TY - JOUR
T1 - Battery safety issue detection in real-world electric vehicles by integrated modeling and voltage abnormality
AU - Li, Da
AU - Zhang, Lei
AU - Zhang, Zhaosheng
AU - Liu, Peng
AU - Deng, Junjun
AU - Wang, Qiushi
AU - Wang, Zhenpo
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Detecting battery safety issues is essential to ensure safe and reliable operation of electric vehicles (EVs). This paper proposes an enabling battery safety issue detection method for real-world EVs through integrated battery modeling and voltage abnormality detection. Firstly, a battery voltage abnormality degree that is adaptive to different battery types and working conditions is defined. Then an integrated battery model is developed by combining an electrochemical model, an equivalent circuit model (ECM), and a data-driven model to evaluate the normal voltage. To ensure normality of input current, a current processing model is presented. The performance of the proposed scheme is examined under random loading profiles using operating data collected from real-world EVs. The results show that the integrated battery model can precisely predict normal battery terminal voltage, with mean-squared-errors of 1.034e−4 V2, 7.221e−5 V2, and 4.612e−5 V2 for driving, quick charging, and slow charging, respectively. The accuracy in classifying faulty and normal batteries is verified based on the operating data collected from 20 EVs.
AB - Detecting battery safety issues is essential to ensure safe and reliable operation of electric vehicles (EVs). This paper proposes an enabling battery safety issue detection method for real-world EVs through integrated battery modeling and voltage abnormality detection. Firstly, a battery voltage abnormality degree that is adaptive to different battery types and working conditions is defined. Then an integrated battery model is developed by combining an electrochemical model, an equivalent circuit model (ECM), and a data-driven model to evaluate the normal voltage. To ensure normality of input current, a current processing model is presented. The performance of the proposed scheme is examined under random loading profiles using operating data collected from real-world EVs. The results show that the integrated battery model can precisely predict normal battery terminal voltage, with mean-squared-errors of 1.034e−4 V2, 7.221e−5 V2, and 4.612e−5 V2 for driving, quick charging, and slow charging, respectively. The accuracy in classifying faulty and normal batteries is verified based on the operating data collected from 20 EVs.
KW - Battery safety
KW - Electric vehicles
KW - Electrochemical model
KW - Equivalent circuit model
KW - Lithium-ion batteries
KW - Radial basis function neural network
UR - http://www.scopus.com/inward/record.url?scp=85166331467&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2023.128438
DO - 10.1016/j.energy.2023.128438
M3 - Article
AN - SCOPUS:85166331467
SN - 0360-5442
VL - 284
JO - Energy
JF - Energy
M1 - 128438
ER -
