Particle swarm optimization for plug-in hybrid electric vehicle control strategy parameter

2008 ◽  
Author(s):  
Xiaolan Wu ◽  
Binggang Cao ◽  
Jianping Wen ◽  
Yansheng Bian
Keyword(s):  
Particle Swarm Optimization ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Particle Swarm ◽  
Vehicle Control ◽  
Swarm Optimization ◽  
Hybrid Electric

scholarly journals Research on Speed Optimization Strategy of Hybrid Electric Vehicle Queue Based on Particle Swarm Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Shaohua Wang ◽  
Chengquan Yu ◽  
Dehua Shi ◽  
Xiaoqiang Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Particle Swarm ◽  
Superior Performance ◽  
Optimization Strategy ◽  
Swarm Optimization ◽  
Speed Optimization ◽  
Hybrid Electric

Traffic lights intersections are common in cities and have an impact on the energy consumption of vehicles, so it is significant to optimize the velocities of vehicles in urban road conditions. The novel speed optimization strategy for hybrid electric vehicle (HEV) queue that helps reduce fuel consumption and improve traffic efficiency is presented in this paper, where real-world traffic signal information is used to construct the research scenario. The initial values of the target velocities are obtained based on the signal phase and timing (SPAT). Then the particle swarm optimization (PSO) algorithm is used to solve the nonlinear constrained problem and obtain the optimal target velocities based on vehicle to vehicle communication (V2V) and vehicle to infrastructure communication (V2I). The lower controller, which applies rule based control strategy, is designed to split the power of the engine and two electric motors in a power split HEV, which is quite promising because of its advantages in fuel economy. Simulation results demonstrate the superior performance of the proposed strategy in reducing fuel consumption of the HEV queue and improving traffic smoothness.

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