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Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries: 3rd Edition
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Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries: 3rd Edition

A Special Issue of Batteries (ISSN 2313-0105) belonging to the section "Energy Storage System Aging, Diagnosis and Safety".

Deadline for manuscript submissions: 10 November 2026 | Viewed by 9643

Editors

Dr. Xin Sui

E-Mail Website
Guest Editor
Department of Energy, Aalborg University, 9220 Aalborg, Denmark
Interests: battery modeling; AI-based battery states estimation; battery health assessment and lifetime prediction; feature engineering and machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Remus Teodorescu

E-Mail Website
Guest Editor
Department of Energy, Aalborg University, 9220 Aalborg, Denmark
Interests: smart batteries; artificial intelligence; power electronics; electric vehicles; lithium-ion batteries; smart battery management; renewable energy; energy storage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Lithium-ion batteries have a wide range of applications, but one of their biggest problems is their limited lifetime due to performance degradation during usage. It is essential, therefore, to determine the battery’s state of health (SOH) so that the battery management system can operate the battery in such a way that enables it to run in its optimal state, and thus prolong its lifetime. Artificial intelligence (AI) technologies possess immense potential in terms of inferring battery SOH, and can extract aging information (i.e., SOH features) from measurements and relate them to battery performance parameters, avoiding a complex battery modeling process. Therefore, this Special Issue aims to showcase manuscripts presenting efficient AI-based SOH estimation methods that exhibit good performance metrics such as high accuracy, high robustness against changes to the working environment, and good generalization, etc.

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

  • The effective data mining of features for AI methods;
  • Network structures (the study of different AI technologies);
  • Learning strategies (supervised, unsupervised, and reinforcement learning);
  • Transferring AI-based models between different battery technologies and applications;
  • Sequentially updated models (probabilistic methods, self-learning, etc.);
  • Physics-informed AI methods for battery SOH estimation;
  • Digital twins for battery cells or systems;
  • The hardware implementation of AI methods.

Dr. Xin Sui
Prof. Dr. Remus Teodorescu
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-anonymized 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

  • lithium-ion battery
  • SOH estimation
  • artificial intelligence
  • lifetime prediction
  • physics-informed AI
  • neural networks
  • supervised learning
  • unsupervised learning
  • self-learning
  • data mining

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Related Special Issue

Published Papers (6 papers)

