Numerical Strategies for Mixed-Integer Optimization of Power-Split and Gear Selection in Hybrid Electric Vehicles

This paper presents numerical strategies for a computationally efficient energy management system that co-optimizes the power split and gear selection of a hybrid electric vehicle (HEV). We formulate a mixed-integer optimal control problem (MIOCP) that is transcribed using multiple-shooting into a m...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 24; no. 3; pp. 1 - 17
Main Authors: Ganesan, Anand, Gros, Sebastien, Murgovski, Nikolce
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Actuators
Dynamic programming
Electric vehicles
Energy management
Gears
hybrid electric vehicle
Hybrid electric vehicles
Mechanical power transmission
Mixed integer
Mixed-Integer nonlinear optimal control
Nonlinear control
nonlinear MPC
nonlinear programming
numerical optimization
Optimal control
optimal gear selection
optimal torque-split
Optimization
Predictive control
Real-time systems
Vehicle dynamics
ISSN:1524-9050, 1558-0016, 1558-0016
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents numerical strategies for a computationally efficient energy management system that co-optimizes the power split and gear selection of a hybrid electric vehicle (HEV). We formulate a mixed-integer optimal control problem (MIOCP) that is transcribed using multiple-shooting into a mixed-integer nonlinear program (MINLP) and then solved by nonlinear model predictive control. We present two different numerical strategies, a Selective Relaxation Approach (SRA), which decomposes the MINLP into several subproblems, and a Round-n-Search Approach (RSA), which is an enhancement of the known 'relax-n-round' strategy. Subsequently, the resulting algorithmic performance and optimality of the solution of the proposed strategies are analyzed against two benchmark strategies; one using rule-based gear selection, which is typically used in production vehicles, and the other using dynamic programming (DP), which provides a global optimum of a quantized version of the MINLP. The results show that both SRA and RSA enable about <inline-formula> <tex-math notation="LaTeX">{3.6}{\%}</tex-math> </inline-formula> cost reduction compared to the rule-based strategy, while still being within <inline-formula> <tex-math notation="LaTeX">\SI{1}{\%}</tex-math> </inline-formula> of the DP solution. Moreover, for the case studied RSA takes about <inline-formula> <tex-math notation="LaTeX">\SI{35}{\%}</tex-math> </inline-formula> less mean computation time compared to SRA, while both SRA and RSA being about <inline-formula> <tex-math notation="LaTeX">99</tex-math> </inline-formula> times faster than DP. Furthermore, both SRA and RSA were able to overcome the infeasibilities encountered by a typical rounding strategy under different drive cycles. The results show the computational benefit of the proposed strategies, as well as the energy saving possibility of co-optimization strategies in which actuator dynamics are explicitly included.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1524-9050
1558-0016
1558-0016
DOI:10.1109/TITS.2022.3229254