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A Recurrent Neural Network For Battery Capacity Estimations In Electrical Vehicles
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This study is an investigation if a recurrent long short-term memory (LSTM) based neural network can be used to estimate the battery capacity in electrical cars. There is an enormous interest in finding the underlying reasons why and how Lithium-ion batteries ages and this study is a part of this broader question. The research questions that have been answered are how well a LSTM model estimates the battery capacity, how the LSTM model is performing compared to a linear model and what parameters that are important when estimating the capacity. There have been other studies covering similar topics but only a few that has been performed on a real data set from real cars driving. With a data science approach, it was discovered that the LSTM model indeed is a powerful model to use for estimation the capacity. It had better accuracy than a linear regression model, but the linear regression model still gave good results. The parameters that implied to be important when estimating the capacity were logically related to the properties of a Lithium-ion battery.En studie över hur väl ett återkommande neuralt nätverk kan estimera kapaciteten hos Litium-ion batteri hos elektroniska fordon, när en en datavetenskaplig strategi har använts.

Place, publisher, year, edition, pages
2019. , p. 53
Keywords [en]
Recurrent Neuralt Network, LSTM, Linear Regression, Lithium-Ion battery, Data pre-processing, Feature Selection.
National Category
Media and Communication Technology
Identifiers
URN: urn:nbn:se:liu:diva-160536ISRN: LIU-ITN-TEK-A-19/046--SEOAI: oai:DiVA.org:liu-160536DiVA, id: diva2:1354631
Subject / course
Computer Engineering
Uppsok
Technology
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Examiners
Available from: 2019-09-25 Created: 2019-09-25 Last updated: 2019-09-25Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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