一种用于高保真锂电池SOC估计的无迹粒子滤波新方法

doi:10.19799/j.cnki.2095-4239.2020.0357

摘要/Abstract

摘要：

动力锂电池作为新能源汽车核心“三电”系统之一，其准确的电池建模与状态预估能确保电池管理系统安全启动及稳定运转。以三元锂电池为研究对象，构建Thevenin等效电路模型，在传统粒子滤波(PF)基础上，选择合适的建议密度函数，提出了一种采用均值、方差更精密计算的无迹粒子滤波算法(UPF)，解决粒子贫化问题，探获锂电池荷电状态(SOC)。该方法通过对理论分析的进一步完善，结合不同工况实验对锂电池工作特性研究，结果表明UPF估算锂电池SOC时，系统鲁棒性提高、跟随效果较好，且估算误差稳定控制在1.5%以内，为动力电池带来了很好的实用价值。

关键词: 三元锂电池, Thevenin模型, 荷电状态, 建议密度函数, 无迹粒子滤波算法

Abstract:

Power lithium-ion battery is one of the core "three-power" systems of new energy vehicles. Its accurate battery modeling and state prediction can ensure the safe start and stable operation of battery management systems. With a ternary lithium-ion battery as the research object, the Thevenin equivalent circuit model is constructed. On the basis of the traditional particle filter (PF), the appropriate recommended density function is selected, and an unscented PF (UPF) with a more precise calculation of the mean and variance is proposed to solve particle depletion and detect the state of charge (SOC) of lithium-ion batteries. The method further improves the theoretical analysis and studies the working characteristics of lithium-ion batteries in combination with experiments under different conditions. The results show that when UPF predicts the lithium-ion battery SOC, the system robustness is improved, the follow-up effect is better, and the prediction error is stably controlled within 1.5%, which brings good practical value to the power battery.

Key words: ternary lithium-ion battery, Thevenin model, state of charge, suggested density function, unscented particle filter algorithm

中图分类号:

谢滟馨, 王顺利, 史卫豪, 熊鑫, 陈先培. 一种用于高保真锂电池SOC估计的无迹粒子滤波新方法[J]. 储能科学与技术, 2021, 10(2): 722-731.

Yanxin XIE, Shunli WANG, Weihao SHI, Xin XIONG, Xianpei CHEN. A new method of unscented particle filter for high-fidelity lithium-ion battery SOC estimation[J]. Energy Storage Science and Technology, 2021, 10(2): 722-731.

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SOC/%	U_OCV/V	R₀/Ω	R_P/Ω	C_P/F
100	4.1877	0.00124	0.000385571	21503.14931
90	4.0535	0.00125	0.000414714	19570.0999
80	3.9363	0.00125	0.000458429	18286.38205
70	3.8268	0.00124	0.000463714	18328.09612
60	3.7406	0.00124	0.000454286	18303.45912
50	3.6504	0.00125	0.000321	25476.63551
40	3.6151	0.00127	0.000310857	24483.91544
30	3.5863	0.0013	0.000335571	23470.41294
20	3.5270	0.00134	0.000405286	20775.46704
10	3.4486	0.00142	0.000648714	11952.87382

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