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Objectified Drivability Analysis and Evaluation of Deceleration Maneuvers for Electric Vehicles
ISSN: 1946-3936, e-ISSN: 1946-3944
Published February 15, 2021 by SAE International in United States
Citation: Ossendorff, D., Heusch, C., Tatas, L., Wick, M. et al., "Objectified Drivability Analysis and Evaluation of Deceleration Maneuvers for Electric Vehicles," SAE Int. J. Engines 14(3):387-403, 2021, https://doi.org/10.4271/03-14-03-0023.
Objectified analysis and evaluation tools offer cost- as well as time-saving potentials regarding the calibration process of vehicle control units. To reduce the time required for the calibration effort, standardized processes including the frontloading of development tasks enable swift calibration procedures and can be used to develop a basis for the comparison of different vehicles and also the calibration quality. In this environment, objectified evaluation methods are also being developed for the investigation of the drivability of electric vehicles.
This article presents a methodology for assessing the longitudinal drive behavior of battery electric vehicles during deceleration maneuvers.
The aim is to objectively evaluate the vehicle deceleration by means of reproducible driving maneuvers. In addition to further measurement signals, the longitudinal acceleration signal serves as the main evaluation basis. Based on characteristic points during the event, the deceleration maneuver is divided into sectors, which is fundamental for further evaluation. Within these sectors, corresponding objective criteria are determined, which describe the behavior of the vehicle during deceleration. Regardless of the sectors, the calculated and determined values are used to evaluate the entire driving maneuver. The determined surrogate parameters allow an objective evaluation of the subjective “drivability” and target-oriented monitoring of the objectives in the development process.
The newly developed methodology is explained in detail in this article and compared with real measured validation data from different electric vehicles.