articleTop 1% cited
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 · Vol. 21 · pp. 510–518
Xiaoyu Li(Beijing Institute of Technology)Lei Zhang✉(Beijing Institute of Technology)Zhenpo Wang✉(Beijing Institute of Technology)Peng Dong(Beijing Institute of Technology)
Advanced Battery Technologies ResearchReliability and Maintenance OptimizationAdvancements in Battery MaterialsBattery (electricity)Artificial neural networkLong short term memoryComputer scienceTerm (time)DecompositionLithium-ion batteryArtificial intelligenceMachine learningRecurrent neural network
Funding
- National Natural Science Foundation of China
Citations
430
FWCI
20.20
field-weighted impact
References
39
Percentile
100%
vs. same field & year
Citations per year
Cited by
A novel deep learning framework for state of health estimation of lithium-ion battery
Journal of Energy Storage · 2020 · 375 citations
Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks
Journal of Energy Storage · 2019 · 320 citations
References
The Development and Future of Lithium Ion Batteries
Journal of The Electrochemical Society · 2016 · 1,862 citations
Gaussian Process Regression for <italic>In Situ</italic> Capacity Estimation of Lithium-Ion Batteries
IEEE Transactions on Industrial Informatics · 2018 · 377 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
Citation Network
How this paper connects to the literature. Drag to explore, click any node to open that paper.