Transferable representation modelling for real-time energy management of the plug-in hybrid vehicle based on K fold fuzzy learning and Gaussian process regression

Zhou, Q., Li, Y., Zhao, D. , Li, J., Williams, H., Xu, H. and Yan, F. (2022) Transferable representation modelling for real-time energy management of the plug-in hybrid vehicle based on K fold fuzzy learning and Gaussian process regression. Applied Energy, 305, 117853. (doi: 10.1016/j.apenergy.2021.117853)

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Abstract

Electric vehicles, including plug-in hybrids, are important for achieving net-zero emission and will dominate road transportation in the future. Energy management, which optimizes the onboard energy usage, is a critical functionality of electric vehicles. It is usually developed following the model-based routine, which is conventionally costly and time-consuming and is hard to meet the increasing market competition in the digital era. To reduce the development workload for the energy management controller, this paper studies an innovative transfer learning routine. A new transferable representation control model is proposed by incorporating two promising artificial intelligence technologies, adaptive neural fuzzy inference system and Gaussian process regression, where the former applies k-fold cross valudation to build a neural fuzzy system for real-time implementation of offline optimization result, and the later connects the neural fuzzy system with a ‘deeper’ architecture to transfer the offline optimization knowledge learnt at source domain to new target domains. By introducing a concept of control utility that evaluates vehicle energy efficiency with a penalty on usage of battery energy, experimental evaluations based on the hardware-in-the-loop testing platform are conducted. Competitive real-time control ultility values (as much as 90% of offline benchmarking results) can be achieved by the proposed control method. They are over 27% higher than that achieved by the neural-network-based model.

Item Type:Articles
Additional Information:The authors are grateful to the State Key Laboratory of Automotive Safety and Energy (KF2029), the EPSRC Fellowship scheme (EP/S001956/1), and the Natural Science Foundation of China (51775393).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Zhao, Dr Dezong
Authors: Zhou, Q., Li, Y., Zhao, D., Li, J., Williams, H., Xu, H., and Yan, F.
College/School:College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
Journal Name:Applied Energy
Publisher:Elsevier
ISSN:0306-2619
ISSN (Online):1872-9118
Published Online:17 September 2021
Copyright Holders:Crown Copyright © 2021
First Published:First published in Applied Energy 305: 117853
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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