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Article Dans Une Revue Electric Power Systems Research Année : 2016

Nonlinear autoregressive neural network in an energy management strategy for battery/ultra-capacitor hybrid electrical vehicles

Résumé

Hybrid electric vehicles are one of the most promising solutions for reducing pollution and fuel consumption. However, their propulsion system comprises a number of different onboard power sources with different dynamic characteristics, meaning that some strategy is required for sharing power between them that takes their characteristics into account. In this paper, a new real time energy management strategy for battery/ultra-capacitor hybrid vehicles is proposed. This strategy is based on sharing the total power between the onboard power systems, namely the battery and the ultra-capacitors, using a Nonlinear Auto-Regressive Neural Network (NARNN) as a time series prediction model, and Discrete Wavelet Transform (DWT) as a time-frequency filter. The objective of this strategy is to lengthen the life of the battery. We simulated this new strategy using actual data from a military hybrid vehicle. The results were found to be promising and show the robustness of the proposed method.
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Dates et versions

hal-02380394 , version 1 (26-11-2019)

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  • HAL Id : hal-02380394 , version 1

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Mona Ibrahim, Samir Jemei, Geneviève Wimmer, Daniel Hissel. Nonlinear autoregressive neural network in an energy management strategy for battery/ultra-capacitor hybrid electrical vehicles. Electric Power Systems Research, 2016, 136, pp.262 - 269. ⟨hal-02380394⟩
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