Fault detection for Li-ion batteries of electric vehicles with segmented regression method

Electric vehicles are increasingly popular for their environmental benefits and cost savings, but the reliability and safety of their lithium-ion batteries are critical concerns. Current regression methods for battery fault detection often analyze charging and discharging as a single continuous process, missing important phase differences. This paper proposes segmented regression to better capture these distinct characteristics for accurate fault detection. The focus is on detecting voltage deviations caused by internal short circuits, external short circuits, and capacity degradation, which are primary indicators of battery faults. Firstly, data from real electric vehicles, operating under normal and faulty conditions, is collected over a period of 18 months. Secondly, the segmented regression method is utilized to segment the data based on the charging and discharging cycles and capture potential dependencies in battery behavior within each cycle. Thirdly, an optimized gated recurrent unit network is developed and integrated with the segmented regression to enable accurate cell voltage estimation. Lastly, an adaptive threshold algorithm is proposed to integrate driving behavior and environmental factors into a Gaussian process regression model. The integrated model dynamically estimates the normal fluctuation range of battery cell voltages for fault detection. The effectiveness of the proposed method is validated on a comprehensive dataset, achieving superior accuracy with values of 99.803% and 99.507% during the charging and discharging phases, respectively.