State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

[1]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[2]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevan e Ve tor Ma hine , 2001 .

[3]  Valerie H. Johnson,et al.  Battery performance models in ADVISOR , 2002 .

[4]  B. Liaw,et al.  Modeling of lithium ion cells: A simple equivalent-circuit model approach , 2004 .

[5]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[6]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[7]  Shen Furao,et al.  A fast nearest neighbor classifier based on self-organizing incremental neural network , 2008, Neural Networks.

[8]  中村 泰,et al.  ハッシュ関数を用いたGaussian Process Regressionの高速化 , 2012 .

[9]  So Young Sohn,et al.  Stock fraud detection using peer group analysis , 2012, Expert Syst. Appl..

[10]  Wang Yun. Toh Battery lifetime prediction , 2012 .

[11]  Jean-Michel Vinassa,et al.  Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks , 2012 .

[12]  Taskin Kavzoglu,et al.  An assessment of the effectiveness of a rotation forest ensemble for land-use and land-cover mapping , 2013 .

[13]  Alberto Del Bimbo,et al.  Object Tracking by Oversampling Local Features , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Min Han,et al.  Ensemble of extreme learning machine for remote sensing image classification , 2015, Neurocomputing.

[15]  Ryosuke Yoshihashi,et al.  Deep Convolutional Neural Networks: A survey of the foundations, selected improvements, and some current applications , 2020, ArXiv.

[16]  Andrea Marongiu,et al.  Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles , 2015 .

[17]  Chao Hu,et al.  Online Estimation of Lithium-Ion Battery Capacity Using Sparse Bayesian Learning , 2015, DAC 2015.

[18]  Nigel P. Brandon,et al.  Novel application of differential thermal voltammetry as an in-depth state-of-health diagnosis method for lithium-ion batteries , 2016 .

[19]  Mihai V. Micea,et al.  Battery state of health estimation: a structured review of models, methods and commercial devices , 2017 .

[20]  Huajing Fang,et al.  A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery , 2017 .

[21]  Francesco Cadini,et al.  Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks , 2017 .

[22]  Michael A. Osborne,et al.  Gaussian process regression for forecasting battery state of health , 2017, 1703.05687.

[23]  Hicham Chaoui,et al.  State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks , 2017, IEEE Transactions on Vehicular Technology.

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

[25]  Jianbo Yu,et al.  State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble , 2018, Reliab. Eng. Syst. Saf..

[26]  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 .

[27]  Hao Yuan,et al.  Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus , 2018, IEEE Transactions on Vehicular Technology.

[28]  Heiko Wersing,et al.  Incremental on-line learning: A review and comparison of state of the art algorithms , 2018, Neurocomputing.

[29]  Zonghai Chen,et al.  State-of-health estimation for the lithium-ion battery based on support vector regression , 2017, Applied Energy.

[30]  Hongye Su,et al.  Neural Network-Based State of Charge Observer Design for Lithium-Ion Batteries , 2018, IEEE Transactions on Control Systems Technology.

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

[32]  Hongwen He,et al.  Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries , 2018, IEEE Transactions on Vehicular Technology.

[33]  M. A. Hannan,et al.  A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations , 2018, Journal of Cleaner Production.

[34]  Furong Gao,et al.  A fast estimation algorithm for lithium-ion battery state of health , 2018, Journal of Power Sources.

[35]  Gregory J. Offer,et al.  Tracking degradation in lithium iron phosphate batteries using differential thermal voltammetry , 2018 .

[36]  Jiuchun Jiang,et al.  State of health estimation of second-life LiFePO4 batteries for energy storage applications , 2018, Journal of Cleaner Production.

[37]  Lin Chen,et al.  Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine , 2018, Energy.

[38]  Jinpeng Tian,et al.  Towards a smarter battery management system: A critical review on battery state of health monitoring methods , 2018, Journal of Power Sources.

[39]  Zheng Chen,et al.  Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine , 2018, Applied Sciences.

