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A Multi-Agent Reinforcement Learning Framework for Lithium-ion Battery Scheduling Problems

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  • Yu Sui

    (1812 Seville Way, San Jose, CA 95131, USA)

  • Shiming Song

    (1812 Seville Way, San Jose, CA 95131, USA)

Abstract

This paper presents a reinforcement learning framework for solving battery scheduling problems in order to extend the lifetime of batteries used in electrical vehicles (EVs), cellular phones, and embedded systems. Battery pack lifetime has often been the limiting factor in many of today’s smart systems, from mobile devices and wireless sensor networks to EVs. Smart charge-discharge scheduling of battery packs is essential to obtain super linear gain of overall system lifetime, due to the recovery effect and nonlinearity in the battery characteristics. Additionally, smart scheduling has also been shown to be beneficial for optimizing the system’s thermal profile and minimizing chances of irreversible battery damage. The recent rapidly-growing community and development infrastructure have added deep reinforcement learning (DRL) to the available tools for designing battery management systems. Through leveraging the representation powers of deep neural networks and the flexibility and versatility of reinforcement learning, DRL offers a powerful solution to both roofline analysis and real-world deployment on complicated use cases. This work presents a DRL-based battery scheduling framework to solve battery scheduling problems, with high flexibility to fit various battery models and application scenarios. Through the discussion of this framework, comparisons have also been made between conventional heuristics-based methods and DRL. The experiments demonstrate that DRL-based scheduling framework achieves battery lifetime comparable to the best weighted-k round-robin (kRR) heuristic scheduling algorithm. In the meantime, the framework offers much greater flexibility in accommodating a wide range of battery models and use cases, including thermal control and imbalanced battery.

Suggested Citation

  • Yu Sui & Shiming Song, 2020. "A Multi-Agent Reinforcement Learning Framework for Lithium-ion Battery Scheduling Problems," Energies, MDPI, vol. 13(8), pages 1-13, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:1982-:d:346625
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    References listed on IDEAS

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    1. Yalian Yang & Xiaosong Hu & Datong Qing & Fangyuan Chen, 2013. "Arrhenius Equation-Based Cell-Health Assessment: Application to Thermal Energy Management Design of a HEV NiMH Battery Pack," Energies, MDPI, vol. 6(5), pages 1-17, May.
    2. Brida V. Mbuwir & Frederik Ruelens & Fred Spiessens & Geert Deconinck, 2017. "Battery Energy Management in a Microgrid Using Batch Reinforcement Learning," Energies, MDPI, vol. 10(11), pages 1-19, November.
    3. Alex Graves & Greg Wayne & Malcolm Reynolds & Tim Harley & Ivo Danihelka & Agnieszka Grabska-Barwińska & Sergio Gómez Colmenarejo & Edward Grefenstette & Tiago Ramalho & John Agapiou & Adrià Puigdomèn, 2016. "Hybrid computing using a neural network with dynamic external memory," Nature, Nature, vol. 538(7626), pages 471-476, October.
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    Cited by:

    1. Jorge Varela Barreras & Ricardo de Castro & Yihao Wan & Tomislav Dragicevic, 2021. "A Consensus Algorithm for Multi-Objective Battery Balancing," Energies, MDPI, vol. 14(14), pages 1-25, July.
    2. Harri Aaltonen & Seppo Sierla & Rakshith Subramanya & Valeriy Vyatkin, 2021. "A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage," Energies, MDPI, vol. 14(17), pages 1-20, September.
    3. Tian, Yuan & Han, Minghao & Kulkarni, Chetan & Fink, Olga, 2022. "A prescriptive Dirichlet power allocation policy with deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 224(C).

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