Battery prognostics and health management from a machine learning perspective

[1]  A. Burke,et al.  Spatial-Temporal Self-Attention Transformer Networks for Battery State of Charge Estimation , 2023, Electronics.

[2]  A. Burke,et al.  Cloud-Based Artificial Intelligence Framework for Battery Management System , 2023, Energies.

[3]  A. Burke,et al.  Battery prognostics and health management for electric vehicles under industry 4.0 , 2023, Journal of Energy Chemistry.

[4]  A. Burke,et al.  Cloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health , 2023, Energies.

[5]  Soohee Han,et al.  Improving Aging Identifiability of Lithium-Ion Batteries Using Deep Reinforcement Learning , 2023, IEEE Transactions on Transportation Electrification.

[6]  H. Chaoui,et al.  Developing an Online Data-Driven State of Health Estimation of Lithium-Ion Batteries Under Random Sensor Measurement Unavailability , 2023, IEEE Transactions on Transportation Electrification.

[7]  Shunli Wang,et al.  Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries , 2023, Reliab. Eng. Syst. Saf..

[8]  Gaoxi Xiao,et al.  Feature Fusion-Based Inconsistency Evaluation for Battery Pack: Improved Gaussian Mixture Model , 2023, IEEE Transactions on Intelligent Transportation Systems.

[9]  A. Burke,et al.  Battery Diagnosis: A Lifelong Learning Framework for Electric Vehicles , 2022, 2022 IEEE Vehicle Power and Propulsion Conference (VPPC).

[10]  A. Burke,et al.  Electric Vehicle Batteries: Status and Perspectives of Data-Driven Diagnosis and Prognosis , 2022, Batteries.

[11]  Liang Zhao,et al.  A Novel State-of-Health Estimation for the Lithium-Ion Battery Using a Convolutional Neural Network and Transformer Model , 2022, SSRN Electronic Journal.

[12]  A. Markham,et al.  Deep learning-based robust positioning for all-weather autonomous driving , 2022, Nature Machine Intelligence.

[13]  Hongwen He,et al.  Battery health-aware and naturalistic data-driven energy management for hybrid electric bus based on TD3 deep reinforcement learning algorithm , 2022, Applied Energy.

[14]  U. Stimming,et al.  Impedance-based forecasting of lithium-ion battery performance amid uneven usage , 2022, Nature Communications.

[15]  Liu Zhiming,et al.  State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm , 2022, Energy.

[16]  Dongliang Gong,et al.  State of health estimation for lithium-ion battery based on energy features , 2022, Energy.

[17]  Danhua Zhou,et al.  Battery health prognosis using improved temporal convolutional network modeling , 2022, Journal of Energy Storage.

[18]  Pingfeng Wang,et al.  Physics-informed machine learning model for battery state of health prognostics using partial charging segments , 2022, Mechanical Systems and Signal Processing.

[19]  X. Mei,et al.  A fast state-of-health estimation method using single linear feature for lithium-ion batteries , 2022, Energy.

[20]  D. Stroe,et al.  An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation , 2022, Energy.

[21]  Xunliang Liu,et al.  Quantitative analysis of the inhibition effect of rising temperature and pulse charging on Lithium dendrite growth , 2022, Journal of Energy Storage.

[22]  A. Burke,et al.  Data-driven prediction of battery failure for electric vehicles , 2022, iScience.

[23]  D. Sauer,et al.  State-of-Health Estimation of Lithium-Ion Batteries by Fusing an Open Circuit Voltage Model and Incremental Capacity Analysis , 2022, IEEE Transactions on Power Electronics.

[24]  Ji Wu,et al.  A multi-feature-based multi-model fusion method for state of health estimation of lithium-ion batteries , 2022, Journal of Power Sources.

[25]  Kai Jiang,et al.  Modeling of solid-state lithium-oxygen battery with porous Li1.3Al0.3Ti1.7(PO4)3-based cathode , 2022, Journal of Energy Storage.

[26]  C. Mi,et al.  Lithium-ion battery capacity estimation based on battery surface temperature change under constant-current charge scenario , 2021, Energy.

[27]  Dirk Uwe Sauer,et al.  Forecasting battery capacity and power degradation with multi-task learning , 2021, Energy Storage Materials.

[28]  Chetan S. Kulkarni,et al.  Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis , 2021, Journal of Power Sources.

[29]  Chen Zewang,et al.  Lithium-ion batteries remaining useful life prediction based on BLS-RVM , 2021 .

