Method, device and electronic equipment for estimating mass and speed of electric bus

Abstract

The present application provides a method, device, and electronic device for estimating the mass and speed of an electric bus. The method obtains the current operating parameters of the electric bus; based on the current operating parameters, the recursive least squares method is combined with a pre-built longitudinal deviation dynamic model to estimate the vehicle mass, obtaining the mass estimation information of the electric bus; the longitudinal acceleration and wheel speed in the current operating parameters are pre-processed to obtain the calibrated acceleration and wheel linear velocity; the pre-processing includes: Hall effect encoding of the wheel speed and calibration of the longitudinal acceleration; based on the calibrated acceleration and wheel linear velocity, the vehicle speed is estimated using a nonlinear digital hybrid filtering algorithm in the current state of the electric bus to obtain the vehicle speed estimation information. The method improves the real-time estimation accuracy of the vehicle mass and speed, thereby improving the response speed and reliability of the vehicle control system and enhancing the driving safety and stability of the electric bus under various complex road conditions.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a method, device, and electronic equipment for estimating the mass and speed of an electric bus. Background Art

[0002] With the widespread use of electric buses in urban and long-distance transport, ensuring their driving stability and safety under various complex road conditions has become increasingly important. Vehicle mass and speed are key parameters that influence vehicle dynamics, and accurately estimating these parameters is crucial for improving the performance of vehicle control systems. However, due to the diverse and dynamic nature of road conditions, accurately estimating mass and speed on unstructured and low-adhesion roads presents significant challenges.

[0003] Existing vehicle mass estimation methods are mostly based on static or quasi-static assumptions on flat roads, making them incapable of handling the dynamic changes brought about by complex road conditions. For example, the traditional Kalman filter method suffers from reduced estimation accuracy when road slope and air resistance vary significantly. Furthermore, while event-triggered mass estimation methods exhibit high accuracy under specific conditions, they require complex sensor configurations, hindering widespread application on economical electric buses.

[0004] Similarly, existing vehicle speed estimation methods primarily rely on data from wheel speed sensors and accelerometers. These methods suffer from significant estimation errors during vehicle acceleration, deceleration, and cornering. Furthermore, wheel slip and sensor signal noise significantly impact the accuracy of speed estimation under low-grip and uneven road conditions. Summary of the Invention

[0005] The purpose of this application is to provide a method, device and electronic equipment for estimating the mass and speed of an electric bus. By combining a longitudinal deviation dynamic model with a recursive least squares algorithm, the current operating parameters of the vehicle are estimated in real time to obtain the vehicle's mass estimation information; and based on the vehicle's longitudinal acceleration and wheel speed, the longitudinal speed is estimated using a nonlinear digital hybrid filtering algorithm under the current state of the electric bus, thereby effectively improving the estimation accuracy and stability under complex road conditions; through accurate mass and speed estimation, the response speed and reliability of the vehicle control system are further improved, and the driving safety and stability of the electric bus under various complex road conditions are enhanced.

[0006] In a first aspect, the present application provides a method for estimating the mass and speed of an electric bus, the method comprising: obtaining current operating parameters of the electric bus; the current operating parameters comprising: longitudinal acceleration, yaw angular velocity, total longitudinal force, vehicle speed, wheel speed, and wheel slip rate; based on the current operating parameters, applying a recursive least squares method in combination with a pre-built longitudinal deviation dynamics model to estimate the vehicle mass, and obtaining mass estimation information of the electric bus; pre-processing the longitudinal acceleration and wheel speed in the current operating parameters to obtain a calibrated acceleration and wheel linear velocity; the pre-processing comprises: Hall effect encoding processing of the wheel speed and calibration processing of the longitudinal acceleration; based on the calibrated acceleration and wheel linear velocity, using a nonlinear digital hybrid filtering algorithm under the current state of the electric bus to estimate the vehicle speed, and obtaining speed estimation information of the electric bus.

[0007] Furthermore, the above-mentioned step of estimating the vehicle mass based on the current operating parameters using a recursive least squares method in combination with a pre-constructed longitudinal deviation dynamic model to obtain the mass estimation information of the electric bus includes: determining whether the current motion state of the electric bus is a longitudinal motion state based on the current operating parameters; if so, extracting the high-frequency components of the longitudinal acceleration and total longitudinal force of the electric bus using a feedforward bandpass filter; estimating the mass estimation information of the electric bus based on the high-frequency components of the longitudinal acceleration and total longitudinal force of the electric bus and a first specified formula; wherein the first specified formula is obtained by processing the pre-constructed longitudinal deviation dynamic model of the electric bus using a recursive least squares algorithm; the first specified formula is as follows:

[0008] ;

[0009] in, represents the mass estimation information of the electric bus, Represents the high-frequency component of the filtered longitudinal acceleration; Represents the high-frequency component of the total longitudinal force after filtering.

[0010] Furthermore, the above-mentioned step of determining whether the current motion state of the vehicle is a longitudinal motion state based on the current operating parameters includes: if the longitudinal acceleration is greater than a first threshold, the yaw angular velocity is less than a second threshold, the total longitudinal force is greater than a third threshold, the vehicle speed is greater than a fourth threshold, and the wheel slip rate is less than a fifth threshold, then determining that the current motion state of the vehicle is a longitudinal motion state.

[0011] Furthermore, the determination process of the first specified formula is as follows:

[0012] The longitudinal deviation dynamic model of the electric bus is constructed, and the corresponding first model formula is as follows:

[0013] ;

[0014] in, Indicates vehicle speed; represents the first derivative of vehicle speed; represents the net longitudinal force acting on the vehicle; represents the effective engine force at the wheels; Indicates the effective braking force at the wheel; represents air resistance; Indicates the air density, represents the effective front surface area, represents the air resistance coefficient; 、 They represent the longitudinal force and rolling resistance caused by the road slope respectively; represents the acceleration due to gravity; Indicates the road slope angle;

[0015] Assume that the total rolling resistance is a constant fraction of the vehicle weight , transform the first model formula to obtain the second model formula, as follows:

[0016] ;

[0017] Among them, the symbol Indicates the deviation of a given quantity; speed deviation is a sinusoidal function of time with a frequency of According to the Bachman-Landau theorem, the second model formula is transformed into the third model formula as follows:

[0018] ;

[0019] in, express The higher-order infinitesimal part of ;

[0020] According to the third model formula, the fourth model formula is obtained:

[0021] ;

[0022] in, represents the second derivative of vehicle speed; represents the total longitudinal force of the vehicle;

[0023] The fourth model equation is transformed based on the least squares method to obtain the first specified equation.

