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An improved adaptive weights correction-particle swarm optimization-unscented particle filter method for high-precision online state of charge estimation of lithium-ion batteries

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Abstract

In the battery management system (BMS), the state of charge (SOC) of lithium-ion batteries is an indispensable part, and the accuracy of SOC estimation has attracted wide attention. Accurate SOC estimation can improve the efficiency of battery use while ensuring battery safety and improving battery life. Taking ternary lithium battery as the research object, this paper proposes a parameter identification method using adaptive forgetting factor recursive least squares and an improved joint unscented particle filter algorithm to estimate SOC. Firstly, an adaptive method is used to select the appropriate forgetting factor value to improve the accuracy of the forgetting factor recursive least squares (FFRLS) method. Meanwhile, the improved particle swarm (IPSO) optimization algorithm that incorporates variable weights and shrinkage factors is utilized to make the best choice of the noise for the unscented Kalman filter (UKF) algorithm in order to improve the estimation accuracy of the UKF algorithm. At the same time, the UKF algorithm is used as the suggestion density function of the particle filter (PF) algorithm to form the unscented particle filter (UPF) algorithm. In this paper, the AFFRLS algorithm and IPSO-SDUPF algorithm are combined to estimate the SOC of Li-ion batteries in real time. Experimental results under different working conditions show that the proposed algorithm has good convergence and high stability for SOC estimation of lithium-ion batteries. The maximum estimation errors of this algorithm are 1.137% and 0.797% for BBDST and DST conditions at 25 °C, and 1.015% and 1.029% for BBDST and DST conditions at 35 °C, which are lower than those of the commonly used algorithms of EKF, SDUKF, IPSO-SDUKF, and SDUPF, and provide a reference for future. The maximum estimation errors are lower than those of the commonly used EKF, SDUKF, IPSO-SDUKF, and SDUPF algorithms, which provide a reference for the future high-precision SOC estimation of Li-ion batteries.

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Funding

The work was supported by the National Natural Science Foundation of China (Nos. 62173281 and 61801407).

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Authors and Affiliations

  1. School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China

    Zehao Li, Shunli Wang, Chunmei Yu, Chuangshi Qi & Xianfeng Shen

  2. School of Electrical Engineering, Sichuan University, Chengdu, 610065, China

    Shunli Wang

  3. School of Pharmacy and Life Sciences, Robert Gordon University, AB10-7GJ, Aberdeen, UK

    Carlos Fernandez

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  1. Zehao Li
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  2. Shunli Wang
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  3. Chunmei Yu
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Contributions

Li was responsible for the article conceptualization, methodology, software, research, formal analysis, and writing-original draft. Wang was responsible for the article’s conceptualization, methodology, resources, and theoretical guidance. Yu was responsible for the linguistic presentation of the article as well as the theoretical guidance. Qi was involved in reviewing the data, revising Figs. 1–4, and adjusting the article presentation. Shen was involved in the validation and collection of experimental data. Fernandez was responsible for the theoretical review, statement checking, and article editing. All authors reviewed the manuscript.

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Correspondence to Shunli Wang.

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Li, Z., Wang, S., Yu, C. et al. An improved adaptive weights correction-particle swarm optimization-unscented particle filter method for high-precision online state of charge estimation of lithium-ion batteries. Ionics 30, 311–334 (2024). https://doi.org/10.1007/s11581-023-05272-9

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