The complexity of EV batteries, which involves electrical, chemical, and thermal mechanisms, leads to a costly and time-consuming process for testing and validation.
In the first part of the EV webinar series, we reviewed the latest research on using AI models to significantly reduce the testing needed for EV batteries. In this follow-up webinar, we demonstrate 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 characterise 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 design of experiments to achieve optimal coverage of the design space, including the use of 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.
Watch on-demand webinar
Learning objectives:
- Learn how to use AI to identify battery failure and precisely estimate battery lifetime quickly.
- Gain insights into developing an AI-powered test plan, optimising the required tests and improving overall battery analytics.
- Understand how ML can enhance decision-making and advance battery product development.
- Learn how to build and share AI pipelines using the Monolith software interactively.
- Leaders in R&D and engineering who work with EV batteries for product validation and certification.
- Business leaders interested in AI trends in the EV market.
- Test engineers looking to use AI for predicting critical and redundant tests.