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Research

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21 pages, 13685 KB  
Article
Early Prediction of Commercial Energy Storage Battery Cycle Life Based on Health Features and Transfer Learning
by Shuping Wang, Xinyue Zhou, Yifeng Cheng, Changhao Li, Guohong Chen, Tian Jiang, Bangyu Li, Feng Ye and Xianzhong Sun
Batteries 2026, 12(7), 253; https://doi.org/10.3390/batteries12070253 - 13 Jul 2026
Viewed by 602
Abstract
As the application scale of battery energy storage gradually increases, the accurate prediction of the remaining service life of large-capacity energy storage batteries is crucial for high-quality development in this field. To address the issues of insufficient reliability and poor generalization in large-capacity [...] Read more.
As the application scale of battery energy storage gradually increases, the accurate prediction of the remaining service life of large-capacity energy storage batteries is crucial for high-quality development in this field. To address the issues of insufficient reliability and poor generalization in large-capacity energy storage battery life prediction, a deep learning framework based on a long short-term memory (LSTM) neural network is developed. Early aging data from the first 150 cycles is used for the model, with outliers removed and noise reduced through Savitzky–Golay (SG) filtering. Data normalization and a sliding window method are employed for training. The model is validated on two batches of large-capacity batteries under GB/T 36276-2023 conditions at 25 °C and 45 °C, achieving the root mean square errors (RMSEs) of 0.86% and 0.50%, respectively, over 1000 cycles. Additionally, the method is tested on small-capacity batteries from an MIT dataset, achieving an RMSE of 4.3%. A transfer learning module fine-tunes the model using cycles 151–300, reducing RMSEs to 0.18%, 0.10%, and 3.1% for the three battery sets. This enhances the model’s generalization and offers a practical solution for life prediction in battery inspection and evaluation. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries: 3rd Edition)
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30 pages, 16529 KB  
Article
Data-Driven Analysis and Machine Learning-Based Estimation of SOC and RUL in Lithium-Ion Batteries Using Heterogeneous Operational Data
by Pierpaolo Dini and Davide Paolini
Batteries 2026, 12(6), 199; https://doi.org/10.3390/batteries12060199 - 30 May 2026
Cited by 1 | Viewed by 928
Abstract
The accurate estimation of State of Charge (SOC) and Remaining Useful Life (RUL) is a key challenge in lithium-ion battery management systems, due to the nonlinear, time-varying, and multi-physics nature of battery dynamics. This work presents a systematic comparative study for SOC and [...] Read more.
The accurate estimation of State of Charge (SOC) and Remaining Useful Life (RUL) is a key challenge in lithium-ion battery management systems, due to the nonlinear, time-varying, and multi-physics nature of battery dynamics. This work presents a systematic comparative study for SOC and RUL estimation based on the analysis of the NASA battery dataset, characterized by significant heterogeneity in operating conditions, temperature regimes, and cycle durations. The study combines a physically informed feature engineering process with machine learning models, including tree-based ensembles, kernel methods, and neural networks. The dataset is analyzed from an electrochemical, thermal, and impedance perspective, highlighting the role of internal resistance evolution, SOC–voltage characteristics, and temperature dynamics as indicators of battery degradation. Based on these observations, two regression problems are formulated: a local window-based representation for SOC estimation and a cycle-level representation for RUL prediction. Particular attention is devoted to the impact of dataset heterogeneity, feature construction, and target representation on the predictive behavior of the considered models. In addition, the work investigates the effect of normalized RUL representations and provides an interpretability-oriented comparison of the learned regressors through feature-importance analysis and parity plots. Experimental results show that SOC estimation is a comparatively well-conditioned problem, achieving high accuracy across nonlinear models, although the dominant role of temporal and current-derived features highlights the strong dependence of the prediction task on the structure of the experimental protocol. In contrast, RUL prediction exhibits significantly higher complexity due to long-term degradation uncertainty and inter-battery variability. The introduction of a normalized RUL representation substantially improves prediction accuracy and stability, particularly for ensemble-based approaches. Feature importance analysis confirms that capacity-related variables dominate RUL estimation, while voltage, temporal, and current-derived features play a central role in SOC prediction. Overall, the results show that physically interpretable feature construction combined with ensemble learning methods provides an effective framework for battery state estimation and degradation analysis under heterogeneous operating conditions. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries: 3rd Edition)
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35 pages, 4516 KB  
Article
Online Internal Temperature Estimation Method for Prismatic Li-Ion Battery Using Embedded Physics-Informed Neural Networks
by Zhengchen Liu, Yan Wang, Ping Gao, Hangyu Luo, Tao Cai, Gen Su, Zhanqiang Wang and Yuxin Meng
Batteries 2026, 12(6), 189; https://doi.org/10.3390/batteries12060189 - 25 May 2026
Viewed by 793
Abstract
Accurate estimation of internal battery temperature is critical for the safety and state-of-health assessment of lithium-ion batteries, yet it remains challenging due to the trade-off between model accuracy and computational feasibility on resource-constrained edge hardware. This work targets stationary large-scale battery energy storage [...] Read more.
Accurate estimation of internal battery temperature is critical for the safety and state-of-health assessment of lithium-ion batteries, yet it remains challenging due to the trade-off between model accuracy and computational feasibility on resource-constrained edge hardware. This work targets stationary large-scale battery energy storage stations (BESS), where ambient temperatures are actively regulated within a narrow range (typically 15–35 °C), and is developed and validated on large-format prismatic LFP cells. We propose ThermaPhysLite, a lightweight physics-informed neural network (PINN) framework with three innovations: (i) a lightweight PINN architecture tailored for edge devices; (ii) integration of a simplified electro–thermal model—a lumped-parameter thermal circuit coupled with the Bernardi heat generation equation—into a multi-scale temporal convolutional network (MS-TCN) through the PINN paradigm; and (iii) real-time online deployment on the ESP32-S3 embedded platform. Ground-truth internal temperatures were obtained via side-drilled thermocouple embedding in disassembled cells. Offline validation under three operating conditions demonstrates RMSE values of 0.15–0.20 °C. Following INT8 quantization (compressed to 84.29 KB), online deployment yields RMSE values of 0.17–0.24 °C with single-cell inference latency of 120 ms, demonstrating practical viability for BMS in large-scale energy storage systems. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries: 3rd Edition)
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Graphical abstract

