Control method for driving motor system of electric tractor

Abstract

The invention discloses an electric tractor driving motor system control method which comprises the following steps: acquiring the rotating speed omega m [n] and the output torque T [n] of a driving motor in real time, calculating the whole vehicle acceleration a [n] and the whole vehicle traction F [n] at the moment n, and calculating the servicing mass estimated value M * [n] and the running resistance estimated value F * L [n] at the moment n according to the whole vehicle acceleration a [n] and the whole vehicle traction F [n], and introducing the curb mass estimated value and the driving resistance estimated value into an open-loop transfer function G (s) of a driving motor control loop. The method has the advantages of weakening the influence caused by the change of the traction mass and the driving resistance, ensuring the consistency of acceleration characteristics and the like.

Description

Technical Field

[0001] The invention relates to the technical field of tractor driving control, in particular to a control method for a driving motor system of an electric tractor. Background Art

[0002] During the actual traction process, the traction mass of the tractor is not fixed, especially when the curb mass is very different between empty and fully loaded. This results in large differences in acceleration characteristics corresponding to different traction masses when the accelerator pedal depth changes by the same amount, especially "acceleration overshoot" when lightly loaded or "acceleration too slow" when heavily loaded, which brings certain difficulties to the driver's operation; when the driving resistance changes, especially when driving on roads with different slopes, the above problems are particularly obvious.

[0003] Therefore, there is an urgent need for a technical solution that can weaken the impact of changes in traction quality and driving resistance. Summary of the invention

[0004] The purpose of the present invention is to solve the above-mentioned deficiencies in the prior art and to provide a method for controlling a drive motor system of an electric tractor which can reduce the influence of changes in traction quality and driving resistance and ensure the consistency of acceleration characteristics.

[0005] The technical solution adopted by the present invention to solve its technical problem is: A method for controlling a driving motor system of an electric tractor, characterized in that: Step S1: Real-time acquisition of the driving motor speed ω m [n] and output torque T[n], calculate the vehicle acceleration a[n] and vehicle traction F[n] at time n, and calculate the estimated curb weight M*[n] and driving resistance F*[n] at time n based on the vehicle acceleration a[n] and vehicle traction F[n]. L [n]; Step S2: Introduce the estimated value of the curb weight and the estimated value of the driving resistance into the open-loop transfer function G(s) of the driving motor control loop; the curb weight and driving resistance of the tractor are estimated online during the driving process of the vehicle, and the estimated curb weight and driving resistance of the tractor are introduced into the driving motor control loop to weaken the influence of the change of the curb weight and driving resistance, which can ensure the consistency of the acceleration characteristics when the tractor has different traction weights and different slopes, and the involved curb weight and driving resistance are estimated only by the motor speed and the motor output torque, which can also ensure the accuracy of the estimated value of the curb weight and driving resistance in the road condition with a slope, without adding additional sensors, and at the same time, whether there is a situation of cargo being left behind can be judged by the change of the curb weight during the driving process. Then, corresponding reminders are given.

[0006] The calculation method of the vehicle acceleration a[n] and the vehicle traction force F[n] at time n in step S1 of the present invention is: a[n] = (ω m [n] - ω m [n-1])*K V / T S ; F[n] = T[n]* K T ; Among them, ω m [n] is the motor speed at time n, ω m [n-1] is the motor speed at time n-1, K V is the vehicle speed and motor speed coefficient, T S is the real-time sampling period, K T It is the conversion coefficient between motor output torque and vehicle driving force.

[0007] In step S1 of the present invention, the initialization of the estimated value of the curb weight M* is the vehicle unloaded weight M min , estimated driving resistance F* L is 0, that is: F* L = 0; M*=M min ; At rest, the estimated driving resistance F* at time n L [n] is the traction force output by the motor system, and the estimated value of the curb weight is M*, that is: F* L = F* L [n]=F[n]; M*[n] =M*.

[0008] In step S1 of the present invention, the estimated value of the curb weight M*[n] and the estimated value of the driving resistance F*[n] at time n are calculated based on the vehicle acceleration a[n] and the vehicle traction force F[n]. L The method of [n] is: F[n-1]-F L [n-1]=M[n-1]*a[n-1]; F[n]-F L [n] = M[n]*a[n]; Among them, F L [n] is the driving resistance at time n, M[n] is the curb weight at time n, F L [n-1] is the driving resistance at time n-1, M[n-1] is the curb weight at time n-1; Let F L [n-1] =F L[n], M[n-1] = M[n], solve for the estimated value of driving resistance F* at time n L [n] and the estimated curb mass M*[n].

