基于改进TOPSIS-模糊贝叶斯网络的电池SOC和SOH联合估计方法
摘要:为实现储能电池全生命周期下的电池状态动态评估，提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性，提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution，TOPSIS)-模糊贝叶斯网络的电池荷电状态(state of charge, SOC)和健康状态(state of health, SOH)联合估计方法。应用多阶电阻-电容电路(resistor-capacitance circuit，RC)模型、使用节点-支路框架构建电池的等效电路模型，通过基尔霍夫定律与欧姆定律对二阶RC电池等效电路模型中的并联回路进行电气特性分析，构建空间状态方程及等效输出方程；对构建的状态方程进行离散化处理，分别定义并联独立回路离散化零输入响应、零状态响应，分析离散化电池模型状态空间方程；将专家打分法引入TOPSIS算法中进行电池SOC量化估计，结合融入模糊尺度的贝叶斯网络，在相同时间分布尺度下通过电池SOH值计算电池观测样本中对应的SOC值，实现电池SOH与SOC联合估计。实验结果表明：所提方法可有效估计不同离散空间尺度下的电池SOC和SOH结果，估计方法具有良好的准确性与较高的精度。
Abstract：In order to realize the dynamic assessment of battery state under the whole life cycle of energy storage batteries, and to improve the adaptability of the lithium-ion battery model and the accuracy of state estimation under complex working conditions, a joint estimation method of battery state of charge (SOC) and state of health (SOH) based on the improved technique for order preference by similarity to an ideal solution (TOPSIS)-fuzzy Bayesian network is proposed. The equivalent circuit model of the battery is constructed by applying the multi-order resistor-capacitance circuit (RC) model and the node-branching framework, and the parallel loop in the equivalent circuit model of the second-order RC battery is characterized by Kirchhoff's law and Ohm's law to construct the spatial equations of state and the equivalent output equations. The constructed equations of state are discretized, and the discretized state-space equation of the battery model is analyzed by defining the discretized zero-input response and zero-state response of the parallel independent loop. The expert scoring method is introduced into the TOPSIS algorithm for the quantitative estimation of battery SOC, and combined with the Bayesian network that integrates into the fuzzy scale, the corresponding SOC values in the observed samples of the batteries are calculated from the battery SOH values under the same time distribution scale, so as to realize the joint estimation of battery SOH and SOC. The experimental results show that the proposed method can effectively estimate the results of battery SOC and SOH in different discrete spatial scales, and the estimation method has good accuracy and high precision.
标题：基于改进TOPSIS-模糊贝叶斯网络的电池SOC和SOH联合估计方法
title:Joint Estimation of Battery SOC and SOH Based on Improved TOPSIS-Fuzzy Bayesian Network
作者：雷咸道,李杰,张二信
authors:LEI Xiandao,LI Jie,ZHANG Erxin
关键词：电池荷电状态(SOC),电池健康状态(SOH),逼近理想解排序法(TOPSIS),模糊贝叶斯网络,联合估计,
keywords:battery state of charge (SOC),battery state of health (SOH),technique for order preference by similarity to an ideal solution (TOPSIS),fuzzy Bayesian network,joint estimation,
发表日期：2024-11-26
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