Gaussian Process Approximate Dynamic Programming for Energy-Optimal Supervisory Control of Parallel Hybrid Electric Vehicles

We propose an energy-efficient supervisory control method for the power management of parallel hybrid electric vehicles (HEVs) to improve the fuel economy and reduce exhaust gas emissions. Plug-in HEVs ((P)HEVs) have multiple power sources (e.g., an engine and motor) that should be cooperatively ope...

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Veröffentlicht in:	IEEE transactions on vehicular technology Jg. 71; H. 8; S. 8367 - 8380
Hauptverfasser:	Bae, Jin Woo, Kim, Kwang-Ki K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Approximate dynamic programming Batteries Control methods Dynamic programming Electric vehicles Energy conversion efficiency Energy efficiency Energy management Engines Exhaust gases Feedback control Fuel consumption Fuel economy Gaussian process gaussian process regression Hybrid electric vehicles Mathematical models Maximum power Optimal control Parallel hybrid electric vehicles Power management Power sources Statistical analysis Supervisory control Traffic speed value function approximation Vehicle dynamics
ISSN:	0018-9545, 1939-9359
Online-Zugang:	Volltext
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