Feature Extraction, Ageing Modelling and Information Analysis of a Large-Scale Battery Ageing Experiment

Jose Genario de Oliveira, Vipul Dhingra and Christoph Hametner
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Jose Genario de Oliveira: Christian Doppler Laboratory for Innovative Control and Monitoring of Automotive Powertrain Systems, TU Wien, 1010 Vienna, Austria
Vipul Dhingra: AVL List GmbH, 8010 Graz, Austria
Christoph Hametner: Christian Doppler Laboratory for Innovative Control and Monitoring of Automotive Powertrain Systems, TU Wien, 1010 Vienna, Austria

Energies, 2021, vol. 14, issue 17, 1-17

Abstract: Large scale testing of newly developed Li-ion cells is associated with high costs for the interested parties, and ideally, testing time should be kept to a minimum. In this work, an ageing model was developed and trained with real data from a large-scale testing experiment in order to answer how much testing time and data would have been really needed to achieve similar model generalisation performance on previously unseen data. A linear regression model was used, and the feature engineering, extraction and selection steps are shown herein, alongside accurate prediction results for the majority of the accelerated ageing experiments. Information analysis was performed to achieve the desired data reduction, obtaining similar model properties with a fifth of the number of cells and half of the testing time. The proposed ageing model uses features commonly found in the literature, and the structure is simple enough for the training to be performed online in an EV. It has good generalisation capabilities. Lastly, the data reduction approach used here is model-independent, allowing a similar methodology to be used with different modelling assumptions.

Keywords: battery ageing; battery modelling; capacity fade estimation; feature engineering (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: 2021
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