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Batteries
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Battery Degradation: Behavior, Mechanisms, Modeling, Estimation, and Optimization Strategies
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Battery Degradation: Behavior, Mechanisms, Modeling, Estimation, and Optimization Strategies

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: 16 July 2026 | Viewed by 2167

Special Issue Editors

Dr. Yujuan Sun

E-Mail Website
Guest Editor
School of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China
Interests: lithium-ion battery management; big data analysis; deep learning algorithms for battery estimation and degradation
Prof. Dr. Caiping Zhang

E-Mail Website
Guest Editor
National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China
Interests: battery modeling and state estimation; aging mechanisms and RUL prediction; charging optimizations; battery fault diagnosis and early warning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Battery degradation is a core challenge limiting the reliability, safety, and service lifecycle of energy storage systems, which are pivotal for electric vehicles, renewable energy integration, and portable electronics. This Special Issue focuses on the multi-dimensional aspects of battery degradation, encompassing intrinsic aging mechanisms, advanced modeling approaches, precise state estimation techniques, and effective optimization strategies. It aims to bridge fundamental research and practical applications, featuring innovative studies on degradation behavior under diverse operating conditions, data-driven and physics-based modeling methods, high-accuracy state-of-health (SOH), state-of-charge (SOC), and remaining useful life (RUL) estimation, and lifecycle extension strategies.

Potential topics include, but are not limited to, the following:

  • Fundamental degradation mechanisms of advanced battery materials and cells.
  • Advanced degradation modeling: physics-based, data-driven, and hybrid models.
  • Precise estimation of SOH, SOC, and RUL under dynamic operating conditions.
  • Degradation behavior and mitigation strategies in extreme scenarios.
  • Optimization of battery management systems (BMS) for degradation mitigation and lifecycle extension.
  • Novel sensing technologies and non-destructive testing methods for in situ/real-time degradation monitoring.
  • Degradation characteristics and management of emerging battery technologies.
  • Case studies on battery degradation in practical applications.

Dr. Yujuan Sun
Prof. Dr. Caiping Zhang
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 degradation
  • SOH estimation
  • remaining useful life (RUL)
  • degradation mechanism
  • battery modeling
  • physics-based model
  • data-driven model
  • battery management system (BMS)
  • in situ monitoring
  • non-destructive testing
  • electric vehicle
  • physics-informed AI

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

Published Papers (3 papers)

