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A control algorithm to follow the limitations of an electric vehicle motor using the TM4 sumo drive as an example

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Warianty tytułu
PL
Algorytm sterowania nadążnego za ograniczeniami silnika pojazdu elektrycznego na przykładzie napędu TM4 sumo
Języki publikacji
EN
Abstrakty
EN
The article discusses development work on the control system of an electric vehicle considering the limitations of the TM4 Sumo power unit. Particular attention was focused on the development of a new algorithm for controlling the final phase of braking (using a retarder) at low speeds, using proprietary regulators based on the prediction of braking force values. The developed algorithm is universal (works with various drive units) automatically adjusting the setting values. At the same time, the authors paid particular attention to the elimination of the phenomenon of oscillation of the engine torque value in the final phase of braking and the synergy of the classic braking system of a commercial vehicle with electric drive braking. The article also discusses proprietary tools and software for monitoring and collecting measurement data from electric vehicles. The control algorithm is one of the products offered on the market as a solution provided by the DIGA Civil Partnership. The presented results were collected from real objects as part of implementations carried out by the authors.
PL
W artykule omówiono prace rozwojowe nad układem sterowania pojazdu elektrycznego z uwzględnieniem ograniczeń jednostki napędowej TM4 Sumo. Szczególną uwagę poświęcono opracowaniu nowego algorytmu sterowania końcową fazą hamowania (za pomocą retardera) przy niskich wartościach prędkości, poprzez zastosowanie autorskich regulatorów opartych na predykcji wartości siły hamowania. Opracowany przez Autorów algorytm jest uniwersalny (działa z różnymi jednostkami napędowymi) automatycznie dostosowując wartości nastaw. Jednocześnie autorzy zwrócili szczególną uwagę na eliminację zjawiska oscylacji wartości momentu obrotowego silnika w końcowej fazie hamowania oraz synergię klasycznego układu hamulcowego pojazdu użytkowego z hamowaniem napędem elektrycznym. W artykule omówiono również autorskie narzędzia i oprogramowanie do monitorowania i zbierania danych pomiarowych z pojazdów elektrycznych. Algorytm sterowania jest jednym z produktów oferowanych na rynku jako rozwiązanie dostarczane przez DIGA Spółka Cywilna. Prezentowane wyniki zostały zebrane z obiektów rzeczywistych w ramach prze-prowadzonych przez autorów wdrożeń.
Rocznik
Strony
9--23
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wykr.
Twórcy
autor
  • DIGA LLP, Zamkowa 1 St., 44-109 Gliwice, Poland
autor
  • The Silesian University of Technology, Faculty of Mechanical Engineering, Department of Process Automation and Integrated Manufacturing Systems, Konarskiego 18A St., 44-100 Gliwice, Poland
  • The Silesian University of Technology, Faculty of Mechanical Engineering, Department of Process Automation and Integrated Manufacturing Systems, Konarskiego 18A St., 44-100 Gliwice, Poland
Bibliografia
  • [1] https://eur-lex.europa.eu/legal-content/PL/TXT/? uri=COM:2020:789:FIN (Announcement from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions - A strategy for sustainable and intelligent mobility - European transport on the road to the future COM/2020/789, Brussels, 9.12.2020; access date: 01.02.2023).
  • [2] https://ec.europa.eu/energy/sites/ener/files/documents/metis_s13_final_report_electromobility_201806.pdf (Effect of electro mobility on the power system and the integration of RES; access date: 01.02.2023).
  • [3] Samanta, A., Williamson S.S. (2021) A Survey of Wireless Battery Management System: Topology, Emerging Trends, and Challenges. Electronics, 10(18), 1-12. https://doi.org/10.3390/electronics10182193.
  • [4] Musti S, Kockelman K.M. (2011) Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas. Transportation Research Part A: Policy and Practice, 45(8), 707-720. https://doi.org/10.1016/j.tra.2011.04.011.
  • [5] Niu G. (2016) Data-driven Technology for Engineering System Health Management. Springer, Cham, ISBN: 978-981-10-2032-2.
