Connection fault diagnosis for lithium-ion battery packs in electric vehicles based on mechanical vibration signals and broad belief network

The connection faults between the cells of a battery pack can increase contact resistance and thus result in abnormal heating at the connections, which can seriously damage or even fail the battery pack. This work therefore proposes a novel connection fault diagnosis method based on mechanical vibration signals rather than voltage and current measurements. Firstly, this work simulates the vibration environment, which resembles that of the actual operation of a lithium-ion battery pack in electric vehicles. The optimal sensor placement is achieved via a sparse-learning algorithm, and the vibration signals are collected on this basis. Following that, this work proposes a broad belief network (BBN) for detecting and locating connection faults in lithium-ion battery packs based on the vibration signals. Since fault diagnosis needs to adapt to new data as they become progressively available in real-time, two incremental-learning algorithms are paired with the BBN, such that the network can achieve fast reconstruction and expansion without re-training from scratch. Empirical evidence suggests that the diagnostic accuracy of the proposed method is 93.25%, which demonstrates the effectiveness and feasibility of the proposed method.