Capacity estimation of lithium-ion batteries with uncertainty quantification based on temporal convolutional network and Gaussian process regression

Dropout: a simple way to prevent neural networks from overfitting

Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky et al. · 2014 · 34.3K citations

Computing, Computer Science, Artificial Intelligence +12

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Shaojie Bai, J. Zico Kolter, Vladlen Koltun · arXiv (Cornell University) · 2018 · 4.3K citations · Full text

Computing, Computer Science, Computer Vision +13

A retrospective on lithium-ion batteries

Jing Xie, Yi‐Chun Lu · Nature Communications · 2020 · 1.1K citations · Full text

Natural Sciences, Chemistry, Materials Science +14

A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery

Kailong Liu, Yunlong Shang, Quan Ouyang et al. · IEEE Transactions on Industrial Electronics · 2020 · 785 citations · Full text

Computing, Computational Engineering, Data Science +17

A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve

Duo Yang, Xu Zhang, Rui Pan et al. · Journal of Power Sources · 2018 · 746 citations

Engineering, Chemical Engineering, Electrochemical Engineering +15