Battery testing with AI: Build a more efficient test plan you can trust

The complexity of EV batteries, which involves electrical, chemical, and thermal mechanisms, leads to a costly and time-consuming process for their testing and validation. In the first part of the EV webinar series (https://www.monolithai.com/webinars/ev-battery-testing), we reviewed the latest research on using AI models to reduce the testing needed for EV batteries significantly. This follow-up webinar will show how to implement these concepts using Monolith software. Monolith Lead Principal Engineer Dr. Joël Henry will demonstrate how to train a model using the latest active learning techniques to characterize battery performance in much fewer test steps than traditional approaches. During the demo, Dr. Henry will explore how to overcome common challenges in EV battery testing: – Learn how to define your experiment design to achieve optimal design space coverage, including using AI models for better results than random or factorial experiments. – Train and optimise an EV battery model in Monolith using real-world test data with no required programming or data science expertise. – Apply robust active learning algorithms for real-time test recommendations to improve the model in much fewer testing steps iteratively. – Share your findings with other team members, departments, or clients.