the convective heat transfer coefficient; Aconv is the
heat exchange area between battery surface and the ambient; and
Tq and Tambient are the battery and the ambient temperature.
The heat transfer between the neighboring batteries and heat
sink can be calculated using the Fourier law [51], which is given
by
Qcond = Acond,bat
For model-based fault diagnosis, a residual signal is typically
calculated by comparing the measured signal with the signal
output by the model [54]. Subsequently, the residual will be
evaluated to determine the diagnosis results [55]. If the residual
between HGRs of the real-world and the normal operation
surpasses a certain value, it is reasonable to deduce that abnormal
reactions occur to the relevant batteries, which may lead to
thermal runaway. For normal operations, due to the existence of
measurement noises and cell inconsistency, the calculated HGR
would be slightly different from the normal HGR. This would
not mislead the diagnosis strategy by setting an appropriate
threshold. Therefore, the MS is established based on (10)–(14),
and the AHG fault is determined by
θ=1
Y
δθ
1
+
Aθ λθ μ=1 Aμ αμ
.
(18)
(14)
Using the local temperature to replace the battery ambient
temperature, the AHG fault judgment rule can be given by
where λbat and Acond,bat are the conductive heat transfer coefficient and heat exchange area between the neighboring batteries;
λsink and Acond,sink are the conductive heat transfer coefficient
and heat exchange area between batteries and heat sink; δ is the
thickness; and Tsink is the temperature of heat sink.
The detailed battery specifications in the MS are given in Table II, including the convective heat transfer coefficient, specific
heat, and mass. The vehicle is stationary during charging and
ΔQge = (Q ge,real − Qge,normal ) > G + αAconv C = M
(19)
where Qge is the residual; M is a constant obtained based on the
statistics of normal EV data. Therefore, using local temperature
as the input of the MS is justified.
In order to avoid the fluctuation of HGR caused by inaccurate
temperature prediction, the Savitzky–Golay filter is used to filter
the data.
+ Acond,sink
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TABLE III
INPUT FACTORS OF THE MODIFIED CNN-LSTM
these factors into fewer new independent factors while retaining
most information. The process of the PCA is given as follows.
The battery temperature and vehicle state-driving behaviorlocal weather factors at all time steps can be capsulated into
⎞
⎛
T1,q F1,1 · · · F1,n
(20)
H l×(n+1) = ⎝ · · · · · · Ft,j · · · ⎠
Tl,q Fl,1 · · · Fl,n
where l is the number of the total time steps.
Hl×(n+1) is further zero-averaged as
⎞
⎛ Z Z
Z
T1,q F1,1 · · · F1,n
Z
··· ⎠
E l×(n+1) = ⎝ · · · · · · Ft,j
Z
Z
Z
Tl,q Fl,1 · · · Fl,n
(21)
Z
Z
where Tt,q
(t = 1, 2, …, l) and Ft,j
(t = 1, 2, …, l; j = 1, 2, …,
n) represent the zero-averaged temperature of battery cell q and
the jth factor at time step t, which can be calculated by
Z
Tt,q
1
= Tt,q −
l
Z
Ft,j
= Ft,j −
1
l
l
Tt,q
(22)
Ft,j .
(23)
t=1
l
t=1
The covariance matrix can be computed by
IV. DATA PREPROCESSING AND PARAMETER OPTIMIZATION OF
THE MODIFIED CNN-LSTM MODEL
A. Data Preprocessing and Input Optimization of the Modified
CNN-LSTM Model
The real-world operating data need to be preprocessed. These
include data parsing, null value filling, outlier correction, and
data filtering. To incorporate the influence of driving behaviors
on battery cells, a vehicle state-driving behavior-local weather
factor analysis is presented to select the inputs of the modified
CNN-LSTM model, which are given in Table III.
As for vehicle states, the terminal voltage is used to represent
the open circuit voltage and the mileage serves to approximately
indicate the battery aging level. Besides, the rotational speed,
voltage, current and temperature of motors are also used. The
driving behaviors can be quantitatively assessed by accelerations and vehicle speeds. In terms of local weather, the local
temperature, barometric pressure, humidity, precipitation, wind
speed, and visibility are obtained from the website “weather
underground” [56] with a sampling interval of 1 h. In order
to correspond to the vehicle operation data, these six weather
parameters with a sampling interval of 10 s are obtained through
linear interpolation.
If these factors are all used as the inputs of the modified
CNN-LSTM model, the entities with limited impact on battery
temperature would increase the computing burden or cause the
overfitting problem [57]. To solve this issue, it is necessary to
extract useful information or compress the factors. The correlation coefficient [58] and GRA [59] are used in [60] and [61].
