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RESEARCH PAPER · Journal of Power Sources · 2022

Machine learning for battery research

Zheng Xi Wei, Qiu He, Yan Zhao

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Machine learning for battery researchOriginalExplainability-driven model improvement for SOH estimation of lithium-ion battery143A review of polymers in sulfide-based hybrid solid-state electrolytes for all-solid-state lithium batteries18Ensemble hybrid machine learning to simulate dye/divalent salt fractionation using a loose nanofiltration membrane24Battery impedance spectrum prediction from partial charging voltage curve by machine learning71Data-Driven State of Health Estimation for Lithium-Ion Batteries Based on Universal Feature Selection10Machine Learning-Assisted Design of Na-Ion-Conducting Glasses21Machine Learning Estimation of Battery Efficiency and Related Key Performance Indicators in Smart Energy Systems33D Printing‐Enabled Design and Manufacturing Strategies for Batteries: A Review88A time-series Wasserstein GAN method for state-of-charge estimation of lithium-ion batteries73Direct Recycling of Lithium-Ion Cathode: A Green Solution (Applied To Laptop Batteries)7Rapid Estimation of Battery Storage Capacity through Multiple Linear Regression6Separation of sulfur compounds from petroleum using hydrodesulfurization method to reduce SO2 emission: Process optimization and validation12Recent advances and applications of machine learning in electrocatalysis36Artificial intelligence-driven rechargeable batteries in multiple fields of development and application towards energy storage34Machine Learning‐Assisted Fabrication of PCBM‐Perovskite Solar Cells with Nanopatterned TiO 2 Layer9Machine learning-based optimization for catalytic sulfur removal: Computational modeling and analysis of fuel purification for reduction of environmental impacts16Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery20Roadmap for rechargeable batteries: present and beyond224A neural-driven stochastic degradation model for state-of-health estimation of lithium-ion battery17AI enabled fast charging of lithium-ion batteries of electric vehicles during their life cycle: review, challenges and perspectives61Progress of machine learning in materials design for Li-Ion battery19The energy storage application of core-/yolk–shell structures in sodium batteries17Unravelling air/moisture stability of cathode materials in sodium ion batteries: characterization, rational design, and perspectives159Automatic Feature Extraction-Enabled Lithium-Ion Battery Capacity Estimation Using Random Fragmented Charging Data16Machine learning for polyphenol-based materials10Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte143An Investigation into the Viability of Battery Technologies for Electric Buses in the UK9Co-free and low strain cathode materials for sodium-ion batteries: Machine learning-based materials discovery28Voltage-fault diagnosis for battery pack in electric vehicles using mutual information22Theoretical investigations on modeling blood flow through vessel for understanding effectiveness of magnetic nanocarrier drug delivery systems3The importance of precise and suitable descriptors in data‐driven approach to boost development of lithium batteries: A perspective12Machine learning for full lifecycle management of lithium-ion batteries43Computational Insights into Electrolyte-Dependent Li-Ion Charge-Transfer Kinetics at the Li x CoO 2 Interface22Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors47Applying Machine Learning to Design Delicate Amorphous Micro-Nano Materials for Rechargeable Batteries20Physical knowledge guided state of health estimation of lithium-ion battery with limited segment data50Electric bikes charging anomaly detection from alternating current side based on big data5Specialized convolutional transformer networks for estimating battery health via transfer learning47High-throughput screening of high energy density LiMn1-xFexPO4 via active learning10Artificial intelligence-driven real-world battery diagnostics100Evolution from passive to active components in lithium metal and lithium-ion batteries separators11Predicting battery applications for complex materials based on chemical composition and machine learning14The ARTISTIC Battery Manufacturing Digitalization Initiative: From Fundamental Research to Industrialization24Application of Deep Learning to Optimize Gradient Porosity Profile for Improved Energy Density of Lithium-Ion Batteries8Estimation of Differential Capacity in Lithium-Ion Batteries Using Machine Learning Approaches3Data refinement for enhanced ionic conductivity prediction in garnet-type solid-state electrolytes12Theoretical investigations on the purification of petroleum using desulfurization process: Analysis and optimization of process9Interpretable Learning of Accelerated Aging in Lithium Metal Batteries47Lithium-ion battery remaining useful life prediction based on interpretable deep learning and network parameter optimization58Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design1Battery pack condition monitoring and characteristic state estimation: Challenges, techniques, and future prospectives22Soc Estimation Using a Neural Network Trained with Model-Based Dataset Implemented in Ground Electric Vehicles BMS0Recent machine learning-driven investigations into high entropy alloys: A comprehensive review37Voltage Prediction of Lithium-Ion Battery Cathode Using Machine Learning1A Feed-Forward Back-Propagation Neural Network Approach for Integration of Electric Vehicles into Vehicle-to-Grid (V2G) to Predict