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Learning-Based Control for Hybrid Battery Management Systems

In: Intelligent Control and Smart Energy Management

Author

Listed:
  • Jonas Mirwald

    (German Aerospace Center (DLR))

  • Ricardo Castro

    (University of California)

  • Jonathan Brembeck

    (German Aerospace Center (DLR))

  • Johannes Ultsch

    (German Aerospace Center (DLR))

  • Rui Esteves Araujo

    (University of Porto)

Abstract

Battery packs of electric vehicles are prone to capacity, thermal, and aging imbalances in their cells, which limit power delivery to the vehicle. To promote a more sustainable transportation, a solution to this problem is necessary. In this chapter, a hybrid battery management system (HBMS), capable of simultaneously equalizing battery state of charge and temperature while enabling hybridization with supercapacitors, is investigated. A model-free reinforcement learning is used to control the HBMS, where the control policy is obtained through direct interaction with the system’s model. The approach of this work exploits the soft actor-critic algorithm to handle continuous control actions and feedback states and deep neural networks as function approximators. The validation of the proposed control method is performed through numerical simulations, making use of numerically efficient models of the energy storage and power converters developed in Modelica language.

Suggested Citation

  • Jonas Mirwald & Ricardo Castro & Jonathan Brembeck & Johannes Ultsch & Rui Esteves Araujo, 2022. "Learning-Based Control for Hybrid Battery Management Systems," Springer Optimization and Its Applications, in: Maude Josée Blondin & João Pedro Fernandes Trovão & Hicham Chaoui & Panos M. Pardalos (ed.), Intelligent Control and Smart Energy Management, pages 187-222, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-84474-5_7
    DOI: 10.1007/978-3-030-84474-5_7
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