TY  - JOUR
A1  - Kreyer, Jörg
A1  - Müller, Marvin
A1  - Esch, Thomas
T1  - A Calculation Methodology for Predicting Exhaust Mass Flows and Exhaust Temperature Profiles for Heavy-Duty Vehicles
JF  - SAE International Journal of Commercial Vehicles
N2  - The predictive control of commercial vehicle energy management systems, such as vehicle thermal management or waste heat recovery (WHR) systems, are discussed on the basis of information sources from the field of environment recognition and in combination with the determination of the vehicle system condition.
In this article, a mathematical method for predicting the exhaust gas mass flow and the exhaust gas temperature is presented based on driving data of a heavy-duty vehicle. The prediction refers to the conditions of the exhaust gas at the inlet of the exhaust gas recirculation (EGR) cooler and at the outlet of the exhaust gas aftertreatment system (EAT). The heavy-duty vehicle was operated on the motorway to investigate the characteristic operational profile. In addition to the use of road gradient profile data, an evaluation of the continuously recorded distance signal, which represents the distance between the test vehicle and the road user ahead, is included in the prediction model. Using a Fourier analysis, the trajectory of the vehicle speed is determined for a defined prediction horizon.
To verify the method, a holistic simulation model consisting of several hierarchically structured submodels has been developed. A map-based submodel of a combustion engine is used to determine the EGR and EAT exhaust gas mass flows and exhaust gas temperature profiles. All simulation results are validated on the basis of the recorded vehicle and environmental data. Deviations from the predicted values are analyzed and discussed.
Y1  - 2020
U6  - http://dx.doi.org/10.4271/02-13-02-0009
SN  - 1946-3928
VL  - 13
IS  - 2
SP  - 129
EP  - 143
PB  - SAE International
CY  - Warrendale, Pa.
ER  - 
TY  - CHAP
A1  - Walter, Peter
A1  - Elsen, Ingo
A1  - Müller, Holger
A1  - Kraiss, Karl-Friedrich
T1  - 3D object recognition with a specialized mixtures of experts architecture
T2  - IJCNN'99. International Joint Conference on Neural Networks. Proceedings
N2  - Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.
Y1  - 1999
SN  - 0-7803-5529-6
U6  - http://dx.doi.org/10.1109/IJCNN.1999.836243
SN  - 1098-7576
N1  - Washington, DC 10-16.07.1999
SP  - 3563
EP  - 3568
PB  - IEEE
CY  - New York
ER  -