[0009] In step S1 of the present invention, the estimated value of the running resistance F* is calculated. L [n] and the estimated value of curb weight M*[n], then F* L [n] and M*[n] are low-pass filtered to obtain M* and F* L .

[0010] The specific method for estimating the curb weight and the driving resistance in step S1 of the present invention is: Step S101: start, execute S102; Step S102: Parameter initialization, F * L = 0, M * =M min , then execute S103; Step S103: According to the sampling period T s Sampling, real-time sampling of the driving motor speed ω m [n] and output torque T[n], and calculate the vehicle acceleration a[n] and vehicle traction F[n] at time n; a[n] = (ω m [n] - ω m [n-1])*K V / T S ; F[n] = T[n]* K T ; Among them, ω m [n] is the motor speed at time n, ω m [n-1] is the motor speed at time n-1, K V is the vehicle speed and motor speed coefficient, T S is the real-time sampling period, K T It is the conversion coefficient between the motor output torque and the vehicle driving force; Step S104: determine whether the vehicle is stationary, if yes, execute S105, if no, execute S106; Step S105: ω m [n] = 0, record the estimated driving resistance value F * L =T[n]* K T , then execute S103; Step S106: According to Newton's second law, a vehicle dynamics model is constructed, and the following equations are formed by the two sets of data calculated by the latest sampling: F[n-1] -F L [n-1] = M[n-1]*a[n-1]; F[n] -F L [n] = M[n]*a[n]; The estimated value of the curb mass M is obtained based on the calculated vehicle acceleration and vehicle traction. * [n] and estimated driving resistance F* L [n]; After the calculation is completed, execute S107; Step S107: Determine the current M * [n] and M * Whether the difference exceeds the set threshold, if it exceeds the set threshold, the quality change flag S_M is set to 1, and S108 is executed; otherwise, the quality change flags S_M and S_M_P are set to 0, that is, S_M=0, S_M_P=0, and S111 is executed; S_M is a status indication that the estimated curb mass value has changed more than the set threshold value compared with the historical estimated curb mass value, and S_M_P indicates whether the existing data after the curb mass change meets the estimated mass and resistance requirements; Step S108: Determine whether the flags S_M and S_M_P are both equal to 1, if yes, execute S109, if not, execute S110; Step S109: Determine M * Whether the difference between [n] and R_M exceeds the set threshold, if yes, execute S103, if no, execute S111; When M * When the difference between [n] and R_M exceeds the set threshold: R_M=M * [n]; R_M records the current estimated instantaneous quality; When R_F * L With F * L When the difference of [n] is within the set threshold range, F is updated according to the following formula * L , F * L = K FIL * F * L [n]+ (1-K FIL )*F * L ; R_F * L = F *L [n]; Where K FIL is the filter coefficient, 0 <K FIL <1, R_F * L Record the current estimated instantaneous driving resistance; Step S110: Record the current estimated value of the curb mass and the estimated value of the driving resistance, and set the mass change flag S_M_P to 1, that is: R_M=M * [n]; R_F * L = F * L [n]; S_M_P=1; and execute S103; Step S111: Correct M using the latest estimated curb weight and running resistance * and F * L , the calculation formula is as follows: F * L = K FIL * F * L [n]+ (1-K FIL )*F * L ; M * = K FIL *M * [n] + (1-K FIL )*M * ; And execute S103.

[0011] If the present invention enters S103 from S102, it is necessary to continuously sample multiple times and record the motor speed and motor output torque of each sample respectively; If S203 is entered from other links, sampling is only required once to record the motor speed and motor output torque.

[0012] In step S106 of the present invention, When F[n] = 0, M * [n] =M * ; F * L [n] = F[n]-M * [n] * a[n]; When a[n] = 0, M * [n] =M * [n-1]; F * L [n] = F[n]; When a[n] ≠ 0 and a[n]=a[n-1], calculate M according to the following formulas: * [n] and F * L [n], M * [n] =M * [n-1]; F * L [n] =F * L [n-1]+F[n]-F[n-1]; When a[n] ≠a[n-1], calculate M according to the following formulas: * [n] and F * L [n], M * [n] = (F[n]-F[n-1] ) / (a[n]-a[n-1]); F * L [n]= (a[n]*F[n-1]-a[n-1]*F[n]) / ((a[n]-a[n-1])).

