AI agent for the calibration of new vehicle functions
Summary
In order to be able to cope with the higher complexity in the future while also increasing development efficiency, Porsche Engineering uses artificial intelligence (AI) to calibrate the control units and input data. The advantages are the high degree of process efficiency of the methodology, as it is self-learning, and the universal applicability to many areas of vehicle development (see also Porsche Engineering Magazine 1/2021 on the principle of PERL). In the mixture formation for an engine, for example, the injection quantity must be correctly adjusted for each speed and torque combination using parameter maps so that the lambda value—the control variable for optimum operation of exhaust gas aftertreatment—matches the target.” The main challenge this involves is the dead time resulting from the spatial distance between the engine and the sensor system at the end of the exhaust tract, coupled with the high speed at which the control system must work, say, during a load change. Dr. Galabina Aleksieva-Rausch, who is responsible for processes, quality, and method development at Porsche, shares his assessment: “As part of a study of the potential offered by PERL, we used it in parallel with conventional calibration to ensure comparability of the results. This means that the newly developed PERL methodology makes a major contribution to a strategic goal of Porsche Engineering: To ensure a short delivery time of high-quality solutions for complex tasks—for the benefit of our customers,” according to Bach.