Monitoring Electric Vehicles on The Go

Aguiari, Davide; Chou, Ka Seng; Tse, Rita; Pau, Giovanni

doi:10.1109/ccnc49033.2022.9700713

Cited by 11 publications

(2 citation statements)

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“…Using an off-the-shelf IoT reader on the interface is generally impossible. As a result, access to the OBD-II data requires a customized IoT device [65]. Studies have also revealed that in order to examine different driving styles, the battery data sampling range should be between 0.1 and 3 s [46].…”

Section: The Ev Datasetmentioning

confidence: 99%

“…Meanwhile, SOH analysis requires the fine-time granularity of the acquired data (e.g., 250 ms sample rate). Currently, internet of things (IoT) devices can perform data collection in various situations and on precise time scales [65]. However, many automotive electronic control units (ECUs) can only operate at a lower sample rate due to the heavy load on the automobile's ECU.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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 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.

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Section: The Ev Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%