Simple and Effective Fault Diagnosis Method of Power Lithium-Ion Battery Based on GWA-DBN

Abstract In order to improve the accuracy of battery pack inconsistency fault detection, an optimal deep belief network (DBN) single battery inconsistency fault detection model based on the gray wolf algorithm (GWA) was proposed. The performance of the DBN model is affected by the weights and bias parameters, and the gray wolf algorithm has a good ability to seek optimization, so the gray wolf algorithm is used to optimize the connection weights of the DBN model. Therefore, the accuracy rate of battery inconsistency diagnosis is improved. The battery voltage characteristic data is used as the input signal of the DBN model. The health and faults of the single cells are used as the output signals of the DBN model. The battery inconsistency fault detection model of GWA-DBN is established. Through the comparison and simulation with other algorithms, it is proved that the designed model has higher diagnostic accuracy, better fitting effect, and good application prospect.