Multi-objective parameter optimization of a series hybrid electric vehicle using evolutionary algorithms

Zhang, Bingzhan; Chen, Zhihang; Mi, Chris; Murphey, Yi Lu

doi:10.1109/vppc.2009.5289749

Cited by 15 publications

(5 citation statements)

References 9 publications

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“…These two algorithms can solve the problem of accuracy and efficiency of the optimization algorithm, but because of the high coupling between the powertrain and control strategy of hybrid electric vehicle, it is not a good choice to separate the two parameters. [4,17,18] considering the coupling problem of hybrid electric vehicle, the powertrain parameters and control strategy parameters, such as transmission ratio and EMS threshold, were considered in the optimization process. [19] directly uses deep learning to train energy management strategies so that they have better performance under comprehensive conditions.…”

Section: Optimization Of Powertrain and Emsmentioning

confidence: 99%

Research on Multi-objective Parameter Dynamic Optimization Design of HEV Considering Mode Transition

Huang¹,

Zhi-yu

Qiu³

et al. 2022

mech

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The parameter optimization of hybrid electric vehicle is very complex and has always been the focus of research, due to the highly intertwined relationship between powertrain configurations and EMS. In addition, automated powertrain is typical cyber-physics system that contains discrete states, such as modes and gears. Transition between discrete states is a contributing factor that causes discomfort. Therefore, in order to solve the above problems, this paper proposes a multi-objective dynamic optimization algorithm considering mode transition. The algorithm consists of two layers. The first layer optimizes fuel economy and dynamic performance, the second layer optimizes ride comfort and smoothness performance, and the results of the second layer are returned to the first layer for re-optimization in real time. The mode transition frequency is used as the comfort objective function, and the threshold value of the engine high efficiency working area is added to the optimization parameter selection. The results show that the optimization method proposed in this paper realizes the parameter decoupling, and the optimization results achieve the global optimization. Effectively improves the vehicle performance and reduces engine start-stop frequently.

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Section: Optimization Of Powertrain and Emsmentioning

confidence: 99%

Research on Multi-objective Parameter Dynamic Optimization Design of HEV Considering Mode Transition

Huang¹,

Zhi-yu

Qiu³

et al. 2022

mech

View full text Add to dashboard Cite

show abstract

“…Evidence indicates that HEVs have higher FE variability due to variation in driving patterns compared to conventional vehicles [17][18][19], but no analysis has been found to explore the reasons for the variability. Research studies have been conducted over the years for the improvement of FE in HEVs [20][21][22][23][24][25][26][27][28][29][30][31], however, FE variability has been overlooked. In few recent papers [32][33][34] a design optimisation methodology has been proposed to reduce FE variability in real-world.…”

Section: Introductionmentioning

confidence: 99%

Understanding Real-World Variability of Hybrid Electric Vehicle Fuel Economy

Roy¹,

McGordon

Jennings

2020

SAE Int. J. Elec. Veh.

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The variability of fuel economy (FE) is of significant importance as that of average FE to realize FE benefits of hybrid electric vehicles (HEVs) consistently by all users in the real world. Over the years, majority of the research has been focused on improving average FE overlooking the variability. Although in recent years few studies have been focused on the reduction of FE variability, no study has been concentrated to understand why certain design has lower FE variability as that of others. This article provides a detailed analysis to decipher the reasons for the FE variability in the real world. This study considered the optimum designs based on two established design optimization methodologies considering Toyota Prius non-plug-in hybrid as a base vehicle. This study analyses the impacts of the parameters of driving patterns and the operation of powertrains on FE variability. The study explains that comparatively bigger internal combustion engine (ICE) in combination with the optimum sizes of generator motor and battery could lead to lower FE variability in the real world due to lesser time of operation of ICE to charge the battery.

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“…The optimization problem is treated as a black box without implicit mathematical derivation, which makes it suitable to optimize vehicle performance as the explicit derivation of the vehicle performance is complex. In the past decade, various applications involving MOEA showed great potential in solving complex vehicle designs [12][13][14][15][16][17][18][19][20][21]. This paper aims to demonstrate the application of evolutionary algorithms to optimize the powertrain and aerodynamic design of racing vehicles.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of virtual formula powertrain design for enhanced performance and fuel efficiency

Tey

Shak

2019

SN Appl. Sci.

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Formula 1 is considered an important motorsport event that has driven the development of cutting-edge automotive technology. Similarly, virtual formula competition is introduced at the university level to harness technological advances for automotive development among universities. Performance and fuel efficiency have always been two conflicting objectives in determining the optimal powertrain design. In this paper, state-of-the-art multi-objective evolutionary algorithms (MOEAs) are employed to optimize the vehicle powertrain design based on competing event and the ontrack performance through software in-the-loop optimization approach. Unlike conventional powertrain optimization approach where the powertrain design is optimized again standard urban driving profiles to achieve optimum driving performance and fuel consumption which is not suitable to be employed to optimize race car powertrain design. The powertrain design is optimized and benchmarked in multiple dynamic test events i.e. Acceleration, Autocross and Endurance. Each event measures different performance parameters (i.e. lap time and fuel consumption) A total of nine design parameters are optimized in this study. Three design parameters (employed to formulate the progressive gear calculation of six gear ratios), five gear shifting rpm (revolutions per minute) and an aerodynamic downforce scale value (for front and rear downforce). Two stages of optimization solution are proposed to handle such complicated design requirement. NSGA-II was identified as the most efficient algorithms among the MOEAs employed in this study; especially in terms of computational time, Pareto distribution and Pareto design. Overall, the optimized powertrain design showed significant improvement in all events i.e. energy efficiency was increased by 13.7% for the endurance event while lap time was reduced by 0.7 and 1.2% in the acceleration and autocross events, respectively.

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Multi-objective parameter optimization of a series hybrid electric vehicle using evolutionary algorithms

Cited by 15 publications

References 9 publications

Research on Multi-objective Parameter Dynamic Optimization Design of HEV Considering Mode Transition

Research on Multi-objective Parameter Dynamic Optimization Design of HEV Considering Mode Transition

Understanding Real-World Variability of Hybrid Electric Vehicle Fuel Economy

Multi-objective optimization of virtual formula powertrain design for enhanced performance and fuel efficiency

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