修正型PNGV构架的锂电池SOC预测及内阻识别  

Research of SOC Prediction and Internal Resistance Identification for Lithium Battery Based on Modified PNGV Framework

作　　者：黄锋[1] 问朋朋[1] 汪兴兴[2] 孙书刚 HUANG Feng;WEN Pengpeng;WANG Xingxing;SUN Shugang(School of Mechanical Engineering,Huzhou Vocational and Technological College,Huzhou 313000,Zhejiang,China;School of Mechanical Engineering,Nantong University,Nantong 226019,Jiangsu,China;Nantong Gaoxin Antiwear Technology Co.,Ltd.,Nantong 226011,Jiangsu,China)

机构地区：[1]湖州职业技术学院机电与汽车工程学院,浙江湖州313000 [2]南通大学机械工程学院,江苏南通226019 [3]南通高欣耐磨科技股份有限公司,江苏南通226011

出　　处：《实验室研究与探索》2020年第1期37-41,共5页Research and Exploration In Laboratory

基　　金：江苏省自然科学基金面上项目(2016246);江苏省重点研发计划项目(BE2016107)。

摘　　要：荷电状态(SOC)是电动汽车动力电池的核心性能指标。为了进一步提高锂离子电池组单体电池荷电状态预测精度,提出一种基于改进PNGV模型的电池内阻辨识与SOC预测。根据锂离子动力电池的特性分析,建立改进型PNGV模型。利用实验采集的数据和最小二乘算法实现内阻的在线识别。通过该内阻辨识算法,更加准确地反映电池的当前电压。根据预测更加准确的电压,从而提出基于数据融合PHM法预测电池的SOC,该方法基于实验数据和灰色预测模型来估算电池的荷电状态。仿真和实验结果表明,基于内阻辨识的SOC预测更准确,具有较强的工程实用性。SOC is the core performance index of electric vehicle power battery,in order to improve lithium ion battery monomer voltage acquisition accuracy and state of charge prediction accuracy further.According to the characteristic analysis of lithium-ion power battery,the real-time identification was achieved by the experimental collected data and recursive least squares algorithm based on the PNGV model.The voltage of the battery can be reflected accurately based on the internal resistance identification algorithm.Therefore,the SOC of battery prediction based on data fusion PHM method is proposed.The method is based on experimental data and grey prediction model to estimate the state of battery.Simulation and experiment show that the SOC prediction method based on internal resistance identification has higher accuracy,can meet the demand of practical application.

关 键 词：锂离子电池 内阻辨识 灰色预测 

分 类 号：TM912[电气工程—电力电子与电力传动]