[30]  Do Soon Kim,et al.  Deep learning models for predicting RNA degradation via dual crowdsourcing , 2021, ArXiv.

[31]  A. Grimaud,et al.  Artificial Intelligence Applied to Battery Research: Hype or Reality? , 2021, Chemical reviews.

[32]  Z. Seh,et al.  Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries , 2021, Advanced materials.

[33]  Alan C. West,et al.  Supervised Learning of Synthetic Big Data for Li‐ion Battery Degradation Diagnosis , 2021, Batteries & Supercaps.

[34]  Anna G. Stefanopoulou,et al.  The challenge and opportunity of battery lifetime prediction from field data , 2021, Joule.

[35]  Xuezhe Wei,et al.  Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning , 2021, Applied Energy.

[36]  Zhizheng Zhang,et al.  Dual-Aspect Self-Attention Based on Transformer for Remaining Useful Life Prediction , 2021, IEEE Transactions on Instrumentation and Measurement.

[37]  W. Shen,et al.  Deep neural network battery charging curve prediction using 30 points collected in 10 min , 2021, Joule.

[38]  Anuradha M. Annaswamy,et al.  One-shot battery degradation trajectory prediction with deep learning , 2021 .

[39]  Zhenpo Wang,et al.  A Novel Consistency Evaluation Method for Series-Connected Battery Systems Based on Real-World Operation Data , 2021, IEEE Transactions on Transportation Electrification.

[40]  I. Kevrekidis,et al.  Physics-informed machine learning , 2021, Nature Reviews Physics.

[41]  G. Offer,et al.  Lithium ion battery degradation: what you need to know. , 2021, Physical chemistry chemical physics : PCCP.

[42]  Zili Wang,et al.  An Intelligent Preventive Maintenance Method Based on Reinforcement Learning for Battery Energy Storage Systems , 2021, IEEE Transactions on Industrial Informatics.

[43]  Jie Zhou,et al.  Bridging Text and Video: A Universal Multimodal Transformer for Audio-Visual Scene-Aware Dialog , 2021, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[44]  Olga Fink,et al.  Learning to Calibrate Battery Models in Real-Time with Deep Reinforcement Learning , 2021, Energies.

[45]  Lifeng Wu,et al.  A novel data-driven method for predicting the circulating capacity of lithium-ion battery under random variable current , 2021 .

[46]  A. Fotouhi,et al.  An Experimental Study on Prototype Lithium–Sulfur Cells for Aging Analysis and State-of-Health Estimation , 2021, IEEE Transactions on Transportation Electrification.

[47]  David Flynn,et al.  Machine learning pipeline for battery state-of-health estimation , 2021, Nature Machine Intelligence.

[48]  Aman Alok,et al.  Protoda: Efficient Transfer Learning for Few-Shot Intent Classification , 2021, 2021 IEEE Spoken Language Technology Workshop (SLT).

[49]  Guang Li,et al.  Fault diagnosis and abnormality detection of lithium-ion battery packs based on statistical distribution , 2021 .

[50]  A. Boes,et al.  11 TOPS photonic convolutional accelerator for optical neural networks , 2021, Nature.

[51]  Xin Tang,et al.  A novel deep learning framework for state of health estimation of lithium-ion battery , 2020 .

[52]  P. Mukherjee,et al.  Degradation-Safety Analytics in Lithium-Ion Cells: Part I. Aging under Charge/Discharge Cycling , 2020 .

[53]  Didier Dumur,et al.  State-of-health estimators coupled to a random forest approach for lithium-ion battery aging factor ranking , 2020, Journal of Power Sources.

[54]  Kwok-Leung Tsui,et al.  Lifespan prediction of lithium-ion batteries based on various extracted features and gradient boosting regression tree model , 2020 .

[55]  Daniel-Ioan Stroe,et al.  State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis , 2020, Applied Energy.

[56]  Wu Yao,et al.  Online state‐of‐health prediction of lithium‐ion batteries with limited labeled data , 2020, International Journal of Energy Research.

[57]  Pai Zheng,et al.  A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives , 2019, Journal of Intelligent Manufacturing.

[58]  W. Dhammika Widanage,et al.  Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems , 2020, Energy and AI.

[59]  Yanfei Gao,et al.  Investigation on capacity loss mechanisms of lithium-ion pouch cells under mechanical indentation conditions , 2020 .

