Optimization for the minimum fuel consumption problem of a hybrid electric vehicle using mixed-integer linear programming

In this study, a mixed-integer linear programming (MILP) formulation for obtaining the operating points of hybrid electric vehicle (HEV) powertrains is proposed. This study focuses on linearizing the nonlinear terms, such as an engine fuel consumption map, or bilinear terms represented by the energy...

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Vydané v:Engineering optimization Ročník 55; číslo 9; s. 1516 - 1534
Hlavní autori: Yamanaka, Gentaro, Kuroishi, Masakatsu, Matsumori, Tadayoshi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Abingdon Taylor & Francis 02.09.2023
Taylor & Francis Ltd
Predmet:
Approximation
Fuel consumption
HEV
hybrid electric vehicle powertrains
Hybrid electric vehicles
Integer programming
Linear programming
Mathematical analysis
Mixed integer
mixed-integer linear programming
Multilayer perceptrons
Multilayers
operating point optimization
Optimization
Powertrain
ISSN:0305-215X, 1029-0273
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Shrnutí:In this study, a mixed-integer linear programming (MILP) formulation for obtaining the operating points of hybrid electric vehicle (HEV) powertrains is proposed. This study focuses on linearizing the nonlinear terms, such as an engine fuel consumption map, or bilinear terms represented by the energy of the motor. To address this problem, a conventional piecewise linear (PWL) method and a multi-layer perceptron (MLP) regression approach are adopted. Although the optimal solution cannot be determined using a PWL approximation for the fuel consumption map, it can be obtained using an MLP regression. Furthermore, the PWL method achieves better results than the MLP approach in terms of the accuracy of its bilinear approximation. Obtaining the optimal solution using MILP helps in acquiring a Lagrange multiplication of the design variables by solving the dual problem, which allows an efficient design revision strategy to be obtained.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2022.2098282