An Improved Fuzzy Energy Management Strategy Based-On Particle Swarm Optimal Algorithm for Electric Vehicle

2016 ◽  
Author(s):  
Yadong Wei ◽  
Hongwei Ma ◽  
Xiaozhong Liao ◽  
Tao Huang
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Energy Management Strategy ◽  
Fuzzy Energy

scholarly journals Multi-objective Parameters Optimization for HEV Based on improved Particle Swarm Algorithm

2019 ◽  
Vol 118 ◽  
pp. 02005
Author(s):  
Ying Ai ◽  
Yuanjie Gao ◽  
dongsheng Liu
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Energy Management Strategy ◽  
Multi Objective

Hybrid electric vehicle fuel consumption and emissions are closely related to its energy management strategy. A fuzzy controller of energy management using vehicle torque request and battery state of charge (SOC) as inputs, engine torque as output is designed in this paper foe parallel hybrid electric vehicle. And a multi-objective mathematical function which purpose on maximize fuel economy and minimize emissions is also established, in order to improve the adaptive ability and the control precision of basic fuzzy controller, this paper proposed an improved particle swarm algorithm that based on dynamic learning factor and adaptive inertia weight to optimize the control parameters. Simulation results based on ADVISOR software platform show that the optimized energy management strategy has a better distribution of engine and motor torque, which helps to improved the vehicle’s fuel economy and exhaust emission performance.

Multi-Objective Optimization Study of Energy Management Strategy for Extended-Range Electric Vehicle

2013 ◽  
Vol 694-697 ◽  
pp. 2704-2709 ◽  
Author(s):  
Ji Gao Niu ◽  
Feng Lai Pei ◽  
Su Zhou ◽  
Tong Zhang
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Energy Management ◽  
Parameter Optimization ◽  
Hybrid Algorithm ◽  
Management Strategy ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Energy Management Strategy ◽  
Extended Range

A new method based on genetic-particle swarm hybrid algorithm was presented for parameter optimization of energy management strategy for extended-range electric vehicle (E-REV). Taking a logic threshold control strategy of an E-REV as example, for the aims of minimizing fuel consumption and emissions, a constrained nonlinear programming parameter optimization model was established. Based on this model, genetic algorithm (GA) and particle swarm optimization (PSO) were improved respectively. Further, a genetic-particle swarm hybrid algorithm was put forward and applied to the multi-objective optimization of E-REV energy management strategy. Optimization results show that the hybrid optimization algorithm can avoid falling into local optimum and its search ability is much better than improved adaptive genetic algorithm (IAGA). This hybrid algorithm is also suitable for the control parameters optimization issues of other types of hybrid electric vehicles.

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