A Recurrent Neural Network For Battery Capacity Estimations In Electrical Vehicles

• Main Author: Corell, Simon
• Format: Others
• Language: English
• Published: Linköpings universitet, Medie- och Informationsteknik 2019
• Subjects: Recurrent Neuralt Network; LSTM; Linear Regression; Lithium-Ion battery; Data pre-processing; Feature Selection.; Media and Communication Technology; Medieteknik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160536

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.