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RESEARCH PAPER · Nature Energy · 2019

Data-driven prediction of battery cycle life before capacity degradation

Kristen Severson, Peter M. Attia, Norman Jin, Nicholas Perkins, Benben Jiang, Zi Yang, Michael H. Chen, Muratahan Aykol, Patrick K. Herring, Dimitrios Fraggedakis, Martin Z. Bazant, Stephen J. Harris, William C. Chueh, Richard D. Braatz

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Data-driven prediction of battery cycle life before capacity degradationOriginalChallenges and opportunities towards fast-charging battery materials1781Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review1183Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network342A deep learning method for online capacity estimation of lithium-ion batteries392State estimation for advanced battery management: Key challenges and future trends850Data-Driven Safety Envelope of Lithium-Ion Batteries for Electric Vehicles220A review on prognostics and health management (PHM) methods of lithium-ion batteries389Investigation of lithium-ion battery degradation mechanisms by combining differential voltage analysis and alternating current impedance313A review of composite solid-state electrolytes for lithium batteries: fundamentals, key materials and advanced structures1030Directly predicting limiting potentials from easily obtainable physical properties of graphene-supported single-atom electrocatalysts by machine learning173Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art503State of Health Monitoring and Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Temporal Convolutional Network241AI-based optimization of PEM fuel cell catalyst layers for maximum power density via data-driven surrogate modeling215Battery Lifetime Prognostics1300Degradation of Commercial Lithium-Ion Cells as a Function of Chemistry and Cycling Conditions605A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries266Strategies to limit degradation and maximize Li-ion battery service lifetime - Critical review and guidance for stakeholders209Closed-loop optimization of fast-charging protocols for batteries with machine learning1153Machine learning: Accelerating materials development for energy storage and conversion370Predicting the state of charge and health of batteries using data-driven machine learning784Mitigating Thermal Runaway of Lithium-Ion Batteries1715Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning877Health Prognosis for Electric Vehicle Battery Packs: A Data-Driven Approach205Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells279A review of key issues for control and management in battery and ultra-capacitor hybrid energy storage systems240Detecting the Onset of Lithium Plating and Monitoring Fast Charging Performance with Voltage Relaxation181Solid–Electrolyte Interphase During Battery Cycling: Theory of Growth Regimes158Technoeconomic model of second-life batteries for utility-scale solar considering calendar and cycle aging177Machine learning for continuous innovation in battery technologies238Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems396A database of battery materials auto-generated using ChemDataExtractor162Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning260Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning379Lifespan prediction of lithium-ion batteries based on various extracted features and gradient boosting regression tree model214Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste: Application of machine learning on waste-to-resource191Challenges and opportunities toward fast-charging of lithium-ion batteries303An Ensemble Learning-Based Data-Driven Method for Online State-of-Health Estimation of Lithium-Ion Batteries158Online capacity estimation of lithium-ion batteries with deep long short-term memory networks362Emerging materials intelligence ecosystems propelled by machine learning441Power Electronics Reliability: State of the Art and Outlook218The Role of Machine Learning in the Understanding and Design of Materials433General Discharge Voltage Information Enabled Health Evaluation for Lithium-Ion Batteries211Machine learning toward advanced energy storage devices and systems199The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety303A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management272Feature Engineering for Machine Learning Enabled Early Prediction of Battery Lifetime178Lithium ion battery degradation: what you need to know1189Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning246A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries418Opportunities and Challenges of Lithium Ion Batteries in Automotive Applications1040Predictive Battery Health Management With Transfer Learning and Online Model Correction257Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries202Early prediction of battery lifetime via a machine learning based framework302Perspective—Combining Physics and Machine Learning to Predict Battery Lifetime264Artificial intelligence to support the integration of variable renewable energy sources to the power system214A Review of Existing and Emerging Methods for Lithium Detection and Characterization in Li‐Ion and Li‐Metal Batteries230Current and future lithium-ion battery manufacturing763Machine learning pipeline for battery state-of-health estimation660A machine-learning prediction method of lithium-ion battery life based on charge process for different applications244A Flexible State-of-Health Prediction Scheme for Lithium-Ion Battery Packs With Long Short-Term Memory Network and Transfer Learning226Lithium-ion battery data and where to find it374A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches242Deep neural network battery charging curve prediction using 30 points collected in 10 min340Application of Digital Twin in Smart Battery Management Systems164One-shot battery degradation trajectory prediction with deep learning228Lithium-ion batteries remaining useful life prediction based on BLS-RVM197Review on state-of-health of lithium-ion batteries: Characterizations, estimations and applications431Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale166Early prediction of remaining useful life for Lithium-ion batteries based on a hybrid machine learning method169The challenge and opportunity of battery lifetime prediction from field data411A Fast Impedance Calculation-Based Battery State-of-Health Estimation Method209End-of-life