Advancing state of charge management in electric vehicles with machine learning : a technological review

Mousaei, Arash and Naderi, Yahya and Bayram, I. Safak (2024) Advancing state of charge management in electric vehicles with machine learning : a technological review. IEEE Access, 12. pp. 43255-43283. ISSN 2169-3536 (https://doi.org/10.1109/ACCESS.2024.3378527)

[thumbnail of Mousaei-etal-IEEEA-2024-charge-management-in-electric-vehicles-with-machine-learning]
Preview
 Text. Filename: Mousaei-etal-IEEEA-2024-charge-management-in-electric-vehicles-with-machine-learning.pdf
Final Published Version
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (3MB)| Preview

Abstract

As the share of electric vehicles increases, electric vehicles are exposed to broader of driving conditions (e.g., extreme weather), which reduce the performance and driving ranges of electric vehicles below their nameplate rating. To ensure customer confidence and support steady growth in electric vehicle adoption rates, accurate estimation of battery state of charge and maintaining battery state of health through optimal charge/discharge decisions are critical. Recently, vehicle manufacturers have begun to employ machine learning techniques to improve state-of-charge management to better inform drivers about both the short-term (state of charge) and long-term (state of health) performance of their vehicles. This comprehensive review article explores the intersection of machine learning and state of charge management in electric vehicles. Recognizing the critical importance of the state of charge in optimizing electric vehicle performance, the article starts by evaluating traditional state of charge estimation methods. Subsequently, it delves into the transformative impact of machine learning techniques and associated algorithms on state of charge management. Through the lens of various case studies, this article demonstrates how machine learning-based state of charge estimation empowers electric vehicles to make informed and dynamic energy usage decisions, enhancing efficiency and extending battery life. The challenges of data availability, model interpretability, and real-time processing constraints are acknowledged as impediments to the widespread adoption of machine learning techniques. Despite these challenges, the future outlook for machine learning in the state of charge management appears promising, with emerging trends such as deep learning and reinforcement learning poised to refine the state of charge estimation accuracy. Moreover, this study sheds light on the transformative potential of machine learning in enhancing the state of charge management efficiency and effectiveness for electric vehicles, offering critical insights. Machine learning emerges as a game-changing force in state of charge management for electric vehicles, paving the way for intelligent and adaptive vehicles that are both environmentally friendly and efficient. This evolving field invites further research and development, making it a vital and exciting area within the automotive industry.

ORCID iDs

Mousaei, Arash, Naderi, Yahya and Bayram, I. Safak ORCID logoORCID: https://orcid.org/0000-0001-8130-5583;
CORE (COnnecting REpositories)