Automotive Innovation ›› 2022, Vol. 5 ›› Issue (4): 359-375.doi: 10.1007/s42154-022-00201-4

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Adaptive Fitting Capacity Prediction Method for Lithium-Ion Batteries

Xiao Chu1 · Fangyu Xue1 · Tao Liu1 · Junya Shao1 · Junfu Li1,2,3
  

  1. 1. School of Automotive Engineering, Harbin Institute of Technology 2. Guangdong Guanghua Sci-Tech Co., Ltd. 3. School of Chemical Engineering and Chemistry, Harbin Institute of Technology
  • 出版日期:2022-11-20 发布日期:2022-12-01

Adaptive Fitting Capacity Prediction Method for Lithium-Ion Batteries

Xiao Chu1 · Fangyu Xue1 · Tao Liu1 · Junya Shao1 · Junfu Li1,2,3   

  1. 1. School of Automotive Engineering, Harbin Institute of Technology 2. Guangdong Guanghua Sci-Tech Co., Ltd. 3. School of Chemical Engineering and Chemistry, Harbin Institute of Technology
  • Online:2022-11-20 Published:2022-12-01

摘要: Lithium-ion batteries have become the mainstream power source for electric vehicles because of their excellent performance. However, lithium-ion batteries still experience aging and capacity attenuation during usage. It is therefore critical to accurately predict battery remaining capacity for increasing battery safety and prolonging battery life. This paper first adopts the metabolism grey algorithm and a simplified electrochemical model to predict battery capacity under different operating conditions. To improve the prediction performance where the capacity changes nonlinearly, a decoupling analysis of battery capacity loss is then conducted based on the simplified electrochemical model. Finally, an adaptive fitting method is developed for capacity prediction, aiming at improving the prediction accuracy at the inflection point of battery capacity diving. The prediction results indicate that the developed adaptive fitting method can achieve high prediction accuracy under battery capacity attenuation at different discharge stages with errors lower than 2.2%. And the battery capacity decay shows linear variation, and the proposed method effectively forecast the inflection point of battery capacity diving.

Abstract: Lithium-ion batteries have become the mainstream power source for electric vehicles because of their excellent performance. However, lithium-ion batteries still experience aging and capacity attenuation during usage. It is therefore critical to accurately predict battery remaining capacity for increasing battery safety and prolonging battery life. This paper first adopts the metabolism grey algorithm and a simplified electrochemical model to predict battery capacity under different operating conditions. To improve the prediction performance where the capacity changes nonlinearly, a decoupling analysis of battery capacity loss is then conducted based on the simplified electrochemical model. Finally, an adaptive fitting method is developed for capacity prediction, aiming at improving the prediction accuracy at the inflection point of battery capacity diving. The prediction results indicate that the developed adaptive fitting method can achieve high prediction accuracy under battery capacity attenuation at different discharge stages with errors lower than 2.2%. And the battery capacity decay shows linear variation, and the proposed method effectively forecast the inflection point of battery capacity diving.