Special Issue "Machine Learning for Battery State Estimation and Lifetime Prediction"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "J: State-of-the-Art Energy Related Technologies".

Deadline for manuscript submissions: 30 June 2022.

Special Issue Editors

Prof. Dr. Daniel-Ioan Stroe
E-Mail Website
Guest Editor
Department of Energy Technology, Aalborg University, Pontoppidanstræde 111, 9220 Aalborg, Denmark
Interests: energy storage; lithium-ion batteries; battery performance and lifetime testing; accelerated aging; battery performance-degradation modeling; state-of-charge estimation; state-of-health estimation; remaining useful lifetime prediction; aging mechanisms; power and energy management strategies; lithium-ion capacitors; hybrid renewable energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Søren B. Vilsen
E-Mail Website
Guest Editor
Department of Mathematical Science, Aalborg University, Skjernvej 4, 9220 Aalborg, Denmark
Interests: applied statistics; statistical learning; machine learning; deep learning; evolutionary computation; battery performance modeling; battery state-of-charge estimation; battery state-of-health estimation; battery lifetime prediction

Special Issue Information

Dear Colleagues,

The Guest Editors of the upcoming Special Issue of Energies, entitled “Machine Learning for Battery State Estimation and Lifetime Prediction”, are pleased to announce that submissions are now open.

Batteries are highly nonlinear energy storage devices and their performance (e.g., capacity, resistance, power, etc.) is very sensitive to the operating conditions (e.g., temperature, C-rate, state-of-charge, etc.). Moreover, the performance of batteries degrades over time, particularly during long-term operation. Consequently, in order to operate batteries with high efficiency, benefit from their advantages, and avoid costly downtime periods, comprehensive knowledge about battery states (e.g., state-of-charge, state-of-health, state-of-power, etc.) is mandatory. Furthermore, accurate prediction of a battery’s remaining useful lifetime (RUL) can allow the user to select the optimal energy/power management strategy, carry out maintenance work, and schedule eventual replacements, which will subsequently have a positive effect on the economic feasibility of the project.

With the increase in cloud computing capabilities and the availability of extensive real field and laboratory battery data, machine learning methods have been utilized as powerful and robust techniques for battery state estimation and RUL prediction.

Thus, this Special Issue seeks manuscripts on the following topics:

  • Machine learning-based battery SOC estimation;
  • Battery lifetime prediction using machine learning;
  • Support vector regression for battery SOH estimation;
  • Neural network approaches for battery SOH estimation;
  • Algorithms for SOH feature selection and reduction.

Prof. Daniel-Ioan Stroe
Dr. Søren B. Vilsen
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 papers will be 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 2000 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.

Published Papers

This special issue is now open for submission.
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