Intelligent Battery Systems: Monitoring, Management, and Control

A special issue of Batteries (ISSN 2313-0105).

Deadline for manuscript submissions: 10 December 2024 | Viewed by 857

Special Issue Editors


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Guest Editor
1.Department of Electrical and Computer Engineering, RPTU Kaiserslautern, 67663 Kaiserslautern, Germany 2.Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
Interests: AC-DC power conversion; modular multilevel converters; battery management system; machine learning

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Guest Editor
Institute of Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany
Interests: battery management system; modular multilevel converters; energy storage system

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Guest Editor
Department of Electrical Engineering, Centro de Energia, Universidad Católica de la Santisima Concepción, Alonso de Ribera 2850, Concepción, Chile
Interests: multilevel converters; modular multilevel converters; battery management system
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Aerospace Engineering, Gyeongsang National University, 501 Jinju-daero, Jinju-si 52828, Republic of Korea
Interests: battery microstructure modelling; computational mechanics; composite materials

Special Issue Information

Dear Colleagues,

The transition from combustion engines to electrified vehicles and renewable energy integration is propelling the demand for compact, high-energy-density storage systems, including batteries. These battery elements are not only key to decarbonizing the transportation sector but also critical for buffering renewable energy. However, there Is still significant room for technological improvement.

A large body of research focuses on the chemistry improvement and production optimization of batteries, whereas concentrating on the chemistry might not be the complete solution. This Special Issue, titled “Intelligent battery Systems: Management, Monitoring, and Control," focuses on advancing battery technologies with a particular emphasis on smart monitoring, management, and control of these systems.

Potential topics include but are not limited to:

  • Advanced sensing or monitoring techniques;
  • Smart estimation and observation techniques;
  • Modelling and design of smart battery elements and systems;
  • Digital twins for battery elements and systems;
  • Statistical analysis and modelling of large battery systems;
  • Machine learning for life/age/charge/temperature prediction;
  • Implementation of artificial intelligence in battery diagnostics;
  • Improved thermal management for battery systems;
  • Optimized control strategies for improved functionalities or life cycle.

Dr. Nima Tashakor
Dr. Zhan Ma
Dr. Ricardo Lizana Fuentes
Dr. Hyoung Jun Lim
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Batteries is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

13 pages, 8198 KiB  
Article
Controlling Algorithm of Reconfigurable Battery for State of Charge Balancing Using Amortized Q-Learning
by Dominic Karnehm, Wolfgang Bliemetsrieder, Sebastian Pohlmann and Antje Neve
Batteries 2024, 10(4), 131; https://doi.org/10.3390/batteries10040131 - 15 Apr 2024
Viewed by 577
Abstract
In the context of the electrification of the mobility sector, smart algorithms have to be developed to control battery packs. Smart and reconfigurable batteries are a promising alternative to conventional battery packs and offer new possibilities for operation and condition monitoring. This work [...] Read more.
In the context of the electrification of the mobility sector, smart algorithms have to be developed to control battery packs. Smart and reconfigurable batteries are a promising alternative to conventional battery packs and offer new possibilities for operation and condition monitoring. This work proposes a reinforcement learning (RL) algorithm to balance the State of Charge (SoC) of reconfigurable batteries based on the topologies half-bridge and battery modular multilevel management (BM3). As an RL algorithm, Amortized Q-learning (AQL) is implemented, which enables the control of enormous numbers of possible configurations of the reconfigurable battery as well as the combination of classical controlling approaches and machine learning methods. This enhances the safety mechanisms during control. As a neural network of the AQL, a Feedforward Neuronal Network (FNN) is implemented consisting of three hidden layers. The experimental evaluation using a 12-cell hybrid cascaded multilevel converter illustrates the applicability of the method to balance the SoC and maintain the balanced state during discharge. The evaluation shows a 20.3% slower balancing process compared to a conventional approach. Nevertheless, AQL shows great potential for multiobjective optimizations and can be applied as an RL algorithm for control in power electronics. Full article
(This article belongs to the Special Issue Intelligent Battery Systems: Monitoring, Management, and Control)
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