Abstract

Mechanical failure prediction of lithium-ion batteries (LIBs) can provide important maintenance information and decision-making reference in battery safety management. However, the complexity of the internal structure of batteries poses challenges to the generalizability and prediction accuracy of traditional mechanical models. In view of these challenges, emerging data-driven methods provide new ideas for the failure prediction of LIBs. This study is based on an experimental data-driven application of machine learning (ML) models to rapidly predict the mechanical behavior and failure of cylindrical cells under different loading conditions. Mechanical abuse experiments including local indentation, flat compression, and three-point bending experiments were conducted on cylindrical LIB samples, and mechanical failure datasets for cylindrical cells were generated, including displacements, voltages, temperatures, and mechanical forces. Six ML models were used to predict the mechanical behavior of cylindrical batteries, four metrics were used to evaluate the prediction performance, the coefficients of determination of eXtreme Gradient Boosting (XGBoost) regression and random forest were 0.999, and the root-mean-square errors (RMSE) were lower than 0.015. It is shown that the integrated tree models tested in this study are suitable for the failure prediction of LIBs under the conditions of mechanical abuse. Also, the random forest prediction model outperforms other ML prediction models with the smallest RMSE values of 0.005, 0.0149, and 0.007 for local indentation, flat compression, and three-point bending, respectively. This work highlights the capability of ML algorithms for LIB safety prediction.

References

1.
Li
,
K. L.
,
Xie
,
N. M.
, and
Tang
,
O.
,
2024
, “
Data-Driven Degradation Trajectory Prediction and Online Knee Point Identification of Battery in Electric Vehicles
,”
Eng. Failure Anal.
,
159
, p.
108154
.
2.
Gotz
,
J. D.
,
Guerrero
,
G. C.
,
de Queiroz
,
J. R. H.
,
Viana
,
D. R.
, and
Borsato
,
M.
,
2023
, “
Diagnosing Failures in Lithium-Ion Batteries With Machine Learning Techniques
,”
Eng. Failure Anal.
,
150
, p.
107309
.
3.
Zhang
,
X. C.
,
Liu
,
N. N.
,
Dong
,
S. J.
,
Zhang
,
T.
,
Yin
,
X. D.
,
Ci
,
T. J.
, and
Wu
,
H. X.
,
2023
, “
Dynamic Crushing Behaviors of Cylindrical Lithium-Ion Battery Under Multiple Impacts: An Experimental Study
,”
ASME J. Electrochem. Energy Convers. Storage
,
20
(
4
), p.
041010
.
4.
Huang
,
Z. X.
,
Zhang
,
X. C.
,
An
,
L. Q.
,
Rao
,
L. X.
,
Gu
,
L. R.
, and
Li
,
C. Y.
,
2024
, “
Dynamic Response Analysis of Cylindrical Lithium-Ion Battery Under Impact Loadings: A Theoretical Study
,”
Thin Walled Struct.
,
205
, p.
112385
.
5.
Lai
,
X.
,
Jin
,
C. Y.
,
Han
,
X. B.
,
Feng
,
X. N.
,
Zheng
,
Y. J.
, and
Quyang
,
M.
,
2021
, “
Mechanism, Modeling, Detection, and Prevention of the Internal Short Circuit in Lithium-Ion Batteries: Recent Advances and Perspectives
,”
Energy Storage Mater.
,
35
, pp.
470
499
.
6.
Huang
,
Z. X.
,
Zhang
,
X. C.
,
Liu
,
N. N.
,
Gu
,
L. R.
,
An
,
L. Q.
, and
Zhou
,
W.
,
2024
, “
Failure Mechanisms and Acoustic Responses of Cylindrical Lithium-Ion Batteries Under Compression Loadings
,”
Eng. Failure Anal.
,
163
, p.
108594
.
7.
Liu
,
B. H.
,
Jia
,
Y. K.
,
Yuan
,
C. H.
,
Wang
,
L. B.
,
Gao
,
X.
,
Yin
,
X.
, and
Xu
,
J.
,
2020
, “
Safety Issues and Mechanisms of Lithium-Ion Battery Cell Upon Mechanical Abusive Loading: A Review
,”
Energy Storage Mater.
,
24
, pp.
85
112
.
8.
Zhang
,
X. C.
,
Huang
,
Z. X.
,
Wang
,
Y. L.
,
Zhang
,
S. F.
,
Zhang
,
T.
