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。
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.
作者
戈斌
罗阳
李家玮
李丽霞
王斌
严亦哲
GE Bin;LUO Yang;LI Jiawei;LI Lixia;WANG Bin;YAN Yizhe(Zhangjiakou Chongli District Power Supply Company,State Grid Jibei Electric Power Co.,Ltd.,Zhangjiakou,Hebei 076350,China;School of Mechanical and Electrical Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China;School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an 710049,China)
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2023年第1期55-65,共11页
Journal of Xi'an Jiaotong University
基金
国家电网有限公司科技资助项目(SGJBZJ00FZJS2250089)
国家自然科学基金资助项目(51907160)。
关键词
风光互补
电动汽车充电站
锂电池
荷电状态
状态估计
等效电路模型
wind-solar complementary
electric vehicle charging stations
Lithium batteries
state of charge
state estimation
equivalent circuit model
