The influences of different design factors,as well as dummy posture,on an occupants' knee slider compression,were studied in this paper.Based on the vehicle geometry data,the simulation model,including both the mu...The influences of different design factors,as well as dummy posture,on an occupants' knee slider compression,were studied in this paper.Based on the vehicle geometry data,the simulation model,including both the multi-rigid-body and finite element(FE)part,was built up and validated with China New Car Assessment Program(C-NCAP)full impact to ensure the accuracy of the model.By adjusting the design parameters and the posture of the femur and lower leg,different factors affecting the passengers' knee slider compression were evaluated,with the help of MAthematical DYnamic MOdel(MADYMO)simulations.The study indicated that the leg posture,the stiffness of the IP and angles of the carpet have significant effects on the knee slider compression in this case.By decreasing the angle between the femur and lower leg from 133° to 124°,the maximum knee slider compression was decreased by 17.3% and by scaling the IP stiffness from 1 to 0.7,it could be decreased by 18.6%.Also,decreasing the angles of the carpet from 28° to 37°can help reduce the knee slider compression by 18.3%.展开更多
Accurate estimation of sideslip angle and vehicle velocity is crucial for effective control of distributed drive electric vehicles.However,as these states are not directly measured,Kalman-based approaches utilizing in...Accurate estimation of sideslip angle and vehicle velocity is crucial for effective control of distributed drive electric vehicles.However,as these states are not directly measured,Kalman-based approaches utilizing in-vehicle sensors have been developed to estimate them.Unfortunately,existing methods tend to ignore the impact of data loss on estimation performance.Furthermore,the process noise,which changes dynamically due to varying driving conditions,is not adequately considered.In response to these constraints,we propose a novel method called the fuzzy adaptive fault-tolerant extended Kalman filter(FAFTEKF).Initially,a fault-tolerant EKF is devised to handle missing measurements.Additionally,a fuzzy logic system that dynamically updates the process noise matrix,is built to improve estimation accuracy under different driving conditions.Extensive experimental results validate the superiority of the FAFTEKF over the traditional EKF across various scenarios with different degrees of data loss.展开更多
基金Supported by the National Natural Science Foundation of China(51405050)Key Laboratory of Advanced Manufacturing Technology for Automobile Parts,Ministry of Education(2016KLMT03)Scientific and Technological Research Program of Chongqing Municipal Education Commission(KJ1500912)
文摘The influences of different design factors,as well as dummy posture,on an occupants' knee slider compression,were studied in this paper.Based on the vehicle geometry data,the simulation model,including both the multi-rigid-body and finite element(FE)part,was built up and validated with China New Car Assessment Program(C-NCAP)full impact to ensure the accuracy of the model.By adjusting the design parameters and the posture of the femur and lower leg,different factors affecting the passengers' knee slider compression were evaluated,with the help of MAthematical DYnamic MOdel(MADYMO)simulations.The study indicated that the leg posture,the stiffness of the IP and angles of the carpet have significant effects on the knee slider compression in this case.By decreasing the angle between the femur and lower leg from 133° to 124°,the maximum knee slider compression was decreased by 17.3% and by scaling the IP stiffness from 1 to 0.7,it could be decreased by 18.6%.Also,decreasing the angles of the carpet from 28° to 37°can help reduce the knee slider compression by 18.3%.
基金Supported by National Natural Science Foundation of China(Grant No.52402482).
文摘Accurate estimation of sideslip angle and vehicle velocity is crucial for effective control of distributed drive electric vehicles.However,as these states are not directly measured,Kalman-based approaches utilizing in-vehicle sensors have been developed to estimate them.Unfortunately,existing methods tend to ignore the impact of data loss on estimation performance.Furthermore,the process noise,which changes dynamically due to varying driving conditions,is not adequately considered.In response to these constraints,we propose a novel method called the fuzzy adaptive fault-tolerant extended Kalman filter(FAFTEKF).Initially,a fault-tolerant EKF is devised to handle missing measurements.Additionally,a fuzzy logic system that dynamically updates the process noise matrix,is built to improve estimation accuracy under different driving conditions.Extensive experimental results validate the superiority of the FAFTEKF over the traditional EKF across various scenarios with different degrees of data loss.
