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System Identification Method for Brake Particle Emission Measurements of Passenger Car Disc Brakes on a Dynamometer
Technical Paper
2018-01-1884
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Besides particulate emissions from engine exhausts, which are already regulated by emission standards, passenger car disc brakes are a source of particulate matter. With the current car fleet it is estimated that up to 21% of the total traffic related PM10 emissions in urban environments originate from brake wear and reduction of brake dust emissions is subject of current research. For the purpose of reducing brake dust emissions by choosing low-emission operating points of the disc brake, the knowledge of the emission behavior depending on brake pressure, wheel speed, temperature and friction history is of interest. According to the current state of research, theoretical white box modeling of the emission behavior is complicated due to the complexity of tribological contact between pad and disc. Thus experimental black box modeling is supposed to describe emission behavior. In order to minimize the influence of disturbances and therefore to improve prediction accuracy of such empirical models, system identification methods based on periodical test signals, such as brake pressure sine, are used for this application. To adopt these test signals, which are established in transfer function measurements, to the application of brake particle measurements and to develop an experimental design, system theoretical quantities, such as cutoff frequency, signal to noise ratio and hysteresis, are determined in dynamometer tests. Therefore measurements of the system’s response to step and sine test signals are analyzed. System identification is executed and the applicability of periodical test signals to brake particle measurements is proven.
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Niemann, H., Winner, H., Asbach, C., Kaminski, H. et al., "System Identification Method for Brake Particle Emission Measurements of Passenger Car Disc Brakes on a Dynamometer," SAE Technical Paper 2018-01-1884, 2018, https://doi.org/10.4271/2018-01-1884.Data Sets - Support Documents
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