[0024] Furthermore, the step of pre-processing the longitudinal acceleration and wheel speed in the current operating parameters to obtain the calibration acceleration and wheel linear velocity includes:

[0025] A 4th-order Butterworth digital filter with a cutoff frequency of 20 Hz is used to filter the wheel speed and longitudinal acceleration;

[0026] Multiply the filtered wheel speed by the corresponding wheel radius to obtain the wheel linear velocity, as follows:

[0027] ;

[0028] in, Indicates the The linear speed of each wheel; Indicates the wheel radius of each wheel; Indicates the The filtered wheel speed of each wheel;

[0029] The filtered longitudinal acceleration is converted into the initial linear acceleration, and the initial linear acceleration is compensated for offset and gain distortion to obtain the linear compensated acceleration as follows:

[0030] ;

[0031] in, represents the linear compensation acceleration; represents the initial linear acceleration; Indicates gain; Indicates offset;

[0032] Input the linear compensation acceleration into the second specified formula for calculation to obtain the calibration acceleration; the second specified formula is as follows:

[0033] ;

[0034] represents the calibration acceleration; is the acceleration due to gravity; and are the distances from the vehicle's center of gravity to the front and rear axles respectively; is the height of the vehicle's center of gravity from the ground; is the spring constant.

[0035] Furthermore, the determination process of the second specified formula is as follows:

[0036] When the pitch angle When it is non-zero, along Linear compensation of axis measurement acceleration As shown in the first formula below:

[0037] ;

[0038] Vertical load variations on the front and rear axles As shown in the second formula below:

[0039] ;

[0040] in, is the load distribution factor;

[0041] According to Hooke's law, the amount of suspension spring compression due to load changes is given by the third equation below:

[0042] ;

[0043] in, Indicates the amount of suspension spring compression;

[0044] Pitch angle It can be approximated as its tangent, as shown in the fourth equation below:

[0045] ;

[0046] The second specified formula is obtained by deducing the first to fourth formulas.

[0047] Furthermore, the above-mentioned step of estimating the vehicle speed based on the calibration acceleration and wheel linear velocity and adopting the nonlinear digital hybrid filtering algorithm under the current state of the electric bus to obtain the speed estimation information of the electric bus includes: in the current estimation cycle, determining the average value of the four wheel linear velocities and the average linear velocity of the two non-driven wheels according to the wheel linear velocities of the four wheels; determining the longitudinal acceleration filter signal according to the calibration acceleration; determining the current electric bus state in the current estimation cycle according to the electric bus state in the previous estimation cycle, the average value of the four wheel linear velocities in the current estimation cycle, and the longitudinal acceleration filter signal; determining the vehicle speed estimation information in the current estimation cycle according to the current electric bus state in the current estimation cycle, the average value of the four wheel linear velocities, the average linear velocity of the two non-driven wheels, and the longitudinal acceleration filter signal.

[0048] Furthermore, the step of determining the current state of the electric bus in the current estimation cycle based on the state of the electric bus in the previous estimation cycle, the average value of the linear velocities of the four wheels in the current estimation cycle, and the longitudinal acceleration filter signal includes:

[0049] The current state of the electric bus in the current estimation cycle is determined according to the following third specified formulas:

[0050] ;

[0051] ;

[0052] ;

[0053] ;

[0054] in, Indicates the current status of the electric bus; represents the state of the electric bus in the last estimation cycle; , represents the average value of the linear speed of the four wheels; Indicates the The linear speed of each wheel; , represents the longitudinal acceleration filtered signal; represents a FIR low-pass filter; = , which also represents the calibration acceleration; Is the longitudinal acceleration filtered signal used to identify braking operations threshold value; and are all constants; , is the hysteresis threshold; Indicates the braking speed threshold.

[0055] Furthermore, the step of determining vehicle speed estimation information in the current estimation period based on the current state of the electric bus, the average of the linear velocities of the four wheels, the average linear velocities of the two non-driven wheels, and the longitudinal acceleration filtering signal in the current estimation period includes:

[0056] The vehicle speed estimation information in the current estimation cycle is calculated according to the following fourth specified formula:

[0057] ;

[0058] in, represents the average linear velocity of the two non-driven wheels; Indicates the vehicle speed estimation information in the current estimation cycle; is the sampling time interval; Indicates the vehicle speed estimation information for the previous estimation cycle.

[0059] On the second aspect, the present application also provides a device for estimating the mass and speed of an electric bus, the device including: a parameter acquisition module for acquiring the current operating parameters of the electric bus; the current operating parameters include: longitudinal acceleration, yaw angular velocity, total longitudinal force, vehicle speed, wheel speed, and wheel slip rate; a mass estimation module for estimating the vehicle mass based on the current operating parameters, applying the recursive least squares method in combination with a pre-built longitudinal deviation dynamic model, and obtaining the mass estimation information of the electric bus; a preprocessing module for preprocessing the longitudinal acceleration and wheel speed in the current operating parameters to obtain the calibrated acceleration and wheel linear speed; the preprocessing includes: Hall effect encoding processing of the wheel speed and calibration processing of the longitudinal acceleration; a speed estimation module for estimating the vehicle speed based on the calibrated acceleration and wheel linear speed, using a nonlinear digital hybrid filtering algorithm under the current working conditions to obtain the speed estimation information of the electric bus.

[0060] In a third aspect, the present application further provides an electronic device comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the method described in the first aspect above.

[0061] In a fourth aspect, the present application also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method described in the first aspect above.

