A weighted Pearson correlation coefficient based multi-fault comprehensive diagnosis for battery circuits

Fault diagnosis for battery circuit is particularly important for the safe management of electric vehicles. Previous correlation based fault diagnosis method only detect some faults, ignores the coupled faults, load connection faults and the problem of current data submerged. In this paper, a multi-fault online diagnosis approach combining a non-redundant measurement topology and weighted Pearson correlation coefficient (WPCC) is adopted to detect various circuit faults by weighted measured data with different forgetting factors. The main advantages are: 1) With adding the connected resistances between the battery pack and the load, the nonredundant measurement topology contains a current sensor and the same number of voltage sensors as those of the battery cells without adding complexity to the system. 2) By adding different weights with bigger forgetting factor to more recent data, a period signal aided WPCC approach is adopted to forget historical data and stress the recent data, so as to online detect the circuit faults. 3) Different from the previous same kind of fault judgement idea, the comprehensive judgement rule are used to online judge the battery abuse faults, connection faults, sensor faults, adjacent homogeneous faults and adjacent hybrid faults. The experiment results show that the investigated method can distinguish and locate the above faults accurately.

Automated summary

In this paper, a multi-fault online diagnosis approach combining a non-redundant measurement topology and weighted Pearson correlation coefficient (WPCC) is proposed to detect various circuit faults. The approach uses weighted measured data with different forgetting factors and can accurately distinguish and locate battery abuse faults, connection faults, sensor faults, adjacent homogeneous faults, and adjacent hybrid faults.