Advancing State of Charge Management in Electric Vehicles With Machine Learning - A Technological Review

Aug 2, 2026

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As the share of electric vehicles expands, battery performance and driving range can drop below nameplate expectations under broader and harsher operating conditions such as extreme weather. Accurate battery state-of-charge estimation and state-of-health maintenance via optimal charge and discharge decisions are therefore essential to sustain customer confidence and adoption growth. Manufacturers increasingly use machine learning to improve both short-term SoC awareness and long-term battery health insights. This review assesses traditional SoC estimation methods, analyzes machine-learning impacts through case studies, and discusses key hurdles including data availability, model interpretability, and real-time constraints. Emerging directions such as deep learning and reinforcement learning are highlighted.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages29

Reading Time1 h 13 min

AccessFree to download

Introduction

State of charge as a key EV metric

Importance of accurate SoC estimation and management

Traditional SoC estimation methods

Machine learning for SoC management

Algorithms and transformative impacts

Case studies and applications

Challenges and future outlook

Data, interpretability, and real-time constraints

Deep learning and reinforcement learning trends

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review ARASH MOUSAEI1, YAHYA NADERI2,(Member, IEEE), AND I. SAFAK BAYRAM3,(Senior Member, IEEE) 1Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran 2Ricardo Plc, G2 4BG Glasgow, U.K. 3Department of Electronic and Electrical Engineering, University of Strathclyde, G1 1XQ Glasgow, U.K. Corresponding author: I. Safak Bayram ([safak.bayram@strath.ac.uk](safak.bayram@strath.ac.uk)) 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 shedslight 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 asa 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. INDEX TERMS Machine learning, state of charge management, electric vehicles, battery management, state of charge estimation order. NOMENCLATURE Abbreviations Definition EV Electric Vehicle. SoC State of Charge. The associate editor coordinating the review of this manuscript and approving it for publication was Xinyu Du . ML Machine Learning. ICE Internal Combustion Engine. HEV Hybrid Electric Vehicle. DC-DC Direct Current to Direct Current. LFP Lithium Iron Phosphate. NMC Nickel Manganese Oxide. LCO Lithium Cobalt Oxide. VOLUME 12, 2024 􀀊 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/ 43255)[ 43255](https://creativecommons.org/licenses/by-nc-nd/4.0/ 43255) Received 22 February 2024, accepted 10 March 2024, date of publication 18 March 2024, date of current version 27 March 2024. Digital Object Identifi

FAQs

Why is state of charge management critical for electric vehicles?

State of charge strongly influences available driving range and overall vehicle performance. Accurate estimation and management also support reliability and durability of the battery system.

What does the review cover about machine learning for SoC management?

It evaluates traditional SoC estimation methods first, then reviews how machine learning techniques and algorithms enhance SoC management. Case studies are used to show how ML enables more informed energy usage decisions.

What challenges limit widespread adoption of ML-based SoC management?

The review points to data availability, difficulties in model interpretability, and real-time processing constraints as major impediments to deployment.

Aug 2, 2026

Upload by Eliana