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Article

Transfer of Statistical Customer Data into Relevant Parameters for the Design of Vehicle Drive Systems

1
Institute of Vehicle System Technology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
2
Mercedes-Benz AG, 70327 Stuttgart, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Ralf Stetter, Udo Pulm and Markus Till
Vehicles 2022, 4(1), 137-144; https://doi.org/10.3390/vehicles4010009
Received: 25 January 2022 / Revised: 4 February 2022 / Accepted: 8 February 2022 / Published: 10 February 2022
(This article belongs to the Special Issue Vehicle Design Processes)
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. View Full-Text
Keywords: statistical customer data; design-relevant parameters; design of vehicle drive systems statistical customer data; design-relevant parameters; design of vehicle drive systems
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MDPI and ACS Style

Mieth, R.; Gauterin, F.; Pauli, F.; Kraus, H. Transfer of Statistical Customer Data into Relevant Parameters for the Design of Vehicle Drive Systems. Vehicles 2022, 4, 137-144. https://doi.org/10.3390/vehicles4010009

AMA Style

Mieth R, Gauterin F, Pauli F, Kraus H. Transfer of Statistical Customer Data into Relevant Parameters for the Design of Vehicle Drive Systems. Vehicles. 2022; 4(1):137-144. https://doi.org/10.3390/vehicles4010009

Chicago/Turabian Style

Mieth, Raphael, Frank Gauterin, Felix Pauli, and Harald Kraus. 2022. "Transfer of Statistical Customer Data into Relevant Parameters for the Design of Vehicle Drive Systems" Vehicles 4, no. 1: 137-144. https://doi.org/10.3390/vehicles4010009

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