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Particle Swarm Optimization for Optimal Powertrain parameters of parallel-series hybrid electric vehicles,"Presenter"

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Available on this site
Format
PDF
Price
Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
Publication code
20181826
Paper/Info type
AVEC

No.ThE1-3

Pages
1-6(Total 6 p)
Date of publication
Jul 2018
Publisher
Others, Unknown
Language
English
Event
AVEC '18

Detailed Information

Category(E)Hybrid EV Control I
Author(E)1) Tianjun Zhu
Abstract(E)This paper proposes a powertrain parameters design approach based on particle swarm optimization (PSO) algorithm. The optimization objective is to optimize transmission ratio and final drive ratio to achieve the minimization of fuel consumption with optimal vehicle performance. The original multi-objective optimization problem is converted into a single-objective problem with a goal-attainment method, and the principal parameters of powertrain are set as the optimized variables by PSO algorithm, with the vehicle performance indexes of parallel-series hybrid electric vehicles (PHEVs) being defined as the constraint conditions. The proposed strategy has been verified by the driving cycle under the MATLAB/Simulink software environment. Simulation results indicate that the proposed PSO-based powertrain parameters optimization method can achieve better fuel efficiency compared with traditional strategies.

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