Fast charging is an exciting topic in the field of electric and hybrid electric vehicles (EVs/HEVs). In order to achieve faster charging times, fast-charging applications involve high-current profiles which can lead to high cell temperature increase, and in some cases thermal runaways. There has been some research on the impact caused by fast-charging profiles. This research is mostly focused on the electrical, thermal and aging aspects of the cell individually, but these factors are never treated together. In this paper, the thermal progression of the lithium-ion battery under specific fast-charging profiles is investigated and modeled. The cell is a Lithium Nickel Manganese Cobalt Oxide/graphite-based cell (NMC) rated at 20 Ah, and thermal images during fast-charging have been taken at four degradation states: 100%, 90%, 85%, and 80% State-of-Health (SoH). A semi-empirical resistance aging model is developed using gathered data from extensive cycling and calendar aging tests, which is coupled to an electrothermal model. This novel combined model achieves good agreement with the measurements, with simulation results always within 2 °C of the measured values. This study presents a modeling methodology that is usable to predict the potential temperature distribution for lithium-ion batteries (LiBs) during fast-charging profiles at different aging states, which would be of benefit for Battery Management Systems (BMS) in future thermal strategies.

With the growing demand for automation in the manufacturing industry, computer vision-based systems have become a popular tool for tasks such as object detection, object picking, and quality control. One of the main challenges in developing such systems is obtaining enough high-quality training data. In this paper, we present a suite of tools that create artificial training data and applies it to solve industrial problems. CAD2Render is a tool for generating synthetic images, starting from a 3D CAD model, which can be used to create large datasets with a large spectrum of controlled variations. CAD2Detect uses these synthetic images to train object detection models. CAD2Pose focuses on estimating the 6 degree of freedom pose of objects in images. Finally, CAD2Defect uses anomaly detection to identify defects in manufactured parts. Overall, the CAD2X suite provides a comprehensive set of tools for training computer vision models for manufacturing applications, while minimizing the need for large amounts of real training data. We demonstrate the effectiveness of our approach on a range of industrial use cases.