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Batteries
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Recent Advances in Numerical Modeling and Experimental Validation of Batteries
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Recent Advances in Numerical Modeling and Experimental Validation of Batteries

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: 28 July 2026 | Viewed by 6328

Special Issue Editors

Dr. François Allard

E-Mail Website
Guest Editor
Centre Énergie, Matériaux et Télécommunications (EMT), Institut National de la Recherche Scientifique (INRS), Varennes, QC J3X 1P7, Canada
Interests: numerical modeling; finite element method; experimental validation; heat and mass transfer; metallic anode; lithium batteries; solid electrolytes; hydrogen production; metal production; all-solid-state batteries; applied electrochemistry
Prof. Dr. Sylvain Franger

E-Mail Website
Guest Editor
Institut de Chimie Moléculaire et des Matériaux d’Orsay, ICMMO (UMR CNRS 8182), Université Paris-Saclay, 17 Avenue des Sciences, 91400 Orsay, France
Interests: materials chemistry; solid-state electrochemistry; impedance spectroscopy; electrochemical modeling; energy storage: Li-based batteries; Na-based batteries; all-solid-state batteries; microbatteries; supercapacitors; energy conversion: electrocatalysis for hydrogen technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Batteries (including metal-ion, all-solid-state, and metal–air batteries) can be engineered using numerical methods (i.e., finite element analysis, mathematical models, and so on) for the sustainable and rapid development of electric vehicles, portable electronics, and renewable energy storage systems. This Special Issue delves into the latest numerical modeling techniques and their critical role in enhancing our understanding of battery performance, efficiency, and lifespan. Contributors highlight innovative computational methods that predict battery behavior under various conditions, thereby aiding in the design of more robust, sustainable, and efficient batteries. A significant focus is placed on the determination of material properties essential for supplying accurate data to numerical models. The Special Issue also emphasizes the importance of experimental validation, presenting studies where theoretical models are rigorously tested against experimental data to ensure accuracy and reliability.

In this Special Issue, scientists and engineers are encouraged to submit articles addressing the following topics:

  1. Advanced numerical modeling techniques: development of new algorithms for simulating battery behavior.
  2. Material property determination: techniques for measuring material properties critical to numerical simulation.
  3. Electrochemical simulation: modeling of electrochemical reactions within the battery.
  4. Thermal management strategies: modeling heat generation and dissipation in lithium batteries.
  5. Degradation mechanisms: investigations into the chemical and mechanical degradation of battery components.
  6. Experimental validation: development of protocols for validating model accuracy and reliability.
  7. Integration of machine learning algorithms: combining machine learning with traditional modeling techniques.
  8. Battery design optimization: application of numerical models to optimize battery design for specific applications (i.e., electric vehicles, energy storage systems, and so on).

Dr. François Allard
Prof. Dr. Sylvain Franger
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

