Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation: 3rd Edition
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D2: Electrochem: Batteries, Fuel Cells, Capacitors".
Deadline for manuscript submissions: 15 August 2025 | Viewed by 389
Special Issue Editor
Interests: Li-ion battery technologies; cell selection; battery sizing; cell characterization; battery state estimation (SoX); battery aging; lifetime modeling; algorithm development; thermal management; diagnosis; prognosis of energy storage devices
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Special Issue Information
Dear Colleagues,
The widespread use of batteries as the most common energy storage systems in automotive and consumer electronics has made them an integral part of our daily business. Crucial concerns like battery performance and lifetime thus require attention, which is often tackled by modeling work. Researchers have made remarkable achievements in developing predictive models that can predict the battery states (SoX), lifetime, remaining useful life, etc., outlining the aging behavior of the battery. Numerous modeling methodologies from physics-based to data-driven types have enriched the prediction modeling accuracy by several folds.
This 3rd edition of the Special Issue highlights the research efforts towards advanced prediction methodologies and/or algorithm development works in terms of contributions (research/perspective/review articles). This is the third volume of the series following up on the excellent collection of works in the previous Special Issues. Novel methodologies and characterization techniques to predict battery aging could also be included for battery diagnosis and prognosis from cell to pack level including the 2nd life of the battery. Authors are encouraged to submit original articles that address, but are not limited to, the following topics:
- Battery state of X (SoC, SoH, SoE, SoP, SoS) estimation;
- Battery aging and lifetime prediction models;
- Early life and remaining useful life (RUL) prediction models;
- Degradation mechanism identification including 2nd life;
- Physical, digitalized, and accelerated aging studies;
- Advancement in the battery management system (BMS) for embedded models;
- Edge and cloud simulation and a combination of the models;
- Diagnosis and prognosis of battery systems including thermal aspects;
- Physics-based, AI, and hybrid modeling work for reliable prediction;
- Reliable service life and monitoring of batteries during 1st and 2nd life.
Prof. Dr. Md Sazzad Hosen
Guest Editor
Manuscript Submission Information
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Keywords
- battery characterization
- lifetime modeling
- aging prediction
- battery state estimation
- remaining useful life prediction
- degradation study
- data-driven battery modeling
- second life battery modeling
- edge and cloud estimation
- diagnosis and prognosis
- embedded models
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