Early-stage lifetime prediction for lithium-ion batteries: A deep learning framework jointly considering machine-learned and handcrafted data features
出版年份 2022 全文链接
标题
Early-stage lifetime prediction for lithium-ion batteries: A deep learning framework jointly considering machine-learned and handcrafted data features
作者
关键词
-
出版物
Journal of Energy Storage
Volume 52, Issue -, Pages 104936
出版商
Elsevier BV
发表日期
2022-06-02
DOI
10.1016/j.est.2022.104936
参考文献
相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。- Multistage State of Health Estimation of Lithium-Ion Battery With High Tolerance to Heavily Partial Charging
- (2022) Zhongbao Wei et al. IEEE TRANSACTIONS ON POWER ELECTRONICS
- Robust State of Health estimation of lithium-ion batteries using convolutional neural network and random forest
- (2022) Niankai Yang et al. Journal of Energy Storage
- Prognostics of battery cycle life in the early-cycle stage based on hybrid model
- (2021) Yu Zhang et al. ENERGY
- Early prediction of battery lifetime via a machine learning based framework
- (2021) Zicheng Fei et al. ENERGY
- Small sample state of health estimation based on weighted Gaussian process regression
- (2021) Hanmin Sheng et al. Journal of Energy Storage
- Development of a battery real-time state of health diagnosis based on fast impedance measurements
- (2021) Edoardo Locorotondo et al. Journal of Energy Storage
- A novel method for state of energy estimation of lithium-ion batteries using particle filter and extended Kalman filter
- (2021) Xin Lai et al. Journal of Energy Storage
- A remaining capacity estimation approach of lithium-ion batteries based on partial charging curve and health feature fusion
- (2021) Lingfeng Fan et al. Journal of Energy Storage
- Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
- (2020) Yunwei Zhang et al. Nature Communications
- A novel deep learning framework for state of health estimation of lithium-ion battery
- (2020) Yaxiang Fan et al. Journal of Energy Storage
- On the feature selection for battery state of health estimation based on charging–discharging profiles
- (2020) Yuanyuan Li et al. Journal of Energy Storage
- A health indicator extraction based on surface temperature for lithium-ion batteries remaining useful life prediction
- (2020) Hailin Feng et al. Journal of Energy Storage
- A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life
- (2020) Lei Ren et al. IEEE Transactions on Industrial Informatics
- Life prediction of lithium-ion batteries based on stacked denoising autoencoders
- (2020) Fan Xu et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning
- (2020) Xiaosong Hu et al. IEEE Transactions on Transportation Electrification
- A Novel Model-Based Voltage Construction Method for Robust State-of-Health Estimation of Lithium-Ion Batteries
- (2020) Xiaolei Bian et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries
- (2019) Fangfang Yang et al. ENERGY
- Data-driven prediction of battery cycle life before capacity degradation
- (2019) Kristen A. Severson et al. Nature Energy
- Image-based defect detection in lithium-ion battery electrode using convolutional neural networks
- (2019) Olatomiwa Badmos et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network
- (2019) Guijun Ma et al. APPLIED ENERGY
- Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks
- (2019) Shuzhi Zhang et al. Journal of Energy Storage
- Remaining Useful Life Prediction of Lithium-Ion Battery Based on Gauss-Hermite Particle Filter
- (2018) Yan Ma et al. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
- A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter
- (2018) Yi Li et al. JOURNAL OF POWER SOURCES
- A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve
- (2018) Duo Yang et al. JOURNAL OF POWER SOURCES
- Remaining useful life estimation in prognostics using deep convolution neural networks
- (2018) Xiang Li et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Remaining Useful Life Prediction for Lithium-ion Battery: A Deep Learning Approach
- (2018) Lei Ren et al. IEEE Access
- Random forest regression for online capacity estimation of lithium-ion batteries
- (2018) Yi Li et al. APPLIED ENERGY
- State-of-health estimation for the lithium-ion battery based on support vector regression
- (2017) Duo Yang et al. APPLIED ENERGY
- An ensemble model for predicting the remaining useful performance of lithium-ion batteries
- (2013) Yinjiao Xing et al. MICROELECTRONICS RELIABILITY
- Online State-of-Health Assessment for Battery Management Systems
- (2011) M V Micea et al. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
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