Earticle

초록

영어

Extended-range electric vehicle is considered to be the ideal transition type for electric vehicle. The optimal operation curve control strategy was proposed for a 12 meter-long range extended electric bus. With exponential function inertia weight adjustment and local chaos substitution, an improved chaotic particle swarm optimization algorithm was applied to optimize the key parameters of energy management strategy. Based on MATLAB/Simulink, full vehicle model and corresponding control strategy were built. The simulation results with typical city driving cycles illustrate that, comparing with standard particle swarm optimization, the new algorithm can greatly improve the convergence speed and optimizing precision, and the optimal parameters can be obtained.

목차

Abstract
 1. Introduction
 2. E-REV Energy Management Control Strategy
  2.1. Power-Train System Structure
  2.2. Optimal Operation Curve Control Strategy
 3. Improved Chaotic Particle Swarm Optimization (ICPSO) Algorithm
  3.1. Chaotic Mapping
  3.2. Exponential Function Inertia Weight Adjustment Strategy
  3.3. Local Chaos Substitution
 4. Parameters Optimization based on ICPSO Algorithm
  4.1. Optimization Objective
  4.2. Parameters Optimization
 5. Simulation and Result Analysis
 6. Conclusions
 Acknowledgements
 References

키워드

저자

• Yongchen Jiang [ Collaborative Innovation Center for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China ]
• Cheng Lin [ Collaborative Innovation Center for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China ]
• Wanke Cao [ Collaborative Innovation Center for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605