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Batteries
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State-of-Health Estimation of Batteries
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State-of-Health Estimation of Batteries

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Performance, Ageing, Reliability and Safety".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 15769

Special Issue Editors

Dr. Vaclav Knap

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27 Prague, Czech Republic
Interests: lithium-ion batteries; lithium-sulfur batteries; energy storage; battery management systems; battery state estimation; degradation and lifetime; battery testing and modelling; ancillary grid services provided by an energy storage
Prof. Dr. Daniel Auger

E-Mail Website1 Website2
Guest Editor
School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK
Interests: the application of reduced-order physics-based models for fast model calibration and estimation; control of hybrid battery systems; electrical and module/pack-level thermal modelling and state estimation; and prognostic/diagnostic techniques for predicting and assessing battery health and remaining useful life
Special Issues, Collections and Topics in MDPI journals
Dr. Abbas Fotouhi

E-Mail Website
Guest Editor
Advanced Vehicle Engineering Centre, Cranfield University, Bedfordshire MK41 0HU, UK
Interests: electric vehicle; sustainable transport systems; battery; energy management; optimization; control; artificial intelligence and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

State-of-health (SOH) estimation of batteries remains a challenging goal. The typical behavior of lithium-ion batteries changes when the anode is doped with a high amount of silicon, highly affecting the accuracy of the estimation. There are novel chemistries worked on or being launched to the market, such as lithium-sulfur or sodium-ion batteries. Concepts of cloud battery management systems open new possibilities, especially in the trending area of machine learning and artificial intelligence. There are new and more demanding applications in the area of aerospace and second-life use. Moreover, ‘smart’ cells or packs are being proposed enhanced with additional sensors to provide extra information. These and more are making the topic of SOH estimation interesting and in high demand. Thus, we would like to encourage you to submit your contributions on the following SOH estimation topics covering:

  • Modern lithium-ion batteries;
  • Lithium-sulfur batteries;
  • Sodium-ion batteries;
  • Machine learning;
  • Artificial intelligence;
  • Cloud BMS;
  • Smart cells and packs;
  • Novel approaches;
  • Remaining useful life.

Dr. Vaclav Knap
Prof. Dr. Daniel Auger
Dr. Abbas Fotouhi
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

  • state-of-health
  • remaining useful life
  • lithium-ion batteries
  • lithium-sulfur batteries
  • sodium-ion batteries
  • machine learning

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Published Papers (6 papers)