[60]  D. Brett,et al.  Data for an Advanced Microstructural and Electrochemical Datasheet on 18650 Li-ion Batteries with Nickel-Rich NMC811 Cathodes and Graphite-Silicon Anodes , 2020, Data in brief.

[61]  Zhenpo Wang,et al.  State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression , 2020, Energy.

[62]  Alan Jenn,et al.  Emissions benefits of electric vehicles in Uber and Lyft ride-hailing services , 2020 .

[63]  Patrick K. Herring,et al.  Machine learning for continuous innovation in battery technologies , 2020, Nature Reviews Materials.

[64]  Xueliang Li,et al.  Data driven battery anomaly detection based on shape based clustering for the data centers class , 2020 .

[65]  Fu-Kwun Wang,et al.  Gradient boosted regression model for the degradation analysis of prismatic cells , 2020, Comput. Ind. Eng..

[66]  Scott J. Moura,et al.  Real-Time Capacity Estimation of Lithium-Ion Batteries Utilizing Thermal Dynamics , 2020, IEEE Transactions on Control Systems Technology.

[67]  Zhong Fan,et al.  Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model , 2020, IEEE Transactions on Smart Grid.

[68]  Rui Xiong,et al.  State-of-Health Estimation Based on Differential Temperature for Lithium Ion Batteries , 2020, IEEE Transactions on Power Electronics.

[69]  Jin Zhao,et al.  Predicting the state of charge and health of batteries using data-driven machine learning , 2020, Nature Machine Intelligence.

[70]  Yujie Wang,et al.  Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles , 2020 .

[71]  Xiaosong Hu,et al.  Battery Lifetime Prognostics , 2020 .

[72]  A. Pérez-Navarro,et al.  Diagnosis of a battery energy storage system based on principal component analysis , 2020 .

[73]  Ricardo Pinto Cunha,et al.  Artificial Intelligence Investigation of NMC Cathode Manufacturing Parametersinterdependencies , 2019, ECS Meeting Abstracts.

[74]  G. Crabtree The coming electric vehicle transformation , 2019, Science.

[75]  Lysandre Debut,et al.  HuggingFace's Transformers: State-of-the-art Natural Language Processing , 2019, ArXiv.

[76]  Andrew Y. Ng,et al.  NGBoost: Natural Gradient Boosting for Probabilistic Prediction , 2019, ICML.

[77]  Jiming Hao,et al.  Air quality and health benefits from fleet electrification in China , 2019, Nature Sustainability.

[78]  Tinne Tuytelaars,et al.  A Continual Learning Survey: Defying Forgetting in Classification Tasks , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[79]  Fei Tao,et al.  Make more digital twins , 2019, Nature.

[80]  Zhe Li,et al.  A review on the key issues of the lithium ion battery degradation among the whole life cycle , 2019, eTransportation.

[81]  Xuning Feng,et al.  Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine , 2019, IEEE Transactions on Vehicular Technology.

[82]  Klemens Böhm,et al.  FOBSS: Monitoring Data from a Modular Battery System , 2019, e-Energy.

[83]  Jun Lu,et al.  Commercialization of Lithium Battery Technologies for Electric Vehicles , 2019, Advanced Energy Materials.

[84]  Kexiang Wei,et al.  Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries , 2019, Energy.

[85]  M. Berecibar Machine-learning techniques used to accurately predict battery life , 2019, Nature.

[86]  Kristen A. Severson,et al.  Data-driven prediction of battery cycle life before capacity degradation , 2019, Nature Energy.

[87]  B. Averbeck,et al.  Reinforcement learning in artificial and biological systems , 2019, Nature Machine Intelligence.

[88]  Lei Yang,et al.  A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction , 2019, Journal of Power Sources.

[89]  Joachim Denzler,et al.  Deep learning and process understanding for data-driven Earth system science , 2019, Nature.

[90]  F. Cadini,et al.  State-of-life prognosis and diagnosis of lithium-ion batteries by data-driven particle filters , 2019, Applied Energy.

[91]  Joeri Van Mierlo,et al.  Random forest regression for online capacity estimation of lithium-ion batteries , 2018, Applied Energy.

[92]  Gopala Krishna Anumanchipalli,et al.  Speech synthesis from neural decoding of spoken sentences , 2018, bioRxiv.

[93]  Cynthia Rudin,et al.  Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.

[94]  Michael A. Osborne,et al.  Battery health prediction under generalized conditions using a Gaussian process transition model , 2018, Journal of Energy Storage.

