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

Sen Yan; Maqsood Hussain Shah; Ji Li; Noel  O’Connor; Mingming Liu

doi:10.1109/CVCI59596.2023.10397371

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

Sen Yan
, Maqsood Hussain Shah
, Ji Li
, Noel O’Connor
, Mingming Liu

Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English
Title of host publication	2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI)
Publisher	IEEE
Number of pages	8
ISBN (Electronic)	9798350340488
ISBN (Print)	9798350340495 (PoD)
DOIs	https://doi.org/10.1109/CVCI59596.2023.10397371
Publication status	Published - 25 Jan 2024
Event	2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI) - Changsha, China Duration: 27 Oct 2023 → 29 Oct 2023

Publication series

Name	Conference on Vehicle Control and Intelligence (CVCI)

Conference

Conference	2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI)
Period	27/10/23 → 29/10/23

Bibliographical note

Funding:
This work is supported by Science Foundation Ireland under Grant No. 21/FFPP/10266 and SFI/12/RC/2289 P2.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 13 Climate Action

Keywords

Green products
Transportation
Pressing
Air pollution
Energy management
Artificial intelligence
Sustainable development

Access to Document

10.1109/CVCI59596.2023.10397371

Cite this

@inproceedings{78d1db0fb7d34449adc6f376cc876fd3,

title = "A Review on AI Algorithms for Energy Management in E-Mobility Services",

abstract = "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.",

keywords = "Green products, Transportation, Pressing, Air pollution, Energy management, Artificial intelligence, Sustainable development",

author = "Sen Yan and Shah, \{Maqsood Hussain\} and Ji Li and Noel O{\textquoteright}Connor and Mingming Liu",

note = "Funding: This work is supported by Science Foundation Ireland under Grant No. 21/FFPP/10266 and SFI/12/RC/2289 P2.; 2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI) ; Conference date: 27-10-2023 Through 29-10-2023",

year = "2024",

month = jan,

day = "25",

doi = "10.1109/CVCI59596.2023.10397371",

language = "English",

isbn = "9798350340495 (PoD)",

series = "Conference on Vehicle Control and Intelligence (CVCI)",

publisher = "IEEE",

booktitle = "2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI)",

}

Yan, S, Shah, MH, Li, J, O’Connor, N & Liu, M 2024, A Review on AI Algorithms for Energy Management in E-Mobility Services. in 2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI)., 10397371, Conference on Vehicle Control and Intelligence (CVCI), IEEE, 2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI), 27/10/23. https://doi.org/10.1109/CVCI59596.2023.10397371

A Review on AI Algorithms for Energy Management in E-Mobility Services. / Yan, Sen; Shah, Maqsood Hussain; Li, Ji et al.
2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI). IEEE, 2024. 10397371 (Conference on Vehicle Control and Intelligence (CVCI)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A Review on AI Algorithms for Energy Management in E-Mobility Services

AU - Yan, Sen

AU - Shah, Maqsood Hussain

AU - Li, Ji

AU - O’Connor, Noel

AU - Liu, Mingming

N1 - Funding: This work is supported by Science Foundation Ireland under Grant No. 21/FFPP/10266 and SFI/12/RC/2289 P2.

PY - 2024/1/25

Y1 - 2024/1/25

N2 - 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.

AB - 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.

KW - Green products

KW - Transportation

KW - Pressing

KW - Air pollution

KW - Energy management

KW - Artificial intelligence

KW - Sustainable development

U2 - 10.1109/CVCI59596.2023.10397371

DO - 10.1109/CVCI59596.2023.10397371

M3 - Conference contribution

SN - 9798350340495 (PoD)

T3 - Conference on Vehicle Control and Intelligence (CVCI)

BT - 2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI)

PB - IEEE

T2 - 2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI)

Y2 - 27 October 2023 through 29 October 2023

ER -

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

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

Fingerprint

Cite this