Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation (Volume II)

Dear Colleagues,

The widespread use of batteries as the most common energy storage systems in automotive and consumer electronics have made it an integral part of our daily business. The crucial concern like battery lifetime, thus, requires principal attention that is often tackled by modeling works. Researchers have made remarkable achievements to develop models that could predict the battery lifetime, state of health (SoH), remaining useful life, etc. outlining the aging behavior. Numerous modeling methodologies from physics-based to black-box types have enriched the prediction modeling accuracy by several folds.

This Special Issue highlights the research efforts towards advanced lifetime prediction methodologies and/or algorithms development works in terms of contributions (research/perspective/review articles). This is the second volume of the series following up the excellent collection of the works in the first issue. Novel methodologies and characterization techniques to predict battery aging could also be included for battery diagnosis and prognosis from cell to pack level. The authors are encouraged to submit original articles addressing potential but not limited to the following topics.

• Battery aging and lifetime prediction models
• Battery state of X (SoC, SoH, SoE, SoP, SoS) estimation
• Early life and Remaining useful life (RUL) prediction
• Rest time based or accelerated aging studies
• Advanced algorithms for on-board predictions
• Diagnosis and prognosis of battery systems
• Physics-based degradation modeling
• Model development using field-data (e-mobility & stationary)
• Machine learning or data-driven battery predictions
• Review of state-of-the-art battery modeling methodologies

Dr. Md Sazzad Hosen
Guest Editor

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. Energies 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 2600 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.

• lifetime modeling
• aging prediction
• battery state estimation
• remaining useful life prediction
• degradation study
• data-driven battery modeling
• capacity fade modeling
• resistance growth modeling
• online estimation
• diagnosis and prognosis
• realistic validation

• Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation in Energies (9 articles)