Artificial Intelligence Based Assessment of LiIon Battery Failure

该项目旨在Acerta Analytics Solutions和Michael Fowler博士支持的研究团队之间建立一种新的合作。到目前为止，双方还没有进行过任何形式的合作。计算技术的进步使数据分析的新形式成为可能。Acerta提供分析解决方案，以确定制造产品在生命周期结束时的保修问题。这意味着Acerta的算法在制成品出厂前分析收集的数据，并根据这些数据确定是否应该将货物发运给客户，或者它是否可能存在保修问题，因此应该在发货前在工厂进行检查。目前，Acerta应用于使用变速箱、发动机和车辆轴承的产品。Acerta正在扩展其产品线，也为电池提供分析。为了将产品线扩展到电池，Acerta需要调整其算法和产品，以提供对电池系统在寿命结束时的质量的准确估计。这需要回答以下问题(其中包括)：(1)电荷的状态是什么，(2)电池的健康状况是什么，以及(3)未来几年预计的降解状态是什么。由于Acerta使用混合了领域知识的机器学习，它需要在特征工程(即，确定应该提供什么传感器值)和特征处理(即，使用特征来提取信息增益)方面的领域专业知识。

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).