基于等效电路模型融合电化学原理的锂离子电池荷电状态估计  

Lithium-ion battery state of charge estimation based on equivalent circuit model

作　　者：李清波 张懋慧 罗英[1] 吕桃林[1] 解晶莹[1] LI Qingbo;ZHANG Maohui;LUO Ying;LYU Taolin;XIE Jingying(State Key Laboratory of Space Power Sources,Shanghai Institute of Space Power-Sources,Shanghai 200245,China)

机构地区：[1]空间电源全国重点实验室,上海空间电源研究所,上海200245

出　　处：《储能科学与技术》2024年第9期3072-3083,共12页Energy Storage Science and Technology

基　　金：国家重点研发计划项目(2022YFB3305400)。

摘　　要：准确高效地评估锂离子电池荷电状态(SOC)是确保电动汽车和储能设备性能和安全的关键。等效电路模型被认为是描述锂离子电池内部复杂反应过程的一种有效方法。针对基于等效电路模型的SOC估计准确性与复杂性难以权衡的问题,本研究采用一阶RC模型作为基础,为了提高整个SOC区间的模型性能表现,通过电化学原理对模型进行优化,通过在一阶RC模型的OCV模块上添加反映电池内部固相扩散过程的改进误差项,在保证较低的计算复杂性的前提下,减小了等效电路模型与更准确的机理模型之间存在的误差。然后基于倍率测试以及脉冲测试数据对电池进行参数辨识,以粒子群算法为基础通过参数解耦的方式降低了参数辨识的复杂度、提升了辨识准确度;同时基于小倍率测试的开路电压(OCV)数据采用多项式方法进行OCV-SOC曲线拟合。随后基于模型参数辨识结果开展SOC估计研究,针对常规卡尔曼滤波准确度不足的问题,在无迹卡尔曼滤波基础上结合加权滑动窗口的思想以提升SOC估计的精确性和鲁棒性,并基于UDDS和DST动态工况测试数据进行算法验证,最终估计效果相对于传统方法呈现出优异的精度与鲁棒性,并且可以在初始SOC有较大偏差时快速收敛至准确值。The accurate and efficient assessment of the state of charge(SOC)of lithium-ion batteries is critical to ensuring the satisfactory performance and safety of electric vehicles and energy storage devices.The equivalent circuit model is considered to be effective for describing complex reaction processes inside Li-ion batteries.To use the equivalent circuit model to address the difficult trade-off between accuracy and complexity in SOC estimation,we use the first-order RC model as the foundation of this study.In order to improve the performance of the model over the SOC interval,the RC model is optimized according to electrochemical principles.By adding an improved error term for the solid-phase diffusion process inside the reactive battery to the open-circuit voltage(OCV)module of the first-order RC model,we reduce the computational complexity.By adding a modified error term that reflects the solid-phase diffusion process inside the cell to the first-order RC model of the OCV module,we also reduce the error between the equivalent circuit model and the more accurate mechanism model while ensuring that the computational complexity remains low.Then,based on the multiplicity test and pulse test data,a particle swarm algorithm is used to reduce the complexity and improve the accuracy of parameter identification through parameter decoupling.At the same time,a polynomial method is used to fit the OCV-SOC curve based on the OCV data from a small-multiplication test.Subsequently,based on the model parameter identification results,SOC estimation research is carried out.To address the insufficient accuracy of conventional Kalman filtering,a weighted sliding window is used with traceless Kalman filtering to improve the accuracy and robustness of the SOC estimation,and the Kalman filtering algorithm is verified based on the UDDS and DST dynamic test data.The final estimation results show excellent accuracy and robustness,unlike the traditional method.The results quickly converge to the accurate value when the initial SOC has a

关 键 词：锂离子电池 融合模型 荷电状态估计 无迹卡尔曼滤波 

分 类 号：TM912[电气工程—电力电子与电力传动]