,
An
,
L. Q.
, and
Wang
,
Q. L.
,
2024
, “
Dynamic Responses of Cylindrical Lithium-Ion Battery Under Localized Impact Loading
,”
Mech. Adv. Mater. Struct.
, pp.
1
10
.
9.
Zhu
,
X. Q.
,
Wang
,
H.
,
Wang
,
X.
,
Gao
,
Y. F.
,
Allu
,
S.
,
Cakmak
,
E.
, and
Wang
,
Z. P.
,
2020
, “
Internal Short Circuit and Failure Mechanisms of Lithium-Ion Pouch Cells Under Mechanical Indentation Abuse Conditions: An Experimental Study
,”
J. Power Sources
,
455
, p.
227939
.
10.
Wang
,
H.
,
Kumar
,
A.
,
Simunovic
,
S.
,
Allu
,
S.
,
Kalnaus
,
S.
,
Turner
,
J. A.
,
Helmers
,
J. C.
,
Rules
,
E. T.
,
Winchester
,
C. S.
, and
Gorney
,
P.
,
2017
, “
Progressive Mechanical Indentation of Large-Format Li-Ion Cells
,”
J. Power Sources
,
341
, pp.
156
164
.
11.
Xiao
,
F. Y.
,
Xing
,
B. B.
,
Kong
,
L.
, and
Xia
,
Y.
,
2021
, “
Impedance-Based Diagnosis of Internal Mechanical Damage for Large-Format Lithium-Ion Batteries
,”
Energy
,
230
, p.
120855
.
12.
Li
,
Y. D.
,
Wang
,
W. W.
,
Lin
,
C.
,
Yang
,
X. G.
, and
Zuo
,
F. H.
,
2021
, “
A Safety Performance Estimation Model of Lithium-Ion Batteries for Electric Vehicles Under Dynamic Compression
,”
Energy
,
215
, p.
119050
.
13.
Gao
,
Z. H.
,
Zhang
,
X. T.
,
Xiao
,
Y.
,
Gao
,
H.
,
Wang
,
H. Y.
, and
Piao
,
C. G.
,
2019
, “
Influence of Low-Temperature Charge on the Mechanical Integrity Behavior of 18650 Lithium-Ion Battery Cells Subject to Lateral Compression
,”
Energies
,
12
(
5
), p.
797
.
14.
Song
,
Y. H.
,
Gilaki
,
M.
,
Keshavarzi
,
M. M.
, and
Sahrael
,
E.
,
2022
, “
A Universal Anisotropic Model for a Lithium-Ion Cylindrical Cell Validated Under Axial, Lateral, and Bending Loads
,”
Energy Sci. Eng.
,
10
(
4
), pp.
1431
1448
.
15.
Keshavarzi
,
M. M.
,
Gilaki
,
M.
, and
Sahraei
,
E.
,
2022
, “
Characterization of In-Situ Material Properties of Pouch Lithium-Ion Batteries in Tension From Three-Point Bending Tests
,”
Int. J. Mech. Sci.
,
219
, p.
107090
.
16.
Goodman
,
J. K. S.
,
Miller
,
J. T.
,
Kreuzer
,
S.
,
Forman
,
J.
,
Wi
,
S.
,
Chai
,
J.
,
Oh
,
J.
, and
White
,
K.
,
2020
, “
Lithium-Ion Cell Response to Mechanical Abuse: Three-Point Bend
,”
J. Energy Storage
,
28
, p.
101244
.
17.
Arief Budiman
,
B.
,
Rahardian
,
S.
,
Saputro
,
A.
,
Hidayat
,
A.
,
Nurprasetio
,
I. P.
, and
Sambegoro
,
P.
,
2022
, “
Structural Integrity of Lithium-Ion Pouch Battery Subjected to Three-Point Bending
,”
Eng. Failure Anal.
,
138
, p.
106307
.
18.
An
,
Z. J.
,
Shi
,
T. J.
,
Du
,
X. Z.
,
An
,
X.
,
Zhang
,
D.
, and
Bai
,
J. H.
,
2024
, “
Experimental Study on the Internal Short Circuit and Failure Mechanism of Lithium-Ion Batteries Under Mechanical Abuse Conditions
,”
J. Energy Storage.
,
89
, p.
111819
.
19.
Xia
,
X.
, and
Tang
,
L.