[0062] The method, device and electronic equipment for estimating the mass and speed of an electric bus provided in this application use a longitudinal deviation dynamics model combined with a recursive least squares algorithm to perform real-time estimation of the vehicle's current operating parameters to obtain vehicle mass estimation information; and based on the vehicle's longitudinal acceleration and wheel speed, use a nonlinear digital hybrid filtering algorithm in the current state of the electric bus to estimate the longitudinal speed to obtain vehicle speed estimation information, thereby effectively improving the estimation accuracy and stability under complex road conditions; through precise mass and speed estimation, the response speed and reliability of the vehicle control system are further improved, enhancing the driving safety and stability of the electric bus under various complex road conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0064] Figure 1A flowchart of a method for estimating the mass and speed of an electric bus provided in an embodiment of the present application;

[0065] Figure 2 A schematic diagram of a process for determining the mass and speed of an electric bus provided in an embodiment of the present application;

[0066] Figure 3 A schematic diagram of a calculation process provided in an embodiment of the present application;

[0067] Figure 4 A structural block diagram of a device for estimating the mass and speed of an electric bus provided in an embodiment of the present application;

[0068] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] The following will clearly and completely describe the technical solutions of this application in conjunction with the embodiments. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0070] Existing vehicle speed estimation methods primarily rely on data from wheel speed sensors and accelerometers. These methods suffer from significant estimation errors during vehicle acceleration, deceleration, and cornering. Furthermore, wheel slip and sensor signal noise significantly impact the accuracy of speed estimation under low-grip and uneven road conditions.

[0071] Based on this, the embodiments of the present application provide a method, device and electronic equipment for estimating the mass and speed of an electric bus. Through the longitudinal deviation dynamic model combined with the recursive least squares algorithm, the current operating parameters of the vehicle are estimated in real time to obtain the vehicle's mass estimation information; and based on the vehicle's longitudinal acceleration and wheel speed, the longitudinal speed is estimated using a nonlinear digital hybrid filtering algorithm under the current state of the electric bus, thereby effectively improving the estimation accuracy and stability under complex road conditions.

[0072] To facilitate understanding of this embodiment, a method for estimating the mass and speed of an electric bus disclosed in an embodiment of the present application is first introduced in detail.

[0073] Figure 1 This is a flow chart of a method for estimating the mass and speed of an electric bus provided in an embodiment of the present application. The method specifically includes the following steps:

[0074] Step S102, obtaining current operating parameters of the electric bus; the current operating parameters include: longitudinal acceleration, yaw angular velocity, total longitudinal force, vehicle speed, wheel speed, and wheel slip rate;

[0075] Before each estimation begins, the longitudinal acceleration is measured using the vehicle's onboard accelerometer, the vehicle's yaw rate is obtained from the yaw rate sensor, the vehicle's total longitudinal force is obtained from the vehicle's electronic control unit, the vehicle speed is obtained from the wheel speed sensor, the wheel slip ratio is obtained from the vehicle's electronic stability controller, and the wheel speed is obtained from the wheel speed detector.

[0076] Step S104 , based on the current operating parameters, the vehicle mass is estimated by applying a recursive least squares method in combination with a pre-built longitudinal deviation dynamics model to obtain mass estimation information of the electric bus;

[0077] The mass estimation formula can be derived by applying the recursive least squares method combined with the pre-built longitudinal deviation dynamic model. Substituting the above operating parameters into the formula, the mass estimation information of the electric bus can be estimated.

[0078] Step S106: Preprocessing the longitudinal acceleration and wheel speed in the current operating parameters to obtain a calibrated acceleration and wheel linear velocity; the preprocessing includes: Hall effect encoding of the wheel speed and calibration of the longitudinal acceleration;

[0079] Step S108 : Based on the calibrated acceleration and the wheel linear velocity, a nonlinear digital hybrid filtering algorithm is used in the current state of the electric bus to estimate the vehicle speed, thereby obtaining estimated speed information of the electric bus.

[0080] The method, device and electronic equipment for estimating the mass and speed of an electric bus provided in this application use a longitudinal deviation dynamics model combined with a recursive least squares algorithm to perform real-time estimation of the vehicle's current operating parameters to obtain vehicle mass estimation information; and based on the vehicle's longitudinal acceleration and wheel speed, use a nonlinear digital hybrid filtering algorithm under the current state of the electric bus to estimate the longitudinal speed, thereby effectively improving the estimation accuracy and stability under complex road conditions; through precise mass and speed estimation, vehicle speed estimation information is obtained, further improving the response speed and reliability of the vehicle control system, and enhancing the driving safety and stability of the electric bus under various complex road conditions.

[0081] The embodiment of the present application also provides another method for estimating the mass and speed of an electric bus, which is implemented on the basis of the above embodiment; this embodiment focuses on describing the module construction, preprocessing, mass estimation, and speed estimation processes.

[0082] The mass estimation process, i.e., the steps of estimating the vehicle mass based on the current operating parameters using the recursive least squares method combined with the pre-built longitudinal deviation dynamics model to obtain the mass estimation information of the electric bus, includes:

[0083] (1) Based on the current operating parameters, determine whether the current motion state of the electric bus is a longitudinal motion state; in specific implementation, if the longitudinal acceleration is greater than a first threshold, the yaw angular velocity is less than a second threshold, the total longitudinal force is greater than a third threshold, the vehicle speed is greater than a fourth threshold, and the wheel slip rate is less than a fifth threshold, then determine that the current motion state of the vehicle is a longitudinal motion state.

[0084] For example, the current operating parameters include: longitudinal acceleration measured using the vehicle's onboard accelerometer , the vehicle's yaw rate obtained from the yaw rate sensor , the total longitudinal force of the vehicle obtained from the vehicle electronic control unit (ECUs) , get the vehicle speed from the wheel speed sensor , wheel slip ratio obtained from the vehicle's electronic stability controller.

