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Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management

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A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Modelling, Simulation, Management and Application".

Deadline for manuscript submissions: 25 November 2025 | Viewed by 21236

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Special Issue Editors

Dr. Xuan Zhou

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Guest Editor

Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, USA
Interests: battery design and manufacturing; battery modelling and control for electric vehicles
Special Issues, Collections and Topics in MDPI journals

Dr. Rongheng Li

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Guest Editor

Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48126, USA
Interests: renewable energy; battery modeling; nanotechnology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Batteries have become an essential power source in many fields, such as electric vehicles and smart grids, due to increasing environmental concerns. The lifespan and cost of batteries play a crucial role in addressing energy crises and environmental issues. The development of models that accurately predict battery life, design effective charging strategies, and assess battery performance now presents considerable challenges in both science and engineering.

This Special Issue of Batteries is open to submissions on the topic of battery numerical modeling, including battery performance modeling, state of charge and health estimation, and charging strategy optimization.

Scientists and engineers are encouraged to submit articles addressing topics in the following areas:

Battery modeling method development, including electrochemical models, data-driven methods, and hybrid modeling approaches.
Simulation of the charging and discharging processes for various types of batteries, including lithium-ion batteries, solid-state batteries, and second-life batteries.
Optimization of the parameters of batteries in specific applications, i.e., electric vehicles, power grid systems, and fuel cell vehicles.
Simulation of the battery degradation process with physical-based models or data-driven approaches.
Charging strategy development in distinct scenarios.
Design of thermal management, including the design of heat dissipation structure and cooling strategies.

Dr. Xuan Zhou
Dr. Rongheng Li
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 100 words) can be sent to the Editorial Office for announcement on this website.

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
electrochemical models
data-driven methods
state of charge and health estimation
charging strategy optimization
thermal management

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Published Papers (11 papers)

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Research

19 pages, 1524 KB

Open AccessArticle

Optimal DC Fast-Charging Strategies for Battery Electric Vehicles During Long-Distance Trips

by David Clar-Garcia, Miguel Fabra-Rodriguez, Hector Campello-Vicente and Emilio Velasco-Sanchez

Batteries 2025, 11(11), 394; https://doi.org/10.3390/batteries11110394 - 24 Oct 2025

Viewed by 351

Abstract

The rapid adoption of electric vehicles (BEVs) has increased the need to understand how fast-charging strategies influence long-distance travel times under real-world conditions. While most manufacturers specify maximum charging power and standardized driving ranges, these figures often fail to reflect actual highway operation, particularly in adverse weather. This study addresses this gap by analyzing the fast-charging behaviour, net battery capacity and highway energy consumption of 62 EVs from different market segments. Charging power curves were obtained experimentally at high-power DC stations, with data recorded through both the charging infrastructure and the vehicles’ battery management systems. Tests were conducted, under optimal conditions, between 10% and 90% state of charge (SoC), with additional sessions performed under both cold and preconditioned battery conditions to show thermal effects on the batteries’ fast-charging capabilities. Real-world highway consumption values were applied to simulate 1000 km journeys at 120 km/h under cold (−10 °C, cabin heating) and mild (23 °C, no AC) weather scenarios. An optimization model was developed to minimize total trip time by adjusting the number and duration of charging stops, including a 5 min detour for each charging session. Results show that the optimal charging cutoff point consistently emerges around 59% SoC, with a typical deviation of 10, regardless of ambient temperature. Charging beyond 70% SoC is generally inefficient unless dictated by charging station availability. The optimal strategy involves increasing the number of shorter stops—typically every 2–3 h of driving—thereby reducing total trip. Full article

(This article belongs to the Special Issue Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management)

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23 pages, 3066 KB

Open AccessArticle

An Empirical Multi-Stage One-Step Battery Thermal Runaway Model Based on Arrhenius Reaction Rate Formalism

by Alexander Ruth, Martin Hantinger, Alexander Machold and Andreas Ennemoser

Batteries 2025, 11(10), 371; https://doi.org/10.3390/batteries11100371 - 9 Oct 2025

