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The past two decades have seen an increasing usage of lithium-ion Li-ion rechargeable batteries in diverse applications including consumer electronics, power backup, and grid-scale energy storage. This paper presents a deep learning method which utilizes deep convolutional neural network DCNN for cell-level capacity estimation based on the voltage, current, and charge capacity measurements during a partial charge cycle. To the best of our knowledge, this is one of the first attempts to apply deep learning to the online capacity estimation of Li-ion batteries. Compared with traditional machine learning methods such as shallow neural networks and relevance vector machine RVM, the proposed deep learning method is demonstrated to produce higher accuracy and robustness in the online estimation of Li-ion battery capacity. A deep learning method for online capacity estimation of lithium-ion batteries.
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This paper proposes a novel parameter identification method and its implementation on SOP prediction for lithium-ion batteries. A rigorous convergence proof of the estimation algorithm is provided. In addition, based on the electrical circuit model with the identified parameters, a battery SOP prediction algorithm is derived, which considers both the voltage and current limitations of the battery. Simulation results for lithium-ion batteries based on real test data from urban dynamometer driving schedule UDDS are provided to validate the proposed parameter identification and SOP prediction methods. The proposed method is suitable for real operation of embedded battery management system due to its low complexity and numerical stability. Minh and J. Han, H. Aki, S. Han, B.
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Here, we visualize lithium growth in a glass capillary cell and demonstrate a change of mechanism from root-growing mossy lithium to tip-growing dendritic lithium at the onset of electrolyte diffusion limitation. In sandwich cells, we further demonstrate that mossy lithium can be blocked by nanoporous ceramic separators, while dendritic lithium can easily penetrate nanopores and short the cell. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material and it is not used for commercial purposes. Fetching data https://www.faastpharmacy.com from CrossRef. This may take some time to load. Journals Books Databases.
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