articleTop 1% cited
A novel deep learning framework for state of health estimation of lithium-ion battery
Journal of Energy Storage · 2020 · Vol. 32 · pp. 101741–101741
Yaxiang Fan(Naval University of Engineering)Fei Xiao✉(Naval University of Engineering)Chaoran Li(Naval University of Engineering)Guorun Yang(Naval University of Engineering)Xin Tang(Naval University of Engineering)
Advanced Battery Technologies ResearchAdvancements in Battery MaterialsElectric Vehicles and InfrastructureBattery (electricity)State of healthArtificial neural networkComputer scienceLithium-ion batteryConvolutional neural networkArtificial intelligenceDeep learningVoltageEngineering
Funding
- National Natural Science Foundation of China
Citations
375
FWCI
19.11
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References
44
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References
Learning long-term dependencies with gradient descent is difficult
IEEE Transactions on Neural Networks · 1994 · 8,303 citations
Degradation diagnostics for lithium ion cells
Journal of Power Sources · 2016 · 1,672 citations
LSTM Fully Convolutional Networks for Time Series Classification
IEEE Access · 2017 · 1,413 citations
Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries
IEEE Transactions on Industrial Electronics · 2017 · 806 citations
Multivariate LSTM-FCNs for time series classification
Neural Networks · 2019 · 1,052 citations
Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
IEEE Transactions on Vehicular Technology · 2018 · 1,213 citations
Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks
Journal of Energy Storage · 2018 · 430 citations
Deep learning
Nature · 2015 · 79,164 citations
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