Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis

Matthieu Dubarry and David Beck
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Matthieu Dubarry: Hawaii Natural Energy Institute, University of Hawai̒i at Mānoa, Honolulu, HI 96822, USA
David Beck: Hawaii Natural Energy Institute, University of Hawai̒i at Mānoa, Honolulu, HI 96822, USA

Energies, 2021, vol. 14, issue 9, 1-24

Abstract: The development of data driven methods for Li-ion battery diagnosis and prognosis is a growing field of research for the battery community. A big limitation is usually the size of the training datasets which are typically not fully representative of the real usage of the cells. Synthetic datasets were proposed to circumvent this issue. This publication provides improved datasets for three major battery chemistries, LiFePO 4 , Nickel Aluminum Cobalt Oxide, and Nickel Manganese Cobalt Oxide 811. These datasets can be used for statistical or deep learning methods. This work also provides a detailed statistical analysis of the datasets. Accurate diagnosis as well as early prognosis comparable with state of the art, while providing physical interpretability, were demonstrated by using the combined information of three learnable parameters.

Keywords: V vs. Q curves; synthetic data; LFP; NCA; NMC 811; BDG tier 1 challenge (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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