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Review

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

by
Seyed Mahdi Miraftabzadeh
,
Michela Longo
*,
Andrea Di Martino
,
Alessandro Saldarini
and
Roberto Sebastiano Faranda
Department of Energy, Politecnico di Milano, 20156 Milano, Italy
*
Author to whom correspondence should be addressed.
Electronics 2024, 13(10), 1973; https://doi.org/10.3390/electronics13101973
Submission received: 11 April 2024 / Revised: 7 May 2024 / Accepted: 10 May 2024 / Published: 17 May 2024
(This article belongs to the Special Issue Advanced Energy Supply and Storage Systems for Electric Vehicles)

Abstract

The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical modeling enhances their applicability across various domains. The vast amount of data available today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable future, the electrification of vehicles and integrating electric systems for energy storage are becoming increasingly important and need to be addressed. The synergy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To the best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles. This study examined all topics to identify the most commonly used methods, which were analyzed based on their characteristics and potential. Future trends were identified by exploring emerging techniques introduced in recent literature contributions published since 2017.
Keywords: artificial intelligence; data management; electric vehicles; energy storage systems; machine learning artificial intelligence; data management; electric vehicles; energy storage systems; machine learning

Share and Cite

MDPI and ACS Style

Miraftabzadeh, S.M.; Longo, M.; Di Martino, A.; Saldarini, A.; Faranda, R.S. Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles. Electronics 2024, 13, 1973. https://doi.org/10.3390/electronics13101973

AMA Style

Miraftabzadeh SM, Longo M, Di Martino A, Saldarini A, Faranda RS. Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles. Electronics. 2024; 13(10):1973. https://doi.org/10.3390/electronics13101973

Chicago/Turabian Style

Miraftabzadeh, Seyed Mahdi, Michela Longo, Andrea Di Martino, Alessandro Saldarini, and Roberto Sebastiano Faranda. 2024. "Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles" Electronics 13, no. 10: 1973. https://doi.org/10.3390/electronics13101973

APA Style

Miraftabzadeh, S. M., Longo, M., Di Martino, A., Saldarini, A., & Faranda, R. S. (2024). Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles. Electronics, 13(10), 1973. https://doi.org/10.3390/electronics13101973

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