Lithium-Ion Batteries

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Lithium-ion battery research spans electrode and electrolyte design, cell operation, degradation, safety, manufacturing, and recycling. Researchers use electrochemical models such as the single-particle model, experiments and materials characterization, and machine-learning methods including sequence networks and physics-informed neural networks. These approaches estimate state of charge and health, forecast remaining life, detect faults and thermal runaway, and speed up materials and design screening, often using cycling datasets and drive-cycle measurements. A central challenge is making predictions that transfer from controlled laboratory tests to different cells, chemistries, and real-world use while remaining physically reliable and computationally efficient.

Latest papers

49 papers1 paper in the last four weeks, down 80% on the four weeks before. 0.0% of all new papers.

Single-Cycle Battery Remaining Useful Life Prediction with Compressed Continuous Wavelet Features and Deep Neural Networks
Oct 25, 2026Sungeun Kim, Mingyu Jung, Yuri Kim +2RUL Estimation Lithium-Ion Batteries
Department of Artificial Intelligence, Ajou University, Suwon, 16499, Republic of Korea · Department of Materials Science & Engineering, Ajou University, Suwon, 16499, Republic of Korea · Department of Software & Computer Engineering, Ajou University, Suwon, 16499, Republic of Korea
Relative Discharge Stage (RDS) Classification: A Practical Indicator of Battery Discharge Progress
Sep 23, 2026Khoa Tran, Tri Le, Hung-Cuong Trinh +1Lithium-Ion Batteries
Data Science Laboratory, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, 70000, Vietnam · AIWARE Limited Company, 17 Huynh Man Dat Street, Hoa Cuong Ward, Hai Chau District, Da Nang, 550000, Vietnam · Natural Language Processing and Knowledge Discovery Research Group, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, 70000, Vietnam +2
Intelligent Degradation Monitoring in Lithium-ion Batteries via Discharge Incremental Capacity Feature Estimation
Sep 19, 2026Amir Madmolilvand, Farzaneh AbdollahiLithium-Ion Batteries
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Joint Remaining Useful Life Prediction and Capacity Estimation of Lithium-Ion Batteries Using Partial-Charging Data
Sep 18, 2026Khoa Tran, Ho-Si-Hung Nguyen, Phone Wai Yan Moe +2RUL Estimation Multi-Task Learning
Data Science Laboratory, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 70000, Vietnam · Faculty of Electrical Engineering, The University of Danang - University of Science and Technology (DUT), 54 Nguyen Luong Bang Street, Lien Chieu District, Da Nang 550000, Vietnam · AIWARE Limited Company, 17 Huynh Man Dat Street, Hoa Cuong Ward, Hai Chau District, Da Nang 550000, Vietnam +2
Prototype-guided transfer of sparse literature knowledge for electrolyte additive discovery
Sep 2, 2026Weixiang Hong, Hongting Du, Jiayue Tang +4Lithium-Ion Batteries Transfer Learning
Sustainable Energy and Environment Thrust and Guangzhou Municipal Key Laboratory of Materials Informatics, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou, 511400, Guangdong, P.R. China · Data Science and Analytics Thrust and Guangzhou Municipal Key Laboratory of Materials Informatics, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou, 511400, Guangdong, P.R. China · Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, P.R. China +1
State of Health Estimation using Convolutional and Bidirectional LSTM Neural Networks tuned by Bayesian Optimization
Aug 31, 2026Panagiotis Eleftheriadis, Foivos Georgios Kyrgios, Sonia LevaConvolutional RNNs Lithium-Ion Batteries
Department of Energy, Politecnico di Milano, via Lambruschini 4a, 20156 Milano, Italy
Data-efficient crack quantification in lithium-ion cathodes using foundation model transfer
Aug 27, 2026Thorsten Tegetmeyer-Kleine, Thomas Schmitt, Phillip Aquino +3Lithium-Ion Batteries Crack Segmentation
Junior Professorship for Artificial Intelligence and Digitalization for Batteries, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany · Center for Aging, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, 52074, Germany · Juelich Aachen Research Alliance, JARA-Energy, Templergraben 55, Aachen, 52056, Germany +5
A Repeated Measurements Approach to $SoH$ Battery Modelling of Cyclic Aged Data in a Laboratory Environment
Aug 20, 2026Mark Cary, Charles BokorLithium-Ion Batteries Mixed-Effects Models
Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, LE11 3TU, UK · School of Engineering, Computing and Mathematics, Oxford Brookes University Oxford OX33 1HX, UK
Enhancing EBSD throughput of battery electrode materials using super-resolution generative adversarial networks
Aug 19, 2026John Mangum, Andrew Glaws, Francois Usseglio-Viretta +2Materials Science Image Super-Resolution
National Laboratory of the Rockies (NLR), 15013 Denver W Parkway, Golden, CO 80401, USA
Degradation-Aligned Self-Supervised Learning for State of Health Estimation of Lithium-Ion Batteries under Label Sparsity
Aug 17, 2026Jiaqi Yao, Julia KowalLithium-Ion Batteries Data-Efficient Learning
Department of Electrical Energy Storage Technology (EET), Technische Universität Berlin, Einsteinufer 11, 10587, Berlin, Germany
Quantifying the Gap Between Laboratory Battery Test Patterns and Field Duty Profiles
Aug 17, 2026Chunyang Zhao, Chresten TræholtLithium-Ion Batteries Benchmark Validity
Department of Wind and Energy Systems Technical University of Denmark Kongens Lyngby, Denmark
Real-Time State-of-Health Estimation and Online Degradation Prognosis from Partial Battery Discharge Using Physics-Informed Neural Networks
Aug 14, 2026Begoña Ispizua, Serio Gil-López, Leire Arrizabalaga +1Lithium-Ion Batteries RUL Estimation
Tecnalia - Basque Research Technology Alliance (BRTA), Derio, 48160, Spain · Unibersity of the Basque Country (EHU), Leioa, 48940, Spain · Mondragon Unibertsitatea, Bilbao, 48001, Spain
