Research on the Role of Artificial Intelligence in Enhancing the Energy Efficiency, Battery Performance, and Driving Range of Electric Vehicles

Gonghe She

doi:10.54254/2755-2721/2026.LD38300

Abstract

Better energy efficiency is becoming increasingly important as electric vehicles are used more widely in everyday transportation. Artificial intelligence (AI) has become an important technology for improving the energy efficiency of electric vehicles (EVs). This study examines the application of AI in battery management systems (BMS), energy management systems (EMS), thermal management, and intelligent driving strategies. Collectively, these technologies improve battery performance, optimize energy utilization, and extend driving range. Analysis shows that AI can provide a multifaceted approach to improving EV energy efficiency by enabling more accurate battery prediction, dynamic energy allocation, and adaptive thermal control. These algorithmic methods can reduce unnecessary energy consumption while maintaining vehicle performance and battery safety. Prognostic AI models can also help predict battery degradation and support more effective maintenance decisions. Better energy efficiency is becoming increasingly important as electric vehicles are used more widely in everyday transportation. Existing challenges include computational cost, data security, and model training. Prospectively, continued advances in AI are expected to support the development of more efficient and sustainable electric vehicles. In addition, improved interoperability between AI models and vehicle systems may further enhance real-time decision-making and energy optimization.

Keywords: Electrification , Intelligence , Optimization , Sustainability

Article Information

Received: 30 September 2026

Published: 27 October 2026

DOI: https://doi.org/10.54254/2755-2721/2026.LD38300

Article Type: Research Article

Cite this article

She,G. (2026). Research on the Role of Artificial Intelligence in Enhancing the Energy Efficiency, Battery Performance, and Driving Range of Electric Vehicles. Applied and Computational Engineering,279,58-62.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About this Volume

Volume Title: ACE Vol.279

Part of Series: Applied and Computational Engineering

ISSN: 2755-2721 (Print) / 2755-273X (Online)

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