Battery Fault Diagnosis for Electric Vehicles Based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model
Da Li, Zhaosheng Zhang, Peng Liu, Zhenpo Wang, Lei Zhang
Abstract
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.