电动汽车锂离子电池模型参数辨识和荷电状态估算

doi:10.16180/j.cnki.issn1007-7820.2020.02.006

摘要/Abstract

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。

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

Abstract:

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%.

Key words: electric vehicle, lithium-ion battery, model parameter identification, MAFF-RLS, SOC, EKF

中图分类号:

TN711.1

蒋芹,张轩雄. 电动汽车锂离子电池模型参数辨识和荷电状态估算[J]. 电子科技, 2020, 33(2): 32-36.

JIANG Qin,ZHANG Xuanxiong. Parameter Identification and State of Charge Estimation of Lithium Ion Battery Model for Electric Vehicles[J]. Electronic Science and Technology, 2020, 33(2): 32-36.

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