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电动汽车锂离子电池模型参数辨识和荷电状态估算 被引量:5

Parameter Identification and State of Charge Estimation of Lithium Ion Battery Model for Electric Vehicles
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摘要 针对电动汽车锂离子电池荷电状态在线估算准确率低、实时性差等问题,文中建立一种精确在线估算荷电状态的有效方法,采用MAFF-RLS和EKF对荷电状态进行估算。建立锂离子电池的等效电路模型,将MAFF-RLS应用在电池等效电路模型的参数辨识上,可以有效在线辨识模型参数。在模型参数辨识的基础上,将辨识出的模型参数作为荷电状态估算的输入,采用EKF估算动力电池实时荷电状态。经过实验仿真发现,采用MAFF-RLS和EKF联合估算荷电状态能够提高估算精确度,估算误差仅在2%以内。 For the problems of low accuracy and poor real-time performance in on-line estimation of lithium-ion battery charging state for electric vehicles,an effective method for accurate on-line estimation of charging state was established.The method of MAFF-RLS and EKF was used to estimate the state of charge for lithium-ion battery.The equivalent circuit model of lithium-ion battery was established,and the MAFF-RLS was applied to the parameter identification for the equivalent circuit model,which could effectively identify model parameters online.Based on the model parameter identification,the identified model parameters are used as the input of the state of charge estimation,and the EKF was used to estimate the real-time state of charge of the power battery.The experimental simulation showed that the combination of MAFF-RLS and EKF could improve the estimation accuracy of the state of charge,and the estimation error was within 2%.
作者 蒋芹 张轩雄 JIANG Qin;ZHANG Xuanxiong(School of Optical and Computer Engineering,University of Shanghai Science and Technology,Shanghai 200093,China)
出处 《电子科技》 2020年第2期32-36,共5页 Electronic Science and Technology
基金 国家自然科学基金(61274105)~~
关键词 电动汽车 锂离子电池 参数辨识 MAFF-RLS SOC估算 扩展卡尔曼 electric vehicle lithium-ion battery model parameter identification MAFF-RLS SOC EKF
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