Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

Ka Seng Chou, Kei Long Wong, Davide Aguiari, Rita Tse, Su Kit Tang, Giovanni Pau

Research output: Contribution to journalReview articlepeer-review

1 Citation (Scopus)

Abstract

In the foreseeable future, electric vehicles (EVs) will play a key role in the decarbonization of transport systems. Replacing vehicles powered by internal combustion engines (ICEs) with electric ones reduces the amount of carbon dioxide (CO2) being released into the atmosphere on a daily basis. The Achilles heel of electrical transportation lies in the car battery management system (BMS) that brings challenges to lithium-ion (Li-ion) battery optimization in finding the trade-off between driving and battery health in both the long- and short-term use. In order to optimize the state-of-health (SOH) of the EV battery, this study focuses on a review of the common Li-ion battery aging process and behavior detection methods. To implement the driving behavior approaches, a study of the public dataset produced by real-world EVs is also provided. This research clarifies the specific battery aging process and factors brought on by EVs. According to the battery aging factors, the unclear meaning of driving behavior is also clarified in an understandable manner. This work concludes by highlighting some challenges to be researched in the future to encourage the industry in this area.

Original languageEnglish
Article number5608
JournalApplied Sciences (Switzerland)
Volume13
Issue number9
DOIs
Publication statusPublished - May 2023

Keywords

  • NMC battery
  • OBDII
  • battery aging
  • driving behavior
  • electric vehicle
  • lithium-ion battery
  • machine learning

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