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Numerical Calculation Method of Model Predictive Control for Integrated Vehicle Thermal Management Based on Underhood Coupling Thermal Transmission

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  • Pengyu Lu

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
    College of Automotive Engineering, Jilin University, Changchun 130025, China)

  • Qing Gao

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
    College of Automotive Engineering, Jilin University, Changchun 130025, China)

  • Liang Lv

    (College of Telecommunication Engineering, Jilin University, Changchun 130025, China)

  • Xiaoye Xue

    (FAW-Volkswagen Automobile Co. LTD, Changchun 130013, China)

  • Yan Wang

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
    College of Automotive Engineering, Jilin University, Changchun 130025, China)

Abstract

The nonlinear model predictive control (NMPC) controller is designed for an engine cooling system and aims to control the pump speed and fan speed according to the thermal load, vehicle speed, and ambient temperature in real time with respect to the coolant temperature and comprehensive energy consumption of the system, which serve as the targets. The system control model is connected to the underhood computational fluid dynamics (CFD) model by the coupling thermal transmission equation. For the intricate thermal management process predictive control and system control performance analysis, a coupling multi-thermodynamic system nonlinear model for integrated vehicle thermal management was established. The concept of coupling factor was proposed to provide the boundary conditions considering the thermal transmission interaction of multiple heat exchangers for the radiator module. Using the coupling factor, the thermal flow influence of the structural characteristics in the engine compartment was described with the lumped parameter method, thereby simplifying the space geometric feature numerical calculation. In this way, the coupling between the multiple thermodynamic systems mathematical model and multidimensional nonlinear CFD model was realized, thereby achieving the simulation and analysis of the integrated thermal management multilevel cooperative control process based on the underhood structure design. The research results indicated an excellent capability of the method for integrated control analysis, which contributed to solving the design, analysis, and optimization problems for vehicle thermal management. Compared to the traditional engine cooling mode, the NMPC thermal management scheme clearly behaved the better temperature controlling effects and the lower system energy consumption. The controller could further improve efficiency with reasonable coordination of the convective thermal transfer intensity between the liquid and air sides. In addition, the thermal transfer structures in the engine compartment could also be optimized.

Suggested Citation

  • Pengyu Lu & Qing Gao & Liang Lv & Xiaoye Xue & Yan Wang, 2019. "Numerical Calculation Method of Model Predictive Control for Integrated Vehicle Thermal Management Based on Underhood Coupling Thermal Transmission," Energies, MDPI, vol. 12(2), pages 1-27, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:2:p:259-:d:197948
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    References listed on IDEAS

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    1. Khaled, Mahmoud & Mangi, Fareed & Hage, Hisham El & Harambat, Fabien & Peerhossaini, Hassan, 2012. "Fan air flow analysis and heat transfer enhancement of vehicle underhood cooling system – Towards a new control approach for fuel consumption reduction," Applied Energy, Elsevier, vol. 91(1), pages 439-450.
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    6. Khaled, Mahmoud & Harambat, Fabien & Hage, Hicham El & Peerhossaini, Hassan, 2011. "Spatial optimization of an underhood cooling module – Towards an innovative control approach," Applied Energy, Elsevier, vol. 88(11), pages 3841-3849.
    7. Khodabakhshian, Mohammad & Feng, Lei & Börjesson, Stefan & Lindgärde, Olof & Wikander, Jan, 2017. "Reducing auxiliary energy consumption of heavy trucks by onboard prediction and real-time optimization," Applied Energy, Elsevier, vol. 188(C), pages 652-671.
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    Cited by:

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