[0013] In step S2 of the present invention, the method of introducing the estimated value of the curb weight and the estimated value of the driving resistance into the open-loop transfer function G(s) of the drive motor control loop is: The speed regulator in the vehicle drive system adopts PI regulator, and the transfer function of PI regulator is (K p +K i / s), K p is the proportional gain, Ki is the integral coefficient, and the estimated value of curb weight M is introduced * With the vehicle unladen mass M min , the ratio of the two adjusts the output value of the PI regulator; The conversion coefficient K between the motor output torque and the vehicle driving force T It is known that the estimated driving resistance value F is introduced * L , by K T After conversion, the resistance torque T is obtained * L ; The drive motor system is given a torque command T G Increase resistance torque T* L ; The drive motor system is given a torque command T G The transfer function of the actual output torque T is 1 / (1+sT m ), T m is the motor torque response time constant; The actual total mass of the vehicle is M; The open-loop transfer function G(s)= (((K p +K i / s)*M * / M min +T * L ) / (1+sT m )*K T -F L ) / M / s.

[0014] In step S2 of the present invention, due to the motor torque response time constant T m is relatively small, and the open-loop transfer function G(s) is simplified to G(s)≈(((K p +K i / s)*M * / M min + T * L )*K T -F L ) / M / s; = (K p +K i / s)*K T / s / M min *M * / M+(F * L -F L ) / M / s; When the estimated value of the curb weight is close to the actual value and the estimated value of the driving resistance is close to the actual value, it can be obtained that G(s)≈(Kp+Ki / s)*K T / M min / s.

[0015] The beneficial effects of the present invention are as follows: the curb weight and driving resistance of the tractor are estimated online during vehicle driving, and the estimated curb weight and driving resistance of the tractor are introduced into the driving motor control loop to weaken the influence of the changes in the curb weight and driving resistance, and the consistency of the acceleration characteristics when the tractor has different traction weights and different slopes can be ensured, and the involved curb weight and driving resistance are estimated only by the motor speed and the motor output torque, and the accuracy of the estimated values ​​of the curb weight and driving resistance can also be ensured in the road conditions with a slope, without adding additional sensors, and at the same time, whether there is a situation of cargo being left behind can be judged by the change in the curb weight during driving, and then corresponding reminders can be given. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of online estimation of curb weight and driving resistance of the present invention.

[0017] Figure 2 It is an open-loop transfer function in which the control system of the present invention does not introduce the estimated value of the curb weight and the estimated value of the running resistance.

[0018] Figure 3 It is an open-loop transfer function in which the control system of the present invention introduces the estimated value of the curb weight and the estimated value of the running resistance. DETAILED DESCRIPTION

[0019] The present invention is described below in conjunction with the accompanying drawings and embodiments.

[0020] A control method for a driving motor system of an electric tractor, Step S1: Real-time acquisition of the driving motor speed ω m [n] and output torque T[n], calculate the vehicle acceleration a[n] and vehicle traction F[n] at time n, and calculate the estimated curb weight M*[n] and driving resistance F*[n] at time n based on the vehicle acceleration a[n] and vehicle traction F[n]. L [n]; Step S2: Introduce the estimated value of the curb weight and the estimated value of the driving resistance into the open-loop transfer function G(s) of the driving motor control loop; the curb weight and driving resistance of the tractor are estimated online during the driving process of the vehicle, and the estimated curb weight and driving resistance of the tractor are introduced into the driving motor control loop to weaken the influence of the change of the curb weight and driving resistance, which can ensure the consistency of the acceleration characteristics when the tractor has different traction weights and different slopes, and the involved curb weight and driving resistance are estimated only by the motor speed and the motor output torque, which can also ensure the accuracy of the estimated value of the curb weight and driving resistance in the road condition with a slope, without adding additional sensors, and at the same time, whether there is a situation of cargo being left behind can be judged by the change of the curb weight during the driving process. Then, corresponding reminders are given.

