Earticle

초록

영어

Compared with the traditional fuel vehicle, the pure electric vehicle has excellent characteristics in the emissions and energy use. But its driving ranges are much shorter than the traditional fuel vehicle. It has become a bottleneck problem for the development of electric vehicle. It is difficult to establish the accurate model of driving ranges in the actual working condition. Its main reason is that the influence factors of electric vehicle driving ranges and driving ranges have a non-linear relationship. BP neural network can map the complex non-linear relationship and has strong non-linear fitting ability. Compared the driving ranges of fuzzy control with fuzzy PI control in pure electric vehicle with dual-energy storage system, the results of simulation experiment indicate that the fuzzy PI control can extend the driving rages of electric vehicle. The maximum value of the average error is 2.66% in BP neural network prediction.

목차

Abstract
 1. The Influence Factors of Driving Ranges
 2. Driving Ranges Calculation
 3. Driving Ranges Prediction Based on BP Neural Network
  3.1. The Neural Network Model
  3.2. The Principle and Steps of BP Neural Network Algorithm
  3.3. Driving Ranges Prediction Based on BP Neural Network
 4. Conclusions
 Acknowledgement
 References

키워드

저자

• Shuang Du [ College of Mechanical Science and Engineering, Jilin University Changchun City, Jilin Province, China, College of Engineering Technology, Jilin Agricultural University Changchun City, Jilin Province, China ]
• Chuncheng Zuo [ College of Mechanical Science and Engineering, Jilin University Changchun City, Jilin Province, China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605