A vehicle-cloud collaborative method for multi-type fault diagnosis of lithium-ion batteries

Abstract

Accurate and reliable fault diagnosis is critical for battery systems to ensure their safe and stable operation. Battery faults cause severe decline of the pack performance and even lead to catastrophic thermal runaway events. This paper presents a vehicle-cloud collaborative method for multi-type fault diagnosis of lithium-ion batteries based on the cell difference model and machine learning. Firstly, experiments of different types of battery module faults are carried out to establish the simulation model of battery system. The charging-discharging conditions of normal and faulty battery modules are simulated to obtain massive cycle data for the algorithm training on the cloud. Then, the cell difference model is used to extract feature differences on the vehicle end. Combined with feature engineering and parameter optimization, the decision tree classifier is trained, and the judgment thresholds in the cloud algorithm are used for real-time tracking of vehicle signals to achieve the purpose of vehicle-cloud collaboration. Finally, the classifier is verified by multiple sets of experiments that can be carried out on the vehicle end. The results show that the proposed method can identify internal short circuit fault before end stage, and accurately distinguish conventional faults, including internal short circuit fault, resistance fault, and capacity fault. • The method improves the efficiency of algorithm development. • The vehicle end estimates cell features in real time. • Massive data is created for cloud algorithm training using battery system simulation model. • The cloud applies fault classifier with high accuracy and low computational complexity. • The method is verified by multiple sets of experiments.