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Energy management strategy for electric vehicles based on deep Q-learning using Bayesian optimization

  • Huifang Kong
  • Jiapeng YanEmail author
  • Hai Wang
  • Lei Fan
Extreme Learning Machine and Deep Learning Networks
  • 54 Downloads

Abstract

In this paper, a deep Q-learning (DQL)-based energy management strategy (EMS) is designed for an electric vehicle. Firstly, the energy management problem is reformulated to satisfy the condition of employing DQL by considering the dynamics of the system. Then, to achieve the minimum of electricity consumption and the maximum of the battery lifetime, the DQL-based EMS is designed to properly split the power demand into two parts: one is supplied by the battery and the other by supercapacitor. In addition, a hyperparameter tuning method, Bayesian optimization (BO), is introduced to optimize the hyperparameter configuration for the DQL-based EMS. Simulations are conducted to validate the improvements brought by BO and the convergence of DQL algorithm equipped with tuned hyperparameters. Simulations are also carried out on both training dataset and the testing dataset to validate the optimality and the adaptability of the DQL-based EMS, where the developed EMS outperforms a previously published rule-based EMS in almost all the cases.

Keywords

Energy management strategy (EMS) Electric vehicle (EV) Deep Q-learning (DQL) Bayesian optimization (BO) 

Notes

Acknowledgements

This research was supported by the National Science and Technology Support Program under grant No 2014BAG06B02, Fundamental Research Funds for the Central Universities under grant No 2014HGCH0003 and the National Natural Science Foundation of China under Grant 61771178.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests in the present work.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electrical Engineering and AutomationHefei University of TechnologyHefeiChina
  2. 2.College of Science, Health, Engineering and EducationMurdoch UniversityPerthAustralia

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