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Research

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17 pages, 7542 KiB  
Article
Electrochemical Impedance Spectroscopy-Based Characterization and Modeling of Lithium-Ion Batteries Based on Frequency Selection
by Yuechan Xiao, Xinrong Huang, Jinhao Meng, Yipu Zhang, Vaclav Knap and Daniel-Ioan Stroe
Batteries 2025, 11(1), 11; https://doi.org/10.3390/batteries11010011 - 29 Dec 2024
Cited by 2 | Viewed by 1253
Abstract
Lithium-ion batteries are commonly employed in electric vehicles due to their efficient energy storage and conversion capabilities. Nevertheless, to ensure reliable and cost-effective operation, their internal states must be continuously monitored. Electrochemical impedance spectroscopy (EIS) is an effective tool for assessing the battery’s [...] Read more.
Lithium-ion batteries are commonly employed in electric vehicles due to their efficient energy storage and conversion capabilities. Nevertheless, to ensure reliable and cost-effective operation, their internal states must be continuously monitored. Electrochemical impedance spectroscopy (EIS) is an effective tool for assessing the battery’s state. Different frequency ranges of EIS correspond to various electrochemical reaction processes. In this study, EIS measurements were conducted at seven temperatures, ranging from −20 °C to 10 °C, and across 21 states of charge (SOCs), spanning from 0% to 100%. A regression model was utilized to examine the unidirectional factorial characteristic impedance relative to temperature and SOC. An analysis of variance (ANOVA) table was created with temperature and SOC as independent variables and the impedance value as the dependent variable. These models accurately capture the behavior of lithium-ion batteries under different conditions. Based on this research, the battery electrochemical processes are better understood. This paper establishes a mathematical expression for a temperature–SOC-based impedance model at specific frequencies, i.e., 1 Hz, 20 Hz, and 3100 Hz. When comparing the models at these three frequencies, it was found that the model fitting accuracy is highest at 20 Hz, making it applicable across a wide range of temperatures and SOCs. Consequently, the accuracy of the impedance model can be enhanced at a specific frequency, simplifying the impedance model and facilitating the development of advanced battery state estimation methods. Full article
(This article belongs to the Special Issue State-of-Health Estimation of Batteries)
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22 pages, 11094 KiB  
Article
State of Health Estimation for Lithium-Ion Batteries Using an Explainable XGBoost Model with Parameter Optimization
by Zhenghao Xiao, Bo Jiang, Jiangong Zhu, Xuezhe Wei and Haifeng Dai
Batteries 2024, 10(11), 394; https://doi.org/10.3390/batteries10110394 - 7 Nov 2024
Viewed by 2038
Abstract
Accurate and reliable estimation of the state of health (SOH) of lithium-ion batteries is crucial for ensuring safety and preventing potential failures of power sources in electric vehicles. However, current data-driven SOH estimation methods face challenges related to adaptiveness and interpretability. This paper [...] Read more.
Accurate and reliable estimation of the state of health (SOH) of lithium-ion batteries is crucial for ensuring safety and preventing potential failures of power sources in electric vehicles. However, current data-driven SOH estimation methods face challenges related to adaptiveness and interpretability. This paper investigates an adaptive and explainable battery SOH estimation approach using the eXtreme Gradient Boosting (XGBoost) model. First, several battery health features extracted from various charging and relaxation processes are identified, and their correlation with battery aging is analyzed. Then, a SOH estimation method based on the XGBoost algorithm is established, and the model’s hyper-parameters are tuned using the Bayesian optimization algorithm (BOA) to enhance the adaptiveness of the proposed estimation model. Additionally, the Tree SHapley Additive exPlanation (TreeSHAP) technique is employed to analyze the explainability of the estimation model and reveal the influence of different features on SOH evaluation. Experiments involving two types of batteries under various aging conditions are conducted to obtain battery cycling aging data for model training and validation. The quantitative results demonstrate that the proposed method achieves an estimation accuracy with a mean absolute error of less than 2.7% and a root mean squared error of less than 3.2%. Moreover, the proposed method shows superior estimation accuracy and performance compared to existing machine learning models. Full article
(This article belongs to the Special Issue State-of-Health Estimation of Batteries)
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20 pages, 5763 KiB  
Article
A Method for Estimating the SOH of Lithium-Ion Batteries Based on Graph Perceptual Neural Network
by Kang Chen, Dandan Wang and Wenwen Guo
Batteries 2024, 10(9), 326; https://doi.org/10.3390/batteries10090326 - 13 Sep 2024
Viewed by 2211
Abstract
The accurate estimation of battery state of health (SOH) is critical for ensuring the safety and reliability of devices. Considering the variation in health degradation across different types of lithium-ion battery materials, this paper proposes an SOH estimation method based on a graph [...] Read more.
The accurate estimation of battery state of health (SOH) is critical for ensuring the safety and reliability of devices. Considering the variation in health degradation across different types of lithium-ion battery materials, this paper proposes an SOH estimation method based on a graph perceptual neural network, designed to adapt to multiple battery materials. This method adapts to various battery materials by extracting crucial features from current, voltage, voltage–capacity, and temperature data, and it constructs a graph structure to encapsulate these features. This approach effectively captures the complex interactions and dependencies among different battery types. The novel technique of randomly removing features addresses feature redundancy. Initially, a mutual information graph structure is defined to illustrate the interdependencies among battery features. Moreover, a graph perceptual self-attention mechanism is implemented, integrating the adjacency matrix and edge features into the self-attention calculations. This enhancement aids the model’s understanding of battery behaviors, thereby improving the transparency and interpretability of predictions. The experimental results demonstrate that this method outperforms traditional models in both accuracy and generalizability across various battery types, particularly those with significant chemical and degradation discrepancies. The model achieves a minimum mean absolute error of 0.357, a root mean square error of 0.560, and a maximum error of 0.941. Full article
(This article belongs to the Special Issue State-of-Health Estimation of Batteries)
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18 pages, 4217 KiB  
Article
Predicting the Future Capacity and Remaining Useful Life of Lithium-Ion Batteries Based on Deep Transfer Learning
by Chenyu Sun, Taolin Lu, Qingbo Li, Yili Liu, Wen Yang and Jingying Xie
Batteries 2024, 10(9), 303; https://doi.org/10.3390/batteries10090303 - 28 Aug 2024
Cited by 3 | Viewed by 2372
Abstract
Lithium-ion batteries are widely utilized in numerous applications, making it essential to precisely predict their degradation trajectory and remaining useful life (RUL). To improve the stability and applicability of RUL prediction for lithium-ion batteries, this paper uses a new method to predict RUL [...] Read more.
Lithium-ion batteries are widely utilized in numerous applications, making it essential to precisely predict their degradation trajectory and remaining useful life (RUL). To improve the stability and applicability of RUL prediction for lithium-ion batteries, this paper uses a new method to predict RUL by combining CNN-LSTM-Attention with transfer learning. The presented model merges the strengths of both convolutional and sequential architectures, and it enhances the model’s capability to grasp comprehensive information by utilizing the attention mechanism, thereby boosting overall performance. The CEEMDAN algorithm is used for NASA batteries with obvious capacity regeneration phenomena to alleviate the difficulties caused by capacity regeneration on model prediction. During the model transfer phase, the CNN and LSTM layers of the pre-trained model from the source domain are kept unchanged during retraining, while the attention and fully connected layers are fine-tuned for NASA batteries and self-tested NCM batteries. The final results indicate that this method achieves superior accuracy relative to other methods while addressing the issue of limited labeled data in the target domain through transfer learning, thereby enhancing the model’s transferability and generalization capabilities. Full article
(This article belongs to the Special Issue State-of-Health Estimation of Batteries)
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16 pages, 5585 KiB  
Article
EIS Ageing Prediction of Lithium-Ion Batteries Depending on Charge Rates
by Olivia Bruj and Adrian Calborean
Batteries 2024, 10(7), 247; https://doi.org/10.3390/batteries10070247 - 11 Jul 2024
Cited by 3 | Viewed by 2537
Abstract
In the automotive industry, ageing mechanisms and diagnosis of Li-ion batteries depending on charge rate are of tremendous importance. With this in mind, we have investigated the lifetime degradation of lithium-ion battery cells at three distinct charging rates using Electrochemical Impedance Spectroscopy (EIS). [...] Read more.
In the automotive industry, ageing mechanisms and diagnosis of Li-ion batteries depending on charge rate are of tremendous importance. With this in mind, we have investigated the lifetime degradation of lithium-ion battery cells at three distinct charging rates using Electrochemical Impedance Spectroscopy (EIS). Impedance spectra of high-energy Panasonic NCR18650B batteries have been analysed in light of two distinct approaches, namely the time-dependent evaluation of the Constant Phase Element (CPE), and the single parameter investigation of resonance frequency of the circuit. SOH percentages were used to validate our approach. By monitoring the CPE-Q parameter at different charge rates of 0.5 C, 1 C, and 1.5 C, respectively, we applied a degradation speed analysis, allowing us to predict a quantitative value of the LIBs. The results are in complete agreement with the resonance frequency single parameter analysis, in which quite a similar trend was obtained after the spline fitting. Full article
(This article belongs to the Special Issue State-of-Health Estimation of Batteries)
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Review

