Reliable Online Internal Short Circuit Diagnosis on Lithium-Ion Battery Packs via Voltage Anomaly Detection Based on the Mean-Difference Model and the Adaptive Prediction Algorithm

Abstract

The safety issue of lithium-ion batteries is a great challenge for the applications of EVs. The internal short circuit (ISC) of lithium-ion batteries is regarded as one of the main reasons for the lithium-ion batteries failure. However, the online ISC diagnosis algorithm for real vehicle data remains highly imperfect at present. Based on the onboard data from the cloud battery management system (BMS), this work proposes an ISC diagnosis algorithm for battery packs with high accuracy and high robustness via voltage anomaly detection. The mean-difference model (MDM) is applied to characterize large battery packs. A diagram of the adaptive integrated prediction algorithm combining MDM and a bi-directional long short-term memory (Bi-LSTM) neural network is firstly proposed to approach the voltage prediction of each cell. The diagnosis of an ISC is realized based on the residual analysis between the predicted and the actual state. The experimental data in DST conditions evaluate the proposed algorithm by comparing it with the solo equivalent circuit-based prediction algorithm and the Bi-LSTM based prediction algorithm. Finally, through the practical vehicle data from the cloud BMS, the diagnosis and pre-warn ability of the proposed algorithm for an ISC and thermal runaway (TR) in batteries are verified. The ISC diagnosis algorithm that is proposed in this paper can effectively identify the gradual ISC process in advance of it.