Data-Driven Modeling, Degradation, Control, and Advanced Management Systems for Batteries

Dear Colleagues,

Batteries play a significant role in modern power systems, transportation systems (land, sea, and air), and space applications. Over the years, battery technology has improved, and advanced management systems have enhanced their performance, longevity, and safety. However, complex battery chemistry, environmental factors, and management strategies have been shown to affect battery performance and health. Understanding and modeling the battery’s physical mechanisms and degradation—such as the capacity and state of health loss and internal resistance growth—considering several environmental factors and radiation (in aviation and space) is essential for mitigating aging and ensuring optimal usage. Data-driven modeling ensures accounting for real-world effects on battery health using computational methods, such as machine learning and AI, to predict and understand battery behavior, enabling more accurate performance insights. Effective control strategies regulate factors, such as charging and discharging currents, depth-of-discharge, and thermal management, ensuring safe and efficient operation across diverse applications. Finally, advanced management systems integrate state-of-the-art technologies like IoT and predictive algorithms to monitor, protect, and optimize battery systems, enhancing reliability, lifespan, and adaptability. This Special Issue aims to unify these approaches to support the development of smarter batteries and more robust and reliable energy solutions for applications such as renewable energy systems, electric transportation systems, and space exploration.

We warmly welcome researchers and practitioners to submit original research articles, comprehensive review papers, and insightful case studies addressing, but not limited to, the following topics:

• Physics-informed machine learning-based hybrid modeling of battery behavior.
• AI and machine learning-based algorithms for accurate battery state estimation.
• Predictive modeling of battery performance under diverse operational conditions.
• Data-driven degradation models, their impact on battery performance, and degradation mitigation strategies.
• Advanced battery control algorithms for optimal charging and discharging profiles.
• Robust control frameworks to minimize battery state-of-health deterioration and enhance lifespan.
• Battery management systems with AI- and learning-based diagnostics and fault detection.
• Predictive maintenance strategies for early fault detection in batteries and its prevention.
• Emerging battery technologies in power systems, transportation, and space applications and their modeling, management, and degradation.

Dr. Diptish Saha
Prof. Dr. Juan C. Vasquez
Dr. Najmeh Bazmohammadi
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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.