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Synthesis and Feature Selection-Supported Validation of Multidimensional Driving Cycles

Author

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  • Jakov Topić

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10002 Zagreb, Croatia)

  • Branimir Škugor

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10002 Zagreb, Croatia)

  • Joško Deur

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10002 Zagreb, Croatia)

Abstract

This paper presents the synthesis and validation of multidimensional driving cycles represented by vehicle velocity, vehicle acceleration, and road slope profiles. For this purpose, a rich set of city bus driving cycles has been recorded. First, a Markov chain model is established based on velocity, acceleration, road slope and road slope time derivative states. Next, a large set of synthetic driving cycles is generated by using a corresponding 8D transition probability matrix, which is implemented in a sparse form based on a dictionary of keys to improve computational efficiency and reduce memory requirements. In support of synthetic driving cycles validation, a number of time- and frequency-domain statistical features are considered, including unique cross-correlation velocity–acceleration–road slope indicators. To predict fuel consumption related to synthetic driving cycles, an accurate neural network model is introduced which uses a fixed 3D histogram of counted discrete velocity, acceleration, and road slope inputs. The significance of each nominated statistical feature and its impact on fuel consumption is revealed by means of linear regression modelling and least absolute shrinkage and selection operator (LASSO) feature selection method. A model having only several most significant features as inputs and fuel consumption as output is proposed to be used for unambiguous single-criterion validation of synthetic driving cycles with respect to recorded ones. Finally, the proposed validation approach is verified against a widely used method relying on minimization of statistical feature deviations with respect to true values. The results point out that, by applying the proposed synthesis and validation method, it is possible to extract most representative synthetic driving cycles in a straightforward and computationally efficient way. The main anticipated applications include various simulation-based analyses that require representative synthetic driving cycles and/or accurate vehicle energy consumption predictions.

Suggested Citation

  • Jakov Topić & Branimir Škugor & Joško Deur, 2021. "Synthesis and Feature Selection-Supported Validation of Multidimensional Driving Cycles," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4704-:d:541579
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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. Claudio Cubito & Federico Millo & Giulio Boccardo & Giuseppe Di Pierro & Biagio Ciuffo & Georgios Fontaras & Simone Serra & Marcos Otura Garcia & Germana Trentadue, 2017. "Impact of Different Driving Cycles and Operating Conditions on CO 2 Emissions and Energy Management Strategies of a Euro-6 Hybrid Electric Vehicle," Energies, MDPI, vol. 10(10), pages 1-18, October.
    3. Jakov Topić & Jure Soldo & Filip Maletić & Branimir Škugor & Joško Deur, 2020. "Virtual Simulation of Electric Bus Fleets for City Bus Transport Electrification Planning," Energies, MDPI, vol. 13(13), pages 1-24, July.
    4. Branimir Škugor & Joško Deur, 2016. "Delivery vehicle fleet data collection, analysis and naturalistic driving cycles synthesis," International Journal of Innovation and Sustainable Development, Inderscience Enterprises Ltd, vol. 10(1), pages 19-39.
    5. Jiankun Peng & Jiwan Jiang & Fan Ding & Huachun Tan, 2020. "Development of Driving Cycle Construction for Hybrid Electric Bus: A Case Study in Zhengzhou, China," Sustainability, MDPI, vol. 12(17), pages 1-19, September.
    6. Jakov Topić & Branimir Škugor & Joško Deur, 2019. "Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range," Energies, MDPI, vol. 12(7), pages 1-20, April.
    7. Hyeonjik Lee & Kihyung Lee, 2020. "Comparative Evaluation of the Effect of Vehicle Parameters on Fuel Consumption under NEDC and WLTP," Energies, MDPI, vol. 13(16), pages 1-19, August.
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    Cited by:

    1. Jakov Topić & Branimir Škugor & Joško Deur, 2022. "Receding-Horizon Prediction of Vehicle Velocity Profile Using Deterministic and Stochastic Deep Neural Network Models," Sustainability, MDPI, vol. 14(17), pages 1-20, August.
    2. Zvonimir Dabčević & Branimir Škugor & Jakov Topić & Joško Deur, 2022. "Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology," Energies, MDPI, vol. 15(11), pages 1-21, June.

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