10th Anniversary of Batteries: Battery Diagnostics and Prognostics

Dear Colleagues,

With the rapid evolution of electric vehicles, renewable energy storage, and portable electronics, ensuring the reliability, safety, and lifetime of battery systems has become a critical challenge. Celebrating the 10th anniversary of Batteries, this Special Issue focuses on advancements in battery diagnostics and prognostics, covering multi-physics modeling, real-time diagnostics, and predictive analytics to enable next-generation battery management.

Key topics include electrochemical–thermal–mechanical coupling for accurate state estimation, degradation-aware digital twins, and AI-driven battery health assessment. Furthermore, stochastic modeling and uncertainty quantification play a crucial role in prognostics of remaining useful life, while embedded sensing and electrochemical impedance spectroscopy (EIS) provide real-time insights into battery health. This collection also explores cloud-integrated and edge computing architectures for predictive battery management systems (BMSs), driving the transition towards intelligent battery technologies. By bridging fundamental research and applied innovations, this Special Issue aims to accelerate the development of more reliable and efficient energy storage solutions.

Dr. Xin Sui
Prof. Dr. Remus Teodorescu
Prof. Dr. Xiaosong Hu
Guest Editors

Manuscript Submission Information

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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.

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