Real-Time Optimization of Energy Management Strategy for Fuel Cell Vehicles Using Inflated 3D Inception Long Short-Term Memory Network-Based Speed Prediction

The performance of speed prediction-based energy management strategy (EMS) for fuel cell vehicles (FCVs) highly relies on the accuracy of predicted speed sequences. Therefore, the future speed sequences are estimated by Inflated 3D Inception long short-term memory (LSTM) network, which can use the h...

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Vydané v:	IEEE transactions on vehicular technology Ročník 70; číslo 2; s. 1190 - 1199
Hlavní autori:	Zhang, Caizhi, Zhang, Yuanzhi, Huang, Zhiyu, Lv, Chen, Hao, Dong, Liang, Chen, Deng, Chenghao, Chen, Jinrui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.02.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Algorithms Degradation Durability Electric vehicles Energy consumption Energy management energy management strategy Fuel cell vehicles Fuel cells Inflated 3D Inception LSTM network Lithium-ion batteries Mechanical power transmission Optimization Powertrain Quadratic programming Real time Real-time optimization Real-time systems Sequences sequential quadratic programming algorithm State of charge
ISSN:	0018-9545, 1939-9359
On-line prístup:	Získať plný text
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