Latent function decomposition for forecasting li-ion battery cells capacity: A multi-output convolved gaussian process approach

Latent function decomposition for forecasting li-ion battery cells capacity: A multi-output convolved gaussian process approach. Chehade, A. & Hussein, A. 2019.
abstract bibtex

Copyright © 2019, arXiv, All rights reserved. —A latent function decomposition method is proposed for forecasting the capacity of lithium-ion battery cells. The method uses the Multi-Output Gaussian Process, a generative machine learning framework for multi-task and transfer learning. The MCGP decomposes the available capacity trends from multiple battery cells into latent functions. The latent functions are then convolved over kernel smoothers to reconstruct and/or forecast capacity trends of the battery cells. Besides the high prediction accuracy the proposed method possesses, it provides uncertainty information for the predictions and captures nontrivial cross-correlations between capacity trends of different battery cells. These two merits make the proposed MCGP a very reliable and practical solution for applications that use battery cell packs. The MCGP is derived and compared to benchmark methods on an experimental lithium-ion battery cells data. The results show the effectiveness of the proposed method.

@misc{
 title = {Latent function decomposition for forecasting li-ion battery cells capacity: A multi-output convolved gaussian process approach},
 type = {misc},
 year = {2019},
 source = {arXiv},
 keywords = {Convolution process,Lithium-ion battery cell,Multi-output Gaussian process,Multi-task learning,Remaining useful life,State-of-charge,Transfer learning,—Capacity},
 id = {44c50cdc-43e4-3a86-9c9d-13dc5535cf65},
 created = {2020-10-27T23:59:00.000Z},
 file_attached = {false},
 profile_id = {ceb22f87-8e17-3a73-8ac5-df584316b424},
 last_modified = {2020-10-28T21:05:06.368Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Copyright © 2019, arXiv, All rights reserved. —A latent function decomposition method is proposed for forecasting the capacity of lithium-ion battery cells. The method uses the Multi-Output Gaussian Process, a generative machine learning framework for multi-task and transfer learning. The MCGP decomposes the available capacity trends from multiple battery cells into latent functions. The latent functions are then convolved over kernel smoothers to reconstruct and/or forecast capacity trends of the battery cells. Besides the high prediction accuracy the proposed method possesses, it provides uncertainty information for the predictions and captures nontrivial cross-correlations between capacity trends of different battery cells. These two merits make the proposed MCGP a very reliable and practical solution for applications that use battery cell packs. The MCGP is derived and compared to benchmark methods on an experimental lithium-ion battery cells data. The results show the effectiveness of the proposed method.},
 bibtype = {misc},
 author = {Chehade, A.A. and Hussein, A.A.}
}

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