ZF has launched TempAI, a production-ready, AI-based temperature administration expertise designed to enhance the efficiency and effectivity of electrical motors in electrical autos. Using a self-learning temperature mannequin, TempAI enhances temperature prediction accuracy by over 15 %, enabling extra exact thermal utilization of electrical motors and rising efficiency with out compromising reliability.
The TempAI platform routinely generates physics-based fashions from in depth measurement knowledge, turning into operational shortly and requiring minimal computing assets. Present management models are adequate, permitting for cost-efficient implementation in collection manufacturing. In keeping with ZF, TempAI delivers focused electrical motor management proper as much as thermal working limits, leading to as much as six % greater peak energy and measurable effectivity enhancements within the Worldwide Harmonized Gentle Automobile Take a look at Process (WLTP) cycle. Beneath dynamic driving situations—similar to high-performance circuits just like the Nürburgring Nordschleife—the expertise reduces power consumption by 6 to 18 %, depending on load situations.
TempAI additionally allows ecological advantages, as its optimized thermal design can considerably cut back the reliance on heavy uncommon earth supplies. The answer additionally gives substantial time financial savings throughout growth, decreasing durations from a number of months to merely a number of days by way of AI-driven modeling.
Throughout motor growth, AI-driven TempAI fashions successfully be taught and predict inside motor thermal processes which can be in any other case troublesome or costly to measure instantly, similar to rotor temperatures. The expertise capitalizes on in depth dataset analyses collected throughout practical checks on check benches and in check autos, involving tens of millions of knowledge factors associated to variables like ambient temperatures, rotor speeds and driver habits patterns.
“This expertise allows us to additional improve the effectivity and reliability of our drives,” stated Dr. Stefan Sicklinger, Head of AI, Digital Engineering, and Validation in R&D, ZF. “On the identical time, TempAI demonstrates how data-driven growth might be not solely quicker, but additionally extra sustainable and extra highly effective.”
Supply: ZF