Viewed by 616

Abstract

This study develops a multi-stage, Arrhenius-type reaction rate model for exothermic heat release during thermal runaway (TR) that depends on the local active material temperature, T_Cell, and the remaining reactant fraction, Y. Model parameters are identified from an accelerating rate calorimetry (ARC) test on an NMC721 pouch cell. Validation across other cell formats (cylindric and prismatic) and cathode chemistries (LCO, LMO, NCA, LFP) is left for future work. Model performance is evaluated in a 3D CFD (AVL FIRE™ M 2021.2) representation of the ARC assembly and benchmarked against Gaussian and polynomial one-step TR formulations that depend solely on T_Cell. The three TR models are further applied to a generic 4S4P pouch cell module under stagnant and actively cooled conditions to assess thermal propagation. In the ARC test, the Arrhenius-type model shows improved agreement with measured cell skin temperatures for the NMC721 cell; in the 4S4P module, it exhibits a trend toward higher thermal propagation rates relative to the Gaussian and polynomial models. Full article

(This article belongs to the Special Issue Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management)

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24 pages, 6368 KB

Open AccessArticle

Electro-Thermal Modeling and Parameter Identification of an EV Battery Pack Using Drive Cycle Data

by Vinura Mannapperuma, Lalith Chandra Gaddala, Ruixin Zheng, Doohyun Kim, Youngki Kim, Ankith Ullal, Shengrong Zhu and Kyoung Pyo Ha

Batteries 2025, 11(9), 319; https://doi.org/10.3390/batteries11090319 - 27 Aug 2025

Viewed by 1326

Abstract

This paper presents a novel electro-thermal modeling approach for a lithium-ion battery pack in an electric vehicle (EV), along with parameter identification using controller area network (CAN) data collected from chassis dynamometer and real-world driving tests. The proposed electro-thermal model consists of a first-order equivalent circuit model (ECM) and a lumped-parameter thermal network in considering a simplified cooling circuit layout and temperature distributions across four distinct zones within the battery pack. This model captures the nonuniform heat transfer between the pack modules and the coolant, as well as variations in coolant temperature and flow rates. Model parameters are identified directly from vehicle-level test data without relying on laboratory-level measurements. Validation results demonstrate that the model can predict terminal voltage with an RMSE of less than 6 V (normalized root mean square error of less than 2%), and battery module surface temperatures with root mean square errors of less than 2 °C for over 90% of the test cases. The proposed approach provides a cost-effective and accurate solution for predicting electro-thermal behavior of EV battery systems, making it a valuable tool for battery design and management to optimize performance and ensure the safety of EVs. Full article

(This article belongs to the Special Issue Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management)

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18 pages, 13041 KB

Open AccessArticle

Experimental Testing and Modeling of Li-Ion Battery Performance Based on IEC 62660-1 Standard

by Zoi Voltsi and Costas Elmasides

Batteries 2025, 11(8), 314; https://doi.org/10.3390/batteries11080314 - 17 Aug 2025

Cited by 1 | Viewed by 2526

Abstract

The adoption of sustainable and environmentally friendly solutions is becoming crucial across several sectors, particularly in transportation. As part of this transition, the transport industry has turned its attention to electric vehicle (EV) development and the deployment of electric batteries. This study provides a comprehensive analysis of the performance of EV batteries, integrating both experimental measurements and simulations. The experimental section involved a series of tests conducted on real batteries under various operating conditions, focusing on different charging and discharging rates. Additionally, the IEC 62660-1 standard was applied, to evaluate their performance under realistic usage scenarios. Moreover, a theoretical model was developed in order to simulate the batteries’ behavior and replicate the observed experimental data. A comparison between the simulation outputs and experimental data was conducted, demonstrating the accuracy of the model. This work provides valuable insights into the performance of EV batteries and lays the foundation for optimization in future applications. Full article

(This article belongs to the Special Issue Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management)

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28 pages, 1412 KB

Open AccessArticle

The Collisional Charging of a Transmon Quantum Battery

by Nicolò Massa, Fabio Cavaliere and Dario Ferraro

Batteries 2025, 11(7), 240; https://doi.org/10.3390/batteries11070240 - 23 Jun 2025

Viewed by 1146

Abstract

Motivated by recent developments in the field of multilevel quantum batteries, we present the model of a quantum device for energy storage with anharmonic level spacing, based on a superconducting circuit in the transmon regime. It is charged via the sequential interaction with a collection of identical and independent ancillary two-level systems. By means of a numerical analysis, we show that, in case these ancillas are coherent, this kind of quantum battery can achieve remarkable performances in terms of the control of the stored energy and its extraction in regimes of parameters within reach in nowadays quantum circuits. Full article

(This article belongs to the Special Issue Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management)

