Citation

Jayaram, Jayapradha and Chetan, J and Nayak, Barun (2024) Electric Vehicle Health Monitoring with Electric Vehicle Range Prediction and Route Planning. Journal of Informatics and Web Engineering, 3 (1). pp. 265-276. ISSN 2821-370X

Text 871-Article Text-6426-1-10-20240207.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

The automotive industry is experiencing a revolutionary wave due to the rapid spread of electric vehicles (EVs), which is paving the way for a fundamental and long-lasting revolution in the way we approach transportation. The global movement to reduce greenhouse gas emissions and lessen the environmental impact of traditional internal combustion engine vehicles has seen a significant boost in the popularity of electric vehicles as people come together to support environmentally conscious and sustainable mobility solutions. But the ecology surrounding electric vehicles must continue to flourish if the particular problems that EVs present are to be successfully addressed. Chief among these are the formidable foes of range anxiety and battery health management. Range anxiety is a real issue felt by many potential EV owners worry about becoming stuck because their battery has run out before reaching their destination. This psychological barrier is very noticeable and makes present and future EV owners doubtful. In addition, the longevity and health of EV batteries are essential to their continued effectiveness and affordability. The driving range and operating efficiency of the vehicle are directly affected by the gradual degradation of the battery due to several factors like aging, charging patterns, and temperature. This research presents an integrative and holistic approach to address these pressing issues, enhancing and elevating the whole EV ownership experience by combining Electric Vehicle Health Monitoring (EVHM) with Electric Vehicle Range Prediction (EVRP) and Route Planning (EVRP). Combining these three essential elements creates an all-encompassing plan created to not only lessen these enormous obstacles but also accelerate the switch to electric vehicles by giving consumers the knowledge and assurance they require for a smooth, eco-friendly, and sustainable mobility in the future.

Item Type:	Article
Uncontrolled Keywords:	A* algorithm, RNN, Long short term memory, Electric Vehicle (EV), Electric vehicle health monitoring (EVHM), Battery health
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL1-484 Motor vehicles. Cycles
Divisions:	Others
Depositing User:	Mr. MUHAMMAD AZRUL MOSRI
Date Deposited:	02 Apr 2024 07:38
Last Modified:	02 Apr 2024 07:38
URII:	http://shdl.mmu.edu.my/id/eprint/12256

Downloads

Edit (login required)