NDLI: Review of the Methods to Optimize Power Flow in Electric Vehicle Powertrains for Efficiency and Driving Performance

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Review of the Methods to Optimize Power Flow in Electric Vehicle Powertrains for Efficiency and Driving Performance

Content Provider	MDPI
Author	Mazali, Izhari Izmi Daud, Zul Hilmi Che Hamid, Mohd Kameil Abdul Tan, Victor Samin, Pakharuddin Mohd Jubair, Abdullah Ibrahim, Khairul Amilin Kob, Mohd Salman Che Xinrui, Wang Talib, Mat Hussin Ab
Copyright Year	2022
Description	Electric vehicles (EV) are quickly gaining a foothold in global markets due to their zero tailpipe emissions and increasing practicality in terms of battery technologies. However, even though EV powertrains emit zero emissions during driving, their efficiency has not been fully optimized, particularly due the commonly used single-speed transmission. Hence, this paper provides an extensive review on the latest works carried out to optimize the power flow in EV powertrains using multispeed discrete transmission, continuously variable transmission and multi-motor configurations. The relevant literatures were shortlisted using a keyword search related to EV powertrain in the ScienceDirect and Scopus databases. The review focused on the related literatures published from 2018 onwards. The publications were reviewed in terms of the methodologies applied to optimize the powertrain for efficiency and driving performance. Next, the significant findings from these literatures were discussed and compared. Finally, based on the review, several future key research areas in EV powertrain efficiency and performance are highlighted.
Starting Page	1735
e-ISSN	20763417
DOI	10.3390/app12031735
Journal	Applied Sciences
Issue Number	3
Volume Number	12
Language	English
Publisher	MDPI
Publisher Date	2022-02-08
Access Restriction	Open
Subject Keyword	Applied Sciences Transportation Science and Technology Electric Vehicle Powertrain Multispeed Discrete Transmission Continuously Variable Transmission Two-motors Configuration Four-motors Configuration
Content Type	Text
Resource Type	Article

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