,
2021
, “
A Fast-Validated Computational Model for Cylindrical Lithium-Ion Batteries Under Multidirectional Mechanical Loading
,”
Int. J. Energy Res.
,
45
(
3
), pp.
4410
4428
.
20.
Mo
,
F. H.
,
Tian
,
Y.
,
Zhao
,
S. Q.
,
Zhao
,
Z.
, and
Ma
,
Z. L.
,
2022
, “
Working Temperature Effects on Mechanical Integrity of Cylindrical Lithium-Ion Batteries
,”
Eng. Failure Anal.
,
137
, p.
106399
.
21.
Zhang
,
X. C.
,
Zhang
,
T.
,
Liu
,
N. N.
,
Yin
,
X. D.
,
Wu
,
X. N.
,
Han
,
H. L.
,
Wang
,
Q. L.
, and
Zhang
,
Y. J.
,
2023
, “
Dynamic Crushing Behaviors and Failure of Cylindrical Lithium-Ion Batteries Subjected to Impact Loading
,”
Eng. Failure Anal.
,
154
, p.
107653
.
22.
Li
,
H. G.
,
Liu
,
B. H.
,
Zhou
,
D.
, and
Zhang
,
C.
,
2020
, “
Coupled Mechanical–Electrochemical–Thermal Study on the Short-Circuit Mechanism of Lithium-Ion Batteries Under Mechanical Abuse
,”
J. Electrochem. Soc.
,
167
(
12
), p.
120501
.
23.
Yin
,
H. F.
,
Ma
,
S.
,
Li
,
H. G.
,
Wen
,
G. L.
,
Santhanagopalan
,
S.
, and
Zhang
,
C.
,
2021
, “
Modeling Strategy for Progressive Failure Prediction in Lithium-Ion Batteries Under Mechanical Abuse
,”
eTransportation
,
7
, p.
100098
.
24.
Zhao
,
J. Y.
,
Feng
,
X. N.
,
Pang
,
Q.
,
Fowler
,
M.
,
Lian
,
Y. B.
,
Quyang
,
M.
, and
Burke
,
A.
,
2024
, “
Battery Safety: Machine Learning-Based Prognostics
,”
Prog. Energy Combust. Sci.
,
102
, p.
101142
.
25.
Buchanan
,
S.
, and
Crawford
,
C.
,
2024
, “
Probabilistic Lithium-Ion Battery State-of-Health Prediction Using Convolutional Neural Networks and Gaussian Process Regression
,”
J. Energy Storage
,
76
, p.
109799
.
26.
Cao
,
J.
,
Wang
,
S. L.
, and
Fernandez
,
C.
,
2024
, “
Multi-Kernel Support Vector Regression Optimization Model and Indirect Health Factor Extraction Strategy for the Accurate Lithium-Ion Battery Remaining Useful Life Prediction
,”
J. Solid State Electrochem.
,
28
(
1
), pp.
19
32
.
27.
Ge
,
Y.
,
Ge
,
J. X.
, and
Sun
,
G. D.
,
2024
, “
A Structural Pruning Method for Lithium-Ion Batteries Remaining Useful Life Prediction Model With Multi-Head Attention Mechanism
,”
J. Energy Storage
,
86
, p.
111396
.
28.
Li
,
D.
,
Liu
,
P.
,
Zhang
,
Z. S.
,
Zhang
,
L.
,
Deng
,
J. J.
,
Wang
,
Z. P.
,
Dorrell
,
D.
,
Li
,
W.
, and
Sauer
D. U.
2022
, “
Battery Thermal Runaway Fault Prognosis in Electric Vehicles Based on Abnormal Heat Generation and Deep Learning Algorithms
,”
IEEE Trans. Power Electron.
,
37
(
7
), pp.
8513
8525
.
29.
Daniels
,
R. K.
,
Kumar
,
V.
,
Chouhan
,
S. S.
, and
Prabhakar
,
A.
,
2024
, “
Thermal Runaway Fault Prediction in Air-Cooled Lithium-Ion Battery Modules Using Machine Learning Through Temperature Sensors Placement Optimization
,”
Appl. Energy
,
355
, p.
122352
.
30.
Tran
,
M. K.
,
Panchal
,
S.
,
Chauhan
,
V.
,
Brahmbhatt
,
N.
,
Mevawalla
,
A.
,
Fraser
,
R.
, and
Fowler
,
M.
2022
, “
Python-Based Scikit-Learn Machine Learning Models for Thermal and Electrical Performance Prediction of High-Capacity Lithium-ion Battery
,”
Int. J. Energy Res.
