Central activity in the development process of automotive drive systems is the validation to meet the customers’ demands. Thereby the transmission topology as well as the operation and drive strategy and their independencies have a great influence on the comfort and the energy efficiency of the vehicle. Especially in modern drivelines, like in hybrid electric vehicles there are great independencies between those factors, which have to be considered and integrated in order to achieve a global optimum. In this contribution an integrated validation and development environment with the focus on hybrid transmission applications is presented. This approach is capable to integrate and consider aspects of complex powertrain systems, as hybrid powertrains, and to cover the competing aspects of NVH, drivability as well as energy efficiency. The approach is an integral combination of new or already published as well as established individual methods that have been extended with challenging aspects of hybrid powertrain development. One integrated method is the time-efficient arrangement of measuring points in order to shorten the global needed conditioning phases. For gearshift evaluation of a dual clutch transmission, this reduces operation time on the roller test bench about 80% compared to an experienced test driver [1]. The integrated approach uses system identification to create time and cost efficient test runs for the test based optimization [2]. Thereby advanced methods for rating the drivability and for automatic identification of NVH-Phenomena [3] are used to optimize multi criteria objective functions. The measurement is done in the IPEK-X-in-the-Loop Framework (XiL) [4], which enables a transmission assessment on the complete vehicle level.

Technical systems must be continuously improved so that they can remain competitive on the market. Also, the time-to-market is an important factor for the success of a product. To achieve this goal, new methods and processes are needed. Especially the testing and calibration are important phases in the development process. This paper introduces a method, which helps to reduce the time effort while increasing the quality of the calibration process. The basic idea is to use measured test data to parameterize a physical (or mostly physical) model structure to create adequate models for the optimization. The main advantage of the method is the reduction of test effort because the number of variations of the design parameter is one, or extremely decreased (depending on the system). Another advantage is that the uncertainty and the limit of the model can be quantified more accurately compared to common approaches based on non-physical model structures. These normally use artificial neuronal networks (ANN) or polynomial approaches for the test-based optimization. This contribution illustrates the method by using the example of the calibration process of a double clutch gearbox (DCT) regarding energy efficiency and drivability on a roller test bench. First step is the test planning and test execution. In this step the method calculates the optimal execution order of the measuring points. In this example 81% timesaving can be achieved compared to the equivalent on the test track. The second step is the automated generation of the simulation model. In this step the unknown parameters of the model structure are calculated. The contribution shows different approaches for the identification of non-linear systems. In the last step the model is used to perform the optimization of the design parameters.