Practical Evaluation of Lithium-Ion Battery State-of-Charge Estimation Using Time-Series Machine Learning for Electric Vehicles

This paper presents a practical usability investigation of recurrent neural networks (RNNs) to determine the best-suited machine learning method for estimating electric vehicle (EV) batteries’ state of charge. Using models from multiple published sources and cross-validation testing with several dri...

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書誌詳細

主要な著者:	Marat Sadykov, Sam Haines, Mark Broadmeadow, Geoff Walker, David William Holmes
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2023-02-01
シリーズ:	Energies
主題:	driving schedulers gradient recurrent unit (GRU) optimisers lithium-ion battery (Li-ion) long short-term memory (LSTM) recurrent neural networks (RNNs)
オンライン･アクセス:	https://www.mdpi.com/1996-1073/16/4/1628
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