In the fast-paced electric vehicle (EV) market, testing batteries has become a significant bottleneck, hindering the timely launch of EVs. The escalating demand and intense competitive pressure to enhance the range and charging times compound this challenge exponentially.
Real-world data from a study conducted by Toyota, MIT, and Stanford demonstrates that AI can reduce testing efforts by a remarkable 98%. (Source)
Monolith CEO Dr. Richard Alhfeld will explain and demonstrate how you can achieve these same results.
Additionally, during the session, Dr. Ahlfeld will discuss how 3 major challenges faced by EV manufacturers can be strategically solved with AI:
- Early Stopping of Tests: Predicting battery failure after a certain number of cycles for efficient testing
- Lifetime Prediction of Batteries: Ensuring optimal performance and reliability by accurately estimating battery lifetimes
- AI-recommended Test Planning: AI-based models act as a "next test recommender" to identify the tests that provide maximum information gain with human-in-the-loop oversight
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Learning Objectives:
- Understand the impact of AI on EV product development.
- Learn how to leverage AI for quicker identification of battery failure and precise estimation of battery lifetime.
- Gain insights into developing an AI-powered test plan, optimising the number of tests required, and improving overall battery analytics.
- Understand how ML can enhance decision-making and advance battery product development.
- Learn how AI can help you make efficient use of limited test stands.
- R&D and engineering leaders involved in EV battery product validation and certification.
- Business leaders who want to learn about the latest trends of AI in the EV market.
- Test engineers who want to learn how to use AI to predict the critical tests to run and redundant tests to avoid.
