Intelligent synthesis of driving cycle for advanced design and control of powertrains

Bolin Zhao, Theo Hofman, Chen Lv, Maarten Steinbuch

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

13 Citations (Scopus)
1 Downloads (Pure)


As an important input for the simulation and design process of powertrains, a driving cycle needs to be representative of real-world driving behavior. For the purpose of reducing the time consumption in the simulation, a novel modeling method is required to get a representative short driving cycle from the driving datasets. In this paper, a stochastic model based driving cycle synthesis is introduced. The Markov Chain process is combined with a transition probability extracted from the input driving data to determine the next possible state of the vehicle. Specifically, the velocity and slope are generated simultaneously using a three-dimensional Markov Chain model. After the generation process, the result is validated by selected criteria. Furthermore, this synthesis can generate the driving cycle with the desired length to compress the original driving cycle. The results show that the successful compression of the driving cycle can be tested for the fuel economic in the powertrain simulation. At last, the standard deviation of acceleration is found that has a positive correlation of the compression capability of the driving cycle.

Original languageEnglish
Title of host publication2018 IEEE Intelligent Vehicles Symposium, IV 2018
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)978-1-5386-4452-2
ISBN (Print)978-1-5386-4453-9
Publication statusPublished - 18 Oct 2018
Event2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China
Duration: 26 Sept 201830 Sept 2018


Conference2018 IEEE Intelligent Vehicles Symposium, IV 2018
CityChangshu, Suzhou


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