Modeling, Diagnosis and Protection for Li-Ion Battery Energy Storage System—2nd Edition

Dear Colleagues,

Lithium-ion batteries are often connected in series and parallel to formulate a lithium-ion battery system (pack) for meeting the high-voltage and high-capacity requirements of energy storage systems. It is particularly important to accurately model the behaviors, estimate the states, diagnose the degradation and faults of the battery energy storage system, and further take necessary measures to prevent any potential safety hazards. However, with an appreciable number of batteries available worldwide, it is challenging to capture the performance, monitor the states, and identify the faults of each battery. Emerging techniques, such as big data mining, artificial intelligence, digital twin, and block chain, bring the potential of addressing these issues through developing new models, state estimation methods, and diagnostic approaches; thus, enabling a promising candidate able to achieve an early fault and safety warning. Furthermore, in order to allow for the application of battery energy storage systems to be safer and more reliable, increasing research has been aimed at optimizing the design and control technologies of battery systems. This Special Issue expects to explore research innovation within the battery system engineering challenge that incorporates modeling, state estimation, diagnostics, prognostics, control engineering, system design, and safety engineering; thus, promoting the mass commercialization and popularity of the Li-ion battery energy storage system. Manuscripts from cross-disciplinary fields, theoretical and practical studies and novel methods are strongly encouraged and welcome.

Topics of interest for publication include, but are not limited to:

• Accurate modeling and fast simulation of Li-ion battery systems;
• Application of digital twin for lithium-ion battery systems;
• The estimation of battery states, such as SOC, SOH, SOF, SOP, and temperature;
• Fast charging and charging optimization methods;
• Wireless charging technology;
• Battery thermal management;
• Design and application of battery virtualization equipment;
• Reliability optimization techniques for lithium-ion battery systems;
• State parameter prognosis and fault diagnosis of lithium-ion battery systems;
• Thermal runaway and thermal failure mechanism;
• Safety protection technology of lithium-ion battery pack;
• Battery management system (BMS) optimization design technology.

Prof. Dr. Bingxiang Sun
Dr. Liye Wang
Dr. Linfeng Zheng
Dr. Haijun Ruan
Dr. Dongsheng Ren
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-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.

• lithium-ion battery
• modeling
• digital twin
• virtual battery
• thermal management, state estimation and prognostics
• faults diagnosis
• safety protection
• BMS

• Modeling, Diagnosis and Protection for Li-Ion Battery Energy Storage System in Energies (6 articles)