IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1476-d751462.html
   My bibliography  Save this article

Efficiency Increase through Model Predictive Thermal Control of Electric Vehicle Powertrains

Author

Listed:
  • Alexander Wahl

    (Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, Forckenbeckstraße 4, 52074 Aachen, Germany)

  • Christoph Wellmann

    (Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, Forckenbeckstraße 4, 52074 Aachen, Germany)

  • Björn Krautwig

    (Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, Forckenbeckstraße 4, 52074 Aachen, Germany)

  • Patrick Manns

    (Chair of Thermodynamics of Mobile Energy Conversion Systems, Faculty of Mechanical Engineering, RWTH Aachen University, Forckenbeckstraße 4, 52074 Aachen, Germany)

  • Bicheng Chen

    (Chair of Thermodynamics of Mobile Energy Conversion Systems, Faculty of Mechanical Engineering, RWTH Aachen University, Forckenbeckstraße 4, 52074 Aachen, Germany)

  • Christof Schernus

    (FEV Europe GmbH, Neuenhofstr. 181, 52078 Aachen, Germany)

  • Jakob Andert

    (Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, Forckenbeckstraße 4, 52074 Aachen, Germany)

Abstract

Battery electric vehicles (BEVs) are currently enjoying rising sales figures. However, BEVs still have problems with customer acceptance, partly due to limited driving ranges. To improve the situation, this paper introduces a novel approach utilising temperature-dependent efficiencies using an economic model predictive control approach (MPC) in combination with an active grille shutter in order to accelerate the heating of the permanent magnet synchronous machine. The measurements of temperature-dependent component efficiencies on a powertrain test bench are presented and analysed in detail in the speed/torque range. Thermal models based on the lumped parameter thermal network approach were developed and validated as part of the system-level validation against a US06 wind tunnel measurement. After the build-up and implementation of the MPC, various simulations were conducted. For the investigations, three driving cycles were considered at component start temperatures of 20–80 °C. The results show that using the MPC with the grille shutter can save 0.69–2.02% energy at the HV level compared to the rule-based control with a shutter, of which up to 1.02% is due to temperature-dependent efficiencies. Comparing the MPC with the grille shutter to a vehicle without a shutter, savings of 2.8–4.2% were achieved, while up to 1.67% was achieved due to temperature effects in the powertrain.

