基于IFFRLS-IMMUKF的商用车磷酸铁锂电池SOC估算

doi:10.19799/j.cnki.2095-4239.2025.0194

储能科学与技术 ›› 2025, Vol. 14 ›› Issue (10): 3996-4008.doi: 10.19799/j.cnki.2095-4239.2025.0194

基于IFFRLS-IMMUKF的商用车磷酸铁锂电池SOC估算

吴华伟¹^,²(), 何成泽¹^,², 洪强¹^,²^,⁴, 周小高³, 李明金⁴, 顾亚娟⁵

^1.湖北文理学院，湖北隆中实验室，湖北襄阳 441053
^2.湖北文理学院，纯电动汽车动力系统设计与测试湖北省重点实验室，湖北襄阳 441053
^3.江西众一智慧科技有限公司，江西鹰潭 335000
^4.无锡明恒混合动力技术有限公司，江苏无锡 214177
^5.江苏省东台中等专业学校，江苏东台 224200

收稿日期:2025-03-03 修回日期:2025-03-25 出版日期:2025-10-28 发布日期:2025-10-20
通讯作者: 吴华伟 E-mail:whw_xy@163.com
作者简介:吴华伟（1979—），男，教授，主要从事车辆动力学与协同控制、地面载运设备服役状态智能监测及预警、新型载运工具动力系统设计及控制方面的研究工作，E-mail：whw_xy@163.com。
基金资助:
国家自然科学基金资助项目(52472405);湖北省自然科学基金(2024AFB219);湖北省自然科学基金创新发展联合基金项目(2024AFD042);湖北省自然科学基金创新发展联合基金项目(2024AFD045);襄阳市科技计划湖北隆中实验室专项

IFFRLS-IMMUKF-based estimation of the state of charge of lithium iron phosphate batteries for commercial vehicles

Huawei WU¹^,²(), Chengze HE¹^,², Qiang HONG¹^,²^,⁴, Xiaogao ZHOU³, Mingjin LI⁴, Yajuan GU⁵

^1.Hubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang 441053, Hubei, China
^2.Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, Hubei, China
^3.Jiangxi Zhongyi Wisdom Technology Co. , Ltd, Yingtan 335000, Jiangxi, China
^4.Wuxi Mingheng Hybrid Technology Co. , Ltd, Wuxi 214177, Jiangsu, China
^5.Dongtai Secondary Vocational School in Jiangsu Province, Dongtai 224200, Jiangsu, China

Received:2025-03-03 Revised:2025-03-25 Online:2025-10-28 Published:2025-10-20
Contact: Huawei WU E-mail:whw_xy@163.com

摘要/Abstract

摘要：

荷电状态(SOC)作为电动汽车剩余容量的表征参数，它的准确预估可以保障电动汽车的安全可靠性。针对复杂环境下电池SOC难以精确估算的问题，本工作基于动力电池特性构建了等效电路模型，并对电池模型状态方程进行了离散化的推演，在获得离散化状态方程的基础上，将金豺优化算法与遗忘因子递推最小二乘法(FFRLS)相结合提出了改进遗忘递推最小二乘法对电池模型进行了参数辨识。同时，联合交互式多模型无迹卡尔曼滤波(IMMUKF)算法对电池SOC进行估算，并在对常温和高温条件下的动态应力(DST)和联邦城市驾驶工况(FUDS)进行试验验证。结果表明，基于IFFRLS-IMMUKF的锂电池SOC估算方法，其平均绝对值误差在0.8%之内，对磷酸铁锂电池有较高的SOC估算精度。

关键词: 金豺优化算法, 遗忘因子递推最小二乘法, 交互式多模型无迹卡尔曼滤波, 荷电状态

Abstract:

State of charge (SOC) is a crucial parameter for characterizing the remaining capacities of electric vehicles (EVs). Accurate SOC estimation ensures EV safety and reliability. To facilitate accurate estimation of battery SOC in complex environments, the equivalent circuit model is constructed based on the characteristics of power batteries, and the equation of state (EOS) of the battery model is discretized. Further, to obtain the discretized EOS, the golden-jackal-optimization algorithm is combined with the forgetting factor recursive least square (FFRLS) algorithm to yield an improved FFRLS method for identifying the parameters of the battery model. Concurrently, the interacting multiple-model unscented Kalman filter (IMMUKF) algorithm is used to estimate the battery SOC, which is experimentally verified via dynamic stress tests (DST) and federal urban driving schedules (FUDS) at room and high temperatures. The experimental results indicate that the mean absolute error of the proposed improved IFFRLS-IMMUKF-based lithium-battery SOC-estimation method is within 0.8% and that the SOC-estimation accuracy for lithium iron phosphate batteries is high.

Key words: golden jackal optimization algorithm, forgetting factor recursive least square, interacting multiple model unscented Kalman filter, state of charge

中图分类号:

TM 912

吴华伟, 何成泽, 洪强, 周小高, 李明金, 顾亚娟. 基于IFFRLS-IMMUKF的商用车磷酸铁锂电池SOC估算[J]. 储能科学与技术, 2025, 14(10): 3996-4008.

Huawei WU, Chengze HE, Qiang HONG, Xiaogao ZHOU, Mingjin LI, Yajuan GU. IFFRLS-IMMUKF-based estimation of the state of charge of lithium iron phosphate batteries for commercial vehicles[J]. Energy Storage Science and Technology, 2025, 14(10): 3996-4008.

图/表 19

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FFRLS	MAE/mV	RMSE/mV	IFFRLS	MAE/mV	RMSE/mV
DST	0.48295	2.50754	DST	0.40793	1.77810
FUDS	0.58532	3.03267	FUDS	0.51131	2.45441

FFRLS	MAE/mV	RMSE/mV	IFFRLS	MAE/mV	RMSE/mV
DST	0.42090	2.00484	DST	0.36123	1.35677
FUDS	0.38668	1.90763	FUDS	0.28619	0.40209

FFRLS	最大误差/%	MAE/%	RMSE/%	IFFRLS	最大误差/%	MAE/%	RMSE/%
DST	4.326	1.470	1.658	DST	2.205	0.702	0.865
FUDS	5.008	1.472	1.722	FUDS	2.468	0.741	0.854

FFRLS	最大误差/%	MAE/%	RMSE/%	IFFRLS	最大误差/%	MAE/%	RMSE/%
DST	5.015	1.304	1.561	DST	2.170	0.573	0.634
FUDS	4.172	1.275	1.514	FUDS	2.435	0.506	0.628

基于IFFRLS-IMMUKF的商用车磷酸铁锂电池SOC估算

IFFRLS-IMMUKF-based estimation of the state of charge of lithium iron phosphate batteries for commercial vehicles

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