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

Nicoleta Stroe 1, 2, 3 Sorin Olaru 2 Guilaume Colin 3 Karim Ben-Cherif 1 Yann Chamaillard 3
1 Technocentre Renault [Guyancourt]
2 L2S - Laboratoire des signaux et systèmes
3 F2ME
PRISME - Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique
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.
Keywords : hybrid electric vehicle energy management predictive control
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Conference papers
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Submitted on : Thursday, July 20, 2017 - 3:58:54 PM
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UNIV-ORLEANS | CNRS | MSL | MSL-THESE | TDS-MACS | L2S | CENTRALESUPELEC | SUP_LSS | UNIV-PARIS-SACLAY | SUP_SYSTEMES | PRISME-CVL

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Nicoleta Stroe, Sorin Olaru, Guilaume 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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