Online diagnosis of state of health for lithium-ion batteries based on short-term charging profiles
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Guang Li | Yonggang Liu | Zheng Chen | Jiangwei Shen | Xing Shu | Yuanjian Zhang | Zheng Chen | Xing Shu | Jiangwei Shen | Yuanjian Zhang | Yonggang Liu | Guang Li
[1] 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.
[2] 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.
[3] 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.
[4] Bangzhu Zhu,et al. Achieving the carbon intensity target of China: A least squares support vector machine with mixture kernel function approach , 2019, Applied Energy.
[5] Lin Chen,et al. Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine , 2018, Energy.
[6] 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 .
[7] Changfu Zou,et al. Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method , 2019, Applied Energy.
[8] 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.
[9] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[10] Chen Lu,et al. Li-ion battery capacity estimation: A geometrical approach , 2014 .
[11] Pengjian Zuo,et al. State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method , 2018 .
[12] Jiuchun Jiang,et al. State of health estimation of second-life LiFePO4 batteries for energy storage applications , 2018, Journal of Cleaner Production.
[13] Zonghai Chen,et al. State-of-health estimation for the lithium-ion battery based on support vector regression , 2017, Applied Energy.
[14] Cheng Chen,et al. A Lithium-Ion Battery-in-the-Loop Approach to Test and Validate Multiscale Dual H Infinity Filters for State-of-Charge and Capacity Estimation , 2018, IEEE Transactions on Power Electronics.
[15] Michael A. Osborne,et al. Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries , 2017, IEEE Transactions on Industrial Informatics.
[16] Amrane Oukaour,et al. State-of-Charge and State-of-Health Lithium-Ion Batteries’ Diagnosis According to Surface Temperature Variation , 2016, IEEE Transactions on Industrial Electronics.
[17] Glenn Fung,et al. Multicategory Proximal Support Vector Machine Classifiers , 2005, Machine Learning.
[18] Johan A. K. Suykens,et al. Least Squares Support Vector Machines , 2002 .
[19] Yu Peng,et al. An On-Line State of Health Estimation of Lithium-Ion Battery Using Unscented Particle Filter , 2018, IEEE Access.
[20] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[21] Shengbo Eben Li,et al. Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles , 2015 .
[22] Joeri Van Mierlo,et al. Online state of health estimation on NMC cells based on predictive analytics , 2016 .
[23] Datong Liu,et al. A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries , 2020 .
[24] 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.
[25] Xiaosong Hu,et al. Battery Lifetime Prognostics , 2020 .
[26] Yi-Ming Wei,et al. An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting , 2016 .
[27] Chao Hu,et al. Online estimation of lithium-ion battery capacity using sparse Bayesian learning , 2015 .
[28] Guangzhao Luo,et al. Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles , 2019, Energy.
[29] Datong Qin,et al. Research on a multi-objective hierarchical prediction energy management strategy for range extended fuel cell vehicles , 2019, Journal of Power Sources.
[30] Akhil Garg,et al. An integrated framework for minimization of inter lithium‐ion cell temperature differences and the total volume of the cell of battery pack for electric vehicles , 2019, Energy Storage.
[31] Ping Shen,et al. The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles , 2018, IEEE Transactions on Vehicular Technology.
[32] 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.
[33] Zhenpo Wang,et al. State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression , 2020 .
[34] Rui Xiong,et al. Fractional-Order Model-Based Incremental Capacity Analysis for Degradation State Recognition of Lithium-Ion Batteries , 2019, IEEE Transactions on Industrial Electronics.
[35] Fu-Kwun Wang,et al. A hybrid model based on support vector regression and differential evolution for remaining useful lifetime prediction of lithium-ion batteries , 2018, Journal of Power Sources.
[36] Zheng Chen,et al. Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine , 2018, Applied Sciences.
[37] Johan A. K. Suykens,et al. Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.