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
Type de document :
Communication dans un congrès
IFAC 2017 - 20th World Congress of the International Federation of Automatic Control, Jul 2017, Toulouse, France. pp.10475-10481, Preprints of the 20th World Congress, The International Federation of Automatic Control, Toulouse, France, July 9-14, 2017. 〈https://www.ifac2017.org/〉
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Contributeur : Guillaume Colin <>
Soumis le : jeudi 20 juillet 2017 - 15:58:54
Dernière modification le : lundi 16 juillet 2018 - 16:00:41

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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, Jul 2017, Toulouse, France. pp.10475-10481, Preprints of the 20th World Congress, The International Federation of Automatic Control, Toulouse, France, July 9-14, 2017. 〈https://www.ifac2017.org/〉. 〈hal-01566029〉

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