Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

Rakshith Subramanya, Seppo A. Sierla, Valeriy Vyatkin

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The transition to renewable production and smart grids is driving a massive investment to battery storages, and reinforcement learning (RL) has recently emerged as a potentially disruptive technology for their control and optimization of battery storage systems. A surge of papers has appeared in the last two years applying reinforcement learning to the optimization of battery storages in buildings, energy communities, energy harvesting Internet of Things networks, renewable generation, microgrids, electric vehicles and plug-in hybrid electric vehicles. This article reviews these applications through 4 different perspectives. Firstly, the type of optimization problem is analyzed; the literature can be divided to approaches that optimize either financial targets or energy efficiency. Secondly, the approaches for handling user comfort are analyzed for applications that may impact a human user. Thirdly, this paper discusses the approach to model and reduce battery degradation. Fourthly, the articles are categorized by application context and applications likely to attract a high amount of research are identified. The paper concludes with a list of unresolved challenges.

Original languageEnglish
Pages (from-to)54484-54506
Number of pages23
JournalIEEE Access
Publication statusPublished - 2022
MoE publication typeA1 Journal article-refereed


  • Battery degradation
  • battery storage
  • electric vehicle
  • microgrid
  • reinforcement learning


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  • Predictricity

    Sierla, S., Hölttä, T., Karhula, N., Aaltonen, H., Subramanya, R. & Vyatkin, V.


    Project: Business Finland: Other research funding

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