电工技术学报  2020, Vol. 35 Issue (23): 4980-4987    DOI: 10.19595/j.cnki.1000-6753.tces.191598
电力系统 |
温度和老化意识融合驱动的电动车辆锂离子动力电池电量和容量协同估计
王榘1, 熊瑞1, 穆浩1,2
1. 电动车辆国家工程实验室(北京理工大学) 北京 100081;
2. 北京空间飞行器总体设计部 北京 100094
Co-Estimation of Lithium-Ion Battery State-of-Charge and Capacity Through the Temperature and Aging Awareness Model for Electric Vehicles
Wang Ju1, Xiong Rui1, Mu Hao1,2
1. National Engineering Laboratory for Electric Vehicles Beijing Institute of Technology Beijing 100081 China;
2. Beijing Institute of Spacecraft System Engineering Beijing 100094 China
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摘要 作为电动车辆的技术瓶颈,动力电池具有强时变非线性特性且可测量有限,使用时易受温度和老化的影响,全寿命周期和宽温度下精确状态估计一直是行业的技术难题。为此,该文首先使用不同温度和不同老化阶段的数据,建立了具有温度和老化意识的多阶段模型;然后利用概率密度函数计算单一模型的权值,提出了多阶段模型融合驱动的动力电池荷电状态(SOC)和容量协同估计方法;最后考虑不确定老化和温度因素的验证结果表明,提出的方法具有较高的SOC和容量估计精度,且对初值误差不敏感,-10%~50%初始误差时SOC估计误差小于2%,收敛速度快。
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关键词 动力电池全寿命周期协同估计荷电状态容量估计    
Abstract:As the technical bottleneck of electric vehicles, batteries have strong time-varying nonlinear characteristics and limited measurability. They are easily affected by temperature and aging during use. Accurate state estimation under the full life cycle and the wide temperature has always been a technical problem in the industry. Therefore, this paper first uses the data of different temperatures and different aging stages to establish a multi-stage model with temperature and aging awareness; then uses the probability density function to calculate the weight of the single models and proposes a multi-stage model fusion-driven battery state of charge (SOC) and capacity estimation method. Finally, the verification results considering uncertainty of aging and temperature factors show that the proposed method has high SOC and capacity estimation accuracy and is not sensitive to the initial error. The SOC estimation error is less than 2% with the -10% to 50% of the initial SOC errors, and the convergence is fast.
Key wordsPower lithium-ion battery    full life    joint estimation    state of charge    capacity estimation   
收稿日期: 2019-11-22     
PACS: TM912.9  
基金资助:国家自然科学基金资助项目(51707011, 51877009)
通讯作者: 熊 瑞 男,1985年生,教授,博士生导师,IET Fellow,研究方向为电动载运装备动力系统、动力电池系统和人工智能。E-mail:rxiong@bit.edu.cn   
作者简介: 王 榘 男,1991年生,博士研究生,研究方向为新能源汽车动力电池系统管理。E-mail:wang_ju@bit.edu.cn
引用本文:   
王榘, 熊瑞, 穆浩. 温度和老化意识融合驱动的电动车辆锂离子动力电池电量和容量协同估计[J]. 电工技术学报, 2020, 35(23): 4980-4987. Wang Ju, Xiong Rui, Mu Hao. Co-Estimation of Lithium-Ion Battery State-of-Charge and Capacity Through the Temperature and Aging Awareness Model for Electric Vehicles. Transactions of China Electrotechnical Society, 2020, 35(23): 4980-4987.
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