[40]  Jinpeng Tian,et al.  A Novel Fractional Order Model for State of Charge Estimation in Lithium Ion Batteries , 2019, IEEE Transactions on Vehicular Technology.

[41]  Yonggang Liu,et al.  Optimal charging strategy design for lithium‐ion batteries considering minimization of temperature rise and energy loss , 2019, International Journal of Energy Research.

[42]  Xiao-Sheng Si,et al.  State-of-Health Estimation for Lithium-Ion Batteries Based on Wiener Process With Modeling the Relaxation Effect , 2019, IEEE Access.

[43]  Hongwen He,et al.  Aging characteristics-based health diagnosis and remaining useful life prognostics for lithium-ion batteries , 2019, eTransportation.

[44]  Xiaofeng Wang,et al.  A Battery Management System With a Lebesgue-Sampling-Based Extended Kalman Filter , 2019, IEEE Transactions on Industrial Electronics.

[45]  Jing Sun,et al.  Current Profile Optimization for Combined State of Charge and State of Health Estimation of Lithium Ion Battery Based on Cramer–Rao Bound Analysis , 2019, IEEE Transactions on Power Electronics.

[46]  Mattia Ricco,et al.  A Simplified Model-Based State-of-Charge Estimation Approach for Lithium-Ion Battery With Dynamic Linear Model , 2019, IEEE Transactions on Industrial Electronics.

[47]  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.

[48]  Lei Zhang,et al.  Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles , 2019, Energy.

[49]  Hongwen He,et al.  Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles , 2019, IEEE Transactions on Vehicular Technology.

[50]  Hongwen He,et al.  An improved state of charge estimation method based on cubature Kalman filter for lithium-ion batteries , 2019, Applied Energy.

[51]  Du Le,et al.  An indirect RUL prognosis for lithium-ion battery under vibration stress using Elman neural network , 2019, International Journal of Hydrogen Energy.

[52]  Ji Wu,et al.  A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain , 2019, IEEE Transactions on Industrial Electronics.

[53]  Michael Pecht,et al.  Online capacity estimation for lithium-ion batteries through joint estimation method , 2019, Applied Energy.

[54]  Thomas M. Jahns,et al.  A Compact Methodology Via a Recurrent Neural Network for Accurate Equivalent Circuit Type Modeling of Lithium-Ion Batteries , 2019, IEEE Transactions on Industry Applications.

[55]  Zhao Yao,et al.  Cycle life prediction of lithium ion battery based on DE-BP neural network , 2019, 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC).

[56]  Michael A. Osborne,et al.  Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries , 2017, IEEE Transactions on Industrial Informatics.

[57]  Guangzhao Luo,et al.  Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles , 2019, Energy.

[58]  Qian Liu,et al.  Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process , 2019, Reliab. Eng. Syst. Saf..

[59]  Qiang Miao,et al.  State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network , 2019, Energy.

[60]  Joeri Van Mierlo,et al.  Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review , 2019, Renewable and Sustainable Energy Reviews.

[61]  Aini Hussain,et al.  Extreme Learning Machine Model for State-of-Charge Estimation of Lithium-Ion Battery Using Gravitational Search Algorithm , 2019, IEEE Transactions on Industry Applications.

[62]  Yonggang Liu,et al.  State of Health Estimation for Lithium-ion Batteries Based on Fusion of Autoregressive Moving Average Model and Elman Neural Network , 2019, IEEE Access.

[63]  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.

[64]  Shouming Zhong,et al.  Lithium-Ion Battery State of Health Monitoring Based on Ensemble Learning , 2019, IEEE Access.

[65]  Yan-Fu Li,et al.  A review on prognostics and health management (PHM) methods of lithium-ion batteries , 2019 .

[66]  Kaike Wang,et al.  Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks , 2019 .

[67]  Rui Xiong,et al.  A review on state of health estimation for lithium ion batteries in photovoltaic systems , 2019, eTransportation.

