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Improving transmission efficiency and reducing energy consumption with automotive continuously variable transmission: A model prediction comprehensive optimization approach

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  • Liu, Hongxiang
  • Han, Ling
  • Cao, Yue

Abstract

This paper proposes an optimal comprehensive control framework to maximize the powertrain transmission efficiency and fuel economy meeting vehicle owners’ travel needs. The proposed control framework consists of vehicle layers and verification. In the vehicle layer, creatively, each set of automotive Continuously Variable Transmission (CVT) supply equipment is equipped with original comprehensive controller that estimates engine fuel consumption and CVT efficiency based on a newly dynamic equation of CVT, engine torque, clamping force, and upcoming trip information. The controller obtains the optimal input of engine torque and clamping force by repeatedly solving the optimization problem of each sampling point in real-time. In the verification layer, with the powertrain system model received from the comprehensive controller, the robustness was verified by bench and hardware-in-the-loop simulation platform. The proposed comprehensive optimization approach can enhance accuracy and solve the multi-objective control problems compared with traditional methods. It is also scalable to the expanding CVT fleet and robust to uncertainties future system condition and in further to meet the vehicle owner’s travel comfort and economy. The proposed method is studied using a prototypical driving platform developed at State Key Laboratory of Automotive Safety and Energy of Tsinghua University and detailed trip information extracted from 2019 BEIJING Transport Annual Report Survey. The main contribution of this study is to explore a novel way to optimize the comprehensive transmission efficiency of engine and CVT, compared with the Proportional Integral Derivative (PID) control, the comprehensive controller can improve the transmission efficiency by 8.92%, and reduce the fuel consumption by 4.9%, it also can save about 90 million liters of fuel and 70 million US dollar for CVT vehicles in Beijing.

Suggested Citation

  • Liu, Hongxiang & Han, Ling & Cao, Yue, 2020. "Improving transmission efficiency and reducing energy consumption with automotive continuously variable transmission: A model prediction comprehensive optimization approach," Applied Energy, Elsevier, vol. 274(C).
    • Handle: RePEc:eee:appene:v:274:y:2020:i:c:s0306261920308151
    DOI: 10.1016/j.apenergy.2020.115303
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    References listed on IDEAS

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    Cited by:

    1. Huijun Yue & Jinyu Lin & Peng Dong & Zhinan Chen & Xiangyang Xu, 2023. "Configurations and Control Strategies of Hybrid Powertrain Systems," Energies, MDPI, vol. 16(2), pages 1-18, January.
    2. Yehui Zhao & Xiaohan Chen & Yue Song & Guangming Wang & Zhiqiang Zhai, 2023. "Energy and Fuel Consumption of a New Concept of Hydro-Mechanical Tractor Transmission," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
    3. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    4. Galindo, José & Serrano, José Ramón & De la Morena, Joaquín & Gómez-Vilanova, Alejandro, 2022. "Physical-based variable geometry turbines predictive control to enhance new hybrid powertrains’ transient response," Energy, Elsevier, vol. 261(PB).
    5. Li, Lei & Huang, Haihong & Zou, Xiang & Zhao, Fu & Li, Guishan & Liu, Zhifeng, 2021. "An energy-efficient service-oriented energy supplying system and control for multi-machine in the production line," Applied Energy, Elsevier, vol. 286(C).
    6. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2023. "Investigation of novel intelligent energy management strategies for connected HEB considering global planning of fixed-route information," Energy, Elsevier, vol. 263(PB).

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