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• W4320061225 abstract "The advent of machine learning (ML) has led to an exponential rise in the exploration of new methods to estimate the states of lithium-ion batteries (LIBs) in electric vehicle (EV) applications. Data-driven methods involving ML are increasingly engaged to estimate the state-of-charge (SOC) and state-of-health (SOH) due to greater availability of battery datasets in the public domain; alongside improvements in computing system efficiency. At present, the battery management system (BMS) of EVs tend to face challenges in attaining highly accurate state estimation results using traditional methods, since the LIBs possess strong time-varying and non-linear traits, whilst remaining susceptible to the influence of external factors. Therefore, this paper provides a comprehensive review of the existing ML methods in SOC and SOH estimation of EV application-based LIBs. The insights gained from this review will contribute towards the future development of advanced LIB state estimation algorithms." @default.
• W4320061225 created "2023-02-12" @default.
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• W4320061225 date "2022-11-01" @default.
• W4320061225 modified "2023-09-26" @default.
• W4320061225 title "A Review of Machine Learning Applications for Li-Ion Battery State Estimation in Electric Vehicles" @default.
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• W4320061225 doi "https://doi.org/10.1109/isgtasia54193.2022.10003481" @default.
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