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A Sticky Sampling and Markov State Transition Matrix Based Driving Cycle Construction Method for EV

Author

Listed:
  • Li Zhao

    (School of Mechanical and electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100192, China)

  • Kun Li

    (School of Mechanical and electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China)

  • Wu Zhao

    (Jiangsu Vocational Institute of Architectural Technology, Xuzhou 221116, China)

  • Han-Chen Ke

    (School of Mechanical and electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China)

  • Zhen Wang

    (School of Mechanical and electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China)

Abstract

Driving cycle (DC) plays an important role in designing and evaluating EVs, and many Markov chain-based DC construction methods describe driving profiles of unfixed-line vehicles with Markov state transition probability. However, for fixed-line electric vehicles, the time-sequence of microtrips brings huge influences on their brake, drive, and battery management systems. Simply describing topography, traffic, location, driving features, and environment in a stochastic manner cannot reflect the continuity characteristics hidden in a fixed route. Thus, in this paper, we propose a sticky sampling and Markov state transition matrix based DC construction algorithm to describe both randomness and continuity hidden in a fixed route, in which a data structure named “driving pulse chain” was constructed to describe the sequence of the driving scenarios and several Markov state transition matrices were constructed to describe the random distribution of velocity and acceleration in same driving scenarios. Simulation and experimental analysis show that with sliding window and driving pulse chain, the proposed algorithm can describe and reflect the continuity characteristics of topography, traffic, and location. At the same time, the stochastic nature of the driving cycle can be preserved.

Suggested Citation

  • Li Zhao & Kun Li & Wu Zhao & Han-Chen Ke & Zhen Wang, 2022. "A Sticky Sampling and Markov State Transition Matrix Based Driving Cycle Construction Method for EV," Energies, MDPI, vol. 15(3), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1057-:d:739349
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    References listed on IDEAS

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    1. José I. Huertas & Michael Giraldo & Luis F. Quirama & Jenny Díaz, 2018. "Driving Cycles Based on Fuel Consumption," Energies, MDPI, vol. 11(11), pages 1-13, November.
    2. José Ignacio Huertas & Luis Felipe Quirama & Michael Giraldo & Jenny Díaz, 2019. "Comparison of Three Methods for Constructing Real Driving Cycles," Energies, MDPI, vol. 12(4), pages 1-15, February.
    3. Hong Zhang & Li Zhao & Yong Chen, 2015. "A Lossy Counting-Based State of Charge Estimation Method and Its Application to Electric Vehicles," Energies, MDPI, vol. 8(12), pages 1-18, December.
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    Cited by:

    1. Mateusz Oszczypała & Jarosław Ziółkowski & Jerzy Małachowski, 2022. "Analysis of Light Utility Vehicle Readiness in Military Transportation Systems Using Markov and Semi-Markov Processes," Energies, MDPI, vol. 15(14), pages 1-24, July.
    2. Mateusz Oszczypała & Jarosław Ziółkowski & Jerzy Małachowski, 2023. "Modelling the Operation Process of Light Utility Vehicles in Transport Systems Using Monte Carlo Simulation and Semi-Markov Approach," Energies, MDPI, vol. 16(5), pages 1-31, February.

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