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	6.3	9.8	2015	16.4 Days	CHF 2700	Submit
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600	Submit
Processes processes	3.4	5.7	2013	14.7 Days	CHF 2400	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit

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Published Papers (4 papers)

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All Batteries Energies Processes Sustainability

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22 pages, 5378 KB  

Open AccessArticle

Computational Fluid Dynamics Analysis of Battery Immersion Cooling: Impact of Dielectric Fluid Thermophysical Properties

by Sara El Afia, Francisco Jurado, R. Mazuir Raja Ahsan Shah and Antonio Cano Ortega

Energies 2026, 19(12), 2770; https://doi.org/10.3390/en19122770 - 9 Jun 2026

Viewed by 759

Abstract

The rapid growth in the electric vehicle sector has increased demand for advanced battery thermal management systems (BTMSs) with high heat-dissipation capacity and temperature uniformity. Immersion cooling using dielectric fluids has recently been recognized as a promising alternative technology to conventional indirect liquid [...] Read more.

The rapid growth in the electric vehicle sector has increased demand for advanced battery thermal management systems (BTMSs) with high heat-dissipation capacity and temperature uniformity. Immersion cooling using dielectric fluids has recently been recognized as a promising alternative technology to conventional indirect liquid cooling methods. This study investigates the thermal and hydrodynamic behaviour of a sixteen-lithium-ion cell battery (LIB) module immersed in low-viscosity dielectric fluids using three-dimensional computational fluid dynamics simulations. In this context, a total of twenty dielectric fluids are evaluated using the ANSYS Fluent solver, with particular emphasis on the effects of key thermophysical properties, including viscosity, density, specific heat capacity, and thermal conductivity. The simulation findings reveal that mineral oil and PAO4 yield the lowest maximum LIB cell temperatures, with a reduction of approximately 4 K compared to the least effective dielectric fluids, such as undecane and cumene. Moreover, in terms of temperature uniformity, mineral oil, Novec 7000, and PAO4 exhibit the most homogeneous temperature distributions among the twenty dielectric fluids. In addition, they show an improvement in the temperature uniformity index of approximately 32.4% compared with the least effective dielectric fluid, cumene. On the other hand, mineral oil and PAO4 generate significantly higher pressure drops because of their relatively high viscosities, which increases hydraulic resistance and pumping power requirements. These findings demonstrate that excellent thermal performance does not necessarily correspond to optimal overall thermo-hydraulic behaviour. Overall, the results confirm that immersion-BTMS performance is governed by a complex interaction between dielectric fluid thermophysical properties and flow behaviour, highlighting the importance of coupled thermo-hydraulic optimization in the selection of dielectric fluids for next-generation immersion-cooled battery systems. Full article

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

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19 pages, 2436 KB  

Open AccessArticle

Modulization and Simulation of Thermal Management System Based on Pumped Two-Phase Loop for Hybrid-Electric Aircraft

by Meissara Houalef and Marco Fioriti

Energies 2026, 19(2), 491; https://doi.org/10.3390/en19020491 - 19 Jan 2026

Cited by 1 | Viewed by 1104

Abstract

An efficient thermal management system (TMS) is essential for ensuring hybrid-electric aircraft (HEA) can handle the significant heat rejection required by electrified propulsion. This paper presents a system-level analysis of a compact P2PL TMS for a 1.4 MW battery generating a 70 kW [...] Read more.

An efficient thermal management system (TMS) is essential for ensuring hybrid-electric aircraft (HEA) can handle the significant heat rejection required by electrified propulsion. This paper presents a system-level analysis of a compact P2PL TMS for a 1.4 MW battery generating a 70 kW heat load. A modular modeling method was used to size the key components, and then dynamic simulations were conducted under varying environmental conditions. The results indicate that a compact TMS weighing 22 kg can be developed, with a condenser heat transfer area of 26.20 m² and operating with a refrigerant mass flow rate of 0.56 kg/s while maintaining low pump power consumption at 22 W. This system can successfully regulate a battery’s temperature so that it remains below 40 °C in both standard (15 °C) and cold (−20 °C) environments. Pressure analysis confirmed the system’s flexibility and its ability to control battery temperature between 27 °C and 38 °C by adjusting the working pressure (6–8 bar). Furthermore, under hot day conditions (40 °C), battery temperature can be maintained at 47.6 °C. Even under extreme conditions (50 °C), the TMS limits the temperature to 57.45 °C, ensuring it stays within the safe operating range. Full article

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

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18 pages, 8082 KB  

Open AccessArticle

Application of Attention Mechanism Models in the Identification of Oil–Water Two-Phase Flow Patterns

by Qiang Chen, Haimin Guo, Xiaodong Wang, Yuqing Guo, Jie Liu, Ao Li, Yongtuo Sun and Dudu Wang

Processes 2026, 14(2), 265; https://doi.org/10.3390/pr14020265 - 12 Jan 2026

Cited by 1 | Viewed by 731

Abstract

Accurate identification of oil–water two-phase flow patterns is essential for ensuring the safety and operational efficiency of oil and gas extraction systems. While traditional methods using empirical models and sensor technologies have provided basic insights, they often struggle to capture the nonlinear features [...] Read more.

Accurate identification of oil–water two-phase flow patterns is essential for ensuring the safety and operational efficiency of oil and gas extraction systems. While traditional methods using empirical models and sensor technologies have provided basic insights, they often struggle to capture the nonlinear features of complex operational conditions. To address the challenge of data scarcity commonly found in experimental settings, this study employs a data augmentation strategy that combines the Synthetic Minority Over-sampling Technique (SMOTE) with Gaussian noise injection, effectively expanding the feature space from 60 original experimental nodes. Next, a physics-constrained attention mechanism model was developed that incorporates a physical constraint matrix to effectively mask irrelevant feature interactions. Experimental results show that while the standard attention model (83.88%) and the baseline BP neural network (84.25%) have limitations in generalizing to complex regimes, the proposed physics-constrained model achieves a peak test accuracy of 96.62%. Importantly, the model demonstrates exceptional robustness in identifying complex transition regions—specifically Dispersed Oil-in-Water (DO/W) flows—where it improved recall rates by about 24.6% compared to baselines. Additionally, visualization of attention scores confirms that the distribution of attention weights aligns closely with fluid-dynamic mechanisms—favoring inclination for stratified flows and flow rate for turbulence-dominated dispersions—thus validating the model’s interpretability. This research offers a novel, interpretable approach for modeling dynamic feature interactions in multiphase flows and provides valuable insights for intelligent oilfield development. Full article

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

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23 pages, 2707 KB  

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

Cited by 1 | Viewed by 1835

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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