基于改进开路电压模型和自适应平方根无迹卡尔曼滤波的锂离子电池宽温度多工况SOC估计

doi:10.19595/j.cnki.1000-6753.tces.231889

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摘要锂离子电池荷电状态（SOC）的准确估计对于电池管理系统（BMS）的性能和可靠性至关重要。现有研究广泛关注电池模型、参数辨识及滤波算法的优化,对于电池开路电压（OCV）与SOC关系的准确映射研究较少,这限制了宽温度多工况下的SOC估计精度。该文创新性地提出一种针对宽温度的OCV-SOC映射修正策略,并构建基于支持向量机回归（SVR）的开路电压模型。该模型能准确地捕捉不同电池温度下的电池行为,提升SOC估计的稳定性、鲁棒性和初始误差修正能力。继而结合二阶RC等效电路模型、动态遗忘因子递归最小二乘法（DFFRLS）及自适应平方根无迹卡尔曼滤波（ASRUKF）实现SOC精确估计。在20%初始误差的条件下,所提方法在宽温度（包含极端温度）及多工况（包含复杂工况）测试中的方均根误差（RMSE）和平均绝对误差（MAE）均小于1.8%,该方法的适应性与准确性高,且算法复杂度未显著增加,为BMS中SOC的高精度估计提供了新的途径。

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关键词 ：锂离子电池, 荷电状态, 开路电压模型, 支持向量机回归, 卡尔曼滤波, 宽温度, 多工况

Abstract：Accurately estimating lithium-ion batteries’ state of charge (SOC) is critical. While existing research primarily focuses on optimizing battery models, parameter identification, and filtering algorithms, this paper introduces an open-circuit voltage (OCV) model. Firstly, an innovative OCV-SOC mapping correction strategy is designed for a wide temperature range. Additionally, an open-circuit voltage model based on support vector machine regression (SVR) is developed to accurately capture varying behaviors of batteries at different temperatures, thereby enhancing the stability, robustness, and initial error correction capability of SOC estimation. The impact of various Kalman filter algorithms and OCV models on SOC estimation across a wide temperature range and multiple working conditions is discussed. By integrating a second-order RC equivalent circuit model, dynamic forgetting factor recursive least squares (DFFRLS), and adaptive square root unscented Kalman filter (ASRUKF), the proposed approach achieves SOC-accurate estimation.
In the wide temperature range tests of the non-complex conditions, the SOC estimation based on ASRUKF achieves a maximum error, mean absolute error (MAE), and root mean square error (RMSE) of below 0.94%. In contrast, at 0℃ or lower temperatures, SOC estimations based on unscented Kalman filter (UKF) and square root unscented Kalman filter (SRUKF) reach an RMSE of 2.36%. Under complex conditions across a wide temperature range, all three filtering algorithms’ maximum RMSE and MAE exceed 3%. Although the estimation time of SVR lags by 0.3 seconds behind extreme gradient boosting (XGBoost) and polynomial regression (PR), indicating a more complex computational process, it remains within acceptable bounds for real-time SOC applications. With an initial deviation of 20%, the SOC estimation based on SVR upholds an RMSE and MAE below 1.8% across a wide temperature range and multiple working conditions, ensuring that errors are contained within 1.2% under the non-complex operating conditions and below 4.3% under the complex operating condition. At 0℃ or lower ambient temperatures, the accuracy of SOC estimation under the complex condition based on PR significantly deteriorates, with both RMSE and MAE exceeding 3%. The SOC estimation based on XGBoost completely diverges under complex condition across a wide temperature range.
Results: (1) UKF, SRUKF, and ASRUKF display the potential to adjust initial discrepancies in fluctuating temperatures and operational conditions. ASRUKF is better at showing the mitigating divergence towards the estimation process's end. (2) OCV models are unsuitable for state-space model-based methods in SOC estimation. Improving and establishing high-accuracy OCV models can effectively enhance the performance of state-space model-based methods. (3) The proposed method achieves real-time, high-accuracy SOC estimation across a wide temperature range and multiple working conditions without significantly increasing algorithmic complexity, exhibiting strong stability, robustness, and initial error correction capability.

Key words： Lithium-ion battery state of charge open-circuit voltage model support vector machine regression Kalman filter wide temperature multiple working condition

收稿日期: 2023-11-13

PACS:

TM91

基金资助:国家自然科学基金（52007106）和山东省自然科学基金（ZR2022ME020）资助项目

通讯作者: 董政男,1988年生,副教授,硕士生导师,研究方向为新能源发电与智能微电网、大功率电力电子装备及其先进控制等。E-mail: dongzheng@sdu.edu.cn

作者简介: 王新栋男,2000年生,硕士研究生,研究方向为锂离子电池状态估计等。E-mail: xindong.wang@mail.sdu.edu.cn

引用本文:

王新栋, 董政, 王书华, 荆峰, 邹兵. 基于改进开路电压模型和自适应平方根无迹卡尔曼滤波的锂离子电池宽温度多工况SOC估计[J]. 电工技术学报, 2024, 39(24): 7950-7964. Wang Xindong, Dong Zheng, Wang Shuhua, Jing Feng, Zou Bing. State-of-Charge Estimation for Lithium-Ion Batteries Across Wide Temperature Range and Multiple Working Conditions Based on Improved Open-Circuit Voltage Model and Adaptive Square Root Unscented Kalman Filter Algorithm. Transactions of China Electrotechnical Society, 2024, 39(24): 7950-7964.

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