[0085] The following logical judgment rules are used to determine whether the vehicle's motion is primarily longitudinal. Specifically, the following conditions are met: the vehicle's yaw rate is less than 1 degree / second, the vehicle's longitudinal acceleration exceeds 1 m / s², the vehicle's speed exceeds 10 km / h, the wheel slip ratio does not exceed 0.03, and the vehicle's total longitudinal force exceeds 500N.

[0086] (2) If yes, use a feedforward bandpass filter to extract the high-frequency components of the longitudinal acceleration and total longitudinal force of the electric bus;

[0087] (3) Estimate the mass information of the electric bus based on the high-frequency components of the longitudinal acceleration and total longitudinal force of the electric bus and a first specified formula. The first specified formula is obtained by processing a pre-built longitudinal deviation dynamic model of the electric bus using a recursive least squares algorithm. The first specified formula is as follows:

[0088] ;

[0089] in, represents the mass estimation information of the electric bus, Represents the high-frequency component of the filtered longitudinal acceleration; Represents the high-frequency component of the total longitudinal force after filtering.

[0090] The following describes in detail the process of determining the first specified formula, see Figure 2 As shown:

[0091] (1) Constructing the longitudinal deviation dynamic model of electric buses:

[0092] The inertial force of vehicle motion plays a major role in longitudinal driving dynamics. Air resistance, additional forces caused by road gradient, and rolling resistance also affect mass estimation. To ensure the accuracy of mass estimation, the following longitudinal deviation dynamics model is constructed. The corresponding first model formula is as follows:

[0093] ;

[0094] in, Indicates vehicle speed; represents the first derivative of vehicle speed; represents the net longitudinal force acting on the vehicle; represents the effective engine force at the wheels; Indicates the effective braking force at the wheel; represents the total longitudinal force of the vehicle; represents air resistance; Indicates the air density, represents the effective front surface area, represents the air resistance coefficient; 、 They represent the longitudinal force and rolling resistance caused by the road slope respectively; represents the acceleration due to gravity; Indicates the road slope angle;

[0095] Assume that the total rolling resistance is a constant fraction of the vehicle weight , transform the first model formula to obtain the second model formula, as follows:

[0096] ;

[0097] Among them, the symbol Indicates the deviation of a given quantity; speed deviation is a sinusoidal function of time with a frequency of According to the Bachman-Landau theorem, the second model formula is transformed into the third model formula as follows:

[0098] ;

[0099] in, express When the frequency of the speed deviation approaches infinity, the inertia force plays a dominant role in the second model formula relative to the air resistance. In addition, assuming that the road slope deviation Specific speed deviation Smaller, that is Based on the above assumptions, in the presence of speed deviation, the mass estimation algorithm is greatly affected by the vehicle's inertia force. Therefore, the longitudinal deviation dynamic equations of the second and third model formulas are used as reference models for mass estimation.

[0100] (2) Design of electric bus mass estimation algorithm:

[0101] To facilitate algorithm design, we first assume that when the electric bus is in a straight-line motion, the high-frequency deviation component of its motion obeys the following relationship, that is, the fourth model formula is obtained based on the third model formula:

[0102] ;

[0103] in, represents the second derivative of vehicle speed; represents the total longitudinal force of the vehicle;

[0104] The quality estimation algorithm proposed in the embodiment of the present application is based on the approximate conditions of the fourth model formula, and the fourth model formula is transformed based on the least squares method to obtain the first specified formula.

[0105] Assumptions , use the recursive least squares algorithm to estimate the vehicle mass:

[0106] .

[0107] The variance of the quality estimate is estimated as follows:

[0108] ;

[0109] In this equation, Represents the prediction variance of the quality estimate. The accuracy of mass estimation is guaranteed within the range of Indicates that at time step The measured filtered longitudinal acceleration, where the denominator is the sum of the squares of this quantity at each estimation time step, and the numerator represents the variance of the measurement / estimation process of the total longitudinal force (effective engine force minus effective brake force). This is the process of mass and mass estimation variance.

[0110] The process of vehicle speed estimation is as follows:

[0111] (1) Data preprocessing:

[0112] The signals required for vehicle speed estimation include: the speed of the four wheels , longitudinal vehicle acceleration Data is acquired by sensors at a sampling rate of 5kHz and needs to be downsampled to the vehicle network bus frequency of 100Hz to serve as input to the vehicle speed estimation algorithm. Data preprocessing consists of two parts: one is processing the Hall effect encoders for wheel speed; the other is calibrating the accelerometer output voltage signal.

[0113] (i) Preprocessing of wheel speed encoder;

[0114] The wheel speed encoder signal is a sinusoidal voltage signal, whose amplitude and frequency are related to the wheel speed. Here, digital signal processing is used to reconstruct the wheel speed signal. The speed of each wheel is obtained. , After that, a 4th order Butterworth digital filter with a cutoff frequency of 20Hz is used for filtering. Then the wheel speed needs to be converted into linear speed, that is, the wheel speed at the tire-road contact point. To do this, the wheel speed of each wheel needs to be converted into linear speed. Multiply by the corresponding wheel radius ,as follows:

[0115] ;

[0116] in, Indicates the The linear speed of each wheel; Indicates the wheel radius of each wheel; Indicates the The filtered wheel speed of each wheel.

[0117] (ii) calibration of the accelerometer;

[0118] The accelerometer signal is very important for vehicle speed estimation, and it is necessary to integrate the signal to obtain the vehicle speed. The acceleration signal is also filtered using a 4th-order Butterworth filter, and the voltage output is converted into linear acceleration to obtain the initial linear acceleration. At the same time, in order to calibrate the signal, it is necessary to compensate for the offset and gain distortion. As shown in the following formula, it is necessary to find the gain and offset So that:

[0119] ;

[0120] in, represents the linear compensation acceleration; represents the initial linear acceleration; Indicates gain; Indicates offset;

[0121] Then, the linear compensation acceleration is input into the second specified formula for calculation to obtain the calibration acceleration; the second specified formula is as follows:

[0122] ;

[0123] represents the calibration acceleration; is the acceleration due to gravity; and are the distances from the vehicle's center of gravity to the front and rear axles respectively; is the height of the vehicle's center of gravity from the ground; is the spring constant.

