AI-Driven Intelligent Energy Management System for Enhancing Electric Vehicle Efficiency and Range

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | 02 | Page :
By

Chetan Choudhary,

Shweta Singh,

  1. Research Scholar, Department of Electrical Engineering, Maharishi University of Information Technology, Lucknow, Uttar Pradesh, India
  2. Assistant Professor, Department of Electrical Engineering, Maharishi University of Information Technology, Lucknow, Uttar Pradesh, India

Abstract

Electric Vehicles (EVs) are crucial in mitigating the emission of greenhouse gases and facilitating sustainable transportation. Their performance is however limited by the capacity of the battery, unpredictable weather conditions and ineffective use of energy. The paper suggests an AI-based Intelligent Energy Management System (IEMS) to increase EV efficiency and driving range. The suggested system combines machine learning (ML), model predictive control (MPC), and real-time data analytics to optimize power distribution among EV subsystems. The architecture integrates the sensing, control and decision layers that include smart grid and vehicle-to-grid (V2G) features. Experiments with simulations show that AI-based energy management may be 10-20 percent more efficient than traditional rule-based systems. The paper emphasizes the use of smart control strategies in realizing the next-generation sustainable electric mobility. It also considers battery state of charge, driving behavior, traffic density, road gradient, ambient temperature, and regenerative braking to improve operational reliability and passenger comfort. The suggested approach lowers battery deterioration and charging expenses while facilitating adaptive decision-making under dynamic operating situations. Predictive optimization further improves the distribution of energy among auxiliary loads, thermal management, and propulsion, guaranteeing steady performance under a variety of operating conditions. These results show how AI-driven energy management could be used in intelligent transportation in the future.

Keywords: Electric Vehicles, Intelligent Energy Management System, Machine Learning, Model Predictive Control, Smart Grid, V2G, Energy Optimization

How to cite this article: Chetan Choudhary, Shweta Singh. AI-Driven Intelligent Energy Management System for Enhancing Electric Vehicle Efficiency and Range. International Journal of Electrical Power and Machine Systems. 2026; 04(02):-.
How to cite this URL: Chetan Choudhary, Shweta Singh. AI-Driven Intelligent Energy Management System for Enhancing Electric Vehicle Efficiency and Range. International Journal of Electrical Power and Machine Systems. 2026; 04(02):-. Available from: https://journals.stmjournals.com/ijepms/article=2026/view=253377

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Ahead of Print Subscription Review Article
Volume 04
02
Received 17/07/2026
Accepted 17/08/2026
Published 26/08/2026
Publication Time 40 Days

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