Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

Chinese Journal of Mechanical Engineering

Original Article

Tianqi Liu¹  Yingmin Zhang¹  Xinghao Du¹  Jichang Peng²  Jinhao Meng³  Shunli Wang⁴  Kailong Liu⁵ 

[1] College of Electrical Engineering, Sichuan University, 610044, Chengdu, China;Nanjing Institute of Technology, 211103, Nanjing, China;School of Electrical Engineering, Xi’an Jiaotong University, 710049, Xi’an, China;Southwest University of Science and Technology, 621010, Mianyang, China;Warwick Manufacturing Group, University of Warwick, Coventry, UK;

关键词: Li-ion battery;    Equivalent circuit model;    Recursive least squares;    Recursive total least squares;   

DOI  :  10.1186/s10033-023-00846-0

 received in 2021-09-23, accepted in 2023-01-10,  发布年份 2023

来源: Springer

【 摘 要 】

Online parameter identification is essential for the accuracy of the battery equivalent circuit model (ECM). The traditional recursive least squares (RLS) method is easily biased with the noise disturbances from sensors, which degrades the modeling accuracy in practice. Meanwhile, the recursive total least squares (RTLS) method can deal with the noise interferences, but the parameter slowly converges to the reference with initial value uncertainty. To alleviate the above issues, this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM. RLS converges quickly by updating the parameters along the gradient of the cost function. RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results prove that the proposed method has good accuracy, a fast convergence rate, and also robustness against noise corruption.

【 授权许可】

CC BY   
© The Author(s) 2023

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