Accurate estimation of state of charge and state of health of electric vehicle power batteries based on dynamic channel pruning informer-stacked denoising autoencoder model

Accurate estimation of state of charge (SOC) and state of health (SOH) of electric vehicle (EV) power batteries is critical for users to plan their trips and improve EV safety. The informer model, an improved variant of the transformer, excels in long-sequence time-series forecasting tasks; however,...

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Vydáno v:	Electrical engineering Ročník 108; číslo 1; s. 19
Hlavní autoři:	Peng, Ziran, Wang, Shunhao, Zhang, Shengchun, Meng, Shuangwu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2026 Springer Nature B.V
Témata:	Accuracy Aging Attention Batteries Cleaning Economics and Management Efficiency Electric charge Electric vehicles Electrical Engineering Electrical Machines and Networks Electrolytes Energy Policy Engineering Estimates Estimation Inference Lithium Machine learning Methods Neural networks Noise reduction Original Paper Power Electronics Pruning Real time Root-mean-square errors Sensors Stability State of charge Transformers Dynamical channel pruning Enhanced informer Stacked denoising autoencoder State of health Electric vehicle State of charge Power batteries
ISSN:	0948-7921, 1432-0487
On-line přístup:	Získat plný text
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