Fast and Accurate Health Assessment of Lithium-Ion Batteries Based on Typical Voltage Segments

Frontiers in Energy Research

Keying Wang¹  Tao Yu¹  Qingquan Luo¹  Ning Yang¹ 

[1] Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou, China;School of Electric Power Engineering South China University of Technology, Guangzhou, China;

关键词: lithium-ion batteries;    state of health;    temporal convolutional networks;    bootstrap aggregating;    segments;    model fusion;   

DOI  :  10.3389/fenrg.2022.925947

来源: DOAJ

【 摘 要 】

Lithium-ion batteries are widely employed in industries and daily life. Research on the state of health (SOH) of batteries is essential for grasping the performance of batteries, better guiding battery health management, and avoiding safety mishaps caused by battery aging. Nowadays, most research adopts a data-driven artificial intelligence approach to assess SOH. However, the majority of approaches are based on entire voltage, current, or temperature curves. In reality, voltage, current, and temperature are frequently presented in segments, leading to the limited flexibility and slow analysis speed of the traditional techniques. This study solves the problem by dividing the whole voltage curve into many typical kinds of segments with equal timescales based on different typical voltage beginning points. On this foundation, the temporal convolution network (TCN) is used to create a sub-model of SOH estimation for several typical kinds of segments. In addition, the sub-models are fused using the bootstrap aggregating (Bagging) approach to boost accuracy. Finally, this research uses a publicly available dataset from Oxford to demonstrate the effectiveness of the suggested strategy.

【 授权许可】

Unknown