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A Comparison of PSO, GA and PSO-GA Hybrid Algorithms for Model-based Fuel Economy Optimization of a Hybrid-Electric Vehicle

Jiang, Siyu
http://rave.ohiolink.edu/etdc/view?acc_num=osu156612591067731

Abstract Details

2019, Master of Science, Ohio State University, Mechanical Engineering.
The automotive industry is driving towards electrification. As the emission and fuel economy standards get more stringent, manufactures are electrifying their vehicle platforms by developing more hybrid electric vehicles. Although new technology boosts the fuel economy, it also brings new challenges. One of them is that customers often find discrepancies between the rated fuel economy number and the number they get during real world operation. Therefore, there is a need to investigate the issue and develop a new calibration process for optimizing the HEV fuel economy over both certification and real-world operation. In this research, a model-based calibration process is developed. The process uses meta-heuristic algorithms to optimize five look-up tables that are relevant to fuel economy of the HEV. Four different meta-heuristic algorithms, namely PSO, GA and two hybrids, are investigated and compared. It is found that PSO has reasonably good performance and can deliver its performance consistently under different conditions. Other algorithms may have better performance under certain scenarios, but they are sensitive to constraints in test problems and fail to get rational solutions in the real problem. The research also investigates methods to reduce number of parameters to optimize, the initialization of the optimization set and ways to generate representative drive cycles based on real-world driving data. The important thing is that these methods are not vehicle-specific and therefore can be migrated to calibration of other HEVs easily.
Giorgio Rizzoni (Advisor)
Marcello Canova (Committee Member)
121 p.
Mechanical Engineering
Hybrid Electric Vehicle; Fuel Economy Optimization; Meta-heuristic algorithms; PSO; GA; Hybrid Algorithm

Recommended Citations

Citations

  • Jiang, S. (2019). A Comparison of PSO, GA and PSO-GA Hybrid Algorithms for Model-based Fuel Economy Optimization of a Hybrid-Electric Vehicle [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu156612591067731

    APA Style (7th edition)

  • Jiang, Siyu. A Comparison of PSO, GA and PSO-GA Hybrid Algorithms for Model-based Fuel Economy Optimization of a Hybrid-Electric Vehicle. 2019. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu156612591067731.

    MLA Style (8th edition)

  • Jiang, Siyu. "A Comparison of PSO, GA and PSO-GA Hybrid Algorithms for Model-based Fuel Economy Optimization of a Hybrid-Electric Vehicle." Master's thesis, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu156612591067731

    Chicago Manual of Style (17th edition)

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© 2019, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.