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Machine Learning-Driven Advancements in Electric Motorcycles: A Systematic Review of Electric Motors, Energy Storage, Charging Technologies, and Electronic Components

Author

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  • Lukasz Pawlik

    (Department of Information Systems, Kielce University of Technology, 7 Tysiąclecia Państwa Polskiego Ave., 25-314 Kielce, Poland)

  • Jacek Lukasz Wilk-Jakubowski

    (Department of Information Systems, Kielce University of Technology, 7 Tysiąclecia Państwa Polskiego Ave., 25-314 Kielce, Poland
    Institute of Crisis Management and Computer Modelling, 28-100 Busko-Zdrój, Poland)

  • Krzysztof Podosek

    (Department of Automotive Engineering and Transport, Kielce University of Technology, 7 Tysiąclecia Państwa Polskiego Ave., 25-314 Kielce, Poland)

  • Grzegorz Wilk-Jakubowski

    (Institute of Crisis Management and Computer Modelling, 28-100 Busko-Zdrój, Poland
    Institute of Internal Security, Old Polish University of Applied Science, 49 Ponurego Piwnika Str., 25-666 Kielce, Poland)

Abstract

The integration of artificial intelligence (AI) and machine learning (ML) technologies is rapidly transforming the design, operation, and optimization of electric motorcycles. This review analyzes research published between 2015 and 2024, focusing on how ML algorithms enhance performance, energy efficiency, diagnostics, and charging strategies across four key domains: electric motors, energy storage, charging systems, and electronic components. The review highlights state-of-the-art solutions such as torque and range prediction using LSTM/GRU models, predictive maintenance via CNNs and autoencoders, energy flow control in hybrid battery–supercapacitor systems using reinforcement learning, and federated learning for privacy-preserving embedded applications. Comparative insights reveal quantifiable performance gains over traditional methods, while integrated frameworks are proposed for linking ML diagnostics, Vehicle-to-Grid (V2G) functionalities, and renewable energy integration. The paper concludes with targeted recommendations for future research, including lightweight edge-deployable models, Explainable AI for safety-critical applications, and the fusion of intelligent charging with eco-design principles, aiming to enable intelligent, sustainable, and high-performance electric motorcycle systems.

Suggested Citation

  • Lukasz Pawlik & Jacek Lukasz Wilk-Jakubowski & Krzysztof Podosek & Grzegorz Wilk-Jakubowski, 2025. "Machine Learning-Driven Advancements in Electric Motorcycles: A Systematic Review of Electric Motors, Energy Storage, Charging Technologies, and Electronic Components," Energies, MDPI, vol. 18(17), pages 1-38, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:17:p:4529-:d:1733170
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    References listed on IDEAS

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    1. Mehmet Cagin Kirca & Andrew McGordon & Truong Quang Dinh, 2024. "Rapid Decision-Making Tool for Electric Powertrain Sizing for Motorcycles during New Product Development," Energies, MDPI, vol. 17(2), pages 1-27, January.
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    3. Jean-Michel Clairand & Javier Rodríguez-García & Carlos Álvarez-Bel, 2018. "Electric Vehicle Charging Strategy for Isolated Systems with High Penetration of Renewable Generation," Energies, MDPI, vol. 11(11), pages 1-21, November.
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