[0021] As attached Figure 1 As shown, the specific method for estimating the curb weight and the driving resistance in step S1 is: Step S101: start, execute S102; Step S102: Parameter initialization, the first power-on initialization curb weight estimation value M* is the vehicle unloaded weight M min , estimated driving resistance F* L is 0, that is: F* L = 0; M*=M min ; Then execute S103; Step S103: According to the sampling period T s Sampling, real-time sampling of the driving motor speed ω m [n] and output torque T[n], and calculate the vehicle acceleration a[n] and vehicle traction F[n] at time n; a[n] = (ω m [n] - ω m [n-1])*K V / T S ; F[n] = T[n]* K T ; Among them, ω m [n] is the motor speed at time n, ω m [n-1] is the motor speed at time n-1, K V is the vehicle speed and motor speed coefficient, T S is the real-time sampling period, K T It is the conversion coefficient between the motor output torque and the vehicle driving force; If S102 enters S103, it is necessary to continuously sample multiple times and record the motor speed and motor output torque of each sample. In this embodiment, three consecutive samples are taken and the motor speed ω of each sample is recorded. m [0],ω m [1] and ω m [2], motor output torque T[0], T[1] and T[2]; Calculate the vehicle acceleration: a[1]=(ω m [1]-ω m [0])*K V / T s ; a[2]=(ω m [2]-ω m [1])*K V / T s ; Calculate vehicle traction: F[0] = T[0]*K T ; F[1] = T[1]*K T ; F[2] = T[2]*K T ; If S103 is entered from other links, sampling is only required once to record the motor speed and motor output torque; Then execute S104; Step S104: determine whether the vehicle is stationary, if yes, execute S105, if no, execute S106; Step S105: In the static state, ω m [n] = 0, estimated driving resistance value F* at time n L [n] is the traction force output by the motor system, and the estimated value of the curb weight is M*, that is: F* L = F* L [n]=F[n]= T[n]* K T ; M*[n] =M*; Then execute S103; Step S106: According to Newton's second law, a vehicle dynamics model is constructed, and the following equations are formed by the two sets of data calculated by the latest sampling: F[n-1] -F L [n-1] = M[n-1]*a[n-1]; F[n] -F L [n] = M[n]*a[n]; Among them, F L [n] is the driving resistance at time n, M[n] is the curb weight at time n, F L [n-1] is the driving resistance at time n-1, M[n-1] is the curb weight at time n-1; Let F L [n-1] =F L [n], M[n-1] = M[n], the estimated value of curb weight M is obtained based on the calculated vehicle acceleration and vehicle traction * [n] and estimated driving resistance F* L [n]; After the calculation is completed, execute S107; In step S106, the following processing methods are: When F[n] = 0, M *[n] =M * ; F * L [n] = F[n]-M * [n] * a[n]; When a[n] = 0, M * [n] =M * [n-1]; F * L [n] = F[n]; When a[n] ≠ 0 and a[n]=a[n-1], calculate M according to the following formulas: * [n] and F * L [n], M * [n] =M * [n-1]; F * L [n] =F * L [n-1]+F[n]-F[n-1]; When a[n] ≠a[n-1], calculate M according to the following formulas: * [n] and F * L [n], M * [n] = (F[n]-F[n-1] ) / (a[n]-a[n-1]); F * L [n]= (a[n]*F[n-1]-a[n-1]*F[n]) / ((a[n]-a[n-1])); Step S107: Determine the current M * [n] and M * Whether the difference exceeds the set threshold, if it exceeds the set threshold, the quality change flag S_M is set to 1, and S108 is executed; otherwise, the quality change flags S_M and S_M_P are set to 0, that is, S_M=0, S_M_P=0, and S111 is executed; S_M is a status indication that the estimated curb mass value has changed more than the set threshold compared with the historical estimated curb mass value. S_M_P indicates whether the existing data after the curb mass change meets the estimated mass and resistance requirements. If the two sets of data before and after the mass change are used for estimation, the estimation result error is large. Therefore, it is necessary to continue sampling after the mass change to ensure the estimation accuracy. Step S108: Determine whether the flags S_M and S_M_P are both equal to 1, if yes, execute S109, if not, execute S110; Step S109: Determine M * Whether the difference between [n] and R_M exceeds the set threshold, if yes, execute S103, if no, execute S111; When M * When the difference between [n] and R_M exceeds the set threshold: R_M=M * [n]; R_M records the current estimated instantaneous quality, which is mainly used in the next cycle, equivalent to temporary storage of historical data; When R_F * L With F * L When the difference of [n] is within the set threshold range, F is updated according to the following formula * L , F * L = K FIL * F * L [n]+ (1-K FIL )*F * L ; R_F * L = F * L [n]; Where K FIL is the filter coefficient, 0 <K FIL <1, R_F * L Record the current estimated instantaneous driving resistance, which is mainly used for the next cycle, equivalent to temporary storage of historical data; Then execute S103; Step S110: Record the current estimated value of the curb mass and the estimated value of the driving resistance, and set the mass change flag S_M_P to 1, that is: R_M=M * [n]; R_F * L = F * L [n]; S_M_P=1; and execute S103; Step S111: Correct M using the latest estimated curb weight and running resistance * and F* L , the calculation formula is as follows: F * L = K FIL * F * L [n]+ (1-K FIL )*F * L ; M * = K FIL *M * [n] + (1-K FIL )*M * ; And execute S103.