  • battery modeling
  • measuring material properties
  • validating model accuracy
  • electrochemical models
  • chemical and mechanical degradation
  • state of charge and health estimation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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27 pages, 4613 KB  
Article
A Reusable Framework for Dynamic Simulation of Grid-Scale Lithium-Ion Battery Energy Storage
by Renos Rotas, Panagiotis Karafotis, Petros Iliadis, Nikolaos Nikolopoulos, Dimitrios Rakopoulos and Ananias Tomboulides
Batteries 2026, 12(2), 63; https://doi.org/10.3390/batteries12020063 - 14 Feb 2026
Viewed by 661
Abstract
This paper presents a modeling framework for large-capacity lithium-ion battery energy storage systems (BESSs), developed within the Modelica LIBSystems library and focused on system-level integration. The framework builds on a combined analysis of the electrical, thermal and degradation behavior at the cell level [...] Read more.
This paper presents a modeling framework for large-capacity lithium-ion battery energy storage systems (BESSs), developed within the Modelica LIBSystems library and focused on system-level integration. The framework builds on a combined analysis of the electrical, thermal and degradation behavior at the cell level to model the BESS interconnection to the electrical grid. A semi-empirical aging model was incorporated following its validation at the cell level against capacity loss experimental measurements. Two case studies were conducted for a 10.5 MW/15 MWh BESS installed in the isolated power system of Terceira Island. The first analyzed the short-term response to a 5% load step decrease under 60% and 80% renewable penetration scenarios, yielding a frequency nadir improvement of 3 mHz and 21 mHz, respectively. The second projected long-term degradation under two dispatch strategies: one derived from historical time series, and another synthetically constructed to induce more frequent and deeper cycling. After 1000 days of operation, the state of health declined to 95.2% in the historical-based case and to 93.5% under the aggressive profile. The proposed framework establishes a unified, cross-domain modeling workbench for Li-ion BESS applications, enabling evaluation of the system design, control strategies, operation conditions, and system-level performance across both dynamic and long-term horizons. Full article
(This article belongs to the Special Issue Recent Advances in Numerical Modeling and Experimental Validation of Batteries)
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19 pages, 3675 KB  
Article
A Multiphysics Aging Model for SiOx–Graphite Lithium-Ion Batteries Considering Electrochemical–Thermal–Mechanical–Gaseous Interactions
by Xiao-Ying Ma, Xue Li, Meng-Ran Kang, Jintao Shi, Xingcun Fan, Zifeng Cong, Xiaolong Feng, Jiuchun Jiang and Xiao-Guang Yang
Batteries 2026, 12(1), 30; https://doi.org/10.3390/batteries12010030 - 16 Jan 2026
Viewed by 1428
Abstract
Silicon oxide/graphite (SiOx/Gr) anodes are promising candidates for high energy-density lithium-ion batteries. However, their complex multiphysics degradation mechanisms pose challenges for accurately interpreting and predicting capacity fade behavior. In particular, existing multiphysics models typically treat gas generation and solid electrolyte interphase [...] Read more.
Silicon oxide/graphite (SiOx/Gr) anodes are promising candidates for high energy-density lithium-ion batteries. However, their complex multiphysics degradation mechanisms pose challenges for accurately interpreting and predicting capacity fade behavior. In particular, existing multiphysics models typically treat gas generation and solid electrolyte interphase (SEI) growth as independent or unidirectionally coupled processes, neglecting their bidirectional interactions. Here, we develop an electro–thermal–mechanical–gaseous coupled model to capture the dominant degradation processes in SiOx/Gr anodes, including SEI growth, gas generation, SEI formation on cracks, and particle fracture. Model validation shows that the proposed framework can accurately reproduce voltage responses under various currents and temperatures, as well as capacity fade under different thermal and mechanical conditions. Based on this validated model, a mechanistic analysis reveals two key findings: (1) Gas generation and SEI growth are bidirectionally coupled. SEI growth induces gas release, while accumulated gas in turn regulates subsequent SEI evolution by promoting SEI formation through hindered mass transfer and suppressing it through reduced active surface area. (2) Crack propagation within particles is jointly governed by the magnitude and duration of stress. High-rate discharges produce large but transient stresses that restrict crack growth, while prolonged stresses at low rates promote crack propagation and more severe structural degradation. This study provides new insights into the coupled degradation mechanisms of SiOx/Gr anodes, offering guidance for performance optimization and structural design to extend battery cycle life. Full article
(This article belongs to the Special Issue Recent Advances in Numerical Modeling and Experimental Validation of Batteries)
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47 pages, 36851 KB  
Article
Comparative Analysis of ML and DL Models for Data-Driven SOH Estimation of LIBs Under Diverse Temperature and Load Conditions
by Seyed Saeed Madani, Marie Hébert, Loïc Boulon, Alexandre Lupien-Bédard and François Allard
Batteries 2025, 11(11), 393; https://doi.org/10.3390/batteries11110393 - 24 Oct 2025
Cited by 3 | Viewed by 1434
Abstract
Accurate estimation of lithium-ion battery (LIB) state of health (SOH) underpins safe operation, predictive maintenance, and lifetime-aware energy management. Despite recent advances in machine learning (ML), systematic benchmarking across heterogeneous real-world cells remains limited, often confounded by data leakage and inconsistent validation. Here, [...] Read more.
Accurate estimation of lithium-ion battery (LIB) state of health (SOH) underpins safe operation, predictive maintenance, and lifetime-aware energy management. Despite recent advances in machine learning (ML), systematic benchmarking across heterogeneous real-world cells remains limited, often confounded by data leakage and inconsistent validation. Here, we establish a leakage-averse, cross-battery evaluation framework encompassing 32 commercial LIBs (B5–B56) spanning diverse cycling histories and temperatures (≈4 °C, 24 °C, 43 °C). Models ranging from classical regressors to ensemble trees and deep sequence architectures were assessed under blocked 5-fold GroupKFold splits using RMSE, MAE, R2 with confidence intervals, and inference latency. The results reveal distinct stratification among model families. Sequence-based architectures—CNN–LSTM, GRU, and LSTM—consistently achieved the highest accuracy (mean RMSE ≈ 0.006; per-cell R2 up to 0.996), demonstrating strong generalization across regimes. Gradient-boosted ensembles such as LightGBM and CatBoost delivered competitive mid-tier accuracy (RMSE ≈ 0.012–0.015) yet unrivaled computational efficiency (≈0.001–0.003 ms), confirming their suitability for embedded applications. Transformer-based hybrids underperformed, while approximately one-third of cells exhibited elevated errors linked to noise or regime shifts, underscoring the necessity of rigorous evaluation design. Collectively, these findings establish clear deployment guidelines: CNN–LSTM and GRU are recommended where robustness and accuracy are paramount (cloud and edge analytics), while LightGBM and CatBoost offer optimal latency–efficiency trade-offs for embedded controllers. Beyond model choice, the study highlights data curation and leakage-averse validation as critical enablers for transferable and reliable SOH estimation. This benchmarking framework provides a robust foundation for future integration of ML models into real-world battery management systems. Full article
(This article belongs to the Special Issue Recent Advances in Numerical Modeling and Experimental Validation of Batteries)
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Review

