首页|Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estima-tion using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capac-ity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incre-mental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to deter-mine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neu-ral network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is val-idated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demon-strate that the proposed method exhibits better estimation performance with an error of less than 1.1％compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.

Electric vehicleLithium-ion battery packCapacity estimationMachine learningField data

Qingguang Qi、Wenxue Liu、Zhongwei Deng、Jinwen Li、Ziyou Song、Xiaosong Hu

展开 >

College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,China

School of Mechanical and Electrical Engineering,University of Electronic Science and Technology of China,Chengdu 611731,Sichuan,China

Department of Mechanical Engineering,National University of Singapore,Singapore 117575,Singapore

National Key Research and Development Program of ChinaProject of basic research funds for central universitiesTalent Plan Project of ChongqingNational Natural Science Foundation of China

2022YFB33054032022CDJDX-006cstc2021ycjhbgzxm029552111530194

2024

10.1016/j.jechem.2024.01.047

能源化学
中国科学院大连化学物理研究所 中国科学院成都有机化学研究所

能源化学

CSTPCDEI
影响因子：0.654
ISSN：2095-4956
年,卷(期)：2024.92(5)