Battery Fault Diagnosis for Electric Vehicles Based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model

• Published in: IEEE transactions on power electronics Vol. 36; no. 2; pp. 1303 - 1315
• Main Authors: Li, Da, Zhang, Zhaosheng, Liu, Peng, Wang, Zhenpo, Zhang, Lei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batteries; Circuit faults; Computational modeling; Computing time; Data acquisition; Data models; Driver behavior; Electric vehicles; Electric vehicles (EVs); equivalent circuit model (ECM); Equivalent circuits; Fault diagnosis; Integrated circuit modeling; lithium-ion battery; long short-term memory recurrent neural network (LSTM); Machine learning; Model accuracy; modified adaptive boosting (MAB); Neural networks; Recurrent neural networks; Reliability; Risk assessment; Thermal runaway
• ISSN: 0885-8993; 1941-0107
• DOI: 10.1109/TPEL.2020.3008194

Battery fault diagnosis is essential for ensuring safe and reliable operation of electric vehicles. In this article, a novel battery fault diagnosis method is presented by combining the long short-term memory recurrent neural network and the equivalent circuit model. The modified adaptive boosting method is utilized to improve diagnosis accuracy, and a prejudging model is employed to reduce computational time and improve diagnosis reliability. Considering the influence of the driver behavior on battery systems, the proposed scheme is able to achieve potential failure risk assessment and accordingly to issue early thermal runaway warning. A large volume of real-world operation data is acquired from the National Monitoring and Management Center for New Energy Vehicles in China to examine its robustness, reliability, and superiority. The verification results show that the proposed method can achieve accurate fault diagnosis for potential battery cell failure and precise locating of thermal runaway cells.