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Applications of Artificial Intelligence (AI) in Energy Storage Systems Design, Operation and Control

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 1267

Special Issue Editors


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Department of Building, Civil, and Environmental Engineering (BCEE), Concordia University, Montreal, Quebec H3G 1M8, Canada
Interests: energy systems engineering; facility management and maintenance; infrastructure management; sustainable cities; communities and buildings
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Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
Interests: urban and building energy system; urban environment; renewable energy

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Department of Building, Civil, and Environmental Engineering (BCEE), Concordia University, Montreal, Quebec H3G 1M8, Canada
Interests: indoor environment; ventilation; filtration; materials; high-performance green buildings; urban ventilation and outdoor environment; GHG

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Department of Engineering, University of Cambridge, Trumpington St, Cambridge CB2 1PZ, UK
Interests: simulation-based optimization methodologies for energy management of buildings; ventilation design strategies; integration of novel building technologies and renewable energy supply systems during design phases; performance assessment of buildings

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Institut National des Sciences Appliquées de Lyon, 69100 Villeurbanne, France
Interests: renewable energy technologies; energy conversion; energy engineering; energy saving; numerical modeling; thermal engineering; engineering thermodynamics; energy efficiency in building; energy; computational fluid dynamics

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Department of Electrical and Information Engineering, Politecnico di Bari, 70126 Bari, Italy
Interests: manufacturing systems; modeling; optimization; logistics; petri nets; fuzzy systems; discrete event systems; automation; systems theory; automatic control

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Dipartimento Energia (DENERG), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: sustainable building; low carbon architecture; energy efficiency in buildings
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Special Issue Information

Dear Colleagues,

Energy storage systems (ESSs) are receiving growing attention as main stream solutions for the widespread use of renewable energies and subsequently as a means of decarbonizing the electrification of society. At the building scale, they are increasingly utilized to enhance heat and cooling energy recovery, promote distributed energy supply and improve the efficiency of cities in reducing their peak demands by helping the utility companies to maintain reliable and resilient generation and distribution capacities. As energy storage systems are well-positioned to bridge the inputs from renewable and recovered energies with the energy demand across varied scales, geographies, and times, there is a pressing need to expand the research in systems’ modeling and analysis of energy storage technologies and their applications. This is of particular importance due to the various types of energy storage technologies in varied sizes and scales, and their diverse operational characteristics and challenges with respect to efficiency and reliability. In doing so, artificial intelligence provides an opportunity to better adapt energy storage systems with changing environmental conditions, dynamic characteristics of the grid, intermittent nature of renewables, thus improving the reliability and resilience of these systems. AI is widely applied in the sizing, scheduling, control, and optimization of energy systems. This Special Issue intends to collect and disseminate the state of the art on research and practice in applications of AI in modeling and analysis of energy storage systems with a focus on the following (and other closely related) topics:

  • Energy supply predictions for integration of renewable energies and ESSs;
  • Energy demand and critical load predictions for ESSs control and operation;
  • Capacity planning and sizing of ESSs;
  • Optimized scheduling of ESSs;
  • Monitoring and control of ESSs;
  • Fault detection, diagnosis, and reliability analysis for ESSs;
  • Distributed management of ESSs;
  • Data analytics for life cycle analysis of ESSs;
  • Location-specific systems analysis for ESSs.

Dr. Fuzhan Nasiri
Prof. Dr. Ryozo Ooka
Prof. Dr. Fariborz Haghighat
prof. Dr. Ruchi Choudhary
Prof. Dr. Frédéric Kuznik
Prof. Dr. Mariagrazia Dotoli
Prof. Dr. Alireza Afshari
Dr. Enrico Fabrizio
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. Energies is an international peer-reviewed open access semimonthly 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 2600 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.

Keywords

  • energy storage systems (ESSs)
  • energy supply predictions for integration of renewable energies and ESSs
  • energy demand and critical load predictions for ESSs control and operation
  • capacity planning and sizing of ESSs
  • optimized scheduling of ESSs
  • monitoring and control of ESSs
  • fault detection, diagnosis, and reliability analysis for ESSs
  • distributed management of ESSs
  • data analytics for life cycle analysis of ESSs
  • location-specific systems analysis for ESSs

Published Papers (1 paper)

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Research

23 pages, 5478 KiB  
Article
The Early Detection of Faults for Lithium-Ion Batteries in Energy Storage Systems Using Independent Component Analysis with Mahalanobis Distance
by Seunghwan Jung, Minseok Kim, Eunkyeong Kim, Baekcheon Kim, Jinyong Kim, Kyeong-Hee Cho, Hyang-A Park and Sungshin Kim
Energies 2024, 17(2), 535; https://doi.org/10.3390/en17020535 - 22 Jan 2024
Viewed by 894
Abstract
In recent years, battery fires have become more common owing to the increased use of lithium-ion batteries. Therefore, monitoring technology is required to detect battery anomalies because battery fires cause significant damage to systems. We used Mahalanobis distance (MD) and independent component analysis [...] Read more.
In recent years, battery fires have become more common owing to the increased use of lithium-ion batteries. Therefore, monitoring technology is required to detect battery anomalies because battery fires cause significant damage to systems. We used Mahalanobis distance (MD) and independent component analysis (ICA) to detect early battery faults in a real-world energy storage system (ESS). The fault types included historical data of battery overvoltage and humidity anomaly alarms generated by the system management program. These are typical preliminary symptoms of thermal runaway, the leading cause of lithium-ion battery fires. The alarms were generated by the system management program based on thresholds. If a fire occurs in an ESS, the humidity inside the ESS will increase very quickly, which means that threshold-based alarm generation methods can be risky. In addition, industrial datasets contain many outliers for various reasons, including measurement and communication errors in sensors. These outliers can lead to biased training results for models. Therefore, we used MD to remove outliers and performed fault detection based on ICA. The proposed method determines confidence limits based on statistics derived from normal samples with outliers removed, resulting in well-defined thresholds compared to existing fault detection methods. Moreover, it demonstrated the ability to detect faults earlier than the point at which alarms were generated by the system management program: 15 min earlier for battery overvoltage and 26 min earlier for humidity anomalies. Full article
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