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Sustainability
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Intelligent Battery Energy Storage Management: Enhancing Performance, Safety, and Sustainability
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Intelligent Battery Energy Storage Management: Enhancing Performance, Safety, and Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 835

Special Issue Editors

Prof. Dr. Bin Duan

E-Mail Website
Guest Editor
School of Control Science and Engineering, Shandong University, Jinan 250061, China
Interests: power electronics; battery technology; modeling; the optimal control of complex nonlinear systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yongzhe Kang

E-Mail Website
Guest Editor
School of Control Science and Engineering, Shandong University, Jinan 250061, China
Interests: lithium battery management; electric vehicles; battery energy storage systems
Dr. Changlong Li

E-Mail Website
Guest Editor
School of Control Science and Engineering, Shandong University, Jinan 250061, China
Interests: battery energy storage systems; modeling and management

Special Issue Information

Dear Colleagues,

Lithium-ion batteries, as a critical component of modern energy storage systems, play an indispensable role in facilitating the transition towards sustainable energy sources. However, the performance optimization and safety enhancement of batteries remain significant challenges in practical applications. Indeed, accurate and reliable battery operation is key to enhancing battery safety, durability, and reliability. These measures are primarily manifested in intelligent management, including accurate“SOX”estimation, early warning and fault diagnosis, energy management and life extension strategies, retired battery sorting, etc. Advances in these areas can not only improve battery performance and lifespan but also ensure safety during use, thereby driving the development and application scope of battery technology.

Thus, we propose the Special Issue, titled “Intelligent Battery Energy Storage Management: Enhancing Performance, Safety, and Sustainability”. The initiative aims to unite scholars focused on similar topics, providing a platform to present their most recent accomplishments and research findings. We strongly welcome submissions of original research articles, reports, and reviews on related topics.

Prof. Dr. Bin Duan
Dr. Yongzhe Kang
Dr. Changlong Li
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. Sustainability 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 2400 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
  • battery management system
  • advanced machine learning
  • optimization and control
  • early warning and fault diagnosis

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Published Papers (1 paper)

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Research

20 pages, 3179 KiB  
Article
Estimation of Lithium-Ion Battery State of Health-Based Multi-Feature Analysis and Convolutional Neural Network–Long Short-Term Memory
by Xin Ma, Xingke Ding, Chongyi Tian, Changbin Tian and Rui Zhu
Sustainability 2025, 17(9), 4014; https://doi.org/10.3390/su17094014 - 29 Apr 2025
Viewed by 222
Abstract
Accurate estimation of battery state of health (SOH) is critical to the efficient operation of energy storage battery systems. Furthermore, precise SOH estimation methods can significantly reduce resource waste by extending the battery service life and optimizing retirement strategies, which is compatible with [...] Read more.
Accurate estimation of battery state of health (SOH) is critical to the efficient operation of energy storage battery systems. Furthermore, precise SOH estimation methods can significantly reduce resource waste by extending the battery service life and optimizing retirement strategies, which is compatible with the sustainable development of energy systems under carbon neutrality goals. Conventional methods struggle to comprehensively characterize the health degradation properties of batteries. To address that limitation, this study proposes a data-driven model based on multi-feature analysis using a hybrid convolutional neural network and long short-term memory (CNN-LSTM) architecture, which synergistically extracts multi-dimensional degradation features to enhance SOH estimation accuracy. The framework begins by systematically collecting the voltage, current, and other parameters during charge–discharge cycles to construct a temporally resolved multi-dimensional feature matrix. A correlation analysis employing Pearson correlation coefficients subsequently identifies key health indicators strongly correlated with SOH degradation. At the same time, the K-means clustering method was adopted to identify and process the outliers of CALCE data, which ensures the high quality of data and the stability of the model. Then, CNN-LSTM hybrid neural network architecture was constructed. The experimental results demonstrated that the absolute value of MBE for the dataset provided by CALCE was less than 0.2%. The MAE was less than 0.3%, and the RMSE was less than 0.4%. Furthermore, the proposed method demonstrated a strong performance on the dataset provided by NASA PCoE. The experimental results indicated that the proposed method significantly reduced the estimation error of SOH across the entire battery lifecycle, and they fully verified the superiority and engineering applicability of the algorithm in battery SOH estimation. Full article
(This article belongs to the Special Issue Intelligent Battery Energy Storage Management: Enhancing Performance, Safety, and Sustainability)
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