Title
Creating a Robust SoC Estimation Algorithm Based on LSTM Units and Trained with Synthetic DataAuthor (from another institution)
xmlui.dri2xhtml.METS-1.0.item-contributorOtherinstitution
https://ror.org/03hp1m080Version
http://purl.org/coar/version/c_970fb48d4fbd8a85
Rights
© 2023 The AuthorsAccess
http://purl.org/coar/access_right/c_abf2Publisher’s version
https://doi.org/10.3390/wevj14070197Published at
World Electric Vehicle Journal Vol. 14. N. 7. N. art. 197Publisher
MDPIKeywords
computer intelligence
Li-ion battery
estimation algorithm
ODS 7 Energía asequible y no contaminante ... [+]
Li-ion battery
estimation algorithm
ODS 7 Energía asequible y no contaminante ... [+]
computer intelligence
Li-ion battery
estimation algorithm
ODS 7 Energía asequible y no contaminante
ODS 9 Industria, innovación e infraestructura
state of charge
synthetic data [-]
Li-ion battery
estimation algorithm
ODS 7 Energía asequible y no contaminante
ODS 9 Industria, innovación e infraestructura
state of charge
synthetic data [-]
Abstract
Creating SoC algorithms for Li-ion batteries based on neural networks requires a large amount of training data, since it is necessary to test the batteries under different conditions so that the algor ... [+]
Creating SoC algorithms for Li-ion batteries based on neural networks requires a large amount of training data, since it is necessary to test the batteries under different conditions so that the algorithm learns the relationship between the different inputs and the output. Obtaining such data through laboratory tests is costly and time consuming; therefore, in this article, a neural network has been trained with data generated synthetically using electrochemical models. These models allow us to obtain relevant data related to different conditions at a minimum cost over a short period of time. By means of the different training rounds carried out using these data, it has been studied how the different hyperparameters affect the behaviour of the algorithm, creating a robust and accurate algorithm. To adapt this approach to new battery references or chemistries, transfer learning techniques can be employed. [-]
xmlui.dri2xhtml.METS-1.0.item-oaire-funderName
European Commissionxmlui.dri2xhtml.METS-1.0.item-oaire-fundingStream
H2020xmlui.dri2xhtml.METS-1.0.item-oaire-awardNumber
963522xmlui.dri2xhtml.METS-1.0.item-oaire-awardURI
https://doi.org/10.3030/963522xmlui.dri2xhtml.METS-1.0.item-oaire-awardTitle
Lightweight Battery System for Extended Range at Improved Safety (LIBERTY)Collections
- Articles - Engineering [639]
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