Earticle

초록

영어

Battery SOC is acted as an essential parameter for EV and HEV, its accurate estimate relates to whether the vehicle control strategy is implemented correctly. There has been no other prediction method, which can obtain a more accurate prediction result. To overcome this deficiency, this paper presents a method to build a battery SOC estimation models using principal component analysis (PCA). PCA cannot manage to extract non-linear factors in the parameters, which may induce prediction error based on the analysis result, in view of this kernel principle component analysis (KPCA) is applied to establish the prediction model. Corresponding simulation and experiments are conducted to verify the proposed model. The simulation results have shown that the revised model can be applied to various working condition with superior real-time ability, reliability and improved precision. The average error of the prediction results can be reduced to 1.46% in comparison with Ah measurement.

목차

Abstract
 1. Introduction
 2. Introduction of Principal Component Analysis and Kernel Principal Component Analysis Principle
  2.1 Principle of Principal Component Analysis
  2.2. Kernel Principal Component Analysis Principle
 3. Battery SOC Estimation Model using Kernel Principal Component Analysis
 4. Model Simulation and Experimental Validation
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Haiying Wang [ School of Automation, Harbin University of Science and Technology, Harbin, China ]
• Chao Xue [ School of Automation, Harbin University of Science and Technology, Harbin, China ]
• Qi Fan [ School of Automation, Harbin University of Science and Technology, Harbin, China ]
• Peng Liu [ School of Automation, Harbin University of Science and Technology, Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605