Transferability of a Battery Cell End-of-Life Prediction Model Using Survival Analysis
Maya Santhira Sekeran,
Milan Živadinović and
Myra Spiliopoulou
Additional contact information
Maya Santhira Sekeran: Digitalization, AVL Software and Functions GmbH, 93059 Regensburg, Germany
Milan Živadinović: PTE/DAB Big Data Intelligence, AVL List GmbH, 8020 Graz, Austria
Myra Spiliopoulou: Knowledge Management and Discovery Lab, Otto-von-Guericke University, 39106 Magdeburg, Germany
Energies, 2022, vol. 15, issue 8, 1-16
Abstract:
Electric vehicles are increasingly becoming the vehicle of choice in today’s environmentally conscious society, and the heart of an electric vehicle is its battery. Today, lithium-ion batteries are mainly used to power electric vehicles for its increased energy storage density and longevity. However, in order to estimate battery life, long and costly battery testing is required. Therefore, there is a need to investigate efficient ways that could reduce the amount of testing required by reusing existing knowledge of aging patterns from different kinds of battery chemistry. This work aims to answer two research questions. The first addresses the challenge of battery cell testing data that contain battery cells that do not reach the End-of-Life (EOL) threshold by the time the testing has been completed. For this challenge, we propose to implement survival analysis that is able to handle incomplete data or what is referred to as censored data. The second addresses how to reuse a model trained on one type of battery cell chemistry to predict the EOL of another battery cell chemistry by implementing transfer learning. We develop a workflow to implement a prediction model for one type of battery cell chemistry and to reuse this pre-trained model to predict the EOL for another type of battery cell chemistry.
Keywords: transfer learning; survival analysis; end-of-life; reliability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:8:p:2930-:d:795215
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