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Data Driven Approach for Battery State Estimation Based on Neural Networks

 
: Heinrich, Felix; Lehmann, Thomas; Jonas, Konstantin; Pruckner, Marco

Bäker, B. ; TU Dresden, Fakultät Verkehrswissenschaften Friedrich List:
Diagnose in mechatronischen Fahrzeugsystemen XIV. Tagungsband : Predictive Maintenance, Remote Diagnose, KI/ Maschinelles Lernen, Standardisierung, 18. und 19. Mai 2021, Dresden, Onlinetagung
Dresden: TUDpress, 2021
ISBN: 978-3-95908-261-7
ISBN: 3-95908-261-4
pp.197-212
Tagung "Diagnose in mechatronischen Fahrzeugsystemen" <14, 2021, Online>
English
Conference Paper
Fraunhofer IVI ()
battery state estimation; neural networks; state of health; battery modeling; battery monitoring

Abstract
To assure safety and performance for battery electric vehicles it is important to monitor the battery state of health. This state is influenced by many different conditions such as state of charge (SOC), time in service, and electrical/ thermal/ mechanical stress. Recent approaches for battery state estimation base on physical models and are subject to lab data under certain testing conditions. However, the stet of health is rarely considered in battery state modeling. In this work, we present a data driven method to continuously estimate the battery state of health (SOH) by applying neural networks on real life in-vehicle time series data. We thereby aim to automatize the complex and time-consuming modeling process. Special adjustments regarding design, composition, and aging of the cell can be omitted.

: http://publica.fraunhofer.de/documents/N-636260.html