Suggested Citation

  • Alexander Wahl & Christoph Wellmann & Björn Krautwig & Patrick Manns & Bicheng Chen & Christof Schernus & Jakob Andert, 2022. "Efficiency Increase through Model Predictive Thermal Control of Electric Vehicle Powertrains," Energies, MDPI, vol. 15(4), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1476-:d:751462
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1476/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/4/1476/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Caiyang Wei & Theo Hofman & Esin Ilhan Caarls & Rokus van Iperen, 2019. "Integrated Energy and Thermal Management for Electrified Powertrains," Energies, MDPI, vol. 12(11), pages 1-24, May.
    2. Ye Yang & Zhongfu Tan & Yilong Ren, 2020. "Research on Factors That Influence the Fast Charging Behavior of Private Battery Electric Vehicles," Sustainability, MDPI, vol. 12(8), pages 1-19, April.
    3. Mariusz Baranski & Wojciech Szelag & Wieslaw Lyskawinski, 2020. "Analysis of the Partial Demagnetization Process of Magnets in a Line Start Permanent Magnet Synchronous Motor," Energies, MDPI, vol. 13(21), pages 1-20, October.
    4. Cedric De Cauwer & Wouter Verbeke & Thierry Coosemans & Saphir Faid & Joeri Van Mierlo, 2017. "A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions," Energies, MDPI, vol. 10(5), pages 1-18, May.
    5. Dominik Dvorak & Daniele Basciotti & Imre Gellai, 2020. "Demand-Based Control Design for Efficient Heat Pump Operation of Electric Vehicles," Energies, MDPI, vol. 13(20), pages 1-18, October.
    6. Ziwen Ling & Christopher R. Cherry & Yi Wen, 2021. "Determining the Factors That Influence Electric Vehicle Adoption: A Stated Preference Survey Study in Beijing, China," Sustainability, MDPI, vol. 13(21), pages 1-22, October.
    7. Hemmati, S. & Doshi, N. & Hanover, D. & Morgan, C. & Shahbakhti, M., 2021. "Integrated cabin heating and powertrain thermal energy management for a connected hybrid electric vehicle," Applied Energy, Elsevier, vol. 283(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shantanu Pardhi & Sajib Chakraborty & Dai-Duong Tran & Mohamed El Baghdadi & Steven Wilkins & Omar Hegazy, 2022. "A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions," Energies, MDPI, vol. 15(24), pages 1-55, December.
    2. Wahl, Alexander & Wellmann, Christoph & Monissen, Christian & Andert, Jakob, 2023. "Active temperature control of electric drivetrains for efficiency increase," Applied Energy, Elsevier, vol. 338(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    2. Abdulaziz Alshammari & Rakan C. Chabaan, 2023. "Metaheruistic Optimization Based Ensemble Machine Learning Model for Designing Detection Coil with Prediction of Electric Vehicle Charging Time," Sustainability, MDPI, vol. 15(8), pages 1-17, April.
    3. Thanh Tung Ha & Thanh Chuong Nguyen & Sy Sua Tu & Minh Hieu Nguyen, 2023. "Investigation of Influential Factors of Intention to Adopt Electric Vehicles for Motorcyclists in Vietnam," Sustainability, MDPI, vol. 15(11), pages 1-16, May.
    4. Alex Wray & Kambiz Ebrahimi, 2022. "Octovalve Thermal Management Control for Electric Vehicle," Energies, MDPI, vol. 15(17), pages 1-23, August.
    5. Seung Jun Choi & Junfeng Jiao, 2024. "Uncovering electric vehicle ownership disparities using K-means clustering analysis: A case study of Austin, Texas," Journal of Computational Social Science, Springer, vol. 7(3), pages 2403-2456, December.
    6. Aree Wangsupphaphol & Surachai Chaitusaney, 2022. "Subsidizing Residential Low Priority Smart Charging: A Power Management Strategy for Electric Vehicle in Thailand," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    7. 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.
    8. Ivan Panfilov & Alexey N. Beskopylny & Besarion Meskhi, 2023. "Numerical Simulation of Heat Transfer and Spread of Virus Particles in the Car Interior," Mathematics, MDPI, vol. 11(3), pages 1-18, February.
    9. Ivan Cvok & Igor Ratković & Joško Deur, 2021. "Multi-Objective Optimisation-Based Design of an Electric Vehicle Cabin Heating Control System for Improved Thermal Comfort and Driving Range," Energies, MDPI, vol. 14(4), pages 1-24, February.
    10. Saleh Mohammed Shahriar & Erphan A. Bhuiyan & Md. Nahiduzzaman & Mominul Ahsan & Julfikar Haider, 2022. "State of Charge Estimation for Electric Vehicle Battery Management Systems Using the Hybrid Recurrent Learning Approach with Explainable Artificial Intelligence," Energies, MDPI, vol. 15(21), pages 1-26, October.
    11. Zhang, Xinfang & Zhang, Zhe & Liu, Yang & Xu, Zhigang & Qu, Xiaobo, 2024. "A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation," Renewable Energy, Elsevier, vol. 234(C).
    12. Liu, Yonggang & Chen, Qianyou & Li, Jie & Zhang, Yuanjian & Chen, Zheng & Lei, Zhenzhen, 2023. "Collaborated eco-routing optimization for continuous traffic flow based on energy consumption difference of multiple vehicles," Energy, Elsevier, vol. 274(C).
    13. Isabel C. Gil-García & Mª Socorro García-Cascales & Habib Dagher & Angel Molina-García, 2021. "Electric Vehicle and Renewable Energy Sources: Motor Fusion in the Energy Transition from a Multi-Indicator Perspective," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    14. Piotr Mynarek & Janusz Kołodziej & Adrian Młot & Marcin Kowol & Marian Łukaniszyn, 2021. "Influence of a Winding Short-Circuit Fault on Demagnetization Risk and Local Magnetic Forces in V-Shaped Interior PMSM with Distributed and Concentrated Winding," Energies, MDPI, vol. 14(16), pages 1-16, August.
    15. Francesco Mocera & Aurelio Somà & Salvatore Martelli & Valerio Martini, 2023. "Trends and Future Perspective of Electrification in Agricultural Tractor-Implement Applications," Energies, MDPI, vol. 16(18), pages 1-36, September.
    16. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng, 2020. "Energy consumption analysis and prediction of electric vehicles based on real-world driving data," Applied Energy, Elsevier, vol. 275(C).
    17. Haber, Marc & Azaïs, Philippe & Genies, Sylvie & Raccurt, Olivier, 2023. "Stress factor identification and Risk Probabilistic Number (RPN) analysis of Li-ion batteries based on worldwide electric vehicle usage," Applied Energy, Elsevier, vol. 343(C).
    18. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    19. Yuan, Hong & Ma, Minda & Zhou, Nan & Xie, Hui & Ma, Zhili & Xiang, Xiwang & Ma, Xin, 2024. "Battery electric vehicle charging in China: Energy demand and emissions trends in the 2020s," Applied Energy, Elsevier, vol. 365(C).
    20. Munseok Chang & Sungwoo Bae & Gilhwan Cha & Jaehyun Yoo, 2021. "Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network," Sustainability, MDPI, vol. 13(24), pages 1-17, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1476-:d:751462. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.