# A Review on AI Algorithms for Energy Management in E-Mobility Services

arXiv: https://arxiv.org/abs/2309.15140 · HTML: https://mlsanity.com/paper/2309.15140/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 43 references that are in the map.
- First posted on arXiv 26 Sep 2023.

## Builds on (most cited first, 2 of 2)

- [A Review and Outlook of Energy Consumption Estimation Models for Electric Vehicles](https://mlsanity.com/paper/2003.12873/) (2020)
- [Estimation of energy consumption of electric vehicles using Deep Convolutional Neural Network to reduce driver's range anxiety](https://mlsanity.com/paper/2008.11112/) (2020)

## Built on by (most cited first, 4 of 4)

- [Optimal Design and Implementation of an Open-source Emulation Platform for User-Centric Shared E-mobility Services](https://mlsanity.com/paper/2403.07964/) (2024)
- [Data-driven Energy Consumption Modelling for Electric Micromobility using an Open Dataset](https://mlsanity.com/paper/2403.17632/) (2024)
- [Leveraging RAG-LLMs for Urban Mobility Simulation and Analysis](https://mlsanity.com/paper/2507.10382/) (2025)
- [Machine Learning in Micromobility: A Systematic Review of Datasets, Techniques, and Applications](https://mlsanity.com/paper/2508.16135/) (2025)

## Abstract (arXiv)

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.