LIBAD: A Multimodal Anomaly Detection Benchmark for Li-Ion Battery Electrode Manufacturing
Aug 8, 2026Wenbo Sui, Daniel Lichau, Harold Phelippeau +1Industrial Anomaly Detection Multimodal Anomaly Detection
Thermo Fisher Scientific, 39 Rue d’Armagnac, Bordeaux, 33800, France · DTU Energy, Technical University of Denmark, Fysikvej 310, Kgs. Lyngby, 2800, Denmark · Thermo Fisher Scientific, 5350 NE Dawson Creek Dr, Hillsboro, OR 97124, USA
PiDDM: Physics-Informed Differentiable Degradation Modeling for Lithium-Ion Battery State-of-Health Prediction
Jul 31, 2026Zeping Chen, Ruda Jian, Sachin Sigdel +3RUL Estimation Physics-Informed ML
Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, USA · Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX, USA · Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN, USA +1
A Density-Matrix Framework for Electronic-Structure Analysis of Electrolytes for Lithium Batteries
Jul 28, 2026Mingkang Liu, Huize Yu, Lei ShenQuantum Chemistry Materials Science
Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore · National University of Singapore (Chongqing) Research Institute, Chongqing 401120, China · National University of Singapore Guangzhou Research Translation and Innovation Institute, Guangzhou, Guangdong 510700, China
Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation
Jul 26, 2026Huy Hoang Le, Kim-Anh NguyenBenchmark Validity Lithium-Ion Batteries
a R&D Department, PowerMore Ltd., Viet Nam · b Faculty of Electrical Engineering, University of Science and Technology, The University of Danang, Viet Nam
AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries
Jul 22, 2026Mengda Xing, Jean-Marie Lagniez, Alejandro FrancoNeural Surrogate Modeling Materials Science
CRIL, UA · CRIL, UMR 8188, Université d’Artois, Rue Jean Souvraz SP 18, F-62307 Lens Cedex, France · LRCS +1
Regime-Aware Physics-Guided Early Warning of Lithium-Ion Battery Thermal Runaway Using Thermo-Mechanical Signals
Jul 21, 2026Syed Sajid Ullah, Muhammad Zunair Zamir, Salman KhanTemporal Convolutional Networks Physics-Informed ML
Physics-Guided Masked Multi-Task Network for Edge-Friendly Battery Health Diagnostics from Sto-chastically Fragmented Charging Profiles
Jul 19, 2026Shuhao Chen, Tianyu Shi, Chengyi TuRUL Estimation Transformer
School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou, 310018, China.
Dynamic Loss Balancing for Joint SOH and RUL Prediction of Lithium-Ion Batteries via a Rotary SOH-Injected Prior Battery Transformer
Jul 19, 2026Shuhao Chen, Tianyu Shi, Yiwen Huang +1RUL Estimation Predictive Maintenance
School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou, 310018, China.
Bridging battery design and health assessment through virtual sensing and physics-informed learning
Jul 18, 2026Wendi Guo, Søren Byg Vilsen, Daniel Ioan Stroe +4RUL Estimation Materials Science
Department of Chemistry, Uppsala University, Sweden · Department of Materials and Production, Aalborg University, Denmark · Department of Energy, Aalborg University, Denmark +2
BattVAE-GP: Generative Modeling of Long-Horizon Battery Degradation with Uncertainty Quantification
Jul 11, 2026Raghvender Raghvender, Mahdi Abid, Ferran Brosa Planella +2Surrogate Modeling Uncertainty Quantification
Laboratoire de Réactivité et de Chimie des Solides (LRCS), UMR CNRS 7314-Université de Picardie Jules Verne, 15 Rue Baudelocque, 80000, Amiens, France · Réseau sur le Stockage Electrochimique de l’Energie (RS2E), CNRS FR 3459, Hub de l’Energie, 15 Rue Baudelocque, 80000, Amiens, France · Predeeption (INRIA Startup Studio / CNRS Innovation), Hub de l’energie, 80000, Amiens, France +2
IonSense-QKG: A Quantum-Readiness Metadata Framework for Lithium-Ion Battery Dataset Discovery
Jul 1, 2026Sakthi Prabhu Gunasekar, Prasanna Kumar RangarajanLithium-Ion Batteries
Amrita School of Computing, Amrita Vishwa Vidyapeetham Coimbatore, India
Physics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with Electrolyte
Jun 26, 2026Gift Modekwe, Qiugang LuPDE Surrogate Modeling Transfer Learning
Department of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, USA
Comparative Study of Neural Surrogate Architectures for Autoregressive Prediction of Internal Battery States
Jun 18, 2026Gihyun Lee, Thorben Menne, Simon Olma +2Neural Surrogate Modeling PDE Surrogate Modeling
Technische Universität Berlin, Berlin, Germany · IAV GmbH, Berlin, Germany
Autonomous End-to-End SOH Prediction Services for Battery Systems via Temporal-Contrastive Representation Learning
Jun 15, 2026Junting Wen, Dan Li, Qihao Quan +8Contrastive Learning Self-Supervised Time Series Representation Learning
School of Software Engineering, Sun Yat-sen University, Zhuhai 519082, China · Tianneng Battery Group Co., Ltd, Zhejiang, China · School of Communication Engineering, Hangzhou Dianzi University, China +1
Battery detection of XRay images using transfer learning
Jun 10, 2026Nermeen Abou Baker, David Rohrschneider, Uwe HandmannTransfer Learning Object Detection
1- Ruhr West University of Applied Sciences - Dept of Computer Science Lutzowstrassse 5, 46236 Bottrop - Germany
Vision-Guided Dual-Arm Humanoid Robotic Disassembly of End-of-Life 18650 Lithium-ion Battery Packs
Jun 6, 2026Yile Chen, Zhihao Liu, Xi Vincent Wang +1Bimanual Robotic Manipulation Robotic Manipulation
KTH Royal Institute of Technology, Brinellvägen 8, 114 28, Stockholm, Sweden
AI-Guided Design and Optimization of Graphite-Based Anodes via Iterative Experimental Feedback
May 29, 2026Qian Du, Mark M. Sullivan, James E. Saal +1Surrogate-Assisted Optimization Materials Science
hte GmbH, Kurpfalzring 104, 69123 Heidelberg, Germany · Citrine Informatics, 2629 Broadway St., Redwood City, CA 94063, USA
MagBridge-Battery: A Synthetic Bridge Dataset for Li-ion Magnetometry and State-of-Health Diagnostics
May 17, 2026Sakthi Prabhu Gunasekar, Prasanna Kumar RangarajanBenchmark Design Synthetic Data Generation
Dept. of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham, India

