Simulation of hybrid electric vehicle based on a series drive train layout

This paper provided a validated modeling and a simulation of a 6 degree freedom vehicle longitudinal model and drive-train component in a series hybrid electric vehicle. The 6-DOF vehicle dynamics model consisted of tire subsystems, permanent magnet synchronous motor which acted as the prime mover coupled with an automatic transmission, hydraulic brake subsystem, battery subsystem, alternator subsystem and internal combustion engine to supply the rotational input to the alternator. A speed and torque tracking control systems of the electric power train were developed to make sure that the power train was able to produce the desired throttle torque in accelerating the vehicle. A human-in-the-loop-simulation was utilized as a mechanism to evaluate the effectiveness of the proposed hybrid electric vehicle. The proposed simulation was used as the preliminary result in identifying the capability of the vehicle in terms of the maximum speed produced by the vehicle and the capability of the alternator to recharge the battery. Several tests had been done during the simulation, namely sudden acceleration, acceleration and braking test and unbounded motion. The results of the simulation showed that the proposed hybrid electric vehicle can produce a speed of up to 70 km/h with a reasonable charging rate to the battery. The findings from this study can be considered in terms of design, optimization and implementation in a real vehicle

Characterization of a Magnetorheological Fluid Damper Applied to Semi-Active Engine Mounting System / M. Hafiz Harun...[et al.]

This study is to propose a hysteresis damper model that can be integrated with the vehicle control system. A prototype of magnetorheological for engine mounting has been designed and tested to realize the objective of this study. The experimental on the prototype of the magnetorheological damper for engine mounts has been conducted in order to investigate the hysteresis of this damper. From the experiment, the results are evaluated in terms of damping force versus piston displacement and also the damping force versus piston velocity. It is significantly shows that the proposed model satisfy the non-linear hysteresis behavior of the MR damper in the form of force-velocity and force-displacement characteristics

Influence of cyclic variance on the performance of URANS for pulsating flow upstream of an automotive catalyst monolith

CFD predictions using the URANS equations have been compared to phase-averaged and instantaneous PIV pulsating flow fields in a planar diffuser upstream of a catalyst monolith. URANS qualitatively capture the velocity and vorticity fields, in particular the spatial and temporal evolution of the main vortices in the diffuser during the acceleration phase of the cycle. However, they over-predict vortex intensity and hence its residual strength at the beginning of successive pulses. Instantaneous PIV measurements show there is a significant cycle-to-cycle variation in the position and structure of the vortices within the diffuser. This serves to "diffuse" the phase-averaged vortex strength. Alternative simulation techniques such as LES or DNS will be needed if the cyclic variation is to be correctly predicted as this will affect conversion efficiency

Evaluation of the convolutional neural network’s performance in classifying steel strip’s surface defects

Steel strip plays a vital role in many industrial fields. Its defects will impact the manifestation of the product and also reduce the features of the product, resulting in a huge economic loss. Deep learning algorithms, such as Convolutional Neural Network (CNN), have successfully been applied to image classification, while featuring a great level of abstraction and learning capabilities. These features are keys to detect and classify surface defects in a robust and reliable manner. The images used for training and testing the model are obtained from the NEU Surface Defect Database which contains six kinds of typical surface defects of steel strips that are rolled-in-scale, patches, crazing, pitted surface, inclusion and scratches. These images are pre-processing to enhance them and extract some useful information from them. After that, the CNN models are trained and tested with these images to evaluate their performance. The specific hyperparameters for the CNN model which are tuned are number of epochs, batch size, number of convolutional layers, input image size and kernel size. For each hyperparameter, the CNN model is trained and tested several times using different values of that hyperparameter. The training accuracy, testing accuracy and training time are recorded and analyzed. Lastly, the final CNN model with high performance is produced. The final CNN model based on the optimum hyperparameters is produced. It has a very high training accuracy of 95.12% and a fairly high testing accuracy of 85.43%. This paper focused on the application of CNN in the classification of the steel strip’s surface defects. The performance of the CNN models with different values of hyperparameters are also evaluated