NIO’s battery swap technology presents a unique opportunity to discover new insights into real-world, daily usage of batteries. It will use Monolith’s Anomaly Detector AI software to monitor performance from data generated in the field. These learnings will build a basis for comparing test-bench results and will be integrated into further verification activities. With Anomaly Detector, NIO and Monolith engineers can recognize abnormalities in cross-channel results at an unprecedented rate based on complex system relationships.
Frank Kindermann, Head of Battery System Europe at NIO, said: “NIO's partnership with Monolith exemplifies our commitment to delivering a premium user experience. 98% of our users across the five key European markets opt for Battery as a Service (BaaS). BaaS allows NIO users in Europe to flexibly charge or swap their batteries as needed, enhancing convenience and cost efficiency.
By combining our expertise in battery performance with Monolith's advanced laboratory capabilities, we're setting new standards in battery monitoring, ensuring unparalleled efficiency and reinforcing NIO's dedication to innovation and quality.”
Battery test data anomalies can now be found rapidly and more efficiently thanks to Monolith self-learning algorithms. It does this by automating the inspection of raw test data to look for potential errors or abnormalities across hundreds of test channels.
Dr. Richard Ahlfeld, CEO and Founder of Monolith, added: “Monolith’s deep-learning algorithms allow for automatic detection of battery issues, such as spontaneous discharge and thermal runaway, making it easier to analyse complex real-world data quickly. This capability not only saves time and resources but also enhances battery safety. As a leader in software-defined vehicles, NIO is the ideal partner to leverage this advanced technology, driving innovation in battery performance and safety.”
In addition to automatic inspection of test data, the Monolith AI platform includes unique, advanced algorithms to reduce the amount of physical testing time and simulations required to successfully develop products with highly complex, intractable physics throughout the design cycle. Using valuable and sometimes limited engineering test data, the AI software makes instant predictions and enables engineers to identify areas where optimisation and development are required, without the extensive need for repetitive, time-consuming physical tests.