[68]  Lei Zhang,et al.  Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks , 2019, Journal of Energy Storage.

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

[70]  Zhenpo Wang,et al.  Battery Aging Assessment for Real-World Electric Buses Based on Incremental Capacity Analysis and Radial Basis Function Neural Network , 2020, IEEE Transactions on Industrial Informatics.

[71]  Yi Li,et al.  Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries , 2020, IEEE Transactions on Industrial Informatics.

[72]  Jian Chen,et al.  State-of-Charge Observer Design for Batteries With Online Model Parameter Identification: A Robust Approach , 2020, IEEE Transactions on Power Electronics.

[73]  Sajeeb Saha,et al.  A Parameter Extraction Method for the Li-Ion Batteries With Wide-Range Temperature Compensation , 2020, IEEE Transactions on Industry Applications.

[74]  Sheng Liu,et al.  Reduced-Coupling Coestimation of SOC and SOH for Lithium-Ion Batteries Based on Convex Optimization , 2020, IEEE Transactions on Power Electronics.

[75]  Kun Li,et al.  A review of the state of health for lithium-ion batteries: Research status and suggestions , 2020 .

[76]  Jun Rao,et al.  Online state-of-health estimation of lithium-ion battery based on dynamic parameter identification at multi timescale and support vector regression , 2020 .

[77]  Penghua Li,et al.  State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network , 2020, Journal of Power Sources.

[78]  Qing-Long Han,et al.  Robust Estimation for State-of-Charge and State-of-Health of Lithium-Ion Batteries Using Integral-Type Terminal Sliding-Mode Observers , 2020, IEEE Transactions on Industrial Electronics.

[79]  Guang Li,et al.  An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation , 2020 .

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

[81]  Ala A. Hussein,et al.  A Collaborative Gaussian Process Regression Model for Transfer Learning of Capacity Trends Between Li-Ion Battery Cells , 2020, IEEE Transactions on Vehicular Technology.

[82]  Fei Feng,et al.  Co-estimation of lithium-ion battery state of charge and state of temperature based on a hybrid electrochemical-thermal-neural-network model , 2020 .

[83]  Meng Li,et al.  Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries , 2020 .

[84]  Wu Wang,et al.  Reliable solar irradiance prediction using ensemble learning-based models: A comparative study , 2020 .

[85]  Furong Gao,et al.  Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter , 2020 .

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

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

[88]  Guang Li,et al.  An adaptive multi-state estimation algorithm for lithium-ion batteries incorporating temperature compensation , 2020 .

[89]  Guang Li,et al.  Online diagnosis of state of health for lithium-ion batteries based on short-term charging profiles , 2020 .

[90]  W. Shen,et al.  Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit , 2020 .

[91]  Yan Xu,et al.  State-of-Health Estimation and Remaining-Useful-Life Prediction for Lithium-Ion Battery Using a Hybrid Data-Driven Method , 2020, IEEE Transactions on Vehicular Technology.

[92]  Yan Xu,et al.  A Hierarchical and Flexible Data-Driven Method for Online State-of-Health Estimation of Li-Ion Battery , 2020, IEEE Transactions on Vehicular Technology.

[93]  Xiaobo Zhao,et al.  Elman neural network using ant colony optimization algorithm for estimating of state of charge of lithium-ion battery , 2020 .

[94]  S M Rakiul Islam,et al.  Precise Online Electrochemical Impedance Spectroscopy Strategies for Li-Ion Batteries , 2020, IEEE Transactions on Industry Applications.

[95]  Dong Zhang,et al.  Battery Adaptive Observer for a Single-Particle Model With Intercalation-Induced Stress , 2020, IEEE Transactions on Control Systems Technology.

[96]  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.

[97]  Guangcai Zhao,et al.  Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries , 2020, IEEE Transactions on Industrial Electronics.

[98]  Xiaosong Hu,et al.  Health Prognosis for Electric Vehicle Battery Packs: A Data-Driven Approach , 2020, IEEE/ASME Transactions on Mechatronics.

