Ausblick – Wo geht es hin?

Pischinger, Stefan; Seiffert, Ulrich

doi:10.1007/978-3-658-09528-4_13

Cited by 10 publications

(15 citation statements)

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“…Here, the aim is to simulate the drive and recuperation torques of the electric machine required by the vehicle. In order to model the exact power flow in the powertrain, a longitudinal dynamics simulation is sufficient when considering normal road traffic [18], whereas only the vehicle speed and thus the acceleration in the direction of travel are taken into account. Only in the application case of motorsports do the lateral dynamics of the vehicle have a significant influence on driving behavior and powertrain [19].…”

Section: Vehicle Modelingmentioning

confidence: 99%

Dependency of Machine Efficiency on the Thermal Behavior of Induction Machines

et al. 2020

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The consideration of the thermal behavior of electric machines is becoming increasingly important in the machine design for electric vehicles due to the adaptation to more dynamic operating points compared to stationary applications. Whereas, the dependency of machine efficiency on thermal behavior is caused due to the impact of temperature on the resulting loss types. This leads to a shift of efficiency areas in the efficiency diagram of electric machines and has a significant impact on the maximum load capability and an impact on the cycle efficiency during operation, resulting in a reduction in the overall range of the electric vehicle. Therefore, this article aims at analyzing the thermal load limits of induction machines in regard to actual operation using measured driving data of battery electric vehicles. For this, a thermal model is implemented using MATLAB® and investigations to the sensitivity of model parameters as well as analysis of the continuous load capacity, thermal load and efficiency in driving cycles under changing boundary conditions are conducted.

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Section: Vehicle Modelingmentioning

confidence: 99%

Dependency of Machine Efficiency on the Thermal Behavior of Induction Machines

et al. 2020

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“…The operating points of a vehicle are individually dependent on the respective application and use case, have a direct impact on the overall machine and powertrain design and should, therefore, be considered in the future design process of electric vehicles. In the following Figure 11, the operating points of the created driving cycle for the taxi driving data are illustrated, which are determined using the machine design tool presented in [19] and a longitudinal dynamics simulation tool based on [20]. It can be seen that in regard to the classification of the ARTEMIS and the KBA, the created driving cycle mostly demonstrates characteristics of an urban or city cycle.…”

Section: Requirements For Electric Machine Designmentioning

confidence: 99%

Requirements for Electric Machine Design based on Operating Points from Real Driving Data in Cities

Kalt

Brenner

Lienkamp

2019

WEVJ

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Increasing environmental awareness leads to the necessity for more efficient powertrains in the future. However, the development of new vehicle concepts generates a trend towards ever shorter development cycles. Therefore, new concepts must be tested and validated at an early stage in order to meet the increasing time pressure. This requires the determination of real driving data in fleet tests in order to generate realistic driving cycles, which correspond as closely as possible to the actual driving behavior of the applications use case. Within the scope of this paper, real driving data are analyzed and used to create a representative driving cycle. The resulting driving cycle based on real driving characteristics is then used to investigate the impact of application-based design for powertrains on the design of electric machines, by illustrating the difference between synthetic operating points and real driving data.

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“…The body roll angle is given by equation 31, where 쳌 t쳌t 쳌‫‬tt (referring to the explanation of anti-roll stiffness in subsection 3.6) is the total resistance against roll. When combining (30) with (31) and inserting the results into (29), it is possible to formulate correlation (32) between the configuration's self-steer characteristics and the needed coefficient for toe kinematics:…”

Section: Suspension Kinematicsmentioning

confidence: 99%

Parameter-adaption for a vehicle dynamics model for the evaluation of powertrain concept designs

et al. 2019

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The powertrain design of multi-motor electric vehicles directly affects not only costs, consumption and acceleration, but also the handling of a vehicle. Therefore, a holistic powertrain design optimization needs to include a vehicle dynamics model in its objective function. While the parameters for the powertrain model result from the design variables that describe the powertrain, the parameters for the vehicle dynamics model must be adapted in a feasible way to ensure comparable results. Therefore, the authors present a method on how to adaptively parametrize a double-track vehicle dynamics model for the use in powertrain design optimization. Automated design calculations for all main chassis and suspension parts are used to determine the parameters for the model. A parameter variation proves the plausibility of the approach. The results show that an adaption of the suspension and chassis parameters due to changes in the powertrain make results more comparable but do not compensate for the effects on the vehicle handling. In particular, the steady state longitudinal load distribution still has major influences on the vehicle handling.

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Ausblick – Wo geht es hin?

Cited by 10 publications

References 0 publications

Dependency of Machine Efficiency on the Thermal Behavior of Induction Machines

Dependency of Machine Efficiency on the Thermal Behavior of Induction Machines

Requirements for Electric Machine Design based on Operating Points from Real Driving Data in Cities

Parameter-adaption for a vehicle dynamics model for the evaluation of powertrain concept designs

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