A Mini Review of the Impacts of Machine Learning on Mobility Electrifications

Kimiya Noor Ali, Mohammad Hemmati, Seyed Mahdi Miraftabzadeh (), Younes Mohammadi and Navid Bayati
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Kimiya Noor Ali: Dipartimento di Sistemi e Informatica (DSI), University of Florence, 50139 Firenze, Italy
Mohammad Hemmati: Center for Industrial Electronics, Institute of Mechanical and Electrical Engineering, University of Southern Denmark, 6400 Sønderborg, Denmark
Seyed Mahdi Miraftabzadeh: Department of Energy, Politecnico di Milano, 20156 Milano, Italy
Younes Mohammadi: Department of Applied Physics and Electronics, Umeå University, 90187 Umeå, Sweden
Navid Bayati: Center for Industrial Electronics, Institute of Mechanical and Electrical Engineering, University of Southern Denmark, 6400 Sønderborg, Denmark

Energies, 2024, vol. 17, issue 23, 1-36

Abstract: Electromobility contributes to decreasing environmental pollution and fossil fuel dependence, as well as increasing the integration of renewable energy resources. The increasing interest in using electric vehicles (EVs), enhanced by machine learning (ML) algorithms for intelligent automation, has reduced the reliance on. This shift has created an interdependence between power, automatically, and transportation networks, adding complexity to their management and scheduling. Moreover, due to complex charging infrastructures, such as variations in power supply, efficiency, driver behaviors, charging demand, and electricity price, advanced techniques should be applied to predict a wide range of variables in EV performance. As the adoption of EVs continues to accelerate, the integration of ML and especially deep learning (DL) algorithms will play a pivotal role in shaping the future of sustainable transportation. This paper provides a mini review of the ML impacts on mobility electrification. The applications of ML are evaluated in various aspects of e-mobility, including battery management, range prediction, charging infrastructure optimization, autonomous driving, energy management, predictive maintenance, traffic management, vehicle-to-grid (V2G), and fleet management. The main advantages and challenges of models in the years 2013–2024 have been represented for all mentioned applications. Also, all new trends for future work and the strengths and weaknesses of ML models in various aspects of mobility transportation are covered. By discussing and reviewing research papers in this field, it is revealed that leveraging ML models can accelerate the transition to electric mobility, leading to cleaner, safer, and more sustainable transportation systems. This paper states that the dependence on big data for training, the high uncertainty of parameters affecting the performance of electric vehicles, and cybersecurity are the main challenges of ML in the e-mobility sector.

Keywords: machine learning; deep learning; mobility; electric vehicle; prediction; battery management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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