Artificial Intelligence Based Assessment of LiIon Battery Failure

点击翻译按钮获取中文摘要

This Engage project aims to establish a new collaboration between Acerta Analytics Solutions and the researchteam supported by Dr. Michael Fowler. The two parties have not engaged before in any form of collaborationto date. Advances in computing technology enable new forms of data analysis. Acerta offers analyticssolutions to identify warranty issues of manufactured goods at the end of life. This means that Acerta'salgorithms analyze data collected from the manufactured good before it leaves the factory and based on the datadetermines whether the good should be shipped to the customer or whether it will likely have a warranty issueand thus should be inspected in the factory prior to shipping. Currently, Acerta is applied to products usinggearboxes, engines, and bearings in vehicles. Acerta is extending its product line to also offer analytics forbatteries. To extend the product line to batteries, Acerta needs to tune its algorithms and products to providemeaningful estimates of the quality of a battery system at the end of life. This requires answering the followingquestions (among others): (1) what is the state of the charge, (2) what is the health of the cells, and (3) what isthe expected state of degradation over the next years. Since Acerta uses machine learning mixed with domainknowledge, it needs domain expertise on feature engineering (i.e., determining what sensor values should beprovided) and feature processing (i.e., use features to extract information gain).