20 pages, 1970 KB  
Article
Toward Generalizable State-of-Charge Prediction of Lithium-Ion Batteries Using Deep Learning and Real-World Data
by Montaha Khedhiri, Rim Slama, Eduardo Redondo-Iglesias and Rochdi Trigui
Batteries 2026, 12(6), 185; https://doi.org/10.3390/batteries12060185 - 22 May 2026
Viewed by 804
Abstract
Recently, numerous approaches have been proposed to improve State of Charge (SoC) prediction, demonstrating the potential of deep learning (DL) techniques for accurate battery state estimation. However, most of these methods are validated on laboratory-controlled or synthetic datasets and do not sufficiently consider [...] Read more.
Recently, numerous approaches have been proposed to improve State of Charge (SoC) prediction, demonstrating the potential of deep learning (DL) techniques for accurate battery state estimation. However, most of these methods are validated on laboratory-controlled or synthetic datasets and do not sufficiently consider real-world battery operating conditions. In practice, batteries operate under highly diverse usage patterns, environmental conditions, and user profiles, which can significantly affect SoC estimation accuracy. In this paper, we address this limitation by leveraging real-world data, which contains measurements from vehicle batteries under heterogeneous user behaviors and operating scenarios. The proposed methodology includes a data cleaning and filtering preprocessing stage, followed by an original DL framework designed to evaluate SoC estimation under different learning conditions. The framework is data driven and built upon a TimerV2-based architecture capable of capturing long-term temporal dependencies and nonlinear relationships in battery signals. Furthermore, transfer learning strategies are explored to enhance adaptability across different battery configurations and datasets for efficient knowledge transfer. Extensive experiments show that the proposed approach achieves high estimation accuracy and strong generalization performance, demonstrating its suitability for reliable real-time SoC estimation in practical battery management systems. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries: 3rd Edition)
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24 pages, 4694 KB  
Article
AI-Driven Thermal Management Optimization for Lithium-Ion Battery Packs: A Surrogate Model Approach to Cell Spacing Design
by Florin Mariasiu, Ioan Szabo and George E. Mariasiu
Batteries 2026, 12(3), 86; https://doi.org/10.3390/batteries12030086 - 2 Mar 2026
Cited by 3 | Viewed by 3013
Abstract
The article presents the possibilities of integrating artificial intelligence (through specific machine learning techniques) in the design and construction process of a battery in order to optimize its thermal management. The workflow starts from CFD thermal simulations (1C-rate) of a battery (16 Li-ion [...] Read more.
The article presents the possibilities of integrating artificial intelligence (through specific machine learning techniques) in the design and construction process of a battery in order to optimize its thermal management. The workflow starts from CFD thermal simulations (1C-rate) of a battery (16 Li-ion cells, type 18650, 4 × 4 arrangement), and based on the results, a complex thermal landscape is created through radial basis function (Rbf) interpolation. Furthermore, a robust neural network (NN) model is proposed and validated through the obtained performances, which is used further for the optimization of the design space (DSO) and multi-objective optimization (MOO) processes. The obtained results show that for DSO, a cell spacing of 1.37 mm is proposed for a maximum cell temperature of 25.53 °C, and in the case of MOO, a cell spacing of 2.64 mm (for minimum fan energy consumption). The main conclusion of the obtained results shows that the use of the NN model as a surrogate (the Digital Twin of a physical model) presents two great advantages in the process of designing a battery: running a CFD simulation for each point on the 2D grid would take hours, while the NN model can generate the entire map and find the optimum in less than 2 s, and moreover, thousands of additional points can be evaluated to find the thin limit of optimal models, effectively filtering out thousands of energy-consuming “suboptimal” configurations. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries: 3rd Edition)
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Review

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48 pages, 5396 KB  
Review
Neural Architectures and Learning Strategies for State-of-Health Estimation of Lithium-Ion Batteries: A Critical Review
by Tai Duc Le, Jin-Hyeok Park and Moo-Yeon Lee
Batteries 2026, 12(2), 76; https://doi.org/10.3390/batteries12020076 - 19 Feb 2026
Cited by 6 | Viewed by 2591
Abstract
Accurate state-of-health (SOH) estimation is a cornerstone of safe, reliable, and cost-effective operation of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems. In recent years, rapid advances in artificial intelligence technology have led to the widespread adoption of neural-network-based SOH estimation [...] Read more.
Accurate state-of-health (SOH) estimation is a cornerstone of safe, reliable, and cost-effective operation of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems. In recent years, rapid advances in artificial intelligence technology have led to the widespread adoption of neural-network-based SOH estimation methods, offering strong nonlinear modeling capability and improved adaptability compared with traditional model-based approaches. However, the growing diversity of neural architectures and learning strategies has led to fragmented development and inconsistent evaluation, hindering their practical deployment. This paper presents a critical and systematic review of the most recent representative studies on neural-network-based SOH estimation for LIBs between 2024 and 2025. A unified taxonomy is introduced to distinguish neural architectures from learning strategies. The neural architectures include artificial neural networks, convolutional and recurrent networks, attention-based models, Transformers, and physics-informed neural networks. The learning strategies encompass transfer learning, physics-constrained/physics-informed learning, robustness-oriented training and efficiency-aware design. The reviewed methods are analyzed in terms of modeling capability, generalization across operating conditions and chemistries, data efficiency, interpretability and deployability within battery management systems. Key challenges including nonlinear degradation, degradation diversity, data scarcity, and limited observability are critically examined. The roles of architecture-strategy co-design in addressing these issues are highlighted. Finally, open research directions and practical recommendations are discussed to guide the development of reliable, scalable and physically consistent SOH estimation frameworks. This review provides a structured reference for researchers and practitioners seeking to advance data-driven battery health monitoring toward real-world applications. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries: 3rd Edition)
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