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Performance Improvements of Electric Vehicles Using Edge Computing and Machine Learning Technologies
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Author(s): Leena Raviprolu (Department of Computer Science, GITAM University (Deemed), Visakhapatnam, India), Nagamani Molakatala (School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India), Rajesh V. Argiddi (Department of Computer Science and Engineering, Walchand Institute of Technology, Solapur, India), Shikalgar Niyaj Dilavar (Department of Automation and Robotics, Dr. D.Y. Patil Institute of Technology, Pune, India)and P. Srinivasan (Department of Chemistry, Kongu Engineering College, Erode, India)
Copyright: 2024
Pages: 34
Source title:
Solving Fundamental Challenges of Electric Vehicles
Source Author(s)/Editor(s): Mazhar Hussain Shaik (Middle East College, Oman)
DOI: 10.4018/979-8-3693-4314-2.ch010
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Abstract
Edge computing and machine learning technologies have significantly improved electric vehicle (EV) performance, enhancing efficiency, reliability, and user experience by processing data closer to the vehicle, reducing latency, and conserving bandwidth. In this chapter, machine learning algorithms in EV edge infrastructure analysis data have been used for predictive analytics and optimization, predicting battery life, optimizing energy consumption, identifying potential failures, enhancing vehicle reliability, and reducing downtime. This chapter also illustrates battery management systems (BMS) using advanced machine learning techniques to monitor health, predict degradation, optimize charging cycles, and enable real-time decision-making for autonomous driving, enhancing safety and preventing overcharging. The practical challenges of integrating edge computing and ML in electric vehicles (EVs), highlighting data privacy, security, and infrastructure requirements, are also elaborated to improve performance.
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