[0124] Furthermore, the determination process of the second specified formula is as follows:

[0125] The standard of the acceleration signal needs to take into account the pitch angle deviation caused by load transfer, and a secondary correction of the pitch angle is required. In fact, during the strong deceleration (acceleration) stage, the suspension elasticity will cause a non-zero body pitch angle. Figure 3 As shown, when the pitch angle When it is non-zero, along Linear compensation of axis measurement acceleration As shown in the first formula below:

[0126] ;

[0127] In order to use a reliable vehicle longitudinal acceleration signal in the estimation algorithm, this effect needs to be compensated. Vertical load variations on the front and rear axles As shown in the second formula below:

[0128] ;

[0129] in, is the load distribution factor;

[0130] According to Hooke's law, the amount of suspension spring compression due to load changes is given by the third equation below:

[0131] ;

[0132] in, Indicates the amount of suspension spring compression;

[0133] Pitch angle It can be approximated as its tangent, as shown in the fourth equation below:

[0134] ;

[0135] According to the derivation from the first to the fourth formula, we can get and The relationship between , that is, the second specified formula mentioned above.

[0136] (2) Vehicle speed estimation algorithm design:

[0137] That is, the above-mentioned step of estimating the vehicle speed based on the calibrated acceleration and the wheel linear velocity using the nonlinear digital hybrid filtering algorithm in the current state of the electric bus to obtain the vehicle speed estimation information of the electric bus includes:

[0138] (i) in the current estimation cycle, determining the average of the four wheel linear velocities and the average linear velocity of the two non-driven wheels based on the wheel linear velocities of the four wheels; and determining the longitudinal acceleration filtered signal based on the calibrated acceleration;

[0139] The vehicle speed is estimated using a nonlinear digital hybrid filtering algorithm. In the electric bus communication network, all signals are floating point numbers with a sampling frequency of 200 Hz. The input signals of the algorithm are the four wheel linear velocities at the tire-road contact point. , and vehicle longitudinal acceleration All signals are rectified according to the preprocessing process. The output signal is the estimated longitudinal vehicle speed In each estimation cycle, the following signals need to be calculated:

[0140] : The average value of the linear speed of the four wheels;

[0141] : the average linear velocity of the two non-driving wheels;

[0142] : Longitudinal acceleration filtered signal, where It is a classic FIR low-pass filter.

[0143] (ii) determining a current state of the electric bus in a current estimation cycle based on the state of the electric bus in a previous estimation cycle, the average of the linear velocities of the four wheels in the current estimation cycle, and the longitudinal acceleration filter signal;

[0144] Secondly, the current state of the electric bus in the current estimation cycle needs to be determined , represented by a 4-valued variable : Indicates that the vehicle speed is very low; Indicates that the vehicle is accelerating; Indicates that the vehicle maintains a constant speed or brakes lightly; Indicates that the vehicle is braking. Specifically, the current state of the electric bus in the current estimation cycle Calculated according to the following third specified formulas:

[0145] ;

[0146] ;

[0147] ;

[0148] ;

[0149] in, Indicates the current status of the electric bus; represents the state of the electric bus in the last estimation cycle; , represents the average value of the linear speed of the four wheels; Indicates the The linear speed of each wheel; , represents the longitudinal acceleration filtered signal; represents a FIR low-pass filter; = , which also represents the calibration acceleration; Is the longitudinal acceleration filtered signal used to identify braking operations The threshold value of As a threshold, it prevents signal jitter when switching between different states; and All are constants and can be adjusted appropriately; , is the hysteresis threshold; the hysteresis threshold of the acceleration signal is introduced and To avoid jitter; Indicates the braking speed threshold.

[0150] (iii) determining vehicle speed estimation information in the current estimation cycle based on the current state of the electric bus, the average linear speed of the four wheels, the average linear speed of the two non-driven wheels, and the longitudinal acceleration filtering signal in the current estimation cycle.

[0151] The vehicle speed estimation information in the current estimation cycle is calculated according to the following fourth specified formula:

[0152] ;

[0153] in, represents the average linear velocity of the two non-driven wheels; Indicates the vehicle speed estimation information in the current estimation cycle; is the sampling time interval; Indicates the vehicle speed estimation information for the previous estimation cycle.

[0154] When the vehicle speed is very low, the vehicle speed is estimated as the average of the four wheel speeds. When the vehicle is accelerating, the vehicle speed is estimated as the average linear speed of the two non-driven wheels. When the vehicle maintains a constant speed or is slightly braking, the vehicle speed is estimated as the average linear speed of the four wheels. When the vehicle is braking, the estimated vehicle speed is based on the open-loop integration of the acceleration signal, and during the open-loop integration phase, the vehicle speed is calibrated backward to correct the initialization error. When the vehicle state switches from 0 to 1, it is assumed that the sampling time points are ; Indicates a sampling moment in the process of switching from state 0 to state 1. Speed estimation Calculated according to the following recursive rules:

[0155] ;

[0156] It should be noted that in , the recursive rule must be initialized. The initial value can be given by:

[0157] ;

[0158] in, express Vehicle speed estimation information at the sampling moment; express Estimated vehicle speed information at the moment; express The standard acceleration at the sampling time.

[0159] The above is the entire process of mass and speed estimation. The mass and speed values output by the estimator are sent to the VCU via the CAN bus for use by other control algorithms of the electric bus.

[0160] In the method for estimating the mass and speed of an electric bus provided in an embodiment of the present application, first, sensors installed on the electric bus are used to collect the current operating parameters of the vehicle in real time. Then, the collected data is processed and calibrated to compensate for measurement errors caused by load transfer and vehicle pitch angle. Finally, based on the longitudinal dynamic characteristics of the vehicle, the recursive least squares method is applied to estimate the vehicle mass and determine the vehicle's motion state to ensure that the vehicle is mainly in a longitudinal motion state. Secondly, the longitudinal acceleration and wheel speed information are processed, and the frequency tracking algorithm is used to convert the encoder signal into linear velocity. The acceleration signal is filtered and corrected to complete the preprocessing process of the input signal. Finally, a nonlinear digital hybrid filtering algorithm is used to estimate the vehicle speed, and the estimation strategies under driving and braking are designed separately to ensure the algorithm's applicability to working conditions. By accurately estimating the vehicle mass and speed of the electric bus under uneven roads and time-varying road conditions, real-time feedback on the vehicle's dynamic characteristics is provided, thereby enhancing the vehicle's control stability and safety.

