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
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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download

https://hal-univ-orleans.archives-ouvertes.fr/hal-01566029
Contributor : Guillaume Colin <>
Submitted on : Thursday, July 20, 2017 - 3:58:54 PM
Last modification on : Monday, July 16, 2018 - 4:00:41 PM

File

IFACWC2017_Stroe_el_al_MPC_HEV...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01566029, version 1

Citation

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⟩

Share

Metrics

Record views

677

Files downloads

540