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

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

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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All Batteries Energies Processes Sustainability

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23 pages, 2707 KiB  

Open AccessArticle

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 - 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

(This article belongs to the Topic Advanced Battery Thermal Management Solution for Electric Vehicles, 2nd Edition)

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