A multi-fault diagnosis method for lithium-ion battery pack using curvilinear Manhattan distance evaluation and voltage difference analysis

In order to ensure the safety and reliability of electric vehicles, and also to reduce the occurrence of accidents, a fast and efficient multi-fault diagnosis methodology for lithium-ion batteries appears to be particularly important. With this motivation, based on curvilinear Manhattan distance and voltage difference analysis technique, a rapid multi-fault diagnosis method for the lithium-ion battery pack is developed. Specifically, the curvilinear Manhattan distance is presented to quantize the charging voltage variation curves, and then detect and locate the faulty cells within the lithium-ion battery pack. The voltage difference analysis approach is proposed to determine the faulty type with different criteria according to three fault characteristics. Diagnosis experiments are implemented to demonstrate the effectiveness of the proposed multi-fault diagnosis technique based on the aging data of the series-connected lithium-ion battery pack. Results of the diagnosis experiments prove that the proposed approach can sensitively and reliably detect and isolate multiple faults with the merits of low computational cost and high accuracy.