,
46
(
2
), pp.
786
794
.
31.
Jia
,
Y. K.
,
Gao
,
X.
,
Mouillet
,
J. B.
,
Terrier
,
J.
,
Lombard
,
P.
, and
Xu
,
J.
,
2021
, “
Effective Thermo-Electro-Mechanical Modeling Framework of Lithium-ion Batteries Based on a Representative Volume Element Approach
,”
J. Energy Storage
,
33
, p.
102090
.
32.
Gilaki
,
M.
,
Song
,
Y. H.
, and
Sahraei
,
E.
,
2022
, “
Homogenized Characterization of Cylindrical Li-Ion Battery Cells Using Elliptical Approximation
,”
Int. J. Energy Res.
,
46
(
5
), pp.
5908
5923
.
33.
Yang
,
W. B.
,
Xia
,
K. W.
, and
Fan
,
S. R.
,
2023
, “
Oil Logging Reservoir Recognition Based on TCN and SA-BiLSTM Deep Learning Method
,”
Eng. Appl. Artif. Intell.
,
121
, p.
105950
.
34.
Guo
,
L.
,
He
,
H. W.
,
Ren
,
Y. R.
,
Li
,
R. Z.
,
Jiang
,
B.
, and
Gong
,
J. Y.
,
2024
, “
Prognostics of Lithium-Ion Batteries Health State Based on Adaptive Mode Decomposition and Long Short-Term Memory Neural Network
,”
Eng. Appl. Artif. Intell.
,
127
, p.
107317
.
35.
Manoharan
,
A.
,
Begam
,
K. M.
,
Aparow
,
V. R.
, and
Sooriamoorthy
,
D.
,
2022
, “
Artificial Neural Networks, Gradient Boosting and Support Vector Machines for Electric Vehicle Battery State Estimation: A Review
,”
J. Energy Storage
,
55
, p.
105384
.
36.
Rauf
,
H.
,
Khalid
,
M.
, and
Arshad
,
N.
,
2023
, “
A Novel Smart Feature Selection Strategy of Lithium-Ion Battery Degradation Modelling for Electric Vehicles Based on Modern Machine Learning Algorithms
,”
J. Energy Storage
,
68
, p.
107577
.
37.
Li
,
Y.
,
Liu
,
K. L.
,
Foley
,
A. M.
,
Zülke
,
A.
,
Berecibar
,
M.
,
Nanini-Maury
,
E.
,
Van Mierlo
,
J.
, and
Hoster
,
H. E.
2019
, “
Data-Driven Health Estimation and Lifetime Prediction of Lithium-Ion Batteries: A Review
,”
Renewable Sustainable Energy Rev.
,
113
, p.
109254
.
38.
Dineva
,
A.
,
Csomós
,
B.
,
Kocsis Sz
,
S.
, and
Vajda
,
I.
,
2021
, “
Investigation of the Performance of Direct Forecasting Strategy Using Machine Learning in State-of-Charge Prediction of Li-Ion Batteries Exposed to Dynamic Loads
,”
J. Energy Storage
,
36
, p.
102351
.
39.
Jafari
,
S.
,
Shahbazi
,
Z.
,
Byun
,
Y. C.
, and
Lee
,
S. J.
,
2022
, “
Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach
,”
Mathematics
,
10
(
6
), p.
888
.
40.
Chung
,
Y. W.
,
Khaki
,
B.
,
Li
,
T. Y.
,
Chu
,
C. C.
, and
Gadh
,
R.
,
2019
, “
Ensemble Machine Learning-Based Algorithm for Electric Vehicle User Behavior Prediction
,”
Appl. Energy
,
254
, p.
113732
.
41.
Xu
,
J.
,
Liu
,
B. H.
,
Wang
,
L. B.
, and
Shang
,
S.
,
2025
, “
Dynamic Mechanical Integrity of Cylindrical Lithium-Ion Battery Cell Upon Crushing
,”
Eng. Failure Anal.
,
53
, pp.
97
110
.
42.
Sahraei
,
E.
,
Meier
,
J.
, and
Wierzbicki
,
T.
,
2014
, “
Characterizing and Modeling Mechanical Properties and Onset of Short Circuit for Three Types of Lithium-Ion Pouch Cells
,”
J. Power Sources
,
247
, pp.
503
516
.
You do not currently have access to this content.