A Battery Model with Adaptive Parameters Based on Equivalent Circuit for State of Charge Estimation

doi:10.7652/xjtuxb201510011

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A Battery Model with Adaptive Parameters Based on Equivalent Circuit for State of Charge Estimation

更新时间：2025-07-09

- A Battery Model with Adaptive Parameters Based on Equivalent Circuit for State of Charge Estimation
- Vol. 49, Issue 10, Pages: 67-71+78(2015)
- 作者机构：
  
  西安交通大学电动汽车与系统控制研究所,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201510011
  CLC： TM912.8
- Online First：10 October 2015，
  
  Published：2015
- 稿件说明：
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A Battery Model with Adaptive Parameters Based on Equivalent Circuit for State of Charge Estimation[J]. 2015, 49(10): 67-71+78.
DOI：

A Battery Model with Adaptive Parameters Based on Equivalent Circuit for State of Charge Estimation[J]. 2015, 49(10): 67-71+78. DOI： 10.7652/xjtuxb201510011.

摘要

针对电池离线参数辨识复杂、模型系统误差无法在线校正等问题

提出基于等效电路的参数自适应电池模型及电池荷电状态估计方法。该方法设计了针对动力电池的自适应参数观测器并证明了稳定性

通过在线估计电池参数从根源校正模型误差

建立滑动平均滤波器对估计参数滤波降噪

利用多时间维度思想周期性更新电池模型

并结合卡尔曼滤波算法进行荷电状态估计。搭建电池充放电测试平台进行实验

实验结果表明:城市道路循环工况下

基于参数自适应电池模型的卡尔曼滤波电池荷电状态估计误差小于3%。该算法简单、准确、适应性强

对于多变环境、长周期使用条件下的动力电池监测具有较高的实用价值。

Abstract

A battery model with adaptive parameters based on equivalent circuit is proposed to solve the problems that it is complex to identify the parameters of a battery model online and errors of the battery model will dramatically enlarge while the parameters of the battery model varies. An observer with adaptive parameters for batteries is designed and is proved to be stable. Parameters are estimated and filtered online by the observer and a moving average filter

respectively. The battery model is periodically updated by previously estimated parameters. Then

the extended Kalman filtering algorithm is adopted to estimate the state of charge(SOC)of the battery. An experimental platform is constructed

and the urban dynamometer driving schedule(UDDS)driving cycle is used to test the algorithm. The results show that the error of SOC estimation based on the proposed model and the dynamic Kalman filter is less than 3%. It can be concluded that the algorithm is accurate and has great value to monitor power batteries in changeful environment.

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references

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