Configuration Analysis and Parameter Matching of Fuel Cell Electric Vehicle Driving System

Zhao, Hongchao; Wang, Yupeng; Huang, Xing; Du, Jing; Ding, Tianwei; Zhao, Zhengxu

doi:10.1109/iecon.2019.8926909

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Formula ( 5) is the objective function with the lowest total cost. Formula ( 6) and (7) indicate that each customer is served and only served once. Formula (8) indicates that the vehicle starts from the distribution center and finally returns to the distribution center.…”

Section: Establish Modelmentioning

confidence: 99%

See 1 more Smart Citation

Path optimization of multi-type vehicle mixed distribution

Teng,

Ren,

Wang

et al. 2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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Section: Establish Modelmentioning

confidence: 99%

“…For example, Liu et al studied the routing optimization of pure electric buses and traditional fuel buses [6]. Zhao et al analyzed the advantages and disadvantages of compensated, hybrid and full-power fuel cell powered vehicles [7]. These studies have laid a theoretical foundation for the study of multi-type vehicle path optimization.…”

Section: Introductionmentioning

confidence: 99%

Path optimization of multi-type vehicle mixed distribution

Teng,

Ren,

Wang

et al. 2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

View full text Add to dashboard Cite

“…In their study, the hybrid, compensation, and full power type of the fuel EV powertrain were analyzed. Their results showed that the cost, performance, and portability were improved in the full-power type powertrain [9].…”

Section: Introductionmentioning

confidence: 99%

Parameter Matching, Optimization, and Classification of Hybrid Electric Emergency Rescue Vehicles Based on Support Vector Machines

et al. 2022

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Based on the requisition for an ideal precise power source for a hybrid electric emergency rescue vehicle (HE-ERV), we present an optimistic parameter matching and optimization schemes for the selection of a HE-ERV. Then, given a set of optimized power source components, they are classified into different types of HE-ERV. In this study, due to the different design objectives of different types of emergency rescue vehicles and the problems of hybrid electric vehicle parameter matching, a multi-island genetic algorithm (MIGA) and non-linear programming quadratic Lagrangian (NLPQL) is proposed for the matched parameters. The vehicle dynamic model is established based on the AVL Cruise simulation platform. The power source performance parameters are matched by theoretical analysis and coupled to the simulation platform. Finally, the optimized matched parameters are classified based on the support vector machines classification model to determine the category of the HE-ERV. The classification results showed that there is an unprecedented level for categorizing several factors of the power source parameters. This research showed that its more logical and reasonable to match HE-ERVs with medium motor/engine power output and battery capacity, as these can attain dynamic performance, extended driving range, and reduced energy consumption.

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