Earticle

초록

영어

This paper proposed a circular particle swarm optimization least squares (CPSOLS) method which is consisted of the regularized least squares (RLS) method and the adaptive particle swarm optimization (APSO) algorithm. The RLS algorithm optimized the parameters of the RBF network, aiming at the phenomenon of RLS trapping in the local minimum, introduced the penalty factor and used the global optimization ability of the particle swarm optimization algorithm to make it out of the local minimum; simplified the structure of the RBF network and improved the generalization ability of the network. The APSO algorithm weakened the precocious converge phenomena of the particle swarm optimization algorithm, adopted the adaptive selection of the nonlinear dynamic inertia weight which is guided by the control factor of the battery external characteristic temperature parameters, optimized the link weight of the RBF network, improved the state of charge (SOC) estimation accuracy and real-time performance of the RBF network. Using the Arbin multifunctional battery test system BT2000 to collect the sample data of the battery external characteristic parameters, and using the sample data to train and optimize the RBF neural network, and estimate the SOC of the batteries. The results showed that the optimized RBF network improved the SOC estimation accuracy and real-time performance.

목차

Abstract
 1. Introduction
 2. Circular Optimization RBF Neural Network Algorithm
  2.1. Principle of RLS Algorithm
  2.2. Adaptive Particle Swarm Optimization
  2.3. Using CPSOLS Optimize RBF
 3. SOC Estimation Experimental Verification
 4. Conclusion
 References

키워드

저자

• Tiezhou Wu [ Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy Hubei University of technology, Wuhan 430068, China ]
• Xiaomin Wu [ Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy Hubei University of technology, Wuhan 430068, China ]
• Mengmeng Yang [ Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy Hubei University of technology, Wuhan 430068, China ]
• Meng Luo [ Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy Hubei University of technology, Wuhan 430068, China ]

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