The Study on Multi-scale Prediction of Future Driving Cycle Based on Markov Chain

Yuecheng Li, Jiankun Peng, Hongwen He*, Shanshan Xie

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

25 Citations (Scopus)

Abstract

In this paper, a multi-scale single-step future driving cycle prediction method is proposed. The driving cycle is discretized at given time scale, and the mesh accuracy of velocity and acceleration is determined to be 1Km/h and 0.05m/s2 respectively, then Markov state transfer matrix can be obtained by categorizing these discretization points and statistical calculation. After that, future driving cycle prediction can be accomplished by proposed method which combines Markov chain and Monte Carlo method. Finally, Root-Mean-Square Deviation is introduced to assess the prediction accuracy. Comparing the prediction accuracy of multi-scale single-step method and traditional fix-scale multi-step method, it can be found that the prediction results of proposed method reach expectant accuracy improving by 7.18% on average.

Original languageEnglish
Pages (from-to)3219-3224
Number of pages6
JournalEnergy Procedia
Volume105
DOIs
Publication statusPublished - 2017
Event8th International Conference on Applied Energy, ICAE 2016 - Beijing, China
Duration: 8 Oct 201611 Oct 2016

Keywords

  • Driving cycle prediction
  • Markov chain
  • Multi-scale single-step prediction
  • Root-mean-square deviation

Fingerprint

Dive into the research topics of 'The Study on Multi-scale Prediction of Future Driving Cycle Based on Markov Chain'. Together they form a unique fingerprint.

Cite this