[0161] Based on the above method embodiment, the present application embodiment also provides a device for estimating the mass and speed of an electric bus, see Figure 4 As shown, the device includes: a parameter acquisition module 42, which is used to obtain the current operating parameters of the electric bus; the current operating parameters include: longitudinal acceleration, yaw angular velocity, total longitudinal force, vehicle speed, wheel speed, and wheel slip rate; a mass estimation module 44, which is used to estimate the vehicle mass based on the current operating parameters by applying the recursive least squares method in combination with a pre-built longitudinal deviation dynamic model to obtain the mass estimation information of the electric bus; a preprocessing module 46, which is used to preprocess the longitudinal acceleration and wheel speed in the current operating parameters to obtain the calibrated acceleration and wheel linear speed; the preprocessing includes: Hall effect encoding processing of the wheel speed and calibration processing of the longitudinal acceleration; a vehicle speed estimation module 48, which is used to estimate the vehicle speed based on the calibrated acceleration and wheel linear speed using a nonlinear digital hybrid filtering algorithm under the current working conditions to obtain the vehicle speed estimation information of the electric bus.

[0162] Furthermore, the mass estimation module 44 is configured to determine, based on current operating parameters, whether the current motion state of the electric bus is a longitudinal motion state; if so, to extract the high-frequency components of the longitudinal acceleration and total longitudinal force of the electric bus using a feedforward bandpass filter; and to estimate the mass of the electric bus based on the high-frequency components of the longitudinal acceleration and total longitudinal force of the electric bus and a first specified formula. The first specified formula is obtained by processing a pre-established longitudinal deviation dynamic model of the electric bus using a recursive least squares algorithm. The first specified formula is as follows:

[0163] ;

[0164] in, represents the mass estimation information of the electric bus, Represents the high-frequency component of the filtered longitudinal acceleration; Represents the high-frequency component of the total longitudinal force after filtering.

[0165] Furthermore, the mass estimation module 44 is configured to determine that the current motion state of the vehicle is a longitudinal motion state if the longitudinal acceleration is greater than a first threshold, the yaw angular velocity is less than a second threshold, the total longitudinal force is greater than a third threshold, the vehicle speed is greater than a fourth threshold, and the wheel slip rate is less than a fifth threshold.

[0166] Furthermore, the determination process of the first specified formula is as follows:

[0167] The longitudinal deviation dynamic model of the electric bus is constructed, and the corresponding first model formula is as follows:

[0168] ;

[0169] in, Indicates vehicle speed; represents the first derivative of vehicle speed; represents the net longitudinal force acting on the vehicle; represents the effective engine force at the wheels; Indicates the effective braking force at the wheel; represents air resistance; Indicates the air density, represents the effective front surface area, represents the air resistance coefficient; 、 They represent the longitudinal force and rolling resistance caused by the road slope respectively; represents the acceleration due to gravity; Indicates the road slope angle;

[0170] Assume that the total rolling resistance is a constant fraction of the vehicle weight , transform the first model formula to obtain the second model formula, as follows:

[0171] ;

[0172] Among them, the symbol Indicates the deviation of a given quantity; speed deviation is a sinusoidal function of time with a frequency of According to the Bachman-Landau theorem, the second model formula is transformed into the third model formula as follows:

[0173] ;

[0174] in, express The higher-order infinitesimal part of ;

[0175] According to the third model formula, the fourth model formula is obtained:

[0176] ;

[0177] in, represents the second derivative of vehicle speed; represents the total longitudinal force of the vehicle;

[0178] The fourth model equation is transformed based on the least squares method to obtain the first specified equation.

[0179] Furthermore, the pre-processing module 46 is used to filter the wheel speed and longitudinal acceleration using a 4th-order Butterworth digital filter with a cut-off frequency of 20 Hz;

[0180] Multiply the filtered wheel speed by the corresponding wheel radius to obtain the wheel linear velocity, as follows:

[0181] ;

[0182] in, Indicates the The linear speed of each wheel; Indicates the wheel radius of each wheel; Indicates the The filtered wheel speed of each wheel;

[0183] The filtered longitudinal acceleration is converted into the initial linear acceleration, and the initial linear acceleration is compensated for offset and gain distortion to obtain the linear compensated acceleration as follows:

[0184] ;

[0185] in, represents the linear compensation acceleration; represents the initial linear acceleration; Indicates gain; Indicates offset;

[0186] Input the linear compensation acceleration into the second specified formula for calculation to obtain the calibration acceleration; the second specified formula is as follows:

[0187] ;

[0188] represents the calibration acceleration; is the acceleration due to gravity; and are the distances from the vehicle's center of gravity to the front and rear axles respectively; is the height of the vehicle's center of gravity from the ground; is the spring constant.

[0189] Furthermore, the determination process of the second specified formula is as follows:

[0190] When the pitch angle When it is non-zero, along Linear compensation of axis measurement acceleration As shown in the first formula below:

[0191] ;

[0192] Vertical load variations on the front and rear axles As shown in the second formula below:

[0193] ;

[0194] in, is the load distribution factor;

[0195] According to Hooke's law, the amount of suspension spring compression due to load changes is given by the third equation below:

[0196] ;

[0197] in, Indicates the amount of suspension spring compression;

[0198] Pitch angle It can be approximated as its tangent, as shown in the fourth equation below:

[0199] ;

[0200] The second specified formula is obtained by deducing the first to fourth formulas.

