Multi-fault detection and diagnosis method for battery packs based on statistical analysis.

Rapid and accurate battery fault diagnosis and distinction is of great importance in electrical vehicles and electrochemical energy storage system. However, misdiagnosis and missed diagnosis happened occasionally. In this paper, a statistical analysis-based multi-fault diagnosis method is proposed to detect and localize short circuit faults, electrical connection faults and voltage sensor faults in LFP battery packs. This method uses non-redundant interleaved voltage measurement topology to detect battery voltages, where every voltage sensor measures the sum of two neighboring batteries and one connection resistor between them. The statistical analysis method sets detection thresholds based on the battery operating data, and captures fault characteristics by analyzing abnormal changes in battery voltage unrelated to current. Theoretical analysis and tests verified that this method can diagnose these three kinds of faults. Sensor faults of excessive error and data sticking can also be distinguished. • The proposed method can diagnose short circuit faults, electrical connection faults and sensor faults. • The non-redundant interleaved voltage measurement topology does not require additional voltage sensors, which saves costs. • The introduction of time window avoids blocking the fault diagnosis process when a fault occurs. • Sensor faults of excessive error and data sticking can be distinguished. [ABSTRACT FROM AUTHOR]