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An Adaptive Energy Management System for Electric Vehicles Based on Driving Cycle Identification and Wavelet Transform

Author

Listed:
  • Qiao Zhang

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, 5988 People Street, Changchun 130022, China
    These authors contributed equally to this work.)

  • Weiwen Deng

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, 5988 People Street, Changchun 130022, China
    These authors contributed equally to this work.)

Abstract

Since driving cycle greatly affects load power demand, driving cycle identification (DCI) is proposed to predict power demand that can be expected to prepare for the power distribution between battery and supercapacitor. The DCI is developed based on a learning vector quantization (LVQ) neural network method, which is assessed in both training and validation based on the statistical data obtained from six standard driving cycles. In order to ensure network accuracy, characteristic parameter and slide time window, which are two important factors ensuring the network accuracy for onboard hybrid energy storage system (HESS) applications in electric vehicles, are discussed and designed. Based on the identification results, Multi-level Haar wavelet transform (Haar-WT) is proposed for allocating the high frequency components of power demand into the supercapacitor which could damage battery lifetime and the corresponding low frequency components into the battery system. The proposed energy management system can better increase system efficiency and battery lifetime compared with the conventional sole frequency control. The advantages are demonstrated based on a randomly generated driving cycle from the standard driving cycle library via simulation.

Suggested Citation

  • Qiao Zhang & Weiwen Deng, 2016. "An Adaptive Energy Management System for Electric Vehicles Based on Driving Cycle Identification and Wavelet Transform," Energies, MDPI, vol. 9(5), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:5:p:341-:d:69536
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    References listed on IDEAS

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    2. Artur Bejger & Tomasz Piasecki, 2020. "The Use of Acoustic Emission Elastic Waves for Diagnosing High Pressure Mud Pumps Used on Drilling Rigs," Energies, MDPI, vol. 13(5), pages 1-16, March.
    3. Wang, Chun & Yang, Ruixin & Yu, Quanqing, 2019. "Wavelet transform based energy management strategies for plug-in hybrid electric vehicles considering temperature uncertainty," Applied Energy, Elsevier, vol. 256(C).
    4. Jorge Garcia & Pablo Garcia & Fabio Giulii Capponi & Giulio De Donato, 2018. "Analysis, Modeling, and Control of Half-Bridge Current-Source Converter for Energy Management of Supercapacitor Modules in Traction Applications," Energies, MDPI, vol. 11(9), pages 1-22, August.
    5. Mario Marchesoni & Massimiliano Passalacqua & Luis Vaccaro, 2020. "A Refined Loss Evaluation of a Three-Switch Double Input DC-DC Converter for Hybrid Vehicle Applications," Energies, MDPI, vol. 13(1), pages 1-13, January.
    6. Ross Milligan & Saioa Etxebarria & Tariq Muneer & Eulalia Jadraque Gago, 2019. "Driven Performance of Electric Vehicles in Edinburgh and Its Environs," Energies, MDPI, vol. 12(16), pages 1-22, August.
    7. Fang Zhou & Feng Xiao & Cheng Chang & Yulong Shao & Chuanxue Song, 2017. "Adaptive Model Predictive Control-Based Energy Management for Semi-Active Hybrid Energy Storage Systems on Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-21, July.

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