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• W4286306593 abstract "For hybrid electric vehicles, optimization of energy consumption is achieved by assigning the operations among the combustion engine and electric machines promptly according to real-time driving condition. In this paper, a real-time energy optimization strategy with model predictive control (MPC) scheme is proposed satisfied by the driver’s power demand, such that improve fuel economy and produce optimal power split. To solve the MPC-based energy optimization problem, the key step is to predict the power demand during the prediction horizon. Therefore, a learning-based Gaussian process (GP) predictor is designed to predict the driver’s demand using vehicle-to-everything (V2X) information. Moreover, an on-board parameter identification using recursive least squares (RLS) method is used to on-line update the parameters of the vehicle model, such that adapt the real traffic conditions. The strategy is further demonstrated with a case study result." @default.
• W4286306593 created "2022-07-21" @default.
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• W4286306593 date "2022-05-04" @default.
• W4286306593 modified "2023-09-27" @default.
• W4286306593 title "MPC based energy management strategy with on-board parameter identification" @default.
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• W4286306593 doi "https://doi.org/10.23919/ascc56756.2022.9828349" @default.
• W4286306593 hasPublicationYear "2022" @default.
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