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Model predictive control for multimode power-split hybrid electric vehicles: Parametric internal model with integrated mode switch and variable meshing losses.

Authors :
Castellano, Antonella
Stano, Pietro
Montanaro, Umberto
Cammalleri, Marco
Sorniotti, Aldo
Source :
Mechanism & Machine Theory. Feb2024, Vol. 192, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

• An MPC energy management strategy for power-split hybrid electric vehicles is proposed. • A universal parametric internal model for multimode power-split transmissions is used. • The discrete mode switch is integrated within the continuous MPC framework. • The transmission meshing losses are included. • A comparison of MPC internal models with different complexity is performed. Model predictive control (MPC) is one of the most promising energy management strategies for hybrid electric vehicles. However, owing to constructive complexity, the multimode power-split powertrain requires dedicated mathematical tools to model the mode switch and transmission power losses within the internal model of the controller. Thus, the transmission losses are usually neglected and the mode switch is optimised through offline simulations. This paper proposes an MPC internal model relying on a parametric approach available in the literature, which provides a unique formulation for modelling any power-split transmission and assesses the transmission meshing losses. The objectives, which cover a gap in the literature, are: 1) to integrate the discrete problem of the mode switch in a continuous formulation of the internal model; 2) to compare MPC internal models with different complexity, and evaluate how the consideration of meshing losses and efficiency of the electric machines affect the controller performance. The results on a case study vehicle, i.e., the Chevrolet Volt, suggest that a simplified internal model deteriorates the fuel consumption performance by less than 2 %, while the integrated mode switch is comparable to the offline strategy. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0094114X
Volume :
192
Database :
Academic Search Index
Journal :
Mechanism & Machine Theory
Publication Type :
Academic Journal
Accession number :
174104923
Full Text :
https://doi.org/10.1016/j.mechmachtheory.2023.105543