一种最优自适应增益非线性观测器的锂电池SOC估算方法  

A State of Charge Estimation Method of Lithium-ion Battery Based on Optimal Adaptive Gain Nonlinear Observer

作　　者：鲍时全 李正明[1] BAO Shi-quan;LI Zheng-ming(School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212000,China)

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212000

出　　处：《软件导刊》2020年第11期60-65,共6页Software Guide

基　　金：国家重点研发计划项目(2017YFB0103200)。

摘　　要：如今电动汽车的发展十分迅速,其动力电池荷电状态SOC关系到锂电池及整车系统的安全、可靠运行,因为SOC表明了电池剩余电量。由于SOC是一个不可直接测量的非线性变量,因此设计一种精度高、可行性强的算法具有十分重要的意义。提出一种最优自适应增益非线性观测器(OAGNO),用差分进化算法(DE)对观测器参数进行寻优。为了验证该方法的先进性,对型号为NCR18650GA的三元锂电池进行工况实验,结果表明,相比无迹卡尔曼滤波(UKF),最优自适应非线性状态观测器具有更高的精度,误差在3%左右。Electric vehicles are developing rapidly nowadays,and the state of charge of power battery is related to the safe and reliable operation of lithium battery and whole vehicle system,because the SOC indicates the remaining power of the battery.Since SOC is a non-linear variable which can not be measured directly,it is very important to design an algorithm with high precision and strong feasi⁃bility.In this paper,an optimal adaptive gain nonlinear observer for SOC estimation of lithium-ion batteries is proposed,parameters of observer is selected by using a differential evolution algorithm.In order to verify the progressiveness of this method,the operating con⁃ditions of a ternary lithium-ion battery with model NCR18650GA are tested.The results show that the proposed optimal adaptive non⁃linear observer has higher accuracy than unscented Kalman filter,and the SOC estimation error is about 3 percent.

关 键 词：电动汽车 荷电状态 无迹卡尔曼滤波 最优自适应非线性观测器 差分进化算法 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]