However, some factors would be lost during the selecting process
of these methods. In this article, the PCA is used to compress
1 T
E E.
(24)
l
The eigenvalues Lj and corresponding feature vectors vj of
J(n+1)×(n+1) are computed and arranged as
J (n+1)×(n+1) =
{L1 ≥ L2 ≥ · · · ≥ Lj ≥ · · · ≥ Ln+1 }
(25)
P(n+1)×(n+1) = v 1 v 2 . . . v j . . . v n+1 .
(26)
The first K columns of P(n+1)×(n+1) are selected and the input
matrix Sl×K of the modified CNN-LSTM model after dimension
reduction is computed by
S l×K = H l×(n+1) P (n+1)×K .
(27)
To determine the number of principal components K, the
cumulative percent variance [62], [63] is computed, which is
described as
K
ηa
a=1
CPV = K+1
× 100%
(28)
ηa
a=1
where CPV is the cumulative percent variance and η a is the ath
diagonal element in the covariance matrix J(n+1)×(n+1) .
The cumulative percent variance is computed based on the
training data of vehicle no.1 as shown in Fig. 4. The number
of principal components K is chosen such that the cumulative
percent variance reaches a predetermined value, e.g., 95% [64].
Under this condition, the computing intensiveness can be reduced by choosing a smaller principal component K. Therefore,
K is determined to be five for the cumulative percent variance
of 97.7%. The dimension-reduced matrix Sl×a is served as the
input of the modified CNN-LSTM model.
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LI et al.: BATTERY THERMAL RUNAWAY FAULT PROGNOSIS IN EVS BASED ON AHG AND DEEP LEARNING ALGORITHMS
Fig. 4.
Covariance radio for different reduced dimension.
Fig. 5.
Sliding window for online implementation.
B. Model Structure Design and Hyperparameter Optimization
of the Modified CNN-LSTM Model
Fig. 6.
Configuration of the modified CNN-LSTM.
Fig. 7.
Procedure of the proposed RAOM.
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To achieve online temperature prediction, battery temperature
needs to be predicted in a timely manner when new data samples
are fed in. A sliding window is applied to feeding the modified
CNN-LSTM-MS with iteratively updated data, which is shown
in Fig. 5.
As for the model structure of the modified CNN-LSTM
model, adding more convolution and recurrent layers can improve modeling accuracy, but excessive layers may cause overfitting and superfluous calculation. The modified CNN-LSTM
model is configured to consist of eleven layers: one input layer;
two convolution layers with relu activation; two pooling layers;
three LSTM recurrent hidden layers; two dense hidden layers
with the tanh activation function; and one output layer. The
detailed configuration is illustrated in Fig. 6.
There are a handful of optimizers available for model parameter optimization [65], [66]. The Adam optimizer is employed
to optimize the weights and biases based on the gradient of the
loss function. The loss function to minimize the MRE is given
by
MRE(y, ŷ) =
1
l
l
t=1
|ŷt − yt |
× 100%
yt
(29)
where yt and ŷt are the real and the predicted temperature at
time step t.
The hyperparameters need to be determined in advance before
model training. Usually, these hyperparameters are determined
based on engineer expertise, babysitting or grid searching, which
always results in low modeling accuracy or excessive use of
labor. The Bayes SMBO proposed in[67] exhibits low accuracy
for small datasets. To deal with the problem, a RAOM is put
forward to optimize the hyperparameters for the modified CNNLSTM model while reducing the computational intensiveness.
A procedure with two hyperparameter dimensions is delineated
in Fig. 7.
The detailed procedures of the RAOM are given as follows.
1) Form the grid A based on the possible hyperparameters.
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2) Initiate the hyperparameters as a sample grid to parameterize and train the modified CNN-LSTM model to get its
MRE.
3) Randomly select a grid near the sample one and calculate
its MRE until there is a grid with the MRE smaller than
that of the sample grid, which is selected as the new sample
grid.
4) Repeat 3) until the MREs of all the grids nearby are bigger
than that of the sample grid, and the hyperparameters of
the current sample grid are considered optimal.
In order to expedite the process mentioned, some hyperparameters of the modified CNN-LSTM model are predetermined
to reduce grid space dimensions. Time step is equal to the sliding
window size. According to the PCA results, the input dimension
is 5 and the output dimension is 1. According to[68], [69], the
neurons number of the dense layer = the neurons number of
the LSTM layer = 100 is set. In this article, the modified CNNLSTM model is trained offline, so the learning rate is assigned
a smaller value of 0.0001 for better modeling performance and
2 025 575 data samples are used for model training. The batch
size is set to be 32. To solve the overfitting problem caused by
too many epochs, the dataset is pretrained and the epoch with the
smallest MRE of validation datasets is taken for formal training.