or second-life options for retired electric vehicle batteries302A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods236The role of artificial intelligence in the mass adoption of electric vehicles172Automated Feature Extraction and Selection for Data-Driven Models of Rapid Battery Capacity Fade and End of Life198Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries412A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries258Artificial Intelligence Applied to Battery Research: Hype or Reality?503Predicting the impact of formation protocols on battery lifetime immediately after manufacturing173Key challenges for a large-scale development of battery electric vehicles: A comprehensive review272Recent Advances of Artificial Intelligence in Manufacturing Industrial Sectors: A Review193Review of fast charging strategies for lithium-ion battery systems and their applicability for battery electric vehicles242Deep neural network battery life and voltage prediction by using data of one cycle only158Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling326Data-Driven Battery State of Health Estimation Based on Random Partial Charging Data359State of health estimation for lithium-ion batteries based on temperature prediction and gated recurrent unit neural network248A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction253Dynamic Equivalent Circuit Model to Estimate State-of-Health of Lithium-Ion Batteries187Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning309Robust State of Health estimation of lithium-ion batteries using convolutional neural network and random forest272Liquid electrolyte: The nexus of practical lithium metal batteries596The challenges and opportunities of battery-powered flight541Battery health estimation with degradation pattern recognition and transfer learning300Tackling Climate Change with Machine Learning1015A review of the recent progress in battery informatics178An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty178A Bayesian Deep Learning Framework for RUL Prediction Incorporating Uncertainty Quantification and Calibration164Prognostics and health management of Lithium-ion battery using deep learning methods: A review295Battery management strategies: An essential review for battery state of health monitoring techniques194Overview of batteries and battery management for electric vehicles715A comprehensive review on batteries and supercapacitors: Development and challenges since their inception472A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction348Data‐Driven Materials Innovation and Applications187Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation815Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning244Review—“Knees” in Lithium-Ion Battery Aging Trajectories488Evaluation guidelines for machine learning tools in the chemical sciences180A Transfer Learning-Based Method for Personalized State of Health Estimation of Lithium-Ion Batteries159A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries288Understanding the mechanism of capacity increase during early cycling of commercial NMC/graphite lithium-ion batteries207Impedance-based forecasting of lithium-ion battery performance amid uneven usage227Aging aware operation of lithium-ion battery energy storage systems: A review210Lithium-Ion Batteries—The Crux of Electric Vehicles with Opportunities and Challenges273Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects193Machine learning for a sustainable energy future517Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network185Machine learning for predicting battery capacity for electric vehicles200A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives175Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects170An encoder-decoder fusion battery life prediction method based on Gaussian process regression and improvement172Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects413Insights into the solvation chemistry in liquid electrolytes for lithium-based rechargeable batteries443Electric vehicle batteries alone could satisfy short-term grid storage demand by as early as 2030255Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries230A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries277Overview on Theoretical Simulations of Lithium‐Ion Batteries and Their Application to Battery Separators160An overview of data-driven battery health estimation technology for battery management system167A data-driven method for extracting aging features to accurately predict the battery health186Data-driven electrolyte design for lithium metal anodes177Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles268A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms250The development of machine learning-based remaining useful life prediction for lithium-ion batteries227Deep learning to estimate lithium-ion battery state of health without additional degradation experiments403Grid-connected battery energy storage system: a review on application and integration305Battery technologies and functionality of battery management system for EVs: Current status, key challenges, and future prospectives288State-of-health estimation of lithium-ion batteries based on electrochemical impedance spectroscopy: a review279Physics-Informed Neural Networks for Prognostics and Health Management of Lithium-Ion Batteries205Thermal state monitoring of lithium-ion batteries: Progress, challenges, and opportunities273Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial243Challenges and opportunities for second-life batteries: Key technologies and economy160Applications of artificial neural network based battery management systems: A literature review256A review of expert hybrid and co-estimation techniques for SOH and RUL estimation in battery management system with electric vehicle application174Battery safety: Machine learning-based prognostics211Revealing the Potential and Challenges of High‐Entropy Layered Cathodes for Sodium‐Based Energy Storage174Challenges and opportunities toward long-life lithium-ion batteries195Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis855State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges184Self-Driving Laboratories for Chemistry and Materials Science674Multi-modal framework for battery state of health evaluation using open-source electric vehicle data209A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis171

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