| The development of electronic technology makes the application of electronic control system more and more in the automobile,especially the dynamic control system.As a typical dynamic control system,ESC is not only playing a key role in the active security field,but also the executive mechanism of the intelligent driving system.The state parameters of the vehicle are the basis of the ESC control,which has a direct impact on the effect of the related functions of the ESC.From the point of view of the cost pair of the engineering application,a lot of the state parameters of the vehicle are unable to measure all the required state parameters by the sensor.Therefore,the real time and accurate estimation of state parameters have become the restrict the application of ESC industrialization.At present,no matter in the field of ESC control research or industrial application,the key technology has not been grasped in China,which make ESC products difficult to meet the industrial needs.Based on the project of cooperation between research group and Zhejiang Asia Pacific Mechatronics Limited by Share Ltd,in order to develop the complete ESC product,the key state parameter estimation methods for these problems were studied.The proposed algorithm for the project applied in implementation and verification of ESC,the real vehicle test under various conditions showed that the proposed algorithm can ensure the safety,real-time,accuracy.And the experiment results pass the EU ECE regulations R13 H by third party certification.The main contents are as follows:First,a multi parameter correction algorithm for longitudinal force compensation in braking condition is proposed.In this paper,the input signals needed for state parameter estimation are divided into two kinds: direct sensor measurement and indirect simple calculation,and the signal processing methods are sorted and analyzed.For the most basic tire force,a simplified calculation is first made,then the longitudinal stiffness,slip rate and adhesion coefficient of tire are taken into account.Finally,a compensation algorithm for longitudinal force under braking condition is put forward.Second,in consideration of road resistance and slope,a vehicle mass recursive estimation method based on working condition suitability is proposed.Sort out the working conditions suitable for quality estimation,use the TSK fuzzy model to judge the suitability of the current working conditions,and get the membership degree as the credibility of the calculation results.The road slope and rolling resistance are considered,and the effective longitudinal acceleration of the current vehicle is obtained,and the mass is calculated according to the longitudinal dynamic equation.The recurrence coefficient is obtained according to the reliability of the calculated results,and the estimated value of the quality is recursively.In a certain state of road resistance and slope,the quality is regarded as the slope of the linear relation curve of longitudinal force and longitudinal acceleration.Select the appropriate sample,combined with statistical covariance and variance to calculate the quality.By the linear relation of slip rate,longitudinal force and vertical force under specific conditions,the ratio of vertical force is calculated by slip rate and longitudinal force.The center of mass of the vehicle is calculated by the force balance,and the estimated value of the center of mass is recurred based on the reliability of the calculated results.According to the reliability of the calculated results,the recurrence coefficient of the next control period is given,and the estimated value of the quality is recursively estimated and amended.Third,a fuzzy recognition method based on wheel dynamic stability is proposed.Based on this method,a fuzzy algorithm of reference speed based on wheel speed reliability is further put forward.Vehicle driving is divided into three conditions: acceleration,deceleration and uniform speed.According to the characteristics of each working condition,the TSK fuzzy model is used to describe the dynamic characteristics of input wheel speed.Through the parameters of wheel speed,acceleration and force change,the reliability of the current input wheel speed is calculated,and the current reference speed is obtained by solving the fuzzy solution.Because the change of ABS wheel speed in the most complex,so estimating was the the most difficult.By analyzing the change law of all wheel speed,the relative stability of the wheel is characterized by priority,and the "preferred" wheel is screened.The results are used in fuzzy rules as one of the reliable judging conditions for wheel speed.Combining the speed reliability of the input wheel and the change of wheel speed,a correction and compensation algorithm for the reference speed and acceleration is proposed,and the results are further optimized.Fourth,in view of engineering application,a vehicle stability state characterization method with multiple correlation parameters is proposed.Based on the variation rules of each parameter,the formulae for calculating the stability state were put forward.The judgment threshold value of the vehicle instability is obtained by the comprehensive stability state characterization.The instability is divided into four parts: insufficient steering,over steering,backward steering and excitation.The difference between yaw rate and actual value is taken as the judging condition.According to the characteristics of all unstable conditions and the judgement of threshold values,judging whether the vehicle was unstable.Fifth,based on the independent design of ESC hardware assembly,the proposed state parameter estimation algorithm is carried out to carry out a real car test.The accuracy,real-time and robustness of the algorithm are verified by various working conditions.It is integrated with the control strategy of ESC.According to the winter and summer matching test of ESC,the comprehensive evaluation of the proposed algorithm is made from the perspective of the complete ESC system application and the experiment results pass the EU ECE regulations R13 H third party certification. |
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