[0022] like Figure 2 As shown in the figure, without introducing the estimated value of curb weight and driving resistance, the open-loop transfer function of the drive motor control loop is G(s) = ((Kp+Ki / s) / (1+sT m )*K T -F L ) / M / s.

[0023] like Figure 3 As shown, in step S2, the method of introducing the estimated value of the curb weight and the estimated value of the driving resistance into the open-loop transfer function G(s) of the drive motor control loop is: The speed regulator in the vehicle drive system adopts PI regulator, and the transfer function of PI regulator is (K p +K i / s), K p is the proportional gain, Ki is the integral coefficient, and the estimated value of curb weight M is introduced * With the vehicle unladen mass M min , the ratio of the two adjusts the output value of the PI regulator; The conversion coefficient K between the motor output torque and the vehicle driving force T It is known that the estimated driving resistance value F is introduced * L , by K T After conversion, the resistance torque T is obtained * L =F * L / K T ; The drive motor system is given a torque command T G Increase resistance torque T * L ; The drive motor system is given a torque command T GThe transfer function of the actual output torque T is 1 / (1+sT m ), T m is the motor torque response time constant; The actual total mass of the vehicle is M; The open-loop transfer function G(s)= (((K p +K i / s)*M * / M min +T * L ) / (1+sT m )*K T -F L ) / M / s.

[0024] Due to the motor torque response time constant T m is relatively small, and the open-loop transfer function G(s) is simplified to G(s)≈(((K p +K i / s)*M * / M min + T * L )*K T -F L ) / M / s; = (K p +K i / s)*K T / s / M min *M * / M+(F * L -F L ) / M / s; When the estimated value of the curb weight is close to the actual value and the estimated value of the driving resistance is close to the actual value, it can be obtained that G(s)≈(Kp+Ki / s)*K T / M min / s, at this time, G(s) is only related to Kp, Ki, K T and M min Related, M min is the unloaded mass of the vehicle. These variables are all constants. The transient and steady-state indicators of the entire drive motor control loop are basically not affected by changes in the curb mass and driving resistance.

[0025] In this embodiment, the change of the tractor's curb weight generally occurs in the following two situations: one is the increase or decrease of the towed cargo when parking; the other is the cargo being left behind during driving. Therefore, the tractor's curb weight will not change frequently during driving, that is, the tractor's curb weight will remain unchanged in a short period of time. Once the curb weight changes, the changed curb weight will also remain unchanged for a period of time.

[0026] In this embodiment, the driving resistance of the tractor includes at least one of wind resistance, friction resistance, and gravity on the slope. The change in the driving resistance of the tractor depends on the change in speed and slope. When the time interval between two adjacent motor speed samples is short enough, it can be considered that the driving resistance corresponding to the two samples is approximately equal.

[0027] In order to improve the estimation accuracy in this embodiment, the sampling period of the motor speed and output torque can be shortened.

[0028] This embodiment is not limited to the control of the drive motor system of an electric tractor, but is also applicable to the control of the drive motor system of a truck, a passenger car, etc.

