Optimizing Discharge Efficiency of Reconfigurable Battery with Deep Reinforcement Learning

Seunghyeok Jeon, Jiwon Kim, Junick Ahn, Hojung Cha

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Cell imbalance in a multicell battery occurs over time due to varying operating environments. This imbalance leads to overall inefficiency in battery discharging due to the relatively weak cells in the battery. Reconfiguring the cells in the battery is one option for addressing the problem, but relevant circuits may lead to severe safety issues. In this article, we aim to optimize the discharge efficiency of a multicell battery using safety-supplemented hardware. To this end, we first design a cell string-level reconfiguration scheme that is safe in hardware operations and also provides scalability due to the low switching complexity. Second, we propose a machine learning-based run-time switch control that considers various battery-related factors, such as the state of charge, state of health, temperature, and current distributions. Specifically, by exploiting the deep reinforcement learning (DRL) technique, we train the complex relationship among the battery factors and derive the best switch configuration in run-time. We implemented a hardware prototype, validated its functionalities, and evaluated the efficacy of the DRL-based control policy. The experimental results showed that the proposed scheme, along with the optimization method, improves the discharge efficiency of multicell batteries. In particular, the discharge efficiency gain is maximized when the cells constituting the battery are unevenly distributed in terms of cell health and exposed temperature.

Original languageEnglish
Article number9211558
Pages (from-to)3893-3905
Number of pages13
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Issue number11
Publication statusPublished - 2020 Nov

Bibliographical note

Funding Information:
Manuscript received April 17, 2020; revised June 17, 2020; accepted July 6, 2020. Date of publication October 2, 2020; date of current version October 27, 2020. This work was supported in part by the Next-Generation Information Computing Development Program funded by the Ministry of Science and ICT under Grant NRF-2017M3C4A7083677; in part by the National Research Foundation of Korea under Grant NRF-2019R1A2C2004619; and in part by the Institute for Information and communications Technology Promotion Grant Funded by the Korea Government [MSIT, Development of High-Assurance (≥EAL6) Secure Microkernel] under Grant 2018-0-00532. This article was presented in the International Conference on Embedded Software 2020 and appears as part of the ESWEEK-TCAD special issue. (Corresponding author: Hojung Cha.) The authors are with the Department of Computer Science, Yonsei University, Seoul 03722, South Korea (e-mail: sh.jeon@yonsei.ac.kr; kim.j@yonsei.ac.kr; j.ahn@yonsei.ac.kr; hjcha@yonsei.ac.kr). Digital Object Identifier 10.1109/TCAD.2020.3012230

Publisher Copyright:
© 1982-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering


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