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40 pages, 574 KiB  
Review
Evaluation of Advances in Battery Health Prediction for Electric Vehicles from Traditional Linear Filters to Latest Machine Learning Approaches
by Adrienn Dineva
Batteries 2024, 10(10), 356; https://doi.org/10.3390/batteries10100356 - 11 Oct 2024
Cited by 6 | Viewed by 3877
Abstract
In recent years, there has been growing interest in Li-ion battery State-of-Health (SOH) estimation due to its critical role in ensuring the safe and reliable operation of Electric Vehicles (EVs). Effective energy management and accurate SOH prediction are essential for the reliability and [...] Read more.
In recent years, there has been growing interest in Li-ion battery State-of-Health (SOH) estimation due to its critical role in ensuring the safe and reliable operation of Electric Vehicles (EVs). Effective energy management and accurate SOH prediction are essential for the reliability and sustainability of EVs. This paper presents an in-depth review of SOH estimation techniques, starting with an overview of seminal methods that lay the theoretical groundwork for battery modeling and SOH prediction. The review then evaluates recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) techniques, emphasizing their contributions to improving SOH estimation. Through a rigorous screening process, the paper systematically assesses the evolution of these advanced methods, addressing specific research questions to evaluate their effectiveness and practical implications. Key findings highlight the potential of hybrid models that integrate Equivalent Circuit Models (ECMs) with Deep Learning approaches, offering enhanced accuracy and real-time performance. Additionally, the paper discusses limitations of current methods, such as challenges in translating laboratory-based models to real-world conditions and the computational complexity of some prospective methods. In conclusion, this paper identifies promising future research directions aimed at optimizing hybrid models and overcoming existing constraints to advance SOH estimation and battery management in Electric Vehicles. Full article
(This article belongs to the Special Issue State-of-Health Estimation of Batteries)
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