Claims

1. A method for controlling a drive motor system of an electric tractor, characterized in that: Step S1: Real-time acquisition of the driving motor speed ω m [n] and output torque T[n], calculate the vehicle acceleration a[n] and vehicle traction F[n] at time n, and calculate the estimated curb weight M*[n] and driving resistance F*[n] at time n based on the vehicle acceleration a[n] and vehicle traction F[n]. L [n]; Step S2: Introduce the estimated value of the curb weight and the estimated value of the driving resistance into the open-loop transfer function G(s) of the drive motor control loop.

2. The electric tractor drive motor system control method according to claim 1, characterized in that: The calculation method of the vehicle acceleration a[n] and the vehicle traction force F[n] at time n in step S1 is: a[n] = (ω m [n] - oh m [n-1])*K V / T S ; F[n] = T[n] * K T ; Among them, ω m [n] is the motor speed at time n, ω m [n-1] is the motor speed at time n-1, K V is the vehicle speed and motor speed coefficient, T S is the real-time sampling period, K T It is the conversion coefficient between motor output torque and vehicle driving force.

3. The method for controlling a driving motor system of an electric tractor according to claim 1 or 2, characterized in that: In step S1, the estimated value of the curb weight M* is initialized to the vehicle unloaded weight M min , estimated driving resistance F* L is 0, that is: F* L = 0； M*=M min ; At rest, the estimated driving resistance F* at time n L [n] is the traction force output by the motor system, and the estimated value of the curb weight is M*, that is: F* L = F* L [n]=F[n]; M*[n] =M*.

4. The method for controlling a driving motor system of an electric tractor according to claim 3, characterized in that: In step S1, the estimated value of the curb weight M*[n] and the estimated value of the driving resistance F* at time n are calculated based on the vehicle acceleration a[n] and the vehicle traction force F[n]. L The method of [n] is: F[n-1]-F L [n-1]=M[n-1]*a[n-1]; F[n]-F L [n] = M[n]*a[n]; Among them, F L [n] is the driving resistance at time n, M[n] is the curb weight at time n, F L [n-1] is the driving resistance at time n-1, M[n-1] is the curb weight at time n-1; Let F L [n-1] =F L [n], M[n-1] = M[n], solve for the estimated driving resistance value F* at time n L [n] and the estimated curb mass M*[n].

5. The method for controlling a driving motor system of an electric tractor according to claim 1, 2 or 4, characterized in that: In step S1, the estimated running resistance value F* is calculated. L [n] and the estimated value of curb weight M*[n], then F* L [n] and M*[n] are low-pass filtered to obtain M* and F* L .

6. The method for controlling a driving motor system of an electric tractor according to claim 1, characterized in that: The specific method for estimating the curb weight and the driving resistance in step S1 is: Step S101: start, execute S102; Step S102: Parameter initialization, F * L = 0, M * =M min , then execute S103; Step S103: According to the sampling period T s Sampling, real-time sampling of the driving motor speed ω m [n] and output torque T[n], and calculate the vehicle acceleration a[n] and vehicle traction F[n] at time n; a[n] = (ω m [n] - oh m [n-1])*K V / T S ; F[n] = T[n] * K T ; Among them, ω m [n] is the motor speed at time n, ω m [n-1] is the motor speed at time n-1, K V is the vehicle speed and motor speed coefficient, T S is the real-time sampling period, K T It is the conversion coefficient between the motor output torque and the vehicle driving force; Step S104: determine whether the vehicle is stationary, if yes, execute S105, if no, execute S106; Step S105: ω m [n] = 0, record the estimated driving resistance F * L =T[n]* K T , then execute S103; Step S106: According to Newton's second law, a vehicle dynamics model is constructed, and the following equations are formed by the two sets of data calculated by the latest sampling: F[n-1] -F L [n-1] = M[n-1]*a[n-1]; F[n] -F L [n] = M[n]*a[n]; The estimated value of the curb mass M is obtained based on the calculated vehicle acceleration and vehicle traction. * [n] and estimated driving resistance F* L [n]; After the calculation is completed, execute S107; Step S107: Determine the current M * [n] and M * Whether the difference exceeds the set threshold, if it exceeds the set threshold, the quality change flag S_M is set to 1, and S108 is executed; otherwise, the quality change flags S_M and S_M_P are set to 0, that is, S_M=0, S_M_P=0, and S111 is executed; S_M is a status indication that the estimated curb mass value has changed more than the set threshold value compared with the historical estimated curb mass value, and S_M_P indicates whether the existing data after the curb mass change meets the estimated mass and resistance requirements; Step S108: Determine whether the flags S_M and S_M_P are both equal to 1, if yes, execute S109, if not, execute S110; Step S109: Determine M * Whether the difference between [n] and R_M exceeds the set threshold, if yes, execute S103, if no, execute S111; When M * When the difference between [n] and R_M exceeds the set threshold: R_M=M * [n]; R_M records the current estimated instantaneous quality; When R_F * L With F * L When the difference of [n] is within the set threshold range, F is updated according to the following formula * L , F * L = K FIL * F * L [n] + (1-K FIL )*F * L ; R_F * L = F * L [n]; Where K FIL is the filter coefficient, 0 <K FIL <1, R_F * L Record the current estimated instantaneous driving resistance; Step S110: Record the current estimated value of the curb mass and the estimated value of the driving resistance, and set the mass change flag S_M_P to 1, that is: R_M=M * [n]; R_F * L = F * L [n]; S_M_P=1; and execute S103; Step S111: Correct M using the latest estimated curb weight and running resistance * and F * L , the calculation formula is as follows: F * L = K FIL * F * L [n] + (1-K FIL )*F * L ; M * = K FIL *M * [n] + (1-K FIL )*M * ; And execute S103.

