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

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 8278

Special Issue Editors

Dr. Xuan Zhou

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48126, USA
Interests: battery design and manufacturing; battery modelling and control for electric vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Rongheng Li

E-Mail Website
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:

  1. Battery modeling method development, including electrochemical models, data-driven methods, and hybrid modeling approaches.
  2. Simulation of the charging and discharging processes for various types of batteries, including lithium-ion batteries, solid-state batteries, and second-life batteries.
  3. Optimization of the parameters of batteries in specific applications, i.e., electric vehicles, power grid systems, and fuel cell vehicles.
  4. Simulation of the battery degradation process with physical-based models or data-driven approaches.
  5. Charging strategy development in distinct scenarios.
  6. 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 (6 papers)

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Research

14 pages, 3925 KiB  
Article
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
Viewed by 249
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, [...] Read more.
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 KiB  
Article
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
Viewed by 1022
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 [...] Read more.
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 KiB  
Article
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
Viewed by 936
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 [...] Read more.
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 KiB  
Article
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 1 | Viewed by 2054
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 [...] Read more.
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 KiB  
Article
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
Viewed by 1467
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 [...] Read more.
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 KiB  
Article
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 2 | Viewed by 1649
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 [...] Read more.
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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