Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data

Zhicheng Xu, Jun Wang*, Peter D. Lund, Yaoming Zhang

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The accuracy of the state of health (SoH) estimation and prediction is of great importance to the operational effectiveness and safety of electric vehicles. Present approaches mostly employ data-driven analysis with laboratory measurements to determine these parameters. Here a novel method is proposed using discrete incremental capacity analysis based on real-life driving data, which enables to estimate the battery SoH without any prior detailed knowledge of battery internal specifics such as current capacity/resistance information. The method accounts for the battery characteristics. It is robust, highly compatible, and has a short computing time and low memory requirement. It's capable to evaluate the SoH of various type of electric vehicles under different charging strategies. The short computing time and low memory needed for the SoH estimation also demonstrates its potential for practical use. Moreover, the clustering analysis is presented, which provides SoH comparison information of certain EV to that of EVs belonging to same type.

Original languageEnglish
Article number120160
Number of pages14
JournalEnergy
Volume225
DOIs
Publication statusPublished - 15 Jun 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • Clustering analysis
  • Discrete incremental capacity analysis
  • Electric vehicles
  • State of health

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