Monolith & CamMotive to improve battery testing with AI
The partners plan on bringing together Monolith’s AI platform and CamMotive’s comprehensive, real-world battery data. The collaboration will reportedly enhance test data validation, thereby helping engineers detect complex failure characteristics during EV battery development.
Specifically, they plan to implement a “hybrid modelling technique for anomaly detection in the battery testing process.” This approach combines physics-based simulations and machine learning methods to identify battery issues that are harder to detect by traditional rule-based detection systems. The companies’ hope is that Monolith’s success in laboratory environments will translate to greater accuracy and insight in real-world scenarios – as well as reduce reliance on physical testing and streamline workflows.
“Our partnership with CamMotive has the potential to make EV battery development faster and more efficient. Training machine learning models with robust, real-world data is what makes AI truly effective, as it means engineers can find reliable ways to save time, achieve performance gains and reduce costs,” said Dr. Richard Ahlfeld, CEO and Founder of Monolith.
Bruce Campbell, Director of CamMotive, added: “Partnering with Monolith gives CamMotive the ability to significantly improve our battery testing process. Monolith’s AI technology allows us to use our state-of-the-art test facility more efficiently while generating higher-quality results. The insights we gain through this collaboration will help us detect potential issues earlier, streamline workflows, and enable our engineers to focus on delivering valuable data analysis for our customers.”
Towards the end of last year, Monolith also entered a partnership with the Chinese EV manufacturer Nio to use artificial intelligence and machine learning to test and improve batteries for electric cars more efficiently. Their partnership started in Europe and may eventually be expanded to China.
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