Thermal Management in Lithium-Ion and Emerging Batteries: Latest Advances and Prospects

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Energy Storage System Aging, Diagnosis and Safety".

Deadline for manuscript submissions: 10 August 2026 | Viewed by 4822

Special Issue Editors


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Guest Editor
Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, USA
Interests: fuel cells; Li-O2 batteries; battery thermal management; heat and mass transfer in porous media
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Guest Editor
Energy Conversion and Storage Systems Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Interests: Li-ion battery; multiphysics modeling; battery safety and thermal management; fluid dynamics and control
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Thermal Systems and Software, General Motors, Detroit, MI 48243, USA
Interests: battery thermal management system; BMS algorithms; modeling and simulation (CFD/FEA); aerodynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapidly expanding battery market presents an urgent requirement for reliable, efficient, and affordable thermal management and protection solutions. These solutions are crucial to improve the cycle life and performance, as well as to mitigate the risk of thermal runaway and catastrophic failures in battery packs. Addressing these pressing needs is of the utmost importance for the power electronics, electric vehicle, and battery industries. By enhancing the safety of battery packs, we can facilitate the widespread adoption of batteries in energy-intensive applications such as electric vehicles and grid-scale energy storage systems. In addition, a higher battery cycle life improves the long-term cost–benefit of a battery electric vehicle or energy storage system. Advanced thermal management strategies can also improve performance, such as fast charging and aggressive discharges across a large temperature band, which further supports battery technology adaptation. Therefore, there is a critical imperative to develop robust, efficient, and cost-effective thermal management strategies that ensure the performance, integrity, and stability of battery systems.

In light of the rapid growth witnessed in the electric vehicle and rechargeable battery markets, this Special Issue, entitled “Thermal Management in Lithium-Ion and Emerging Batteries: Latest Advances and Prospects”, presents an opportune platform to explore diverse thermal management technologies. It specifically focuses on their application in battery and electronics systems for transportation applications, including passenger cars, trucks, buses, locomotives, boats, aircraft, and beyond, encompassing both established industry practices and emerging solutions for future advancements. By examining conventional approaches alongside cutting-edge innovations, this timely collection of articles aims to address the evolving demands of the field and foster insights into effective thermal management strategies.

We encourage contributions from diverse global stakeholders to foster a comprehensive and inclusive dialogue. Valuable insights and expertise from academic institutions, industry organizations, and national laboratories are highly valued and encouraged. Both experimental studies and numerical simulations are welcomed. Topics of interest for publication include, but are not limited to, the following:

  • Assessment of industry approaches and emerging advancements;
  • Single-phase cooling (passive, direct active, indirect active, and hybrid);
  • Multi-phase cooling (phase change materials, evaporative cooling, immersion/submerged cooling);
  • Innovative cooling materials and structures;
  • Advanced sensors, thermal control, and fault detection;
  • Battery materials and designs with improved thermal properties;
  • Strategies to mitigate thermal runaway and propagation;
  • Cutting-edge models to gain insights into thermal-related degradation mechanisms;
  • New mechanistic models to understand degradation caused by thermal issues;
  • Fusion of machine learning algorithms to enhance the precision and efficiency of detection and prediction;
  • Extreme conditions such as extremely fast charge and low temperatures.

Dr. Xianglin Li
Dr. Chuanbo Yang
Dr. Prahit Dubey
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 submissions that pass pre-check are 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 250 words) can be sent to the Editorial Office for assessment.

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. Batteries is an international peer-reviewed open access monthly 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 2700 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.

Keywords

  • Lithium-ion batteries
  • rechargeable batteries
  • thermal management
  • thermal runaway
  • battery safety
  • advanced cooling systems

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Published Papers (1 paper)

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Review

69 pages, 2859 KB  
Review
Advances in Battery Modeling and Management Systems: A Comprehensive Review of Techniques, Challenges, and Future Perspectives
by Seyed Saeed Madani, Yasmin Shabeer, Ananthu Shibu Nair, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, Shi Xue Dou, Khay See, Saad Mekhilef and François Allard
Batteries 2025, 11(11), 426; https://doi.org/10.3390/batteries11110426 - 20 Nov 2025
Cited by 23 | Viewed by 4385
Abstract
Energy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency [...] Read more.
Energy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency and preventing malfunctions. This paper thoroughly examines the most recent advancements in battery and BMS modeling, including data-driven, thermal, and electrochemical methods. Advanced modeling approaches are explored, including physics-based models that incorporate mechanical stress and aging effects, as well as artificial intelligence (AI)-driven state estimation. New technologies that facilitate data-driven decision-making, real-time monitoring, and simplified systems include digital twins (DTs), cloud computing, and wireless BMSs. Nonetheless, there are still issues with cost optimization, cybersecurity, and computing efficiency. This study presents key advancements in battery modeling and BMS applications, including defect diagnostics, temperature management, and state-of-health (SOH) prediction. A comparison of machine learning (ML) methods for SOH prediction is given, emphasizing how well neural networks (NNs) and transfer learning function with real-world datasets. Additionally, future research objectives are described, with an emphasis on next-generation sensor technologies, cloud-based BMSs, and hybrid algorithms. Distinct from existing reviews, this paper integrates academic modeling with industrial benchmarking and highlights the convergence of hybrid physics-informed and data-driven techniques, multi-physics simulations, and intelligent architecture. For high-performance EV applications, this analysis offers insight into creating more intelligent, adaptable, and secure BMSs by addressing current constraints and utilizing state-of-the-art technologies. Full article
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