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A novel torque distribution strategy based on deep recurrent neural network for parallel hybrid electric vehicle

Kong, H., Fang, Y., Fan, L., Wang, H.ORCID: 0000-0003-2789-9530, Zhang, X. and Hu, J. (2019) A novel torque distribution strategy based on deep recurrent neural network for parallel hybrid electric vehicle. IEEE Access, 7 . pp. 65174-65185.

Link to Published Version: https://doi.org/10.1109/ACCESS.2019.2917545
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Abstract

In this paper, energy management strategy (EMS) model based on deep recurrent neural network (DRNN) is presented to learn optimal torque distribution for the single-axle parallel hybrid electric vehicle. The model has two distinguishing properties: 1) because the EMS is formulated as a time series prediction problem, taking historical data as input of the EMS model captures the input-and-output dynamic characteristics and enhances the prediction capability and 2) the EMS model based on end-to-end framework directly generates torque distribution results without extracting features of driving cycles and other artificial interference. The extensive simulations are conducted to demonstrate the accuracy and generalization capability of the EMS model in public platform TensorFlow. Comparing with other energy management strategies, our proposed model yields better performance in terms of fuel economy and accuracy. The simulation results show that our proposed EMS model provides a novel way to study the energy management strategy.

Item Type: Journal Article
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: IEEE
Copyright: © Copyright 2019 IEEE
URI: http://researchrepository.murdoch.edu.au/id/eprint/46353
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