Vehicle drive systems are often oversized for common customer operation in order to cover the high demands of rare driving events such as towing a trailer, high acceleration or steep inclines. This high torque and power requirement affects the efficiency map and the highest efficiency is around the area of increased torque and speed. However, in everyday use, drive systems are mostly driven by customers at low speed and load, and therefore are not operating in the most efficient area. Designing a drive system that only covers the area of highest customer operation can increase efficiency by moving the sweet spot of efficiency to the relevant area, and thus reduce energy consumption. Therefore, customer data need to be analyzed in order to identify customer requirements and to localize the area of greatest operation. The method presented in this paper analyzes customer data in order to identify design-relevant parameters for a customer-specific drive system design. The available customer data results from event-based counts and are submitted as a statistical frequency distribution. These statistics are compared with discrete time series recorded during test drives in order to derive representative time series that correspond to customer behavior. By applying the time frame-based load analysis to these relevant time series, the desired design-relevant parameters are pointed out.

Only a small part of the high performance of electric drive systems in vehicles is used in everyday operation by customers. As a result, most drives are not operated in the optimum efficiency range. Designing a suitable drive system, whose performance is aligned with actual customer requirements, presents the potential to increase efficiency. Based on the findings of previous research, this paper serves to complement an existing method, which already introduced the basic method of transferring statistical customer data into relevant parameters for the design of a customer-specific drive system. In order to improve the method, further criteria for the selection of relevant time series come into place. Furthermore, the impact on maximum loads resulting from various sequences of the selected time series is identified and evaluated with time frame-based analysis. A new approach for the effective computation of maximum design-relevant loads in the admissible time frame range is introduced and validated. By taking this approach, the sensitivity of the derived design parameters regarding various time series sequence is evaluated in the context of selected datasets. In addition, concatenations of time series are identified which may have a relevant influence on the maximum loads. Consequently, the design process is safeguarded thoroughly against potential maximum loads as well as the associated thermal stresses.