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14 pages, 3925 KB

Open AccessArticle

Capacity and State-of-Health Prediction of Lithium-Ion Batteries Using Reduced Equivalent Circuit Models

by Hakeem Thomas and Mark H. Weatherspoon

Batteries 2025, 11(4), 162; https://doi.org/10.3390/batteries11040162 - 19 Apr 2025

Cited by 3 | Viewed by 1878

Abstract

Knowledge of battery health and its degradation has been a research focus since it enables users to use batteries optimally. The dynamic electrochemical properties within a cell can be represented by an equivalent circuit to observe the impedance over a range of frequencies, which is an indicator of the cell’s degradation buildup from an electrical framework. This process provides information on the different electrochemical processes observed at different frequency ranges, which can be used to optimally predict the capacity fade of a cell. With the increasing demand for batteries, faster and less computationally intensive means are being explored to predict the capacity degradation of batteries. The proposed method in this article introduces an effective reduced equivalent circuit model (ER-ECM) for battery prognosis studies. The ER-ECM measures the parameters of impedance spectra from the high- to mid-frequency regions for data input. These parameters are then used to accurately predict the capacity of the battery and its state of health. The results show that the overarching charge transfer resistance provides the most salient data for capacity predictions, having an average capacity error of 1.4%, which is a 40% reduction compared to using all the parameters of the ER-ECM. The ECMs used in this study also provide faster model training and testing by 6% compared to using global impedance spectra. Full article

(This article belongs to the Special Issue Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management)

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24 pages, 17820 KB

Open AccessArticle

Research on the Fast Charging Strategy of Power Lithium-Ion Batteries Based on the High Environmental Temperature in Southeast Asia

by Qin Zhang and Yuyang Yu

Batteries 2025, 11(1), 5; https://doi.org/10.3390/batteries11010005 - 25 Dec 2024

Cited by 2 | Viewed by 2471

Abstract

To address the problem of excessive charging time for electric vehicles (EVs) in the high ambient temperature regions of Southeast Asia, this article proposes a rapid charging strategy based on battery state of charge (SOC) and temperature adjustment. The maximum charging capacity of the cell is exerted within different SOCs and temperature ranges. Taking a power lithium-ion battery (LIB) with a capacity of 120 Ah as the research object, a rapid charging model of the battery module was established. The battery module was cooled by means of a liquid cooling system. The combination of the fast charging strategy and the cooling strategy was employed to comprehensively analyze the restrictions of the fast charging rate imposed by the battery SOC and temperature. The results indicate that when the coolant flow rate was 12 L/min and the inlet coolant temperature was 22 °C, the liquid cooling system possessed the optimal heat exchange capacity and the lowest energy consumption. The maximum temperature (Tmax) of the battery during the charging process was 50.04 °C, and the charging time was 2634 s. To lower the Tmax of the battery during the charging process, a charging rate limit was imposed on the temperature range above 48 °C based on the original fast charging strategy. The Tmax decreased by 0.85 °C when charging with the optimized fast charging strategy. Full article

(This article belongs to the Special Issue Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management)

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26 pages, 6088 KB

Open AccessArticle

A Genetic Algorithm Based ESC Model to Handle the Unknown Initial Conditions of State of Charge for Lithium Ion Battery Cell

by Kristijan Korez, Dušan Fister and Riko Šafarič

Batteries 2025, 11(1), 1; https://doi.org/10.3390/batteries11010001 - 24 Dec 2024

Cited by 1 | Viewed by 1626

Abstract

Classic enhanced self-correcting battery equivalent models require proper model parameters and initial conditions such as the initial state of charge for its unbiased functioning. Obtaining parameters is often conducted by optimization using evolutionary algorithms. Obtaining the initial state of charge is often conducted by measurements, which can be burdensome in practice. Incorrect initial conditions can introduce bias, leading to long-term drift and inaccurate state of charge readings. To address this, we propose two simple and efficient equivalent model frameworks that are optimized by a genetic algorithm and are able to determine the initial conditions autonomously. The first framework applies the feedback loop mechanism that gradually with time corrects the externally given initial condition that is originally a biased arbitrary value within a certain domain. The second framework applies the genetic algorithm to search for an unbiased estimate of the initial condition. Long-term experiments have demonstrated that these frameworks do not deviate from controlled benchmarks with known initial conditions. Additionally, our experiments have shown that all implemented models significantly outperformed the well-known ampere-hour coulomb counter integration method, which is prone to drift over time and the extended Kalman filter, that acted with bias. Full article

(This article belongs to the Special Issue Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management)