Here, the epoch is set to 54. The other hyperparameters are
derived based on the RAOM. A three-dimensional (3-D) grid
space built for the number of kernels, kernel size and sliding
window size, and the initial values of the hyperparameters are:
the number of kernels = 16; the kernel size = 2; and sliding
window size = 360. The optimized hyperparameters are: the
learning rate = 0.0001; the sliding window size = 540; the
number of kernels = 22; the kernel size = 3; the batch size = 32;
the epoch = 54; the input dimension = 5; the output dimension
= 1; and the neurons number of the dense layer = the neurons
number of the LSTM layer = 100.
V. TEMPERATURE PREDICTION RESULTS AND DISCUSSIONS
A. Temperature Prediction Based on the Modified CNN-LSTM
Model
Model validation is conducted to examine the temperature
prediction performance of the modified CNN-LSTM model in
Section III-A. The ten-fold cross-validation and MRE are used
to evaluate the modeling accuracy. As for one-time-step temperature prediction, the proposed modified CNN-LSTM model
exhibits high accuracy in ten-fold cross-validation for vehicle
no.1 with an MRE of 0.06%.
The relative error (RE) is used to evaluate the modeling error
for each time step, which is given by
RE(y, ŷ) =
ŷt − yt
× 100%.
yt
Fig. 8.
One-time-step temperature prediction results for vehicle no.1.
Fig. 9.
MREs of different PWSs.
B. PWS Comparison of the Modified CNN-LSTM Model
To compare the prediction accuracy for different PWSs of the
modified CNN-LSTM model, the MREs are calculated under
the PWSs of 6, 12, …, 84, 90, representing the time-ahead
temperature predictions of 1 min, 2 min, …, 14 min, 15 min.
The MREs under different PWSs are shown in Fig. 9. With the
increasing PWS, the MRE shows a growing trend. To provide
a driver with sufficient time to respond and deal with possible
thermal runaway, the PWS should be set as large as possible. On
the other hand, to achieve accurate temperature prediction and
AHG diagnosis, the PWS needs to be set as small as possible
to meet the MRE requirement. Therefore, the PWS can be
determined balancing the requirements of the PWS and MRE.
In this article, the MRE of the temperature sensor is used
as the threshold for the MRE of temperature prediction. The
accuracy of the temperature sensor is 0.1°C, and the MRE is
0.31% at the average temperature of 32.4°C. Therefore, the
MRE of temperature prediction should better be set smaller than
0.31%. Here, PWS = 48 is chosen to balance the requirements
of the prediction horizon and accuracy. Correspondingly, the
MREs of the training and verification datasets are 0.27% and
0.28%, respectively. This indicates that there is no overfitting
during the model training process so that the regularization and
dropout factors are not set.
(30)
The validation data of vehicle no.1 covering a duration from
April 18, 2019 00:07:06 to April 18, 2019 22:22:16 are collected. The one-time-step prediction temperatures and REs are
illustrated in Fig. 8. The small REs show that the modified CNNLSTM model can achieve accurate temperature prediction.
C. Superiority Verification of the Modified CNN-LSTM Model
To verify the superiority of the modified CNN-LSTM model,
various prediction methods presented in the literature are also
used for comparison. The parameters of the Kalman filter are set
according to [25], while the other methods are parameterized
using the RAOM. The ten-fold cross-validation is employed
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LI et al.: BATTERY THERMAL RUNAWAY FAULT PROGNOSIS IN EVS BASED ON AHG AND DEEP LEARNING ALGORITHMS
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TABLE IV
MRES OF VARIOUS TEMPERATURE PREDICTION METHODS
and the MREs are given in Table IV. The Kalman filter is based
on the replenished battery parameters and can only achieve
one-step temperature prediction. For one-time-step temperature
prediction, the MREs of the LSTM-oriented model [26], the
proposed modified CNN-LSTM, the modified CNN-LSTM
without PCA, the CNN-LSTM, the back propagation neural
network model [34], the multiscale deep CNN [70], the
least squares support vector machine [33], and the random
forest [32] are 0.08%, 0.06%, 0.06%, 0.06%, 0.09%, 0.07%,
0.08%, and 0.07%, respectively; these methods show similar
accuracy. However, for 48-time-step temperature prediction,
their MREs are 0.59%, 0.28%, 0.46%, 0.41%, 1.04%, 0.44%,
0.96%, and 0.94%, which indicates that the proposed modified
CNN-LSTM model exhibits the best performance. In addition,
the MRE of the modified CNN-LSTM with PCA is further
reduced by 39.1%. This can be ascribed to that the spatial
and temporal features of the input factors can be extracted by
combining the CNN with the LSTM, which can contribute
to prediction accuracy improvement for multitime-step
temperature prediction. Furthermore, the prediction accuracy
of the modified CNN-LSTM model is improved by feeding the
states into three gates in the memory cell of the LSTM.
