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Sensorless control strategy for light-duty EVs and efficiency loss evaluation of high frequency injection under standardized urban driving cycles

Author

Listed:
  • Trancho, E.
  • Ibarra, E.
  • Arias, A.
  • Kortabarria, I.
  • Prieto, P.
  • Martínez de Alegría, I.
  • Andreu, J.
  • López, I.

Abstract

Sensorless control of Electric Vehicle (EV) drives is considered to be an effective approach to improve system reliability and to reduce component costs. In this paper, relevant aspects relating to the sensorless operation of EVs are reported. As an initial contribution, a hybrid sensorless control algorithm is presented that is suitable for a variety of synchronous machines. The proposed method is simple to implement and its relatively low computational cost is a desirable feature for automotive microprocessors with limited computational capabilities. An experimental validation of the proposal is performed on a full-scale automotive grade platform housing a 51 kW Permanent Magnet assisted Synchronous Reluctance Machine (PM-assisted SynRM). Due to the operational requirements of EVs, both the strategy presented in this paper and other hybrid sensorless control strategies rely on High Frequency Injection (HFI) techniques, to determine the rotor position at standstill and at low speeds. The introduction of additional high frequency perturbations increases the power losses, thereby reducing the overall efficiency of the drive. Hence, a second contribution of this work is a simulation platform for the characterization of power losses in both synchronous machines and a Voltage Source Inverters (VSI). Finally, as a third contribution and considering the central concerns of efficiency and autonomy in EV applications, the impact of power losses are analyzed. The operational requirements of High Frequency Injection (HFI) are experimentally obtained and, using state-of-the-art digital simulation, a detailed loss analysis is performed during real automotive driving cycles. Based on the results, practical considerations are presented in the conclusions relating to EV sensorless control.

Suggested Citation

  • Trancho, E. & Ibarra, E. & Arias, A. & Kortabarria, I. & Prieto, P. & Martínez de Alegría, I. & Andreu, J. & López, I., 2018. "Sensorless control strategy for light-duty EVs and efficiency loss evaluation of high frequency injection under standardized urban driving cycles," Applied Energy, Elsevier, vol. 224(C), pages 647-658.
    • Handle: RePEc:eee:appene:v:224:y:2018:i:c:p:647-658
    DOI: 10.1016/j.apenergy.2018.05.019
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    1. Wei, Zhen & Xu, John & Halim, Dunant, 2017. "HEV power management control strategy for urban driving," Applied Energy, Elsevier, vol. 194(C), pages 705-714.
    2. Tsiakmakis, Stefanos & Fontaras, Georgios & Ciuffo, Biagio & Samaras, Zissis, 2017. "A simulation-based methodology for quantifying European passenger car fleet CO2 emissions," Applied Energy, Elsevier, vol. 199(C), pages 447-465.
    3. Wang, Yubo & Shi, Wenbo & Wang, Bin & Chu, Chi-Cheng & Gadh, Rajit, 2017. "Optimal operation of stationary and mobile batteries in distribution grids," Applied Energy, Elsevier, vol. 190(C), pages 1289-1301.
    4. Wang, Hewu & Zhang, Xiaobin & Ouyang, Minggao, 2015. "Energy consumption of electric vehicles based on real-world driving patterns: A case study of Beijing," Applied Energy, Elsevier, vol. 157(C), pages 710-719.
    5. Capasso, Clemente & Veneri, Ottorino, 2014. "Experimental analysis on the performance of lithium based batteries for road full electric and hybrid vehicles," Applied Energy, Elsevier, vol. 136(C), pages 921-930.
    6. Veneri, Ottorino & Capasso, Clemente & Patalano, Stanislao, 2017. "Experimental study on the performance of a ZEBRA battery based propulsion system for urban commercial vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 2005-2018.
    7. González Palencia, Juan C. & Otsuka, Yuki & Araki, Mikiya & Shiga, Seiichi, 2017. "Scenario analysis of lightweight and electric-drive vehicle market penetration in the long-term and impact on the light-duty vehicle fleet," Applied Energy, Elsevier, vol. 204(C), pages 1444-1462.
    8. Ding, Xiaofeng & Guo, Hong & Xiong, Rui & Chen, Feida & Zhang, Donghuai & Gerada, Chris, 2017. "A new strategy of efficiency enhancement for traction systems in electric vehicles," Applied Energy, Elsevier, vol. 205(C), pages 880-891.
    9. Falcão, Eduardo Aparecido Moreira & Teixeira, Ana Carolina Rodrigues & Sodré, José Ricardo, 2017. "Analysis of CO2 emissions and techno-economic feasibility of an electric commercial vehicle," Applied Energy, Elsevier, vol. 193(C), pages 297-307.
    10. Tsokolis, D. & Tsiakmakis, S. & Dimaratos, A. & Fontaras, G. & Pistikopoulos, P. & Ciuffo, B. & Samaras, Z., 2016. "Fuel consumption and CO2 emissions of passenger cars over the New Worldwide Harmonized Test Protocol," Applied Energy, Elsevier, vol. 179(C), pages 1152-1165.
    11. Palmer, Kate & Tate, James E. & Wadud, Zia & Nellthorp, John, 2018. "Total cost of ownership and market share for hybrid and electric vehicles in the UK, US and Japan," Applied Energy, Elsevier, vol. 209(C), pages 108-119.
    12. Ko, Jinyoung & Jin, Dongyoung & Jang, Wonwook & Myung, Cha-Lee & Kwon, Sangil & Park, Simsoo, 2017. "Comparative investigation of NOx emission characteristics from a Euro 6-compliant diesel passenger car over the NEDC and WLTC at various ambient temperatures," Applied Energy, Elsevier, vol. 187(C), pages 652-662.
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    2. Dwi Sudarno Putra & Seng-Chi Chen & Hoai-Hung Khong & Chin-Feng Chang, 2023. "Realization of Intelligent Observer for Sensorless PMSM Drive Control," Mathematics, MDPI, vol. 11(5), pages 1-20, March.
    3. Michele De Santis & Sandro Agnelli & Fabrizio Patanè & Oliviero Giannini & Gino Bella, 2018. "Experimental Study for the Assessment of the Measurement Uncertainty Associated with Electric Powertrain Efficiency Using the Back-to-Back Direct Method," Energies, MDPI, vol. 11(12), pages 1-19, December.
    4. Endika Robles & Markel Fernandez & Edorta Ibarra & Jon Andreu & Iñigo Kortabarria, 2019. "Mitigation of Common Mode Voltage Issues in Electric Vehicle Drive Systems by Means of an Alternative AC-Decoupling Power Converter Topology," Energies, MDPI, vol. 12(17), pages 1-27, August.

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