Integrated Intelligent Energy ›› 2025, Vol. 47 ›› Issue (8): 10-20.doi: 10.3969/j.issn.2097-0706.2025.08.002

• Multi-dimensional Energy Storage Technology • Previous Articles     Next Articles

Fault diagnosis technology for lithium-ion batteries based on electro-thermal coupling model

YU Ziyi1(), PAN Tinglong1,*(), GE Ke2(), DOU Zhenlan3(), XU Dezhi4()   

  1. 1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
    2. Jiangsu Haiji New Energy Company Limited, Wuxi 214422, China
    3. State Grid Shanghai Power Supply Company, Shanghai 200122, China
    4. School of Electrical Engineering, Southeast University, Nanjing 210018, China
  • Received:2024-09-19 Revised:2024-10-21 Published:2025-06-03
  • Contact: PAN Tinglong E-mail:6221915027@stu.jiangnan.edu.cn;tlpan@jiangnan.edu.cn;geke123@163.com;douzhl@126.com;xudezhi@seu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(62222307)

Abstract:

With the rapid development of new energy vehicles, the safety of lithium-ion batteries, a core component of power batteries, has become increasingly crucial. Therefore, a fault diagnosis technology for lithium-ion batteries based on an electro-thermal coupling model was proposed. By integrating the electrical and thermal characteristics of lithium-ion batteries, an electro-thermal coupling model was established. The relative errors of the voltage and surface temperature predicted by the model were both less than 1%, providing a more accurate description of battery performance. The model combined a second-order Thevenin equivalent circuit model with a lumped parameter thermal model, dynamically reflecting the influence of current and voltage on temperature while accounting for the feedback effects of temperature on electrical parameters. The model parameters were identified using the Forgetting Factor Recursive Least Squares (FFRLS) algorithm, and state estimation was conducted with an Adaptive Extended Kalman Filter (AEKF). The differences between measured and estimated values enabled accurate fault diagnosis of the batteries. Simulation results demonstrated that the proposed method successfully monitored battery states under various fault conditions and identified and diagnosed faults through combined voltage and temperature monitoring.

Key words: lithium-ion battery, electro-thermal coupling model, parameter identification, state estimation, fault diagnosis

CLC Number: