Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation

Dear Colleagues,

The widespread use of batteries, which are the most common energy storage systems in automotive and consumer electronics, have made them an integral part of our daily lives. Crucial concerns, such as battery life, thus require significant attention that is often tackled by modeling. Researchers have made remarkable advancements to develop models that can predict the battery lifetime, state of health (SoH), remaining useful life, etc. outlining the aging behavior. Numerous modeling methodologies from physics-inspired to black-box methods have improved the prediction modeling accuracy by several folds.

This Special Issue highlights research efforts towards advanced battery lifetime prediction methodologies and/or algorithm development studies, in terms of contributions (i.e., research/perspective/review articles). Novel methodologies and characterization techniques to predict battery aging could also be included for battery diagnosis and prognosis from cell to pack level. Authors are encouraged to submit original articles addressing including, but not limited to, the following topics:

• Battery aging and lifetime prediction models;
• Battery state of health/power estimation;
• Remaining useful life prediction;
• Rest time based or accelerated aging investigation;
• Advanced algorithms for battery life prediction;
• Diagnosis and prognosis of battery systems;
• Physics-informed aging modeling;
• AI or data-driven battery life prediction.

Dr. Md Sazzad Hosen
Dr. Theodoros Kalogiannis
Guest Editors

Manuscript Submission Information

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