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A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network

Author

Listed:
  • Hongwen He

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Chao Sun

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Xiaowei Zhang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Driving patterns exert an important influence on the fuel economy of vehicles, especially hybrid electric vehicles. This paper aims to build a method to identify driving patterns with enough accuracy and less sampling time compared than other driving pattern recognition algorithms. Firstly a driving pattern identifier based on a Learning Vector Quantization neural network is established to analyze six selected representative standard driving cycles. Micro-trip extraction and Principal Component Analysis methods are applied to ensure the magnitude and diversity of the training samples. Then via Matlab/Simulink, sample training simulation is conducted to determine the minimum neuron number of the Learning Vector Quantization neural network and, as a result, to help simplify the identifier model structure and reduce the data convergence time. Simulation results have proved the feasibility of this method, which decreases the sampling window length from about 250–300 s to 120 s with an acceptable accuracy. The driving pattern identifier is further used in an optimized co-simulation together with a parallel hybrid vehicle model and improves the fuel economy by about 8%.

Suggested Citation

  • Hongwen He & Chao Sun & Xiaowei Zhang, 2012. "A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network," Energies, MDPI, vol. 5(9), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:9:p:3363-3380:d:19897
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    References listed on IDEAS

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    1. Abdelsalam Ahmed Abdelsalam & Shumei Cui, 2012. "A Fuzzy Logic Global Power Management Strategy for Hybrid Electric Vehicles Based on a Permanent Magnet Electric Variable Transmission," Energies, MDPI, vol. 5(4), pages 1-24, April.
    2. Hongwen He & Zhentong Liu & Liming Zhu & Xinlei Liu, 2012. "Dynamic Coordinated Shifting Control of Automated Mechanical Transmissions without a Clutch in a Plug-In Hybrid Electric Vehicle," Energies, MDPI, vol. 5(8), pages 1-16, August.
    3. M. Montazeri-Gh & M. Asadi, 2011. "Intelligent approach for parallel HEV control strategy based on driving cycles," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(2), pages 287-302.
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    Cited by:

    1. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2022. "Real-Time Energy Management Strategy Based on Driving Conditions Using a Feature Fusion Extreme Learning Machine," Energies, MDPI, vol. 15(12), pages 1-22, June.
    2. Jau-Woei Perng & Yi-Horng Lai, 2016. "Robust Longitudinal Speed Control of Hybrid Electric Vehicles with a Two-Degree-of-Freedom Fuzzy Logic Controller," Energies, MDPI, vol. 9(4), pages 1-15, April.
    3. Xiao Hu & Shikun Liu & Ke Song & Yuan Gao & Tong Zhang, 2021. "Novel Fuzzy Control Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Considering State of Health," Energies, MDPI, vol. 14(20), pages 1-20, October.
    4. 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.

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