Enhancing state of charge and state of energy estimation in Lithium-ion batteries based on a TimesNet model with Gaussian data augmentation and error correction

Accurately estimating the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries is crucial for their safe and stable operation. This study proposes a hybrid deep learning model based on Gaussian data augmentation (GDA), the TimesNet model, error correction (EC), and an improved Ba...

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Vydané v:	Applied energy Ročník 359; s. 122669
Hlavní autori:	Zhang, Chu, Zhang, Yue, Li, Zhengbo, Zhang, Zhao, Nazir, Muhammad Shahzad, Peng, Tian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.04.2024
Predmet:	Error correction Gaussian data augmentation State of charge State of energy TimesNet State of energy Gaussian data augmentation TimesNet Error correction State of charge
ISSN:	0306-2619, 1872-9118
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