Black Box Efficiency Modelling of an Electric Drive Unit Utilizing Methods of Machine Learning

Abstract

The increasing electrification of powertrains leads to increased demands for the test technology to ensure the required functions. For conventional test rigs in particular, it is necessary to have knowledge of the test technology's capabilities that can be applied in practical testing. Modelling enables early knowledge of the test rigs dynamic capabilities and the feasibility of planned testing scenarios. This paper describes the modelling of complex subsystems by experimental modelling with artificial neural networks taking transmission efficiency as an example. For data generation, the experimental design and execution is described. The generated data is pre-processed with suitable methods and optimized for the neural networks. Modelling is executed with different variants of the inputs as well as different algorithms. The variants compare and compete with each other. The most suitable variant is validated using statistical methods and other adequate techniques. The result represents reality well and enables the performance investigation of the test systems in a realistic manner.

Knee Joint Osteoarthritis Diagnosis Based on Selected Acoustic Signal Discriminants Using Machine Learning

“…Artificial neural networks are one type of highly parameterized statistical models. They have been developed based on information about the functioning of the nervous system of living organisms and are capable of mapping extremely complex functions (Bauer, Stütz & Kley, 2021;Dudek-Dyduch, Tadeusiewicz & Horzyk, 2009;Tadeusiewicz, 1993). Technically, an artificial neuron is an element whose properties correspond to selected properties of its biological counterpart.…”