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01-09-2023 | Article

Transfer Learning from Lab to Fleet Data - Battery Life Prediction and Optimal Use in the Battery Management System

Authors: Alexander Palmisano, Milan Živadinović, Gerhard Schagerl, Christian Rupert Rehrl

Published in: ATZextra worldwide | Special Issue 1/2023

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Excerpt

The article delves into the application of transfer learning to predict battery life and optimize usage in electric vehicles. By combining data from lab tests and fleet operations, advanced machine learning models can accurately forecast battery health and lifespan. This approach not only enhances sustainability and reduces costs but also provides insights into battery behavior and safety. The use of cloud-based platforms enables real-time monitoring and data analysis, allowing for timely interventions such as temperature control during charging. The article highlights the effectiveness of transfer learning methods in reducing data requirements and improving prediction accuracy, even with limited fleet data. This makes it particularly relevant for industries looking to optimize battery performance and extend their lifespan in a data-driven manner.
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Metadata
Title
Transfer Learning from Lab to Fleet Data - Battery Life Prediction and Optimal Use in the Battery Management System
Authors
Alexander Palmisano
Milan Živadinović
Gerhard Schagerl
Christian Rupert Rehrl
Publication date
01-09-2023
Publisher
Springer Fachmedien Wiesbaden
Published in
ATZextra worldwide / Issue Special Issue 1/2023
Print ISSN: 2195-1470
Electronic ISSN: 2195-1489
DOI
https://doi.org/10.1007/s40111-023-0903-y

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