VCM模型下的IBAS-EKF锂电池荷电状态估计

寇发荣; 王思俊; 王甜甜; 洪锋; 杨慧杰

doi:10.13433/j.cnki.1003-8728.20200287

VCM模型下的IBAS-EKF锂电池荷电状态估计

doi: 10.13433/j.cnki.1003-8728.20200287

西安科技大学机械工程学院, 西安 710054

基金项目:

国家自然科学基金项目 51775426

陕西省重点研发计划项目 2020GY-128

西安市科技计划项目 21XJZZ0039

详细信息

作者简介:
寇发荣(1973-), 教授, 博士, 研究方向为车辆动力学及电池管理系统, koufarong@xust.edu.cn

中图分类号: TM912
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- 文章访问数: 140
- HTML全文浏览量: 26
- PDF下载量: 18
- 被引次数: 0
出版历程
- 收稿日期: 2020-06-03
- 刊出日期: 2021-12-05

IBAS-EKF Estimation of Lithium Battery State of Charge under VCM

College of Mechanical Engineering, Xi′an University of Science and Technology, Xi′an 710054, China

摘要

摘要: 为解决锂电池荷电状态(SoC)难以精确估计的问题，提出了极化电压修正模型(VCM)和改进天牛须优化扩展Kalman滤波算法(IBAS-EKF)共同实现电池SoC的精确估计。在建立3阶RC电池模型和参数辨识的基础上，使用Elman循环神经网络对模型极化电压实现在线修正和优化，形成VCM模型；采用改进天牛须搜索算法优化扩展Kalman滤波算法的系统噪声协方差矩阵和量测噪声协方差矩阵，形成IBAS-EKF锂电池SoC估计算法。在测试平台上进行城市道路循环工况试验，结果表明：基于VCM模型的IBAS-EKF锂电池SoC估计算法的各项误差指标均低于传统的SoC估计算法，估计误差在0.6%以内，效果满足实际工程要求。
- SoC估计 /
- 天牛须搜索算法 /
- 扩展Kalman滤波 /
- Elman神经网络
Abstract: To solve the problem that lithium battery state of charge (SoC) is difficult to be estimated, put forward the polarization voltage correction model (VCM) and improved beetle antennae search optimized extended Kalman filtering algorithm (IBAS-EKF) can realize battery SoC accurate estimates. Based on the third-order RC battery model and parameter identification, Elman recurrent neural network is used to realize on-line correction and optimization of model polarization voltage to form VCM model. The system noise covariance matrix and measurement noise covariance matrix of the improved beetle antennae search optimized extended Kalman filtering algorithm is used to form the IBAS-EKF lithium battery SoC estimation algorithm. Working urban dynamometer driving schedule test on the test platform, the results show that each error index of IBAS-EKF lithium battery SoC estimation algorithm based on VCM model is lower than the traditional SoC estimation algorithm, and the estimation error is within 0.6 %, which meets the requirements of practical engineering.
- state of charge estimation /
- beetle antennae search /
- extended Kalman filtering /
- Elman neural network

HTML全文

图 1 3阶RC等效电路模型

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图 2 基于Elman神经网络的极化电压修正流程

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图 3 IBAS优化EKF噪声协方差算法

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图 4 改进天牛须搜索算法最优适应度迭代曲线

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图 5 ITECH动力电池测试系统

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图 6 常规电性试验步骤

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图 7 联合多模型OCV-SoC曲线

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图 8 混合动力脉冲测试电压、电流

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图 9 分段三次Hermite法插值3阶RC模型曲线

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图 10 美国城市道路循环工况

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图 11 极化电压修正模型、3阶RC模型端电压与试验电压对比曲线及其误差

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图 12 UDDS工况下各模型负载端电压误差统计分析

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图 13 IBAS-EKF算法、传统EKF算法估计SoC值与SoC试验值对比曲线及误差

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图 14 UDDS工况下锂电池SoC估计误差统计分析

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