[99]  Minggao Ouyang,et al.  Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter , 2020 .

[100]  Noureddine Zerhouni,et al.  An ensemble prognostic method for lithium-ion battery capacity estimation based on time-varying weight allocation , 2020 .

[101]  Fengjun Yan,et al.  State-of-Health Estimation of Lithium-Ion Batteries Using Incremental Capacity Analysis Based on Voltage–Capacity Model , 2020, IEEE Transactions on Transportation Electrification.

[102]  Guangzhao Luo,et al.  Multiobjective Optimization of Data-Driven Model for Lithium-Ion Battery SOH Estimation With Short-Term Feature , 2020, IEEE Transactions on Power Electronics.

[103]  Yuanjian Zhang,et al.  Capacity Prediction and Validation of Lithium-Ion Batteries Based on Long Short-Term Memory Recurrent Neural Network , 2020, IEEE Access.

[104]  Yao Zhang,et al.  Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning , 2020, Nature Communications.

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

[106]  Mihai V. Micea,et al.  Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks , 2020, International Journal of Energy Research.

[107]  Ebrahim Farjah,et al.  Online Parameter Estimation for Supercapacitor State-of-Energy and State-of-Health Determination in Vehicular Applications , 2020, IEEE Transactions on Industrial Electronics.

[108]  Yan Xu,et al.  Data-Driven Online Health Estimation of Li-Ion Batteries Using A Novel Energy-Based Health Indicator , 2020, IEEE Transactions on Energy Conversion.

[109]  Pengliang Qin,et al.  State of health prediction for lithium‐ion batteries with a novel online sequential extreme learning machine method , 2020, International Journal of Energy Research.

[110]  Guang Li,et al.  A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization , 2020 .

[111]  Ruikai Zhao,et al.  An open circuit voltage-based model for state-of-health estimation of lithium-ion batteries: Model development and validation , 2020 .

[112]  Boyang Liu,et al.  Joint estimation of battery state-of-charge and state-of-health based on a simplified pseudo-two-dimensional model , 2020 .

[113]  Kashem M. Muttaqi,et al.  Real-Time Estimation of Model Parameters and State-of-Charge of Li-Ion Batteries in Electric Vehicles Using a New Mixed Estimation Model , 2020, IEEE Transactions on Industry Applications.

[114]  E. Li,et al.  A Method of State-of-Charge Estimation for EV Power Lithium-Ion Battery Using a Novel Adaptive Extended Kalman Filter , 2020, IEEE Transactions on Vehicular Technology.

[115]  Jie Tang,et al.  A data-driven fuzzy information granulation approach for battery state of health forecasting , 2020 .

[116]  Shun Jia,et al.  A sample entropy based prognostics method for lithium-ion batteries using relevance vector machine , 2021 .

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

[118]  Henk Jan Bergveld,et al.  Joint Estimation of Battery Parameters and State of Charge Using an Extended Kalman Filter: A Single-Parameter Tuning Approach , 2021, IEEE Transactions on Control Systems Technology.

[119]  Meng Wei,et al.  Remaining useful life prediction of lithium-ion batteries based on Monte Carlo Dropout and gated recurrent unit , 2021 .

[120]  Joeri Van Mierlo,et al.  Battery lifetime prediction and performance assessment of different modeling approaches , 2021, iScience.

[121]  Lin Chen,et al.  Li-Ion Battery State of Health Estimation and Remaining Useful Life Prediction Through a Model-Data-Fusion Method , 2021, IEEE Transactions on Power Electronics.

[122]  Li Zhao,et al.  A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life , 2021, IEEE Transactions on Industrial Informatics.

[123]  Hanzhengnan Yu,et al.  State of charge prediction framework for lithium-ion batteries incorporating long short-term memory network and transfer learning , 2021 .

