Advanced Numerical Approaches for Analysis of Vehicle Ride Comfort, Wheel Bearings and Steering Control
Mahala, Manoj Kumar
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Suspension systems and wheels play a critical role in vehicle dynamics performance of a car in areas such as ride comfort and handling. Lumped parameter models (LPMs) are commonly used for assessing the performance of vehicle suspension systems. However, there is a lack of clarity with regard to the relative capabilities of different LPM configurations. A comprehensive comparative study of three most commonly used LPMs of increasing complexity has been carried out in the current work. The study reported here has yielded insights into the capabilities of the considered LPMs in predicting response time histories which may be used for assessing ride comfort. A shortcoming of available suspension system models appears to be in representation of harsh situations such as jounce movement which cause full compression of springs leading to ‘jerks’ manifested as high values of rate of change of acceleration of sprung mass riding on a wheel. In the current research work, a modified nonlinear quarter-car model is proposed to account for the contact force that results in jerk-type response. The numerical solution algorithm is validated through the simulation of an impact test on a car McPherson strut in a Drop Weight Impact Testing Tower developed in CAR Laboratory, CPDM. This is followed by a detailed comparison of HCM and QCM to examine their suitability for such analysis. For decades, wheel bearings in vehicles have been designed using simplified analytical approaches based on Hertz contact theory and test data. In the present work, a hybrid approach has been developed for assessing the load bearing capacity of a wheel ball bearing set. According to this approach, the amplitude of dynamic wheel load can be obtained from a lumped parameter analysis of a suspension system, which can then be used for detailed static finite element analysis of a wheel bearing system. The finite element modelling approach has been validated by successfully predicting the load bearing capacity of an SKF ball bearing set for an acceptable fatigue life. For the first time, using a powerful commercial explicit finite element analysis tool, a detailed dynamic analysis has been carried of a deep groove ball bearing with a rotating inner race. The analysis has led to a consistent representation of complex motions consisting of rotations and revolutions of rolling elements, and generated insights into the stresses developed in the various components such as balls and races. In conclusion, a simple yet effective fuzzy logic-based yaw control algorithm has been presented in the current research. According to this algorithm, two inputs i.e. a yaw rate error and a driver steering angle are used for generating an output in the form of an additive steering angle which potentially can aid a driver in avoiding straying from an intended path.