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Energy Management Strategy for Hybrid Energy Storage System based on Model Predictive Control

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Abstract

Electric vehicle (EV) is developed because of its environmental friendliness, energy-saving and high efficiency. For improving the performance of the energy storage system of EV, this paper proposes an energy management strategy (EMS) based model predictive control (MPC) for the battery/supercapacitor hybrid energy storage system (HESS), which takes stabilizing the DC bus voltage and improving the efficiency of the system as two major optimization goals. In addition, an enumeration algorithm is presented to solve the optimization function. The experimental results show the performance of the proposed EMS which is able to enhance the overall instantaneous power and prevent the battery from overloading. Meanwhile, compared with the results of a single battery storage system, the maximum amplitude of the battery current in the HESS is reduced by 40.81% and whole system energy loss is reduced by 24.13% with the proposed power management strategy.

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Availability of data and material

All data generated or analysed during this study are included in this published article.

Code availability

This research is done on the basis of Matlab 2019a and Code Composer Studio 9.1.0

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China(62273313, 52177068), Science and Technology Development Project of Henan Province (222102240005), Young Backbone Teacher Training Program of Henan Province(2021GGJS089), and Zhengzhou Collaborative Innovation Project (2021ZDPY0204).

Funding

This work was supported by the National Natural Science Foundation of China General Program (Grant No. 62273313, 52177068); Science and Technology Development Project of Henan Province (222102240005); Zhengzhou Collaborative Innovation Project (2021ZDPY0204); Young Backbone Teacher Training Program of Henan Province(2021GGJS089).

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Authors and Affiliations

  1. Department of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China

    Yongpeng Shen, Yuanfeng Li, Yanfeng Wang, Jianbin Sun & Songnan Sun

  2. Department of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China

    Dongqi Liu

Authors
  1. Yongpeng Shen
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  2. Yuanfeng Li
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  3. Dongqi Liu
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  4. Yanfeng Wang
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  5. Jianbin Sun
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  6. Songnan Sun
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [YS], [YL] and [DL]. The first draft of the manuscript was written by [YL] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yuanfeng Li.

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Shen, Y., Li, Y., Liu, D. et al. Energy Management Strategy for Hybrid Energy Storage System based on Model Predictive Control. J. Electr. Eng. Technol. 18, 3265–3275 (2023). https://doi.org/10.1007/s42835-023-01445-8

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  • DOI: https://doi.org/10.1007/s42835-023-01445-8

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