[124]  Bhim Singh,et al.  An Online Method of Estimating State of Health of a Li-Ion Battery , 2021, IEEE Transactions on Energy Conversion.

[125]  Yu Peng,et al.  Model-Based Health Diagnosis for Lithium-Ion Battery Pack in Space Applications , 2020, IEEE Transactions on Industrial Electronics.

[126]  Anuradha M. Annaswamy,et al.  Online capacity estimation of lithium-ion batteries with deep long short-term memory networks , 2021, Journal of Power Sources.

[127]  Yonggang Liu,et al.  Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter , 2021 .

[128]  Jun Wang,et al.  Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data , 2021, Energy.

[129]  Yunhong Che,et al.  Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning , 2020, IEEE Transactions on Transportation Electrification.

[130]  Xiaosong Hu,et al.  Feature Analyses and Modeling of Lithium-Ion Battery Manufacturing Based on Random Forest Classification , 2021, IEEE/ASME Transactions on Mechatronics.

[131]  Michael Pecht,et al.  Capacity-Fading Behavior Analysis for Early Detection of Unhealthy Li-Ion Batteries , 2021, IEEE Transactions on Industrial Electronics.

[132]  Saad Mekhilef,et al.  Combined State of Charge and State of Energy Estimation of Lithium-Ion Battery Using Dual Forgetting Factor-Based Adaptive Extended Kalman Filter for Electric Vehicle Applications , 2021, IEEE Transactions on Vehicular Technology.

[133]  Zhenpo Wang,et al.  Online accurate state of health estimation for battery systems on real-world electric vehicles with variable driving conditions considered , 2021 .

[134]  YongFang Guo,et al.  A state-of-health estimation method of lithium-ion batteries based on multi-feature extracted from constant current charging curve , 2021 .

[135]  Xudong Zhang,et al.  Real-time predictive energy management of plug-in hybrid electric vehicles for coordination of fuel economy and battery degradation , 2021 .

[136]  Fengjun Yan,et al.  A Novel Model-Based Voltage Construction Method for Robust State-of-Health Estimation of Lithium-Ion Batteries , 2021, IEEE Transactions on Industrial Electronics.

[137]  Hari Om Bansal,et al.  Fuzzy logic and Elman neural network tuned energy management strategies for a power-split HEVs , 2021 .

[138]  Matteo Corno,et al.  Model-Based Estimation of Lithium Concentrations and Temperature in Batteries Using Soft-Constrained Dual Unscented Kalman Filtering , 2021, IEEE Transactions on Control Systems Technology.

[139]  Mingqiang Lin,et al.  State of health estimation of lithium-ion battery based on an adaptive tunable hybrid radial basis function network , 2021, Journal of Power Sources.

[140]  Patrick K. Herring,et al.  Perspective—Combining Physics and Machine Learning to Predict Battery Lifetime , 2021, Journal of The Electrochemical Society.

[141]  Fengchun Sun,et al.  Electrode ageing estimation and open circuit voltage reconstruction for lithium ion batteries , 2021 .

[142]  Yonggang Liu,et al.  A Flexible State-of-Health Prediction Scheme for Lithium-Ion Battery Packs With Long Short-Term Memory Network and Transfer Learning , 2021, IEEE Transactions on Transportation Electrification.

[143]  Sheng Hong,et al.  A health assessment framework of lithium-ion batteries for cyber defense , 2021, Appl. Soft Comput..

[144]  Zhile Yang,et al.  A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system , 2021, Energy.

[145]  C. Mademlis,et al.  A Novel On-Board Electrochemical Impedance Spectroscopy System for Real-Time Battery Impedance Estimation , 2021, IEEE Transactions on Power Electronics.

[146]  Zhenpo Wang,et al.  Battery Fault Diagnosis for Electric Vehicles Based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model , 2021, IEEE Transactions on Power Electronics.

[147]  Lin Chen,et al.  Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation , 2021 .