D. Robustness and Adaptability Verification of the Modified
CNN-LSTM Model
The robustness and adaptability of the modified CNN-LSTM
model are further examined using the data collected from vehicle
no. 2. The data are grouped into four datasets according to
different seasons and their respective MREs are calculated. The
MREs for the Spring, Summer, Autumn, and Winter are 0.29%,
0.24%, 0.28%, and 0.31%, respectively. The MRE for the summer is the lowest while the temperature prediction accuracy is
the best. In contrast, the MRE for the winter is a little higher. It is
because the battery temperature shows different characteristics
in different seasons and the local temperature would impose an
unavoidable impact on the prediction performance. However, the
MREs for all four seasons are relatively small, so the presented
temperature prediction model can achieve accurate 48-time-step
temperature prediction for vehicles with the same specifications.
Fig. 10. 48-time-step temperature prediction for vehicle of the same specification under four seasons. (a) Spring. (b) Summer. (c) Autumn. (d) Winner.
This verifies its robustness and adaptability. The real and predicted temperatures around March 15th, June 15th, September
15th, December 15th are shown in Fig. 10 as the representative
results for the spring, summer, autumn, and winter. The small
REs show that the modified CNN-LSTM model can provide
accurate temperature prediction for AHG prognosis at each time
step.
VI. REAL-WORLD THERMAL RUNAWAY PROGNOSIS RESULT
AND DISCUSSION OF THE MODIFIED CNN-LSTM-MS
A. Battery Simulation for Normal HGR Based on Simulink
To diagnose the AHG fault for thermal runaway prognosis,
the HGR of a real-world battery cell need to be compared
with its normal HGR as indicated in (19). To compute the
normal HGR under different initial state-of-charges (SOCs), battery temperatures, and ambient temperatures, an electro-thermal
model is established and given in Fig. 11, which consists of a
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IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 37, NO. 7, JULY 2022
Fig. 11.
Established electrothermal model for simulation.
Fig. 12.
Thevenin equivalent circuit model.
Fig. 15. AHG prognosis results for cell no. 1. (a) Heat generation/convection
rate-temperature. (b) Battery temperature.
Thevenin equivalent circuit model [71] and a thermal model. The
Thevenin equivalent circuit model provides the terminal and the
open-circuit voltage for the thermal model while the thermal
model feedbacks temperature into the equivalent circuit model.
As shown in Fig. 12, the Thevenin equivalent circuit model
can be given by
dUpo
Upo
1
dt = − Rpo ·Cpo + Cpo · I
(31)
U = Uoc − R0 · I − Upo
Fig. 13. Heat generation and dissipation rate curves at different initial SOCs,
initial battery temperatures, and ambient temperatures.
where Uoc, U, I, Upo , R0 , Rpo , and Cpo are the open-circuit
voltage, terminal voltage, charging current, polarization voltage,
ohmic resistance, polarization resistance, and polarization capacitance of the battery, respectively.
As for the thermal model, the HGR is set according to [72],
and the heat dissipation and the heat absorption rate are obtained
based on the proposed MS in Section III-B. Therefore, the
thermal model is given by
Ps = Qab + Qdis
Fig. 14. Comparison of the HGRs based on the real-world data and the
Simulink.
(32)
where Ps is the power dissipated inside the battery.
Based on the established electrothermal model, simulations
are implemented in MATLAB/Simulink to calculate the normal
heat generation and dissipation curves for different initial battery
SOCs and temperatures and ambient temperatures. Some results
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LI et al.: BATTERY THERMAL RUNAWAY FAULT PROGNOSIS IN EVS BASED ON AHG AND DEEP LEARNING ALGORITHMS
Fig. 16.
AHG prognosis result of cell no. 1.
are shown in Fig. 13. The ambient temperature and battery SOC
intervals between the curves are 5°C and 10%, respectively, and
the initial battery temperature is equal to the ambient and heat
sink temperature.
B. Cross-Verification for Accuracy of Normal HGRs by
Real-World Data and Simulink
To determine the threshold M in (19), the historical data of
the normal vehicle no.1 is used and the biggest residual ΔQg
of 0.935 is assigned to M for AHG diagnosis of other EVs. To
verify the accuracy of the normal HGRs by real-world data and
the Simulink, the data are collected from another normal vehicle
no. 3, which covers a duration from March 4, 2019 12:12:54 to
March 4, 2019 14:52:04. The heat generation/dissipation ratetemperature plot during the charging process is shown in Fig. 14.
