Enhanced Lithium-Ion Battery Parameter Estimation and SOC Prediction via Variable Forgetting Factor Bi-Loop Recursive Least Squares

被引：0

作者：

Xia, Wei ^{[1
]}

Xu, Jinli ^{[1
]}

Liu, Baolei ^{[1
]}

Duan, Huiyun ^{[2
]}

机构：

[1] Wuhan Univ Technol, Sch Mech & Elect Engn, Wuhan, Peoples R China

[2] Jiangxi Polytech Univ, Jiujiang, Peoples R China

来源：

ENERGY SCIENCE & ENGINEERING | 2025年

关键词：

ECM; lithium-ion battery; recursive least squares (RLS); variable forgetting factor (VFF); VFFBRLS algorithm; CHARGE ESTIMATION METHODS; EXTENDED KALMAN FILTER; ENERGY ESTIMATION; STATE;

D O I：

10.1002/ese3.70218

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The reliability of parameter accuracy in lithium-ion battery models plays a crucial role in the efficiency of state-of-charge (SOC) estimation methods that employ model-based strategies. To address the limitations of traditional recursive least squares (RLS) algorithms in tracking dynamic battery characteristics, This paper introduces a bi-loop recursive least square (Bi-RLS) method with a variable forgetting factor (VFF) based on an enhanced equivalent circuit model (ECM). The Bi-RLS structure optimizes the intermediate iterative process to enhance the estimation accuracy and robustness. The VFF mechanism dynamically adjusts parameter weights to balance tracking accuracy and noise suppression. Experimental results demonstrate that the proposed method achieves reduction in voltage prediction error compared to conventional RLS and ECM-based approaches, validated under incremental OCV tests and dynamic stress test profiles. The framework provides a practical solution for high-precision battery modeling in real-world applications.

引用

页数：11

共 33 条

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