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38 pages, 2474 KB  
Review
A Comprehensive Review of Equivalent Circuit Models and Neural Network Models for Battery Management Systems
by Davide Pio Laudani, Davide Milillo, Michele Quercio, Francesco Riganti Fulginei and Lorenzo Sabino
Batteries 2026, 12(1), 37; https://doi.org/10.3390/batteries12010037 - 22 Jan 2026
Viewed by 1665
Abstract
Lithium-ion batteries are the most widely used electrochemical energy storage technology due to their excellent performance. They play a crucial role in enabling the widespread adoption of sustainable transportation and renewable energy storage. Comprehensive battery monitoring, encompassing both performance and safety aspects, presents [...] Read more.
Lithium-ion batteries are the most widely used electrochemical energy storage technology due to their excellent performance. They play a crucial role in enabling the widespread adoption of sustainable transportation and renewable energy storage. Comprehensive battery monitoring, encompassing both performance and safety aspects, presents various challenges. Generally, this task is handled by a battery management system (BMS). Therefore, this paper provides a brief introduction to the key battery state parameters, such as the state of charge (SOC), state of health (SOH), and state of power (SOP). Subsequently, after a brief overview of BMS structural and software architectures, this work focuses on a detailed description of equivalent circuit models (ECMs) and artificial neural networks (ANNs), which represent part of the modeling approaches available in the literature, providing a characterization of the complex and nonlinear dynamics underlying lithium-ion batteries. These approaches are systematically evaluated, including hybrid methods to highlight their respective advantages, limitations, and suitability for different BMS functionalities. Full article
(This article belongs to the Special Issue Recent Advances in Numerical Modeling and Experimental Validation of Batteries)
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