[0201] Furthermore, the above-mentioned step of estimating the vehicle speed based on the calibration acceleration and wheel linear velocity and adopting the nonlinear digital hybrid filtering algorithm under the current state of the electric bus to obtain the speed estimation information of the electric bus includes: in the current estimation cycle, determining the average value of the four wheel linear velocities and the average linear velocity of the two non-driven wheels according to the wheel linear velocities of the four wheels; determining the longitudinal acceleration filter signal according to the calibration acceleration; determining the current electric bus state in the current estimation cycle according to the electric bus state in the previous estimation cycle, the average value of the four wheel linear velocities in the current estimation cycle, and the longitudinal acceleration filter signal; determining the vehicle speed estimation information in the current estimation cycle according to the current electric bus state in the current estimation cycle, the average value of the four wheel linear velocities, the average linear velocity of the two non-driven wheels, and the longitudinal acceleration filter signal.

[0202] Furthermore, the step of determining the current state of the electric bus in the current estimation cycle based on the state of the electric bus in the previous estimation cycle, the average value of the linear velocities of the four wheels in the current estimation cycle, and the longitudinal acceleration filter signal includes:

[0203] The current state of the electric bus in the current estimation cycle is determined according to the following third specified formulas:

[0204] ;

[0205] ;

[0206] ;

[0207] ;

[0208] in, Indicates the current status of the electric bus; represents the state of the electric bus in the last estimation cycle; , represents the average value of the linear speed of the four wheels; Indicates the The linear speed of each wheel; , represents the longitudinal acceleration filtered signal; represents a FIR low-pass filter; = , which also represents the calibration acceleration; Is the longitudinal acceleration filtered signal used to identify braking operations threshold value; and are all constants; , is the hysteresis threshold; Indicates the braking speed threshold.

[0209] Furthermore, the vehicle speed estimation module 48 is configured to calculate the vehicle speed estimation information in the current estimation cycle according to the following fourth specified formula:

[0210] ;

[0211] in, represents the average linear velocity of the two non-driven wheels; Indicates the vehicle speed estimation information in the current estimation cycle; is the sampling time; Indicates the vehicle speed estimation information for the previous estimation cycle.

[0212] The device provided in the embodiment of the present application has the same implementation principle and technical effects as those in the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the embodiment of the device, reference can be made to the corresponding content in the aforementioned method embodiment.

[0213] The present application also provides an electronic device, such as Figure 5 As shown, it is a structural diagram of the electronic device, wherein the electronic device includes a processor 51 and a memory 50, the memory 50 stores computer executable instructions that can be executed by the processor 51, and the processor 51 executes the computer executable instructions to implement the above method.

[0214] exist Figure 5 In the illustrated embodiment, the electronic device further includes a bus 52 and a communication interface 53 , wherein the processor 51 , the communication interface 53 and the memory 50 are connected via the bus 52 .

[0215] Among them, the memory 50 may include high-speed random access memory (RAM), and may also include non-volatile memory (non-volatile memory), such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 53 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. can be used. The bus 52 can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 52 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0216] The processor 51 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 51 or by software instructions. The above processor 51 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly implemented as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor 51 reads the information in the memory and completes the steps of the method of the above embodiment in combination with its hardware.

[0217] An embodiment of the present application also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the above-mentioned method. The specific implementation can be found in the above-mentioned method embodiment, which will not be repeated here.

[0218] The computer program products of the methods, devices, and electronic devices provided in the embodiments of the present application include a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the previous method embodiments. For specific implementation, please refer to the method embodiments and will not be repeated here.

[0219] Unless otherwise specifically stated, the relative steps, numerical expressions and values of the components and steps set forth in these embodiments do not limit the scope of the present application.

[0220] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0221] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate the description of this application and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0222] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed in the present application, or perform equivalent replacements for some of the technical features thereof. These modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application shall be subject to the scope of protection of the claims.

Claims

1. A method for estimating the mass and speed of an electric bus, characterized in that: The method comprises: Obtaining current operating parameters of the electric bus; the current operating parameters include: longitudinal acceleration, yaw angular velocity, total longitudinal force, vehicle speed, wheel speed, and wheel slip rate; Based on the current operating parameters, a recursive least squares method is used in combination with a pre-built longitudinal deviation dynamic model to estimate the vehicle mass, thereby obtaining mass estimation information of the electric bus; Preprocessing the longitudinal acceleration and wheel speed in the current operating parameters to obtain a calibrated acceleration and wheel linear velocity; the preprocessing includes: Hall effect encoding processing of the wheel speed and calibration processing of the longitudinal acceleration; Based on the calibrated acceleration and the wheel linear velocity, a nonlinear digital hybrid filtering algorithm under the current state of the electric bus is used to estimate the vehicle speed to obtain vehicle speed estimation information of the electric bus; The step of preprocessing the longitudinal acceleration and wheel speed in the current operating parameters to obtain the calibration acceleration and wheel linear speed includes: Filtering the wheel speed and the longitudinal acceleration using a 4th-order Butterworth digital filter with a cutoff frequency of 20 Hz; Multiply the filtered wheel speed by the corresponding wheel radius to obtain the wheel linear velocity, as follows: ； in, Indicates the The linear speed of each wheel; Indicates the wheel radius of each wheel; Indicates the The filtered wheel speed of each wheel; The filtered longitudinal acceleration is converted into an initial linear acceleration, and the initial linear acceleration is compensated for offset and gain distortion to obtain a linear compensated acceleration, as follows: ； in, represents the linear compensation acceleration; represents the initial linear acceleration; Indicates gain; Indicates offset; The linear compensation acceleration is input into a second specified formula for calculation to obtain a calibration acceleration; the second specified formula is as follows: ； represents the calibration acceleration; is the acceleration due to gravity; represents the mass estimation information of the electric bus; and are the distances from the vehicle's center of gravity to the front and rear axles respectively; is the height of the vehicle's center of gravity from the ground; is the spring constant.

