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A two-layer predictive control for hybrid electric vehicles energy management

Abstract : In this paper, a two-layer predictive energy management strategy for hybrid electric vehicles without an external recharge is introduced. The low-level layer exploits telemetry data over a short-term horizon in a model predictive control structure that provides the engine torque, but also the stop-start decision. The upper layer uses a tuning mechanism with a longer horizon to calculate the MPC weighting factor that ensures a balance between the fuel and battery consumption. An analysis of this upper-level tuning prediction horizon dependence on the drive cycle characteristics is performed. The robustness with respect to state-of-charge and engine torque estimation is also proven by a sensitivity analysis.
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Contributor : Guillaume Colin Connect in order to contact the contributor
Submitted on : Thursday, July 20, 2017 - 3:58:54 PM
Last modification on : Thursday, October 6, 2022 - 10:42:07 AM


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  • HAL Id : hal-01566029, version 1


Nicoleta Stroe, Sorin Olaru, Guillaume Colin, Karim Ben-Cherif, Yann Chamaillard. A two-layer predictive control for hybrid electric vehicles energy management. IFAC 2017 - 20th World Congress of the International Federation of Automatic Control, The International Federation of Automatic Control, Jul 2017, Toulouse, France. pp.10475-10481. ⟨hal-01566029⟩



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