Many surveys on vehicle traffic safety have shown that the tire road friction coefficient(TRFC)is correlated with the probability of an accident.The probability of road accidents increases sharply on slippery road sur...Many surveys on vehicle traffic safety have shown that the tire road friction coefficient(TRFC)is correlated with the probability of an accident.The probability of road accidents increases sharply on slippery road surfaces.Therefore,accurate knowledge of TRFC contributes to the optimization of driver maneuvers for further improving the safety of intelligent vehicles.A large number of researchers have employed different tools and proposed different algorithms to obtain TRFC.This work investigates these different methods that have been widely utilized to estimate TRFC.These methods are divided into three main categories:off-board sensors-based,vehicle dynamics-based,and data-driven-based methods.This review provides a comparative analysis of these methods and describes their strengths and weaknesses.Moreover,some future research directions regarding TRFC estimation are presented.展开更多
Four-wheel independently driven electric vehicles(FWID-EV)endow a flexible and scalable control framework to improve vehicle performance.This paper integrates the torque vectoring and active suspension system(ASS)to e...Four-wheel independently driven electric vehicles(FWID-EV)endow a flexible and scalable control framework to improve vehicle performance.This paper integrates the torque vectoring and active suspension system(ASS)to enhance the vehicle’s longitudinal and vertical motion control performance.While the nonlinear characteristic of the tire model leads to a relatively heavier computational burden.To facilitate the controller design and ease the load,a half-vehicle dynamics system is built and simplified to the linear-time-varying(LTV)model.Then a model predictive controller is developed by formulating the objective function by comprehensively considering the safety,energy-saving and comfort requirements.The in-wheel motor efficiency and the power loss of tire slip are treated as optimization indices in this work to reduce energy consumption.Finally,the effectiveness of the proposed controller is verified through the rapid-control-prototype(RCP)test.The results demonstrate the enhancement of the energy-saving as well as comfort on the basis of vehicle stability.展开更多
Realistic faults and failures often occur probabilistically in the lane-keeping system of autonomous electric vehicles,reducing system reliability and posing significant challenges to driving safety.To enhance the sys...Realistic faults and failures often occur probabilistically in the lane-keeping system of autonomous electric vehicles,reducing system reliability and posing significant challenges to driving safety.To enhance the system resilience,this paper proposes a novel robust fuzzy fault-tolerant control strategy that incorporates the adaptive event-trigger(AET)mechanism to realize stable,reliable,and precise lane-keeping control in the presence of multiple system uncertainties and probabilistic faults.First,to capture the uncertain and time-varying nature of tire cornering stiffness,an effective Takagi-Sugeno(T-S)fuzzy tire model is developed.Then,by employing the distribution-based probabilistic approach,two sets of unrelated random variables,random sensor and actuator faults in the control system,are modeled.Next,to improve communication efficiency and address ineluctable network-induced delays,an AET control framework with a well-designed triggering condition is established.Subsequently,a robust fuzzy output feedback fault-tolerant lane-keeping controller that satisfies the H∞per-formance is designed by using the Lyapunov-Krasovski functional method.Furthermore,the mean-square ex-ponential stability of the closed-loop system is rigorously guaranteed.Finally,real-time simulations based on Carsim/Simulink co-simulation platform under dynamic driving conditions demonstrate the feasibility and ef-fectiveness of the proposed control strategy.展开更多
基金Supported by the National Natural Science Funds for Distinguished Young Scholar of China(Grant No.52025121)National Natural Science Foundation of China(Grant Nos.51975118,52002066).
文摘Many surveys on vehicle traffic safety have shown that the tire road friction coefficient(TRFC)is correlated with the probability of an accident.The probability of road accidents increases sharply on slippery road surfaces.Therefore,accurate knowledge of TRFC contributes to the optimization of driver maneuvers for further improving the safety of intelligent vehicles.A large number of researchers have employed different tools and proposed different algorithms to obtain TRFC.This work investigates these different methods that have been widely utilized to estimate TRFC.These methods are divided into three main categories:off-board sensors-based,vehicle dynamics-based,and data-driven-based methods.This review provides a comparative analysis of these methods and describes their strengths and weaknesses.Moreover,some future research directions regarding TRFC estimation are presented.
基金Supported by National Natural Science Foundation of China(Grant Nos.51975118,52025121)Foundation of State Key Laboratory of Automotive Simulation and Control of China(Grant No.20210104)+1 种基金Foundation of State Key Laboratory of Automobile Safety and Energy Saving of China(Grant No.KFZ2201)Special Fund of Jiangsu Province for the Transformation of Scientific and Technological Achievements of China(Grant No.BA2021023).
文摘Four-wheel independently driven electric vehicles(FWID-EV)endow a flexible and scalable control framework to improve vehicle performance.This paper integrates the torque vectoring and active suspension system(ASS)to enhance the vehicle’s longitudinal and vertical motion control performance.While the nonlinear characteristic of the tire model leads to a relatively heavier computational burden.To facilitate the controller design and ease the load,a half-vehicle dynamics system is built and simplified to the linear-time-varying(LTV)model.Then a model predictive controller is developed by formulating the objective function by comprehensively considering the safety,energy-saving and comfort requirements.The in-wheel motor efficiency and the power loss of tire slip are treated as optimization indices in this work to reduce energy consumption.Finally,the effectiveness of the proposed controller is verified through the rapid-control-prototype(RCP)test.The results demonstrate the enhancement of the energy-saving as well as comfort on the basis of vehicle stability.
基金Supported by National Natural Science Foundation of China(Grant Nos.52025121,52394263)National Key R&D Plan of China(Grant No.2023YFD2000301)+2 种基金Foundation of State Key Laboratory of Automobile Safety and Energy Saving of China(Grant No.KFZ2201)Jiangsu Provincial Scientific Research Center of Applied Mathematics(Grant No.BK20233002)Special Fund of Jiangsu Province for the Transformation of Scientific and Technological Achievements(Grant No.BA2021023).
文摘Realistic faults and failures often occur probabilistically in the lane-keeping system of autonomous electric vehicles,reducing system reliability and posing significant challenges to driving safety.To enhance the system resilience,this paper proposes a novel robust fuzzy fault-tolerant control strategy that incorporates the adaptive event-trigger(AET)mechanism to realize stable,reliable,and precise lane-keeping control in the presence of multiple system uncertainties and probabilistic faults.First,to capture the uncertain and time-varying nature of tire cornering stiffness,an effective Takagi-Sugeno(T-S)fuzzy tire model is developed.Then,by employing the distribution-based probabilistic approach,two sets of unrelated random variables,random sensor and actuator faults in the control system,are modeled.Next,to improve communication efficiency and address ineluctable network-induced delays,an AET control framework with a well-designed triggering condition is established.Subsequently,a robust fuzzy output feedback fault-tolerant lane-keeping controller that satisfies the H∞per-formance is designed by using the Lyapunov-Krasovski functional method.Furthermore,the mean-square ex-ponential stability of the closed-loop system is rigorously guaranteed.Finally,real-time simulations based on Carsim/Simulink co-simulation platform under dynamic driving conditions demonstrate the feasibility and ef-fectiveness of the proposed control strategy.