7. The method for controlling a driving motor system of an electric tractor according to claim 6, characterized in that: If entering S103 from S102, it is necessary to continuously sample multiple times and record the motor speed and motor output torque of each sample respectively; If S203 is entered from other links, sampling is only required once to record the motor speed and motor output torque.

8. The method for controlling a driving motor system of an electric tractor according to claim 6 or 7, characterized in that: In step S106, When F[n] = 0, M * [n] =M * ; F * L [n] = F[n] -M * [n] * a[n]; When a[n] = 0, M * [n] =M * [n-1]; F * L [n] = F[n]; When a[n] ≠ 0 and a[n] =a[n-1], calculate M according to the following formulas: * [n] and F * L [n], M * [n] =M * [n-1]; F * L [n] =F * L [n-1]+F[n]-F[n-1]; When a[n] ≠a[n-1], calculate M according to the following formulas: * [n] and F * L [n], M * [n] = (F[n] -F[n-1] ) / (a[n]-a[n-1]); F * L [n]= (a[n]*F[n-1]-a[n-1]*F[n]) / ((a[n]-a[n-1]))。 9. A method for controlling a driving motor system of an electric tractor according to claim 1, 2, 4, 6 or 7, characterized in that: In step S2, the method of introducing the estimated value of the curb weight and the estimated value of the driving resistance into the open-loop transfer function G(s) of the drive motor control loop is: The speed regulator in the vehicle drive system adopts PI regulator, and the transfer function of PI regulator is (K p +K i / s), K p is the proportional gain, Ki is the integral coefficient, and the estimated value of curb weight M is introduced * With the vehicle unladen mass M min , the ratio of the two adjusts the output value of the PI regulator; The conversion coefficient K between the motor output torque and the vehicle driving force T It is known that the estimated driving resistance value F is introduced * L , by K T After conversion, the resistance torque T is obtained * L ; The drive motor system is given a torque command T G Increase resistance torque T * L ; The drive motor system is given a torque command T G The transfer function of the actual output torque T is 1 / (1+sT m ), T m is the motor torque response time constant; The actual total mass of the vehicle is M; The open-loop transfer function G(s)= (((K p +K i / s)*M * / M min +T * L ) / (1+sT m )*K T -F L ) / M / s.

10. The method for controlling a driving motor system of an electric tractor according to claim 9, characterized in that: In step S2, due to the motor torque response time constant T m is relatively small, and the open-loop transfer function G(s) is simplified to G(s)≈(((K p +K i / s)*M * / M min + T * L )*K T -F L ) / M / s; = (K p +K i / s)*K T / s / M min *M * / M+(F * L -F L ) / M / s; When the estimated value of the curb weight is close to the actual value and the estimated value of the driving resistance is close to the actual value, we can get G(s) ≈(Kp+Ki / s)*K T / M min / s.

Images