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

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. (C) 2016 Elsevier B.V. All rights reserved.