Article
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Preserved in Portico This version is not peer-reviewed
Benchmark Synthetic Training Data for Artificial Intelligence-based Li-ion Diagnosis and Prognosis
Version 1
: Received: 6 May 2020 / Approved: 7 May 2020 / Online: 7 May 2020 (06:20:09 CEST)
A peer-reviewed article of this Preprint also exists.
Abstract
Accurate lithium battery diagnosis and prognosis is critical to increase penetration of electric vehicles and grid-tied storage systems. They are both complex due to the intricate, nonlinear, and path-dependent nature of battery degradation. Data-driven models are anticipated to play a significant role in the behavioral prediction of dynamical systems such as batteries. However, they are often limited by the amount of training data available. In this work, we generated the first big data comprehensive synthetic datasets to train diagnosis and prognosis algorithms. The proof-of-concept datasets are over three orders of magnitude larger than what is currently available in the literature. With benchmark datasets, results from different studies could be easily equated, and the performance of different algorithms can be compared, enhanced, and analyzed extensively. This will expend critical capabilities of current AI algorithms, tools, and techniques to predict scientific data.
Supplementary and Associated Material
http://dx.doi.org/10.17632/bs2j56pn7y.1: diagnosis dataset
http://dx.doi.org/10.17632/6s6ph9n8zg.1: prognosis dataset
Keywords
Machine Learning; Training Data; alawa; AI
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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