Topic Editors

Department of Electric Engineering and Energy Technology (ETEC), Mobility, Logistics and Automotive Technology Research Centre (MOBI), Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium
Prof. Dr. Masud Behnia
Center for Turbulence Research (CTR), Stanford University, Stanford, CA, USA
Department of Electric Engineering and Energy Technology (ETEC), Mobility, Logistics and Automotive Technology Research Centre (MOBI), Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium

Advanced Battery Thermal Management Solution for Electric Vehicles, 2nd Edition

Abstract submission deadline
31 March 2026
Manuscript submission deadline
30 June 2026
Viewed by
217

Topic Information

Dear Colleagues,

This Topic is a continuation of the previous successful Topic “Advanced Battery Thermal Management Solution for Electric Vehicles”. We are pleased to invite you to contribute to this Topic again, which is open to researchers and authors who would like to submit their research and review articles in the field of battery, electric vehicles, heat transfer, thermal management systems, energy storage, biothermal engineering, and nanoscale energy transfer.

The present Topic will consider how combined and progressive thermal management technologies can control and use excess energy in a comprehensive range of industrial and non-industrial applications. It will encompass various subjects, comprising battery thermal management systems, battery electrochemistry, battery electrothermal systems, energy generation, applied thermal applications, thermal energy storage, thermal management and conversion, heat transfer applications, and renewable energies.

Dr. Hamidreza Behi
Prof. Dr. Masud Behnia
Dr. Danial Karimi
Topic Editors

Keywords

  • battery thermal management
  • renewable energy technologies
  • energy and thermal storage
  • battery electro-thermal model
  • battery electrochemical application
  • electronic cooling

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Batteries
batteries
4.8 6.6 2015 18.5 Days CHF 2700 Submit
Energies
energies
3.2 7.3 2008 16.2 Days CHF 2600 Submit
Processes
processes
2.8 5.5 2013 16 Days CHF 2400 Submit
Sustainability
sustainability
3.3 7.7 2009 19.3 Days CHF 2400 Submit

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Published Papers (1 paper)

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23 pages, 2707 KiB  
Article
Performance Analysis of Battery State Prediction Based on Improved Transformer and Time Delay Second Estimation Algorithm
by Bo Gao, Xiangjun Li, Fang Guo and Xiping Wang
Batteries 2025, 11(7), 262; https://doi.org/10.3390/batteries11070262 (registering DOI) - 13 Jul 2025
Abstract
As energy storage technology advances rapidly, the power industry demands accurate state estimation of lithium batteries in energy storage power stations. This study aimed to improve such estimations. An improved Transformer structure was employed to estimate the battery’s state of charge (SOC). The [...] Read more.
As energy storage technology advances rapidly, the power industry demands accurate state estimation of lithium batteries in energy storage power stations. This study aimed to improve such estimations. An improved Transformer structure was employed to estimate the battery’s state of charge (SOC). The Time Delay Second Estimation (TDSE) algorithm optimized the improved Transformer model to overcome traditional models’ limitations in extracting long-term dependency. Innovative particle filter algorithms were proposed to handle the nonlinearity, uncertainty, and dynamic changes in predicting remaining battery life. Results showed that for LiNiMnCoO2 positive electrode datasets, the model’s max SOC estimation error was 2.68% at 10 °C and 2.15% at 30 °C. For LiFePO4 positive electrode datasets, the max error was 2.79% at 10 °C (average 1.25%) and 2.35% at 30 °C (average 0.94%). In full lifecycle calculations, the particle filter algorithm predicted battery capacity with 98.34% accuracy and an RMSE of 0.82%. In conclusion, the improved Transformer and TDSE algorithm enable advanced battery state prediction, and the particle filter algorithm effectively predicts remaining battery life, enhancing the adaptability and robustness of lithium battery state analysis and offering technical support for energy storage station management. Full article
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