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Next Generation of Electric Vehicles: AI-Driven Approaches for Predictive Maintenance and Battery Management

Author

Listed:
  • Muhammed Cavus

    (Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle Upon Tyne NE1 8SA, UK)

  • Dilum Dissanayake

    (School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK; d.dissanayake@bham.ac.uk)

  • Margaret Bell

    (School of Engineering, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK)

Abstract

This review explores recent advancements in electric vehicles (EVs), focusing on the transformative role of artificial intelligence (AI) in battery management systems (BMSs) and system control technologies. While EVs are integral to sustainable transportation, challenges remain in optimising battery longevity, energy efficiency, and safety. AI-driven techniques—such as machine learning (ML), neural networks (NNs), and reinforcement learning (RL)—enhance battery state of health (SOH) and state of charge (SOC) predictions, as well as temperature regulation, offering superior accuracy over traditional methods. Additionally, AI-powered control frameworks optimise energy distribution, regenerative braking, and power allocation under varying driving conditions. Deep RL enables adaptive, self-learning capabilities that improve energy efficiency and extend battery life, even in dynamic environments. This review also examines the integration of the Internet of Things (IoT) and big data analytics in EV systems, enabling predictive maintenance and fleet-level optimisation. By analysing these advancements, this paper highlights AI’s pivotal role in shaping next-generation, energy-efficient EVs.

Suggested Citation

  • Muhammed Cavus & Dilum Dissanayake & Margaret Bell, 2025. "Next Generation of Electric Vehicles: AI-Driven Approaches for Predictive Maintenance and Battery Management," Energies, MDPI, vol. 18(5), pages 1-41, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1041-:d:1596391
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    References listed on IDEAS

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