[148]  Xiaoyu Li,et al.  Lithium Battery State-of-Health Estimation via Differential Thermal Voltammetry With Gaussian Process Regression , 2021, IEEE Transactions on Transportation Electrification.

[149]  Mingyu Gao,et al.  Improved parameter identification and state-of-charge estimation for lithium-ion battery with fixed memory recursive least squares and sigma-point Kalman filter , 2021 .

[150]  Yan Xu,et al.  An Ensemble Learning-Based Data-Driven Method for Online State-of-Health Estimation of Lithium-Ion Batteries , 2021, IEEE Transactions on Transportation Electrification.

[151]  Yijia Cao,et al.  Optimization of Variable-Current Charging Strategy Based on SOC Segmentation for Li-ion Battery , 2021, IEEE Transactions on Intelligent Transportation Systems.

[152]  Fengjun Yan,et al.  A Two-Step Parameter Optimization Method for Low-Order Model-Based State-of-Charge Estimation , 2021, IEEE Transactions on Transportation Electrification.

[153]  Xudong Zhang,et al.  Bi-level Energy Management of Plug-in Hybrid Electric Vehicles for Fuel Economy and Battery Lifetime with Intelligent State-of-charge Reference , 2021 .

[154]  Yong Tian,et al.  State-of-charge estimation tolerant of battery aging based on a physics-based model and an adaptive cubature Kalman filter , 2021 .

[155]  Kashem M. Muttaqi,et al.  State-of-Charge Estimation of Li-ion Battery Using Gated Recurrent Unit With One-Cycle Learning Rate Policy , 2021, IEEE Transactions on Industry Applications.

[156]  Yunlong Shang,et al.  A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery , 2021, IEEE Transactions on Industrial Electronics.

[157]  G. Rizzoni,et al.  Prediction of remaining useful life for a composite electrode lithium ion battery cell using an electrochemical model to estimate the state of health , 2021 .

[158]  Kwok-Leung Tsui,et al.  Early prediction of battery lifetime via a machine learning based framework , 2021, Energy.

[159]  Oh Ki Yong,et al.  Physics-informed Neural Network for Estimation of Lithium-Ion Battery State-of-health , 2021 .

[160]  Kai Liu,et al.  Evaluation and observability analysis of an improved reduced-order electrochemical model for lithium-ion battery , 2021 .

[161]  Xiaosong Hu,et al.  General Discharge Voltage Information Enabled Health Evaluation for Lithium-Ion Batteries , 2021, IEEE/ASME Transactions on Mechatronics.

[162]  Dongdong Zhao,et al.  A Nonlinear Observer SOC Estimation Method Based on Electrochemical Model for Lithium-Ion Battery , 2021, IEEE Transactions on Industry Applications.

[163]  Daniel-Ioan Stroe,et al.  Incremental Capacity Analysis Applied on Electric Vehicles for Battery State-of-Health Estimation , 2021, IEEE Transactions on Industry Applications.

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

[165]  Guang Li,et al.  Stage of Charge Estimation of Lithium-Ion Battery Packs Based on Improved Cubature Kalman Filter With Long Short-Term Memory Model , 2021, IEEE Transactions on Transportation Electrification.

[166]  Lifeng Wu,et al.  Prognostics of battery cycle life in the early-cycle stage based on hybrid model , 2021 .

[167]  Xiaosong Hu,et al.  Predictive Battery Health Management With Transfer Learning and Online Model Correction , 2021, IEEE Transactions on Vehicular Technology.

[168]  Ping Fu,et al.  Research on state of health prediction model for lithium batteries based on actual diverse data , 2021 .

[169]  Nan Chen,et al.  Coestimation of State-of-Charge and State-of-Health for Power Batteries Based on Multithread Dynamic Optimization Method , 2021, IEEE Transactions on Industrial Electronics.

[170]  Remus Teodorescu,et al.  An Automatic Weak Learner Formulation for Lithium-Ion Battery State of Health Estimation , 2021, IEEE Transactions on Industrial Electronics.