Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Electric Vehicles and Infrastructure · Advancements in Battery Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China
Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy
Autonomous Robotics Research Center, Technology Innovation Institute (TII), Abu Dhabi P.O. Box 9639, United Arab Emirates
Samueli Computer Science Department, University of California, Los Angeles, CA 90095, USA
References (99)
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