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Automotive battery testing with AI

Source:Hannover Messe Release Date:2024-03-04 455
MetalworkingSoftware & CNC System Pan Era, additives, packagingAutomationSoftware & Control
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Richard Ahlfeld, CEO of Monolith AI, discusses the rising importance of battery testing for automotive applications and the application of AI and machine learning in this field.

 

Richard Ahlfeld, CEO of Monolith AI, discusses the rising importance of battery testing for automotive applications and the application of AI and machine learning in this field. He shares insights on optimizing test plans, the technical aspects of battery testing with AI, and the challenges of deploying a cloud-based toolbox for battery testing.

The Rising Importance of Batteries in the Industrial AI Space

Monolith AI, a machine learning platform, has noticed a significant increase in interest and demand for battery testing. As batteries have become a hot topic in recent years, Monolith AI has sharpened their focus on supporting engineering users in building machine learning models based on battery data. The company has developed a unique toolbox that helps engineering companies test and understand battery behavior. They address the challenge of selecting the right battery chemistry and the difficulty in modeling and predicting battery behavior. With the increasing amount of battery test data being generated, Monolith AI's platform utilizes machine learning algorithms to optimize test plans, recommend which tests to prioritize, and analyze large amounts of battery test data.

Addressing the Challenges of Battery Testing and Modeling

Battery testing poses several challenges for engineering companies, particularly those transitioning to battery-powered propulsion systems. Understanding battery behavior is crucial, but it can be complex and difficult to predict. Battery testing involves cycling batteries in various conditions, including different temperatures, to identify when batteries fail and under what circumstances. This extensive testing generates a large amount of data, making it challenging to derive meaningful insights and accurately model battery behavior. Monolith AI's toolbox leverages machine learning to help engineers navigate these challenges by optimizing test plans, identifying the best battery chemistry, and analyzing and modeling battery behavior.

Monolith AI's Toolbox for Battery Testing and Optimization

Monolith AI offers a toolbox specifically designed to assist engineers in the battery industry. The toolbox utilizes machine learning algorithms that have been pre-trained and calibrated using extensive battery test data. These algorithms actively work with test engineers, analyzing and recommending the most valuable and informative tests to conduct. By optimizing test plans, engineers can save time and resources by eliminating unnecessary tests while focusing on critical ones. Monolith AI's cloud-based platform allows for the storage and analysis of large battery test data sets, providing engineers with valuable insights to improve battery safety, performance, and durability. While Monolith AI initially focused on batteries, their approach is applicable to other industries and products, highlighting their vision of assisting research and development engineers across various domains.

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