The need of quick innovation in the automotive domain made simulation necessary at early stages of the development cycle. Vehicles and powertrains are complex systems where different domains are involved. Representati...
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The need of quick innovation in the automotive domain made simulation necessary at early stages of the development cycle. Vehicles and powertrains are complex systems where different domains are involved. Representative phenomenological models of powertrains have been developed and have been used in the design phase under domain dedicated tools. However, their use for controls validation using Model-In-the-loop (MIL) and Hardware-In-the-loop (HIL) was prevented due to performance limitation of widely used single-solver/single-core simulation approaches. This paper proposes a simulation approach for complex systems simulation, taking into account the fact that the computational power improvement in today processors is mainly driven by the augmentation of the number of cores per processor, rather than in cores frequency increase. It first focuses into numerical solvers characteristics, especially the time-step and order management, to identify their influence on the accuracy and the simulation speed. Then, a parallel simulation method is proposed in order to exploit more efficiently the parallelism provided by multi-core architectures, as well as involved complex systems nature (different dynamics and time scales), while avoiding disrupting simulation results. Finally, this method is validated on phenomenological engine test cases, and its effectiveness is illustrated.
This paper presents a 9-degree of freedom (DOF) vehicle model combined with a closed loop driver model for the purpose of developing vehicle lateral control. The driver model was developed to control the steering angl...
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ISBN:
(纸本)9781424478149
This paper presents a 9-degree of freedom (DOF) vehicle model combined with a closed loop driver model for the purpose of developing vehicle lateral control. The driver model was developed to control the steering angle and uses the lookup table path as a reference for the control input. The proposed outer-loop controller structure for the driver model is a combination of proportional gain control with a yaw effect adaptive fuzzy logic control. A stepper motor model, rack and pinion model, and kinematics model of the steering system are also briefly introduced as an inner-loop sub-system for stepper motor actuated steering (SMAS) system. The proposed inner-loop controller is a closed-loop positioning control for the stepper motor. The performance of the outer-loop and inner-loop controllers were evaluated using predefined trajectory for lanekeeping and double lane change (DLC) maneuvers at 80 km/h constant speed. Both of the controller's software-in-the-loop simulations (SILS) results were validated using an instrumented automatic steering test rig through the hardware-in-the-loopsimulation (HILS). The SILS and HILS results show that the proposed driver model is capable of improving the Y-axis trajectory error and maneuvers significantly and the proposed SMAS system is capable of tracking the desired steering angle position and producing the front wheel steer angle for the use of vehicle model.
To evaluate the software behavior of the electronic control unit （ECU） of automotive electrical parking brake （EPB）, a software- in-the-loop （SiL） simulation system is built. The EPB is simulated by ARX （auto-r...
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To evaluate the software behavior of the electronic control unit （ECU） of automotive electrical parking brake （EPB）, a software- in-the-loop （SiL） simulation system is built. The EPB is simulated by ARX （auto-regressive with auxiliary input） model, ARMAX （auto-regressive moving average with auxiliary input） model, and NNARMAX （neural network ARMAX） model. By system identification, the ARX（3,4,2）, ARX（4,4,2）, ARMAX（3,3,1,1）, and ARMAX（4,4,3,2） models are derived. Validation results show that the four-order ARMAX model and the NNARMAX model better simulate the actuator of the EPB.
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