  • [6] Lee J., Wu F., Zhao W., Ghaffari M., Liao L., Siegel D. (2014) Prognostics and health management design for rotary machinery systems-reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1-2), 314-334. https://doi.org/10.1016/j.ymssp.2013.06.004.
  • [7] Pecht M. (2008) Encyclopedia of structural health monitoring, in Prognostics and Health Management of Electronics. John Wiley & Sons, ISBN: 9780470058220.
  • [8] Li B. et al. (2017) Big Data Analytics for Electric Vehicle Integration in Green Smart Cities. IEEE Communications Magazine, 55(11), 19-25. https://doi.org/10.1109/MCOM.2017.1700133.
  • [9] Rahimi-Eichi H., Chow M.Y. (2014) Big-Data Framework for Electric Vehicle Range Estimation. Proceedings Annual Conference IEEE Industrial Electronics Society, 14951288, 5628-5634, 2014. https://doi.org/10.1109/IECON.2014.7049362.
  • [10] Tran D.D., et al. (2020) Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains. Topologies and integrated energy management strategies. Renewable and Sustainable Energy Reviews, 119, 1-29. https://doi.org/10.1016/j.rser.2019.109596.
  • [11] Un-Noor F., Padmanaban S., Mihet-Popa L., Mollah M. N., Hossain E. (2017) A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development, MDPI Energies, 10(8). https://doi.org/10.3390/en10081217.
  • [12] Knowles M. (2013) Through-life management of electric vehicles. Procedia CIRP, 11, 260-265. https://doi.org/10.1016/j.procir.2013.07.074.
  • [13] Wang C., Ji T., Mao F., Wang Z., Li Z. (2021) Prognostics and Health Management System for Electric Vehicles with a Hierarchy Fusion Framework: Concepts, Architectures, and Methods. Advanced Infrastructure Systems Integrating Hardware and Software Platforms, 2021, 1-11. https://doi.org/10.1155/2021/6685900.
  • [14] Atamuradov V., et al. (2017) Prognostics and Health Management for Maintenance Practitioners, Implementation and Tools Evaluation. International Journal of Prognostics and Health Management – IJPHM, Special Issue on Railway Systems & Mass Transportation, 8(3), 1-32. https://doi.org/10.36001/ijphm.2017.v8i3.2667.
  • [15] Zhang Z., Son J.H., Li Y., Trayer M., Pi Z., Hwang D.Y., Moon J. K. (2014) Training-Free Non-Intrusive Load Monitoring of Electric Vehicle Charging with Low Sampling Rate. IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society, 14963859, 1-12. https://doi.org/10.1109/IECON.2014.7049328.
  • [16] Borén S. (2019) Electric buses’ sustainability effects, noise, energy use, and costs. International Journal of Sustainable Transportation, 14(12), 956-971. https://doi.org/10.1080/15568318.2019.1666324.
  • [17] Liu Z., Tao W., Jiang L., Zhu C. (2014) Design and application on electric vehicle real-time condition monitoring system by Internet of Things technology. 2014 IEEE 5th International Conference on Software Engineering and Service Science, 14698751, 21-29. https://doi.org/10.1109/ICSESS.2014.6933674.
  • [18] Antoine G., Mikeka C., Bajpai G., Jayavel K. (2021) Speed Management Strategy: Designing an IoT-Based Electric Vehicle Speed Control Monitoring System. Sensors, 21(19), 1-17. https://doi.org/10.3390/s21196670.
  • [19] Yan N., et al. (2022) Online battery health diagnosis for electric vehicles based on DTW-XGBoost. Energy Reports, 8(8), 121-128. https://doi.org/10.1016/j.egyr.2022.09.126.
  • [20] Zhao J., Ling H., Wang J., Burke A.F., Lian Y. (2022) Data-driven prediction of battery failure for electric vehicles. iScience, CellPress Open Access, 25(4). https://doi.org/10.1016/j.isci.2022.104172.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7b402cd1-b50f-4683-9087-ce5419c96da2
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