Real-Time State Estimation of Lithium Batteries Used for Energy Storage in Electric Vehicle Charging Stations with Wind-Solar Complementary Power System

GE Bin; LUO Yang; LI Jiawei; LI Lixia; WANG Bin; YAN Yizhe

doi:10.7652/xjtuxb202301006

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Real-Time State Estimation of Lithium Batteries Used for Energy Storage in Electric Vehicle Charging Stations with Wind-Solar Complementary Power System

更新时间：2025-07-09

- Real-Time State Estimation of Lithium Batteries Used for Energy Storage in Electric Vehicle Charging Stations with Wind-Solar Complementary Power System
- Vol. 57, Issue 1, Pages: 55-65(2023)
- 作者机构：
  
  1. 国网冀北电力有限公司张家口市崇礼区供电分公司,河北,张家口,076350
  2. 西安建筑科技大学机电工程学院,西安,710055
  3. 西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202301006
  CLC： TM912
- Online First：10 January 2023，
  
  Published：2023
- 稿件说明：
移动端阅览
GE Bin, LUO Yang, LI Jiawei, et al. Real-Time State Estimation of Lithium Batteries Used for Energy Storage in Electric Vehicle Charging Stations with Wind-Solar Complementary Power System[J]. 2023, 57(1): 55-65.
DOI：

GE Bin, LUO Yang, LI Jiawei, et al. Real-Time State Estimation of Lithium Batteries Used for Energy Storage in Electric Vehicle Charging Stations with Wind-Solar Complementary Power System[J]. 2023, 57(1): 55-65. DOI： 10.7652/xjtuxb202301006.

摘要

为了解决风光互补式电动汽车充电站储能系统中

传统静态锂电池模型不能实时更新参数导致相应的开路电压和荷电状态(state of charge

SOC)估计误差大等问题

提出一种基于动态一阶RC等效电路模型的锂电池自适应实时状态估计方法。首先

采用滑模控制方法追踪锂电池的实时输出电压

基于动态一阶RC等效电路模型

考虑锂电池内部参数欧姆内阻、极化内阻、极化电容和开路电压的动态变化情况

修正锂电池的端电压状态估计方程; 然后

通过李雅普诺夫函数和稳定性判据推导出状态估计方程参数与实时电压追踪误差、工作电流之间的关系

得出锂电池内部参数的实时更新方法; 进一步

通过实验确定开路电压与锂电池SOC之间的函数关系; 在此基础上

实现锂电池状态的自适应实时估计。仿真结果表明:在风光互补式电动汽车充电站储能系统的连续变化负载工况下

所提自适应实时状态估计方法可以使锂电池估计状态快速收敛至模型参考值

避免了开路电压估计值波动问题; 以安时积分和卡尔曼滤波方法修正的SOC为参考

自适应实时估计SOC的最大误差为0.72%

均方根误差和平均绝对误差分别为0.002 3和0.001 9; 与开路电压-内阻模型估计SOC进行比较

自适应实时估计SOC的精度提高了一个数量级。

Abstract

As for the application of an energy storage system in an electric vehicle charging station with a wind-solar complementary power system

this paper proposes an adaptive real-time state estimation method for the lithium battery based on a dynamic first-order RC equivalent circuit model

so as to solve the problem of big error in estimating the corresponding open-circuit voltage and the state of charge(SOC)due to the incapability of the traditional static lithium battery model to update the parameter states in real-time. First

the sliding mode control method is used for tracking the real-time output voltage of the lithium battery and based on the dynamic first-order RC equivalent circuit model

the terminal voltage state estimation equation of the lithium battery is modified by considering the dynamic variation of such internal parameters as ohmic resistance

polarized resistance

polarized capacitance and open circuit voltage. Then

the relationship among the state estimation equation parameters

the real-time voltage error

and the operating current is derived based on the Lyapunov function and stability criterion

and the real-time update method for the internal parameters of the lithium battery is obtained. Next

the functional relationship between the open-circuit voltage and SOC of the lithium battery is established based on the experimental results. On this basis

the adaptive real-time state estimation of the lithium battery is realized. Simulation results show that the adaptive real-time state estimation method can make the estimated states of the lithium battery converge to the model reference values quickly under the continuous load conditions of the energy storage system in an electric vehicle charging station with a wind-solar complementary power system. At the same time

the adaptive real-time state estimation method can effectively avoid the fluctuation of the estimated open-circuit voltage. By using the corrected SOC based on ampere-hour integration and Kalman filter methods as a reference

the maximum error of the adaptive real-time SOC estimation is 0.72%

and the root mean square error and the average absolute error are 0.0023 and 0.0019

respectively. Compared with the estimated SOC based on the open circuit voltage-internal resistance model

the estimated SOC accuracy based on the adaptive real-time estimation is improved by an order of magnitude.

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