Abstract:

Accurate performance evaluation of power battery cells is of great significance to ensuring the safety of power batteries. For the existing data-driven battery fault diagnosis algorithms， mostly individual cells are compared with each other and the outlier cells are identified as faulty cells by classification， based on differences in characteristic parameters such as single cell voltage. However， if there are multiple cells of similar abnormally performance in the power battery pack， or all individual batteries show an overall performance deterioration， it is difficult to distinguish individual cells or even there is no significant outliers， and the application of the mutual comparison strategy is limited. A power battery fault diagnosis method is proposed based on 1dCNN-LSTM to quantify the abnormality of a single cell in this paper. Combining the three types of characteristics of vehicle motion status， drive system status and power battery electrical signal， the 1dCNN-LSTM fusion model is established to estimate the individual cell voltage under ideal conditions as reference. The difference between the real-time voltage reference value and the measured voltage value is used to quantify the abnormality of each cell. Combined with actual cases， it is shown that for thermal runaway case due to single cell failure， the abnormal performance of the faulty cell compared to others can be identified 7 days before accident， and potential risk can be recognized in discharge processes from a year of more before the accident. For overall deterioration cases without obvious individual cells inconsistency， the deterioration evolution within the last 7 days can be tracked.

Key words: power battery, fault diagnosis, cell inconsistency, fused model, real-time voltage estimation