A Review on AI Algorithms for Energy Management in E-Mobility Services
Sen Yan Maqsood Hussain Shah Ji Li Noel O'Connor Mingming Liu
E-mobility, or electric mobility, has emerged as a pivotal solution to
address pressing environmental and sustainability concerns in the
transportation sector. The depletion of fossil fuels, escalating greenhouse gas
emissions, and the imperative to combat climate change underscore the
significance of transitioning to electric vehicles (EVs). This paper seeks to
explore the potential of artificial intelligence (AI) in addressing various
challenges related to effective energy management in e-mobility systems (EMS).
These challenges encompass critical factors such as range anxiety, charge rate
optimization, and the longevity of energy storage in EVs. By analyzing existing
literature, we delve into the role that AI can play in tackling these
challenges and enabling efficient energy management in EMS. Our objectives are
twofold: to provide an overview of the current state-of-the-art in this
research domain and propose effective avenues for future investigations.
Through this analysis, we aim to contribute to the advancement of sustainable
and efficient e-mobility solutions, shaping a greener and more sustainable
future for transportation.