[95]  Lior Rokach,et al.  Ensemble learning: A survey , 2018, WIREs Data Mining Knowl. Discov..

[96]  Zonghai Chen,et al.  A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve , 2018 .

[97]  Jun Lu,et al.  Batteries and fuel cells for emerging electric vehicle markets , 2018 .

[98]  Stefan Wermter,et al.  Continual Lifelong Learning with Neural Networks: A Review , 2018, Neural Networks.

[99]  Abhinav Nellore,et al.  Cloud computing for genomic data analysis and collaboration , 2018, Nature Reviews Genetics.

[100]  Andreas Krause,et al.  A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions , 2016, bioRxiv.

[101]  Hans-Peter Kriegel,et al.  DBSCAN Revisited, Revisited , 2017, ACM Trans. Database Syst..

[102]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[103]  M. Pecht,et al.  Cycle life testing and modeling of graphite/LiCoO 2 cells under different state of charge ranges , 2016 .

[104]  M. Dubarry,et al.  Fast charging technique for high power LiFePO4 batteries: A mechanistic analysis of aging , 2016 .

[105]  Pan Chaofeng,et al.  On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis , 2016 .

[106]  Jean-Michel Vinassa,et al.  State of health assessment for lithium batteries based on voltage–time relaxation measure , 2016 .

[107]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[108]  Jonn Axsen,et al.  Moving beyond alternative fuel hype to decarbonize transportation , 2016, Nature Energy.

[109]  Joshua B. Tenenbaum,et al.  Human-level concept learning through probabilistic program induction , 2015, Science.

[110]  Taejung Yeo,et al.  A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation , 2015 .

[111]  Elizabeth Gibney,et al.  European labs set sights on continent-wide computing cloud , 2015, Nature.

[112]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[113]  Wei Xie,et al.  An Integrated Probabilistic Approach to Lithium-Ion Battery Remaining Useful Life Estimation , 2015, IEEE Transactions on Instrumentation and Measurement.

[114]  Nadia Drake,et al.  Cloud computing beckons scientists , 2014, Nature.

[115]  Zhe Li,et al.  A comparative study of commercial lithium ion battery cycle life in electrical vehicle: Aging mechanism identification , 2014 .

[116]  Alois Knoll,et al.  Gradient boosting machines, a tutorial , 2013, Front. Neurorobot..

[117]  Matthieu Dubarry,et al.  Synthesize battery degradation modes via a diagnostic and prognostic model , 2012 .

[118]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[119]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[120]  Peter Stone,et al.  Transfer Learning for Reinforcement Learning Domains: A Survey , 2009, J. Mach. Learn. Res..

[121]  I. Bloom,et al.  Differential voltage analyses of high-power, lithium-ion cells: 1. Technique and application , 2005 .

[122]  Bernard Zenko,et al.  Is Combining Classifiers with Stacking Better than Selecting the Best One? , 2004, Machine Learning.

[123]  L. Breiman Random Forests , 2001, Machine Learning.

[124]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.

[125]  Dazhong Wu,et al.  Battery health management using physics-informed machine learning: Online degradation modeling and remaining useful life prediction , 2022, Mechanical Systems and Signal Processing.

[126]  Cheng Cheng,et al.  Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning , 2022, Energy & Environmental Science.

[127]  Won Tae Joe,et al.  A Deep Reinforcement Learning Framework for Fast Charging of Li-ion Batteries , 2022, IEEE Transactions on Transportation Electrification.

[128]  Licheng Wang,et al.  State of health estimation of lithium-ion battery based on improved ant lion optimization and support vector regression , 2022, Journal of Energy Storage.

[129]  Xiuze Zhou,et al.  Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries , 2022, IEEE Access.

[130]  Philip V. Orlik,et al.  Remaining Useful Life Estimation for LFP Cells in Second-Life Applications , 2021, IEEE Transactions on Instrumentation and Measurement.

[131]  Zhenpo Wang,et al.  State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression , 2020 .

[132]  Krishnan S. Hariharan,et al.  Deep Gaussian process regression for lithium-ion battery health prognosis and degradation mode diagnosis , 2020 .

[133]  Joeri Van Mierlo,et al.  A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter , 2018 .

[134]  E. Zio,et al.  Prognostics and Health Management of Industrial Equipment , 2013 .

[135]  Seungjin Choi,et al.  Supervised Learning , 2015, Encyclopedia of Biometrics.

[136]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.