Applying machine learning for cars’semi-active air suspension under soft and rigid roads

To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized rules of the fuzzy control(FC)method and car dynamic model for application in SASs.The root-mean-square(RMS)acceleration of the driver’s seat and car’s pitch angle are chosen as the objective functions.The results indicate that a soft surface obviously influences a car’s ride quality,particularly when it is traveling at a high-velocity range of over 72 km/h.Using the ML method,the car’s ride quality is improved as compared to those of FC and without control under different simulation conditions.In particular,compared with those cars without control,the RMS acceleration of the driver’s seat and car’s pitch angle using the ML method are respectively reduced by 30.20% and 19.95% on the soft road and 34.36% and 21.66% on the rigid road.In addition,to optimize the ML efficiency,its learning data need to be updated under all various operating conditions of cars.

Method of ameliorating the lubrication and friction performance of an engine based on different microtextures

A design of different microtextures on the surface of the crankpin bearing(CB)is proposed to ameliorate the lubrication and friction performance(LFP)of engines.On the basis of the CB s hydrodynamic lubrication model,the bearing surface of CB using different microtextures,such as wedge-shaped textures(WSTs),square textures(STs),circular textures(CTs),and combined square-circular textures(CSCTs),is simulated and assessed under various external loads of the CB at an engine speed of 2000 r/min.The pressure of the oil film,the frictional force,the force of the solid asperity contact,and the friction coefficient of the CB are used as objective functions.Results indicate that the bearing surface designed by the STs remarkably improves the CB s LFP in comparison with other structures of WSTs,CTs,and CSCTs.Particularly,the average values of the frictional force,solid asperity contact,and friction coefficient of the CB using the STs are greatly reduced by 28.5%,14.5%,and 33.2%and by 34.4%,26.3%,and 43.6%in comparison with the optimized CB dimensions and CTs,respectively.Therefore,the application of the STs on the CB surfaces can enhance the LFP of engines.

Comparison of the vibration isolation performance of a seat suspension with various design modes

Three design modes of seat suspension,i.e.,negative stiffness elements(NSEs),damping elements(DEs),and negative stiffness-damping elements(NSDEs),are proposed to evaluate the ride performance of a vehicle.Based on a dynamic model of a seat suspension and indexes of the root mean square deformation and acceleration of the seat suspension(x RMS)and driver s seat(a RMS),the influence of the design parameters of the NSEs,DEs,and NSDEs on the driver s ride comfort is evaluated.A genetic algorithm is then applied to optimize the parameters of the NSEs,DEs,and NSDEs.The study results indicate that the design parameters of the NSEs and NSDEs remarkably influence x RMS and a RMS,whereas those of the DEs insignificantly influence x RMS and a RMS.Based on the optimal results of the NSEs,DEs,and NSDEs,the damping force of the DEs is 98.3%lower than the restoring force of the NSEs.Therefore,the DEs are ineffective in decreasing x RMS and a RMS.Conversely,the NSEs combined with the damping coefficient of the seat suspension strongly reduce x RMS and a RMS.Consequently,the NSEs can be added to the seat suspension,and the damping coefficient of the seat suspension can also be optimized or controlled to further enhance the vehicle s ride performance.