State of Charge for Lithium-Ion Batteries14Property Prediction for Complex Compounds Using Structure-Free Mendeleev Encoding and Machine Learning9Accelerating battery innovation: AI-powered molecular discovery58Elucidating the impact of metal doping in Li 1.15 (Ni 0.35 Mn 0.65 ) 0.85 O 2 cathodes using high-throughput experiments and machine learning13From atoms to algorithms: a review of machine learning approaches to cathode material innovation in zinc-ion batteries6Machine learning based prediction of MnO 2 cathode discharge capacity for high-performance zinc-ion batteries7Machine learning-assisted design and prediction of materials for batteries based on alkali metals8Quantitative Diagnosis of Early Internal Short Circuit for Lithium-Ion Batteries Based on Multirate Discharge6Recent Advances of Deposition Methods for High‐Performance Lithium–Sulfur Batteries – A Review8Optimising the regeneration process of spent lithium‑cobalt oxide cathode through performance analysis model10Influence of uncertainties in a battery pack with air cooling for electric vehicles on temperature difference and volume of battery module4Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion28Kolmogorov-Arnold networks for algorithm design in battery energy storage system applications13Machine Learning-Driven Optimization of Spent Lithium Iron Phosphate Regeneration12Feature-enhanced encoder-decoder model for accurate lithium-ion battery state of health estimation5Utilizing Machine Learning to Predict the Charge Storage Capability of Lithium-Ion Battery Materials0Integrating Machine Learning Techniques for the Prediction of Key Performance Indicators in Battery Materials0Data-Driven Strategies for Next-Generation Aviation Batteries: Improving Energy Density and Cycle Stability in Electric Propulsion Systems0Developing safe and high-performance lithium-ion batteries: Strategies and approaches97State‐of‐the‐Art Machine Learning Technology for Sustainable Lithium Battery Cathode Design: A Perspective13Localized feature selection augmented dual-stream fusion network for state of health estimation of lithium-ion batteries10Artificial Intelligence‐Driven Development in Rechargeable Battery Materials: Progress, Challenges, and Future Perspectives53Applications of Virtual Reality Simulations and Machine Learning Algorithms in High-Risk Environments2Generative-Enhanced Physics-Informed Neural Network for Lithium-Ion Battery State of Health Estimation12A review on electrolyte innovation enabled by machine learning for energy storage applications5Comparative Modeling of Vanadium Redox Flow Batteries Using Multiple Linear Regression and Random Forest Algorithms2A dual-driven interpretable ensemble model for efficient and reliable SOH estimation of lithium-ion batteries4How Machine Learning Has Driven the Development of Rechargeable Ion Batteries21Reliability Assessment Model of Energy Storage Battery Based on Multidimensional Generalized Generating Function0Extremely Fast-Charging Batteries: Principle, Strategies, Detection, and Prediction64Lithium-ion battery remaining useful life prediction based on dynamic filter frequency mixing learner and dual-stream Mamba3Path to Machine Learning-Driven Autonomous Systems for Solid-State Electrolyte Batteries: Design, Fabrication, and Lifetime Prediction13Recent advances in the high entropy materials for advanced energy storage with machine learning6Design optimization of electric-fish-inspired power sources using statistical and machine learning approaches2Convergence of Computational Materials Science and AI for Next-Generation Energy Storage Materials6PredPotS: web tool for predicting one-electron standard reduction potentials for organic molecules in aqueous phase1Relaxation voltage-based dual-stream transfer attention network for state of health estimation of lithium-ion batteries4Physics-informed neural networks in the energy sector: Progress, trends, and future directions4Closed-loop regeneration of spent nickel manganese cobalt oxide cathodes enabled by artificial neural network modelling2Computation-assisted probing of the solid-solid interface in all-solid-state sodium-ion batteries1Generative AI-Driven Discovery of Next-Generation Electrolytes for Alkali Metal Batteries4Machine Learning Framework for Optimising Regeneration of Lithium‐Ion Battery Cathode Materials0A novel machine learning approach and experimental framework for predicting and validating the electrochemical supercapacitor electrode performance of MnO2 nanorods2Electrochemical Corrosion and Safety Hazards in Sustainable Batteries: Corrosion Mechanisms, Safety Challenges, and Protection1LiClustCondAI: A clustering-based ensemble regression framework for ionic conductivity predictions in solid-state electrolytes0Instance-Weighted Transfer Few-Shot Adaptation for Hard Carbon Capacity Retention Prediction0Machine Learning Guided Design of Doped Lithium Lanthanum Zirconium Oxide ( LLZO ) for Improved Performance0Estimating Series Battery Pack Capacity From Charge–Discharge Behavior at Unilateral Boundaries0Lessons From Deep Eutectic Solvents to Design High Entropy Electrolytes for Electrochemical Energy Storage0Artificial intelligence for energy storage systems: State of the art and future perspectives1Early‐stage lifetime classification for batteries: Machine‐learned features versus human‐selected features0Physics‐Guided Descriptors Enable Data‐Efficient Prediction of Battery Coulombic Efficiency0Machine learning-genetic algorithm coupled optimization of heteroatom-doped carbon electrodes for vanadium redox flow batteries0

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