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

Bolin Zhao, Theo Hofman, Chen Lv, Maarten Steinbuch

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

13 Citaten (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.

Originele taal-2Engels
Titel2018 IEEE Intelligent Vehicles Symposium, IV 2018
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Aantal pagina's6
ISBN van elektronische versie978-1-5386-4452-2
ISBN van geprinte versie978-1-5386-4453-9
StatusGepubliceerd - 18 okt. 2018
Evenement2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China
Duur: 26 sep. 201830 sep. 2018


Congres2018 IEEE Intelligent Vehicles Symposium, IV 2018
StadChangshu, Suzhou


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