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Research

35 pages, 9480 KB  
Article
Battery State of Charge Estimation in Electric Vehicles Using Machine Learning with Feature Engineering and Seasonal Analysis Under On-Road Conditions
by Feristah Dalkilic, Kadriye Filiz Balbal, Kokten Ulas Birant, Elife Ozturk Kiyak, Yunus Dogan, Semih Utku and Derya Birant
Batteries 2026, 12(5), 159; https://doi.org/10.3390/batteries12050159 - 29 Apr 2026
Viewed by 307
Abstract
Estimating the state of charge (SoC) is a critical task for effective management of electric vehicle batteries. Simple machine learning methods (LR, KNN, etc.) often suffer from limited prediction accuracy, while deep learning approaches (LSTM, CNN, etc.) generally require high computational resources and [...] Read more.
Estimating the state of charge (SoC) is a critical task for effective management of electric vehicle batteries. Simple machine learning methods (LR, KNN, etc.) often suffer from limited prediction accuracy, while deep learning approaches (LSTM, CNN, etc.) generally require high computational resources and behave as black-box models with limited explainability. To overcome these limitations, the present work proposes a SoC estimation approach based on the Light Gradient Boosting Machine (LightGBM). The proposed model provides a balanced trade-off between prediction accuracy and computational efficiency. Furthermore, feature engineering is performed to derive additional informative features, improving the model’s ability to learn driving conditions and battery dynamics. In addition, the study incorporates a seasonal analysis by evaluating the model under both summer and winter conditions, allowing the impact of environmental variations on SoC estimation performance to be investigated. Moreover, Explainable Artificial Intelligence (XAI) techniques are employed to interpret the model predictions. Evaluation on real-world on-road data demonstrated that the proposed model achieved substantial improvements in estimation performance compared to recent studies. Full article
(This article belongs to the Special Issue Battery Degradation: Behavior, Mechanisms, Modeling, Estimation, and Optimization Strategies)
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29 pages, 2247 KB  
Article
Physics-Informed and Explainable Machine Learning for State-of-Health Estimation of Second-Life Lithium-Ion Batteries Under Sparse Cycling
by Md Sabbir Hossen, Md Tanjil Sarker, Gobbi Ramasamy and Ngu Eng Eng
Batteries 2026, 12(5), 149; https://doi.org/10.3390/batteries12050149 - 23 Apr 2026
Viewed by 517
Abstract
Reliable state-of-health (SOH) estimation is a key prerequisite for the safe and effective reuse of second-life lithium-ion batteries. However, practical assessment during early-stage screening is often constrained by extremely limited cycling data, where only a few discharge cycles are available due to time [...] Read more.
Reliable state-of-health (SOH) estimation is a key prerequisite for the safe and effective reuse of second-life lithium-ion batteries. However, practical assessment during early-stage screening is often constrained by extremely limited cycling data, where only a few discharge cycles are available due to time and cost limitations. This study investigates SOH estimation under an extreme sparse-cycling scenario in which only three discharge cycles per battery are available, reflecting realistic constraints in early-stage second-life battery screening. Under such severe data limitations, conventional data-driven models become unreliable, motivating the need for data-efficient and interpretable approaches. To address this challenge, a physics-aware and explainable machine learning framework is proposed, integrating physically interpretable feature extraction with lightweight regression models and Shapley Additive exPlanations SHAP-based interpretability analysis. Electrochemically motivated and mathematically derived features are extracted from voltage, current, and capacity measurements to ensure robustness under severe data scarcity. Multiple model classes, including linear regression, support vector regression, tree-based ensembles, and deep learning architectures, are systematically evaluated to assess their suitability in this constrained regime. Experimental results on real second-life battery datasets demonstrate that physics-aware linear models provide stable and interpretable SOH estimates under extreme data sparsity, whereas more complex nonlinear and deep learning models exhibit higher variability due to insufficient training data. These findings highlight that model suitability is strongly dependent on data availability and support the adoption of interpretable, physics-aware approaches for early-stage second-life battery screening rather than long-term degradation modeling. Full article
(This article belongs to the Special Issue Battery Degradation: Behavior, Mechanisms, Modeling, Estimation, and Optimization Strategies)
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31 pages, 6189 KB  
Article
A Data-Driven Method Based on Feature Engineering and Physics-Constrained LSTM-EKF for Lithium-Ion Battery SOC Estimation
by Yujuan Sun, Shaoyuan You, Fangfang Hu and Jiuyu Du
Batteries 2026, 12(2), 64; https://doi.org/10.3390/batteries12020064 - 14 Feb 2026
Cited by 1 | Viewed by 914
Abstract
Accurate estimation of the State of Charge (SOC) for lithium-ion batteries is a core function of the Battery Management System (BMS). However, LiFePO4 batteries present specific challenges for SOC estimation due to the characteristic plateau in their open-circuit voltage (OCV) versus SOC [...] Read more.
Accurate estimation of the State of Charge (SOC) for lithium-ion batteries is a core function of the Battery Management System (BMS). However, LiFePO4 batteries present specific challenges for SOC estimation due to the characteristic plateau in their open-circuit voltage (OCV) versus SOC relationship. Moreover, data-driven estimation approaches often face significant difficulties stemming from measurement noise and interference, the highly nonlinear internal dynamics of the battery, and the time-varying nature of key battery parameters. To address these issues, this paper proposes a Long Short-Term Memory (LSTM) model integrated with feature engineering, physical constraints, and the Extended Kalman Filter (EKF). First, the model’s temporal perception of the historical charge–discharge states of the battery is enhanced through the fusion of temporal voltage information. Second, a post-processing strategy based on physical laws is designed, utilizing the Particle Swarm Optimization (PSO) algorithm to search for optimal correction factors. Finally, the SOC obtained from the previous steps serves as the observation input to EKF filtering, enabling a probabilistically weighted fusion of the data-driven model output and the EKF to improve the model’s dynamic tracking performance. When applied to SOC estimation of LiFePO4 batteries under various operating conditions and temperatures ranging from 0 °C to 50 °C, the proposed model achieves average Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as low as 0.46% and 0.56%, respectively. These results demonstrate the model’s excellent robustness, adaptability, and dynamic tracking capability. Additionally, the proposed approach only requires derived features from existing input data without the need for additional sensors, and the model exhibits low memory usage, showing considerable potential for practical BMS implementation. Furthermore, this study offers an effective technical pathway for state estimation under a “physical information–data-driven–filter fusion” framework, enabling accurate SOC estimation of lithium-ion batteries across multiple operating scenarios. Full article
(This article belongs to the Special Issue Battery Degradation: Behavior, Mechanisms, Modeling, Estimation, and Optimization Strategies)
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