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15 pages, 4490 KB

Open AccessArticle

Simulation of Dendrite Growth with a Diffusion-Limited Aggregation Model Validated by MRI of a Lithium Symmetric Cell during Charging

by Rok Peklar, Urša Mikac and Igor Serša

Batteries 2024, 10(10), 352; https://doi.org/10.3390/batteries10100352 - 8 Oct 2024

Cited by 5 | Viewed by 3572

Abstract

Lithium metal batteries offer high energy density but are challenged by dendrite growth, which can lead to short circuits and battery failure. Multiple models with varying degrees of accuracy and computational cost have been developed to understand and predict dendrite growth. This study presents a simple model to simulate macroscale dendrite growth on lithium metal electrodes. The model uses a 3D single-particle Diffusion-Limited Aggregation (DLA) algorithm with an electric field bias to simulate dendrite growth. The electric field bias was introduced into the model with an important parameter, namely the biasing factor c, which determines the balance between diffusion and electric field effects. Before performing the simulation with the proposed model, the dendrite growth in a lithium symmetric cell during charging was measured by sequential 3D magnetic resonance imaging (MRI). These data were then used to validate the simulation, as the dendrite structure in each measured MRI time frame was used a starting point for a new simulation, the results of which were then validated with the measured dendrite structure of the next time frame. The best agreement between the simulated and measured dendrite structures using the overlap and displacement of deposition sites metrics was obtained at the biasing factor c = 0.7. This agreement was also good in terms with the fractal dimension of the dendrite structures. The proposed method offers a simple, accurate, and scalable framework for predicting dendrite growth over long deposition periods, making it a valuable tool for studying dendrite suppression under real-world battery charging conditions. Full article

(This article belongs to the Special Issue Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management)

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28 pages, 14953 KB

Open AccessArticle

Enhancing State of Health Prediction Accuracy in Lithium-Ion Batteries through a Simplified Health Indicator Method

by Dongxu Han, Nan Zhou and Zeyu Chen

Batteries 2024, 10(10), 342; https://doi.org/10.3390/batteries10100342 - 27 Sep 2024

Cited by 1 | Viewed by 1996

Abstract

Accurately predicting the state of health (SOH) of lithium-ion batteries is crucial for optimizing battery performance and achieving efficient energy management, especially in electric vehicle applications. However, the existing incremental capacity analysis methods, which are mostly based on curve multi-parameter analysis, still have limitations in terms of computation, prediction accuracy, and adaptability to actual operating conditions. This paper conducts an in-depth analysis of the incremental capacity (IC) curve and proposes a feature parameter based on the area under the IC curve. By incorporating charge and discharge data, a weighted health indicator sequence is constructed and three mathematical models are proposed to link health indicators with cycle number, capacity, and SOH. The feasibility of using impedance as an additional input is also explored, despite the challenges of measurement, revealing its potential applications. Validation of the models with different datasets shows that the proposed method achieves both average relative error and root mean square error within 5%, outperforming other methods in terms of minimizing error and ensuring stability. The results demonstrate that the area-weighted incremental capacity method significantly enhances battery health monitoring accuracy, contributing to the development of sustainable and efficient energy storage systems. Full article

(This article belongs to the Special Issue Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management)

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17 pages, 11349 KB

Open AccessArticle

Rapid Estimation of Static Capacity Based on Machine Learning: A Time-Efficient Approach

by Younggill Son and Woongchul Choi

Batteries 2024, 10(6), 191; https://doi.org/10.3390/batteries10060191 - 31 May 2024

Cited by 3 | Viewed by 2005

Abstract

With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical than ever. A crucial step for battery reuse or recycling is the precise estimation of static capacity at retirement. Traditional methods are time-consuming, often taking several hours. To address this issue, a machine learning-based approach is introduced to estimate the static capacity of retired batteries rapidly and accurately. Partial discharge data at a 1 C rate over durations of 6, 3, and 1 min were analyzed using a machine learning algorithm that effectively handles temporally evolving data. The estimation performance of the methodology was evaluated using the mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). The results showed reliable and fairly accurate estimation performance, even with data from shorter partial discharge durations. For the one-minute discharge data, the maximum RMSE was 2.525%, the minimum was 1.239%, and the average error was 1.661%. These findings indicate the successful implementation of rapidly assessing the static capacity of EV batteries with minimal error, potentially revitalizing the retired battery recycling industry. Full article

(This article belongs to the Special Issue Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management)

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