2. The method according to claim 1, characterized in that The step of estimating the vehicle mass by applying a recursive least squares method in combination with a pre-built longitudinal deviation dynamic model based on the current operating parameters to obtain mass estimation information of the electric bus includes: determining, based on the current operating parameters, whether the current motion state of the electric bus is a longitudinal motion state; If yes, extracting high-frequency components of the longitudinal acceleration and total longitudinal force of the electric bus using a feedforward bandpass filter; The mass estimation information of the electric bus is estimated based on the high-frequency components of the longitudinal acceleration and the total longitudinal force of the electric bus and a first specified formula. The first specified formula is obtained by processing a pre-built longitudinal deviation dynamic model of the electric bus using a recursive least squares algorithm. The first specified formula is as follows: ； in, Represents the high-frequency component of the filtered longitudinal acceleration; Represents the high-frequency component of the total longitudinal force after filtering.

3. The method according to claim 2, characterized in that The step of determining whether the current motion state of the vehicle is a longitudinal motion state according to the current operating parameters includes: If the longitudinal acceleration is greater than a first threshold, the yaw angular velocity is less than a second threshold, the total longitudinal force is greater than a third threshold, the vehicle longitudinal speed is greater than a fourth threshold, and the wheel slip rate is less than a fifth threshold, it is determined that the current motion state of the vehicle is a longitudinal motion state.

4. The method according to claim 1, characterized in that The process of determining the second specified formula is as follows: When the pitch angle When it is non-zero, along Linear compensation of axis measurement acceleration As shown in the first formula below: ； Vertical load variations on the front and rear axles As shown in the second formula below: ； in, is the load distribution factor; According to Hooke's law, the amount of suspension spring compression due to load changes is given by the third equation below: ； in, Indicates the amount of suspension spring compression; Pitch angle It can be approximated as its tangent, as shown in the fourth equation below: ； The second specified formula is obtained by deducing the first to fourth formulas.

5. The method according to claim 1, characterized in that: The step of estimating the vehicle speed based on the calibration acceleration and the wheel linear velocity using a nonlinear digital hybrid filtering algorithm in the current state of the electric bus to obtain vehicle speed estimation information of the electric bus includes: In a current estimation cycle, based on the wheel linear velocities of the four wheels, an average of the four wheel linear velocities and an average linear velocity of the two non-driven wheels are determined; based on the calibration acceleration, a longitudinal acceleration filter signal is determined; Determine the current state of the electric bus in the current estimation cycle based on the state of the electric bus in the previous estimation cycle, the average value of the linear speeds of the four wheels in the current estimation cycle, and the longitudinal acceleration filter signal; The vehicle speed estimation information in the current estimation cycle is determined according to the current state of the electric bus, the average linear speed of the four wheels, the average linear speed of the two non-driving wheels and the longitudinal acceleration filtering signal in the current estimation cycle.

6. The method according to claim 5, characterized in that The step of determining the current state of the electric bus in the current estimation cycle according to the state of the electric bus in the previous estimation cycle, the average value of the linear velocities of the four wheels in the current estimation cycle, and the longitudinal acceleration filtering signal comprises: The current state of the electric bus in the current estimation cycle is determined according to the following third specified formulas: ； ； ； ； in, Indicates the current status of the electric bus; represents the state of the electric bus in the last estimation cycle; , represents the average value of the linear speed of the four wheels; Indicates the The linear speed of each wheel; , represents the longitudinal acceleration filtered signal; represents a FIR low-pass filter; = , which also represents the calibration acceleration; Is the longitudinal acceleration filtered signal used to identify braking operations threshold value; and are all constants; , is the hysteresis threshold; Indicates the braking speed threshold.

7. The method according to claim 5, characterized in that The step of determining vehicle speed estimation information in the current estimation cycle according to the current state of the electric bus, the average linear speed of the four wheels, the average linear speed of the two non-driven wheels, and the longitudinal acceleration filtering signal in the current estimation cycle includes: The vehicle speed estimation information in the current estimation cycle is calculated according to the following fourth specified formula: ； in, represents the average linear velocity of the two non-driven wheels; Indicates the vehicle speed estimation information in the current estimation cycle; is the sampling time interval; Indicates the vehicle speed estimation information for the previous estimation cycle.

8. A device for estimating the mass and speed of an electric bus, characterized in that: The device comprises: A parameter acquisition module is used to obtain the current operating parameters of the electric bus; the current operating parameters include: longitudinal acceleration, yaw angular velocity, total longitudinal force, vehicle speed, wheel speed, and wheel slip rate; a mass estimation module, configured to estimate the vehicle mass based on the current operating parameters by applying a recursive least squares method in combination with a pre-built longitudinal deviation dynamics model to obtain mass estimation information of the electric bus; a preprocessing module, configured to preprocess the longitudinal acceleration and wheel speed in the current operating parameters to obtain a calibrated acceleration and wheel linear velocity; the preprocessing includes: Hall effect encoding of the wheel speed and calibration of the longitudinal acceleration; a vehicle speed estimation module, configured to estimate the vehicle speed based on the calibrated acceleration and the wheel linear velocity using a nonlinear digital hybrid filtering algorithm under current operating conditions to obtain vehicle speed estimation information of the electric bus; The preprocessing module is further used for: Filtering the wheel speed and the longitudinal acceleration using a 4th-order Butterworth digital filter with a cutoff frequency of 20 Hz; Multiply the filtered wheel speed by the corresponding wheel radius to obtain the wheel linear velocity, as follows: ； in, Indicates the The linear speed of each wheel; Indicates the wheel radius of each wheel; Indicates the The filtered wheel speed of each wheel; The filtered longitudinal acceleration is converted into an initial linear acceleration, and the initial linear acceleration is compensated for offset and gain distortion to obtain a linear compensated acceleration, as follows: ； in, represents the linear compensation acceleration; represents the initial linear acceleration; Indicates gain; Indicates offset; The linear compensation acceleration is input into a second specified formula for calculation to obtain a calibration acceleration; the second specified formula is as follows: ； represents the calibration acceleration; is the acceleration due to gravity; represents the mass estimation information of the electric bus; and are the distances from the vehicle's center of gravity to the front and rear axles respectively; is the height of the vehicle's center of gravity from the ground; is the spring constant.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 7.

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