Abstract:

To solve the issues that the bias, drift, gain, sticking and mutation fault modes of the current sensor in a battery pack are difficult to detect, recognize and evaluate, a comprehensive diagnosis strategy based on model fusion was proposed. A normal battery model with current as input and voltage as output (CIVO) was established. Based on the one-to-many relationship between the current sensor and batteries in the pack, the cumulative sum of the log-likelihood ratios of the residuals of the voltage of each cell was used as the detection index. A bias/drift fault model and a gain fault model with voltage as input and current as output (VICO) were established. Based on the residual variance of fault current, the model matching was performed on each fault mode. The quantitative evaluation of the bias, drift and gain modes were achieved by introducing a fault parameter to the fault model. The results show that based on CIVO, the five fault modes can be reliably detected. The sticking mode takes the shortest detection time and the drift mode requires the longest detection time, attributed to the slow-change characteristics of the fault current. Based on VICO, five fault modes can be accurately recognized. The quantitative evaluations of the bias, drift and gain modes are highly accurate, with the evaluation results of 0.396 2 A (experimental value 0.4 A), 1.641 7×10^-4 (experimental value 1.5×10^-4) and 0.201 6 (experimental value 0.2), respectively.

Key words: battery pack, current sensors, fault modes, comprehensive diagnostics, model fusion