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Energy-Based Design of Powertrain for a Re-Engineered Post-Transmission Hybrid Electric Vehicle

Author

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  • Laura Tribioli

    (Department of Engineering, Niccolò Cusano University, via Don Carlo Gnocchi 3, 00166 Rome, Italy)

Abstract

This paper presents a systematic approach for the design of post-transmission hybrid electric vehicle powertrains, as an instrument aiding the designer in making the right decision. In particular, a post-transmission series/parallel hybrid electric powertrain is considered, and all of the possible energy paths are taken into account, in order to automatically select the configuration that gives the lowest fuel consumption, thus better fitting to the considered mission. The optimization problem is solved with the Dijkstra algorithm, which is more computationally efficient than other optimization algorithms in the case of massive design spaces. In this way, it is possible to design a vehicle in terms of architecture and component sizes, without making any a priori choices, which are usually based on common sense, likely compromising the overall system efficiency. In order to demonstrate the effectiveness of the methodology, different driving cycles have been simulated, and some results are presented. The methodology is particularly applied to re-engineered vehicles, aimed at maximizing the benefits of the vehicle hybridization process. Results show how the introduction, in the optimization algorithm, of the engine load factor and sharing factor, for the engine torque split between the generator and the wheels, is crucial. For example, a 10% reduction of the original engine size, suggested by a low load factor, is able to allow for a 24% reduction in the fuel consumption. On the other hand, the sharing factor is of particular importance in suggesting if the vehicle architecture should be series, parallel or rather combined.

Suggested Citation

  • Laura Tribioli, 2017. "Energy-Based Design of Powertrain for a Re-Engineered Post-Transmission Hybrid Electric Vehicle," Energies, MDPI, vol. 10(7), pages 1-22, July.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:918-:d:103482
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    References listed on IDEAS

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    1. Weichao Zhuang & Xiaowu Zhang & Huei Peng & Liangmo Wang, 2016. "Simultaneous Optimization of Topology and Component Sizes for Double Planetary Gear Hybrid Powertrains," Energies, MDPI, vol. 9(6), pages 1-17, May.
    2. Zhuang, Weichao & Zhang, Xiaowu & Ding, Yang & Wang, Liangmo & Hu, Xiaosong, 2016. "Comparison of multi-mode hybrid powertrains with multiple planetary gears," Applied Energy, Elsevier, vol. 178(C), pages 624-632.
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    Citations

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    Cited by:

    1. Ioan Aschilean & Mihai Varlam & Mihai Culcer & Mariana Iliescu & Mircea Raceanu & Adrian Enache & Maria Simona Raboaca & Gabriel Rasoi & Constantin Filote, 2018. "Hybrid Electric Powertrain with Fuel Cells for a Series Vehicle," Energies, MDPI, vol. 11(5), pages 1-12, May.
    2. Wiesław Grzesikiewicz & Lech Knap & Michał Makowski & Janusz Pokorski, 2018. "Study of the Energy Conversion Process in the Electro-Hydrostatic Drive of a Vehicle," Energies, MDPI, vol. 11(2), pages 1-22, February.
    3. Ming Ye & Yitao Long & Yi Sui & Yonggang Liu & Qiao Li, 2019. "Active Control and Validation of the Electric Vehicle Powertrain System Using the Vehicle Cluster Environment," Energies, MDPI, vol. 12(19), pages 1-21, September.
    4. Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.
    5. Edwin R. Grijalva & José María López Martínez, 2019. "Analysis of the Reduction of CO 2 Emissions in Urban Environments by Replacing Conventional City Buses by Electric Bus Fleets: Spain Case Study," Energies, MDPI, vol. 12(3), pages 1-31, February.
    6. Sahar Koohfar & Wubeshet Woldemariam & Amit Kumar, 2023. "Performance Comparison of Deep Learning Approaches in Predicting EV Charging Demand," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
    7. Yunyan Li & Yuansheng Huang & Meimei Zhang, 2018. "Short-Term Load Forecasting for Electric Vehicle Charging Station Based on Niche Immunity Lion Algorithm and Convolutional Neural Network," Energies, MDPI, vol. 11(5), pages 1-18, May.
    8. Fabio Vacca & Stefano De Pinto & Ahu Ece Hartavi Karci & Patrick Gruber & Fabio Viotto & Carlo Cavallino & Jacopo Rossi & Aldo Sorniotti, 2017. "On the Energy Efficiency of Dual Clutch Transmissions and Automated Manual Transmissions," Energies, MDPI, vol. 10(10), pages 1-22, October.

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