Faster Battery Development: How AI Transforms Engineering

In this newsletter, I share the learnings from Monolith from over 300+ AI projects with engineering leaders and provide:

• Helpful?insights?from our learnings working at the forefront of product development
• Expected benefits from adopting a more data-driven workflow
• Guidelines?to evaluate a suitable use case for AI

Follow me for more insights on AI in Engineering Product Development.

Using AI to Reduce Battery Testing ??

In the 2023 State of AI in Engineering study conducted by Forrester Consulting, more than two thirds of US and European automotive engineering leaders stated they need to find new ways to speed up the ideation and launch of complex new products like battery electric vehicles (BEVs) in order to stay competitive.

NTR gives active recommendations on the validation tests to run during the development of hard-to-model, nonlinear products such as batteries and fuel cells, and found that AI can reduce testing by up to 73%.

Predict Next Battery Tests Using Machine Learning & Active Learning ??

Engineers can now access ranked validation test recommendations, reducing testing by up to 73% and speeding up time to market. With human-in-the-loop inspection, domain experts collaborate with machine learning, optimising test plans for complex products in automotive, aerospace, and industrial domains.

Powered by proprietary active learning technology, the new Machine Learning method offers impactful test recommendations, cutting costs and boosting efficiency in complex product development.

Learn how your team of engineers can skip unnecessary tests: https://www.monolithai.com/webinars/next-test-recommender

Active Learning vs. Factorial Design of Experiment for Battery Charging Tests ??

Most companies who test battery cells will perform a factorial Design of Experiment. This is visualised below by the red grid slowly covering the whole design space. You can easily see that this is a very rigorous but also very slow approach.?

Active Learning is a machine learning approach where you update your model after each test to decide where you should test next. This feedback loop accelerates your learning speed considerably. You can see this here by the red points who quickly spread across the entire design space.?

Read more in the article below??

Feel free to contact the experts at Monolith to discuss your AI Use Case and run a feasibility analysis.

If you’d like to see more tips on AI in Engineering and Machine Learning for Battery Design, Lifetime and Charging Strategies, consider subscribing to this newsletter!

Test Less. Learn More.

Richard