It can be seen that the residual is low and the accuracies of the
HGRs based on the real-world data and the Simulink are high
when the battery temperature is lower than 35°C or higher than
37°C. When the battery temperature is between 35°C and 37°C,
the calculation error is relatively high with a maximum value of
0.401. It is due to that the predicted temperature has been filtered
and the residual is higher when the HGR changes quickly. In
general, the MRE of the HGRs based on the real-world data and
the Simulink is 1.42%, which indicates the accuracy of the HGR
based on the Simulink is sufficient for AHG diagnosis.
C. Real-World Thermal Runaway Prognosis
To verify the effectiveness of the presented CNN-LSTMMS scheme for thermal runaway prognosis, the data collected
from vehicle no. 2 that experienced thermal runaway are used,
which covers a duration from 23:26:40 January 20, 2019 to
01:56:40 January 21, 2019. Battery cell no. 1 experienced
thermal runaway on January 21, 2019 at 01:56:40. The heat
generation/dissipation rate-temperature relationship and the corresponding measured temperature for cell no. 1 are depicted in
Fig. 15. As shown in Fig. 15(a), as the temperature rises, the
residual between the real-world and the normal HGR becomes
larger and exceeds the threshold at 01:37:30. The proposed
model is able to perform effective thermal runaway prognosis by
combining the AHG diagnosis with temperature prediction. The
thermal runaway can be predicted by the AHG diagnosis method
8523
19 min in advance, while the CNN-LSTM model can predict the
AHG 8 min ahead. Therefore, thermal runaway can be accurately
prognosed 27 min ahead, which provides sufficient time for
occupants to take proactive actions. According to Fig. 15(b),
the presented CNN-LSTM-MS scheme can prognose thermal
runaway even when there is no obvious rise in temperature measurement. This verifies the effectiveness and superiority of the
proposed method over threshold-based safety monitoring strategies that need obvious temperature abnormality as a premise
for accurate thermal runaway prognosis. The MRE between the
real and the predicted temperature is 0.21%. This indicates that
the modified CNN-LSTM shows competitive performance for
temperature prediction in thermal runaway condition.
To avoid heat generation and dissipation rate fluctuations
caused by inaccurate temperature prediction, the predicted temperature is filtered with the filtering method introduced in
Section III-C with the predicted, filtered, and real temperatures
delineated in Fig. 16. The MREs of the predicted and filtered
temperatures are 0.22% and 0.21%, respectively. This proves
that the filter can effectually smooth the temperature measurement without significant impact on temperature prediction.
VII. CONCLUSION
In this article, an enabling battery thermal runaway prognosis
scheme is presented. It comprises of temperature prediction
using a modified CNN-LSTM neural network model and AHG
diagnosis using an MS. First, the memory cell of the LSTM is
modified, and is further combined with the CNN to extract temporal and spatial features of the real-world factors related to battery temperature evolution. The vehicle state-driving behaviorlocal weather analysis, PCA, and RAOM are proposed to optimize the model inputs and hyperparameters of the modified
CNN-LSTM model for higher modeling accuracy. The modified
CNN-LSTM model is offline trained and can be updated using
real-world data. Compared with other temperature prediction
methods, the modified CNN-LSTM model can achieve accurate
temperature prediction for eight minutes with an MRE of 0.28%.
The robustness and adaptation are also verified using different
datasets collected throughout a complete calendar year. Then,
an MS is presented to calculate battery HGR, based on which a
strategy is devised to realize online AHG diagnosis. Finally,
the effectiveness and timeliness of the CNN-LSTM-MS are
verified using the operating data collected from a real-world
EV with thermal runaway occurrence. The results show that
thermal runaway can be accurately prognosed 27 min ahead,
which provides sufficient time for occupants to take proactive
actions. The average computing time of the presented scheme
is 0.758 s based on a Dell laptop equipped with an Intel (R)
Core (TM) i5-7300HQ CPU, a 16 GB RAM, and a 4 GHz
discrete graphics card. This computing time for thermal runaway
prognosis is efficient, compared with the sampling interval of
10 s.
The modified CNN-LSTM model is trained using one-year
operating data of an EV and then used for other EVs with the
same specifications. With the increasing number of EVs, more
vehicle data of different specifications, regions, and climates can
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be used for model training. With the development of advanced
sensors, such as fibre optical sensor, the internal temperature can
be obtained for more accurate battery thermal modeling.
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