Oct 25, 2026J/K move · Enter open · S save

Single-Cycle Battery Remaining Useful Life Prediction with Compressed Continuous Wavelet Features and Deep Neural Networks

Sungeun Kim, Mingyu Jung, Yuri Kim, Jin-Sung Park+1

Department of Artificial Intelligence, Ajou University, Suwon, 16499, Republic of Korea · Department of Materials Science & Engineering, Ajou University, Suwon, 16499, Republic of Korea · Department of Software & Computer Engineering, Ajou University, Suwon, 16499, Republic of Korea

RUL Estimation Lithium-Ion Batteries Frequency-Domain Feature Learning

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Lithium-ion batteries are widely used in industrial applications, but their performance gradually degrades over time, making accurate lifetime prediction important for safety and reliability. However, reliable lifetime assessment generally requires extensive degradation testing, which is both costly and time-consuming. To reduce this burden, recent data-driven approaches have sought to predict the end-of-life cycle of batteries from only the initial few cycles. Nevertheless, identifying reliable degradation patterns from such limited early-cycle data remains challenging because early aging dynamics are often subtle and nonlinear. To address this issue, we adopt the continuous wavelet transform to convert cycle-wise voltage, current, and capacity signals into time-frequency representations, enabling multi-resolution characterization of degradation behavior. Building on this representation, we apply scale compression to reduce redundancy while preserving structural information in the scalograms. We evaluate the proposed framework on eight benchmark datasets derived from seven public battery datasets covering diverse electrode chemistries, cell form factors, and cycling protocols. Experimental results show that the proposed framework consistently outperforms prior approaches in single-cycle remaining useful life prediction and remains robust as the number of input cycles increases from 1 to 100. In addition, redundancy-fidelity analysis characterizes the trade-off introduced by scale compression, and gradient-based visual explanations indicate that the model focuses on localized time-frequency regions associated with structured signal transitions during charge-discharge operation. These results demonstrate the effectiveness and interpretability of the proposed framework for early-cycle battery remaining useful life prediction.