BAVARIAN RESEARCH ASSOCIATION FOR CUSTOMIZED DIGITAL ENGINEERING FOR BAVARIAN SMES USING THE EXAMPLE OF THE DRIVE TRAIN OF BATTERY ELECTRIC VEHICLES
SP4 Methodology for the development of innovative drive systems and components
Field of work:Working Group Context
The development of innovative drive systems and components requires not only the application of simulation tools of the product development process but also a large amount of data from production and series. For SMEs in particular, the resulting high costs and lack of specialist knowledge represent an insurmountable hurdle in the development process. Methods of digitalization available today, e.g. the networked application of design software or data networking through artificial intelligence (AI), offer possibilities to overcome these difficulties. This makes it possible to speed up the development process and improve the achievable results with a minimum of manpower. However, a lack of know-how in the application of innovative methods of digital engineering is an obstacle to their use today. In order to realize maximum benefit from the possibilities of digital engineering, a guideline for the efficient use of the new technologies in the modular development of powertrains is to be created. The procedure will be demonstrated using the example of a BEV drive with a focus on transmission development.
In practice, in addition to empirical knowledge, test bench trials are often used as a supplement to calculation and simulation in the development process. In order to create a specifically applicable procedure for data acquisition and management, a parameter identification on the demonstrator for the electric powertrain will be carried out within the scope of this TP, using existing calculation programs for transmission design. By evaluating these parameters with regard to their significance and quality of information, e.g. by means of FMEA, information is derived which is missing in current development processes or requires further validation. Based on this information, it is identified which parameters can be determined by model-based algorithms and which by physical testing. For parameters required on the test bench side, which cannot or can only insufficiently be determined with state-of-the-art measurement technology (e.g. local contact sizes), appropriate sensors and measurement technology are adapted and developed based on preliminary work on model test benches.