Advanced Modeling and State Estimation Technologies for Next-Generation Battery Management
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D2: Electrochem: Batteries, Fuel Cells, Capacitors".
Deadline for manuscript submissions: 7 March 2026 | Viewed by 184
Special Issue Editors
Interests: lithium-ion batteries; battery management; electrified vehicles
Special Issues, Collections and Topics in MDPI journals
Interests: lithium-ion batteries; thermal monitoring; state estimation
Interests: smart batteries; artificial intelligence; power electronics; electric vehicles; lithium-ion batteries; smart battery management; renewable energy; energy storage
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Lithium-ion batteries (LIBs) have been widely deployed in various energy storage applications, ranging from customer electronics, electric vehicles, grid energy storage systems, and even flying cars. However, several common concerns, such as short driving mileage, potential safety hazards, and unpredictable degradation, greatly hinder the further adoption of battery-powered devices. Advanced battery management is a viable solution to mitigating these awkward situations, which entails accurate and computationally efficient battery models and state estimation algorithms. involves multi-level battery models (i.e., cell/module/pack level) and key states like state of charge (SOC), state of temperature (SOT), state of health (SOH), state of power (SOP), remaining useful lifetime (RUL), and state of safety (SOS). Conventional approaches face significant challenges in striking the trade-off between prediction fidelity, computational complexity, implementation difficulty, and generalization capability.
Recent developments in data science and hardware capabilities offer transformative opportunities to enhance battery management over the entire lifespan. Advanced strategies and techniques, such as cloud-end collaboration and physics–data hybridization, make good use of separate methods to improve performance (like simulation accuracy and generalizability) without the sacrifice of computational efficiency. These emerging approaches hold great promise for enabling next-generation smart battery management.
This Special Issue aims to provide an exchange platform for researchers and practitioners to present cutting-edge developments in the field of battery modeling, state estimation, safety evaluation, and risk warning.
The scope of this Special Issue aligns with the journal’s mission to advance theoretical innovation, technological development, and practical applications in the field of energy storage and management. We particularly welcome interdisciplinary research that integrates electrochemistry, data science, and system engineering to address critical challenges in battery modeling, state estimation, and safety management.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Physics-based/physics-data hybrid modeling methods for fast battery digital twins;
- Model simplification and reformulation for real-time control purposes;
- Model-based multi-state joint estimation (like SOC/SOH/SOP/SOS);
- Machine learning enabled state estimation and prediction (like SOH/RUL);
- Sensorless temperature estimation and the reconstruction of temperature distribution of large-format battery cells/packs;
- Safety quantification and risk warning based on multi-source information fusion and cloud-end collaboration;
- Degradation-aware state monitoring and performance management strategies.
We look forward to receiving your contributions.
Dr. Wenxue Liu
Dr. Yusheng Zheng
Prof. Dr. Remus Teodorescu
Dr. Hamidreza Movahedi
Guest Editors
Manuscript Submission Information
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Keywords
- lithium-ion batteries
- physics-based modeling
- model reformulation
- machine learning
- state estimation
- safety management
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