Combinatorial Optimization Algorithm of MIGA and NLPQL for a Plug-in Hybrid Electric Bus Parameters Optimization

Hongwen He; Lu Yi; Jiankun Peng

doi:10.1016/j.egypro.2017.03.708

Combinatorial Optimization Algorithm of MIGA and NLPQL for a Plug-in Hybrid Electric Bus Parameters Optimization

Hongwen He^*, Lu Yi, Jiankun Peng

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

7 Citations (Scopus)

Abstract

In this paper, the fuel economy is chosen as the optimization target of a Plug-in hybrid electric bus (PHEB). The optimization mathematical model of PHEB powertrain parameters is established, which is based on optimal energy management strategy, and the energy management strategy of this model is formulated by dynamic programming (DP) algorithm. Firstly, PHEB fuel economy is chosen as the objective function of parameter optimization. Then, combinatorial optimization algorithm is designed by Multi-Island genetic algorithm (MIGA) and Sequential Quadratic Programming-NLPQL. MIGA is used for global optimization firstly, and the NLPQL is used for local optimization. Finally, experiments results prove that PHEB fuel consumption per 100 km has reduced to 17.41 L diesel from 18.51 L diesel, and electricity consumption per 100 km remains the same level.

Original language	English
Pages (from-to)	2460-2465
Number of pages	6
Journal	Energy Procedia
Volume	105
DOIs	https://doi.org/10.1016/j.egypro.2017.03.708
Publication status	Published - 2017
Event	8th International Conference on Applied Energy, ICAE 2016 - Beijing, China Duration: 8 Oct 2016 → 11 Oct 2016

Keywords

Multi-Island genetic algorithm
Parameters optimization
Plug-in Hybrid electric bus
Sequential Quadratic Programming-NLPQL

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.egypro.2017.03.708

Cite this

@article{7b786a030ad24f739e8a8a7432d178ae,

title = "Combinatorial Optimization Algorithm of MIGA and NLPQL for a Plug-in Hybrid Electric Bus Parameters Optimization",

abstract = "In this paper, the fuel economy is chosen as the optimization target of a Plug-in hybrid electric bus (PHEB). The optimization mathematical model of PHEB powertrain parameters is established, which is based on optimal energy management strategy, and the energy management strategy of this model is formulated by dynamic programming (DP) algorithm. Firstly, PHEB fuel economy is chosen as the objective function of parameter optimization. Then, combinatorial optimization algorithm is designed by Multi-Island genetic algorithm (MIGA) and Sequential Quadratic Programming-NLPQL. MIGA is used for global optimization firstly, and the NLPQL is used for local optimization. Finally, experiments results prove that PHEB fuel consumption per 100 km has reduced to 17.41 L diesel from 18.51 L diesel, and electricity consumption per 100 km remains the same level.",

keywords = "Multi-Island genetic algorithm, Parameters optimization, Plug-in Hybrid electric bus, Sequential Quadratic Programming-NLPQL",

author = "Hongwen He and Lu Yi and Jiankun Peng",

note = "Publisher Copyright: {\textcopyright} 2017 The Authors.; 8th International Conference on Applied Energy, ICAE 2016 ; Conference date: 08-10-2016 Through 11-10-2016",

year = "2017",

doi = "10.1016/j.egypro.2017.03.708",

language = "English",

volume = "105",

pages = "2460--2465",

journal = "Energy Procedia",

issn = "1876-6102",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - Combinatorial Optimization Algorithm of MIGA and NLPQL for a Plug-in Hybrid Electric Bus Parameters Optimization

AU - He, Hongwen

AU - Yi, Lu

AU - Peng, Jiankun

PY - 2017

Y1 - 2017

N2 - In this paper, the fuel economy is chosen as the optimization target of a Plug-in hybrid electric bus (PHEB). The optimization mathematical model of PHEB powertrain parameters is established, which is based on optimal energy management strategy, and the energy management strategy of this model is formulated by dynamic programming (DP) algorithm. Firstly, PHEB fuel economy is chosen as the objective function of parameter optimization. Then, combinatorial optimization algorithm is designed by Multi-Island genetic algorithm (MIGA) and Sequential Quadratic Programming-NLPQL. MIGA is used for global optimization firstly, and the NLPQL is used for local optimization. Finally, experiments results prove that PHEB fuel consumption per 100 km has reduced to 17.41 L diesel from 18.51 L diesel, and electricity consumption per 100 km remains the same level.

AB - In this paper, the fuel economy is chosen as the optimization target of a Plug-in hybrid electric bus (PHEB). The optimization mathematical model of PHEB powertrain parameters is established, which is based on optimal energy management strategy, and the energy management strategy of this model is formulated by dynamic programming (DP) algorithm. Firstly, PHEB fuel economy is chosen as the objective function of parameter optimization. Then, combinatorial optimization algorithm is designed by Multi-Island genetic algorithm (MIGA) and Sequential Quadratic Programming-NLPQL. MIGA is used for global optimization firstly, and the NLPQL is used for local optimization. Finally, experiments results prove that PHEB fuel consumption per 100 km has reduced to 17.41 L diesel from 18.51 L diesel, and electricity consumption per 100 km remains the same level.

KW - Multi-Island genetic algorithm

KW - Parameters optimization

KW - Plug-in Hybrid electric bus

KW - Sequential Quadratic Programming-NLPQL

UR - http://www.scopus.com/inward/record.url?scp=85020744534&partnerID=8YFLogxK

U2 - 10.1016/j.egypro.2017.03.708

DO - 10.1016/j.egypro.2017.03.708

M3 - Conference article

AN - SCOPUS:85020744534

SN - 1876-6102

VL - 105

SP - 2460

EP - 2465

JO - Energy Procedia

JF - Energy Procedia

T2 - 8th International Conference on Applied Energy, ICAE 2016

Y2 - 8 October 2016 through 11 October 2016

ER -

Combinatorial Optimization Algorithm of MIGA and NLPQL for a Plug-in Hybrid Electric Bus Parameters Optimization

Abstract

Keywords

UN SDGs

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