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A Scoping Review of Energy-Efficient Driving Behaviors and Applied State-of-the-Art AI Methods

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  • Zhipeng Ma

    (SDU Center for Energy Informatics, The Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark, DK-5230 Odense, Denmark)

  • Bo Nørregaard Jørgensen

    (SDU Center for Energy Informatics, The Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark, DK-5230 Odense, Denmark)

  • Zheng Ma

    (SDU Center for Energy Informatics, The Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark, DK-5230 Odense, Denmark)

Abstract

The transportation sector remains a major contributor to greenhouse gas emissions. The understanding of energy-efficient driving behaviors and utilization of energy-efficient driving strategies are essential to reduce vehicles’ fuel consumption. However, there is no comprehensive investigation into energy-efficient driving behaviors and strategies. Furthermore, many state-of-the-art AI models have been applied for the analysis of eco-friendly driving styles, but no overview is available. To fill the gap, this paper conducts a thorough literature review on ecological driving behaviors and styles, and analyzes the driving factors influencing energy consumption and state-of-the-art methodologies. With a thorough scoping review process, thirty-seven articles with full text were assessed, and the methodological and related data are compared. The results show that the factors that impact driving behaviors can be summarized into eleven features including speed, acceleration, deceleration, pedal, steering, gear, engine, distance, weather, traffic signal, and road parameters. This paper finds that supervised/unsupervised learning algorithms and reinforcement learning frameworks have been popularly used to model the vehicle’s energy consumption with multi-dimensional data. Furthermore, the literature shows that the driving data are collected from either simulators or real-world experiments, and the real-world data are mainly stored and transmitted by meters, controller area networks, onboard data services, smartphones, and additional sensors installed in the vehicle. Based on driving behavior factors, driver characteristics, and safety rules, this paper recommends nine energy-efficient driving styles including four guidelines for the drivers’ selection and adjustment of the vehicle parameters, three recommendations for the energy-efficient driving styles in different driving scenarios, and two subjective suggestions for different types of drivers and employers.

Suggested Citation

  • Zhipeng Ma & Bo Nørregaard Jørgensen & Zheng Ma, 2024. "A Scoping Review of Energy-Efficient Driving Behaviors and Applied State-of-the-Art AI Methods," Energies, MDPI, vol. 17(2), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:500-:d:1322678
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    References listed on IDEAS

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    1. Turkensteen, Marcel, 2017. "The accuracy of carbon emission and fuel consumption computations in green vehicle routing," European Journal of Operational Research, Elsevier, vol. 262(2), pages 647-659.
    2. Li, Jie & Wu, Xiaodong & Xu, Min & Liu, Yonggang, 2022. "Deep reinforcement learning and reward shaping based eco-driving control for automated HEVs among signalized intersections," Energy, Elsevier, vol. 251(C).
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    1. Juan Carlos Almachi & Jonathan Saguay & Edwin Anrango & Edgar Cando & Salvatore Reina, 2025. "Clustering-Based Urban Driving Cycle Generation: A Data-Driven Approach for Traffic Analysis and Sustainable Mobility Applications in Ecuador," Sustainability, MDPI, vol. 17(8), pages 1-19, April.

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