AI enabled fast charging of lithium-ion batteries of electric vehicles during their life cycle: review, challenges and perspectives

Sun, Daoming; Guo, Dongxu; Lu, Yufang; Chen, Jiali; Lu, Yao; Han, Xuebing; Feng, Xuning; Lu, Languang; Wang, Hewu; Ouyang, Minggao

doi:10.1039/d4ee03063j

Energy Environ. Sci.

2024

DOI: 10.1039/d4ee03063j

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AI enabled fast charging of lithium-ion batteries of electric vehicles during their life cycle: review, challenges and perspectives

Daoming Sun,

Dongxu Guo,

Yufang Lu

et al.

Abstract: This review presents a thorough investigation of factors affecting fast charging, battery modeling, key state estimation and fast charging control strategies and provides a forward-looking perspective on AI enabled fast charging technology of LIBs.

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Cited by 2 publications

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Fast-charging lithium-ion batteries electrodes enabled by self-regulating micro-channels networks

Xu,

Zheng,

Feng

et al. 2024

Chinese Chemical Letters

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Fast-charging lithium-ion batteries electrodes enabled by self-regulating micro-channels networks

Xu,

Zheng,

Feng

et al. 2024

Chinese Chemical Letters

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State-of-Health Estimation for Lithium-Ion Batteries in Hybrid Electric Vehicles—A Review

Zhang,

2024

Energies

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This paper presents a comprehensive review of state-of-health (SoH) estimation methods for lithium-ion batteries, with a particular focus on the specific challenges encountered in hybrid electric vehicle (HEV) applications. As the demand for electric transportation grows, accurately assessing battery health has become crucial to ensuring vehicle range, safety, and battery lifespan, underscoring the relevance of high-precision SoH estimation methods in HEV applications. The paper begins with outlining current SoH estimation methods, including capacity-based, impedance-based, voltage and temperature-based, and model-based approaches, analyzing their advantages, limitations, and applicability. The paper then examines the impact of unique operating conditions in HEVs, such as frequent charge–discharge cycles and fluctuating power demands, which necessitate tailored SoH estimation techniques. Moreover, this review summarizes the latest research advances, identifies gaps in existing methods, and proposes scientifically innovative improvements, such as refining estimation models, developing techniques specific to HEV operational profiles, and integrating multiple parameters (e.g., voltage, temperature, and impedance) to enhance estimation accuracy. These approaches offer new pathways to achieve higher predictive accuracy, better meeting practical application needs. The paper also underscores the importance of validating these estimation methods in real-world scenarios to ensure their practical feasibility. Through systematic evaluation and innovative recommendations, this review contributes to a deeper understanding of SoH estimation for lithium-ion batteries, especially in HEV contexts, and provides a theoretical basis to advance battery management system optimization technologies.

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AI enabled fast charging of lithium-ion batteries of electric vehicles during their life cycle: review, challenges and perspectives

Abstract: This review presents a thorough investigation of factors affecting fast charging, battery modeling, key state estimation and fast charging control strategies and provides a forward-looking perspective on AI enabled fast charging technology of LIBs.

Cited by 2 publications

References 252 publications

Fast-charging lithium-ion batteries electrodes enabled by self-regulating micro-channels networks

Fast-charging lithium-ion batteries electrodes enabled by self-regulating micro-channels networks

State-of-Health Estimation for Lithium-Ion Batteries in Hybrid Electric Vehicles—A Review

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