Data-driven prediction of battery cycle life before capacity degradation
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Summary
A machine-learning method to predict battery life before the onset of capacity degradation with high accuracy is reported, highlighting the promise of combining deliberate data generation with data-driven modelling to predict the behaviour of complex dynamical systems.
- Type
- article
- Published
- 2019-03-25
- Cited by
- 2,722
- References
- 75
- Access
- Open access
- OpenAlex
- https://openalex.org/W2924382816
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:135445087
Keywords
Battery (electricity), Degradation (telecommunications), Computer science, Reliability engineering, Lithium iron phosphate
References
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- A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning
- Battery Durability and Reliability under Electric Utility Grid Operations: Path Dependence of Battery Degradation
- High-performance ternary nickel-cobalt-manganese oxide nanoparticles-anchored reduced graphene oxide composite as Li-ion battery cathode: Simple preparation and comparative study
- Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
- Perspective—From Calorimetry Measurements to Furthering Mechanistic Understanding and Control of Thermal Abuse in Lithium-Ion Cells
- State of Health Estimation for Lithium-ion Batteries Based on Fusion of Autoregressive Moving Average Model and Elman Neural Network
- Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network
- Modeling long-term capacity degradation of lithium-ion batteries
- A deep learning method for online capacity estimation of lithium-ion batteries
- Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method
- State estimation for advanced battery management: Key challenges and future trends
- Data-Driven Safety Envelope of Lithium-Ion Batteries for Electric Vehicles
- Amorphous Sb 2 S 3 Anodes by Reactive Radio Frequency Magnetron Sputtering for High‐Performance Lithium‐Ion Half/Full Cells
- A review on prognostics and health management (PHM) methods of lithium-ion batteries
- Beyond Expert‐Level Performance Prediction for Rechargeable Batteries by Unsupervised Machine Learning
- Insights from machine learning of carbon electrodes for electric double layer capacitors
- Integrated design and control optimization of hybrid electric marine propulsion systems based on battery performance degradation model
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