Sensorless-based MCU load estimation method and system

Abstract

The application provides a sensorless MCU load estimation method and system, which comprises the following steps: based on the vehicle longitudinal force balance principle and the permanent magnet synchronous motor operation characteristics, a vehicle longitudinal dynamics initial mathematical model is constructed, and a vehicle weight estimation core solving equation is obtained; through the MCU, motor operation parameters and vehicle driving basic parameters of the vehicle CAN bus are collected in real time, and input parameters corresponding to the vehicle weight estimation core solving equation are calculated based on the collected parameters; the time domain signals corresponding to the collected parameters are subjected to working condition effectiveness determination, so as to screen out continuous sampling data meeting the preset effective working condition condition; the filtered effective sampling time domain signals are subjected to filtering and noise reduction processing, so as to obtain target sampling data after phase alignment; the target sampling data are introduced into a preset recursive least square method iteration model with an adaptive forgetting factor, and real-time vehicle weight estimation results are output. The application can output accurate estimation results.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, and in particular to a sensorless MCU load estimation method and system. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the control requirements for vehicle power, economy, and driving safety are continuously increasing. The total vehicle weight (including curb weight and payload) is a key input parameter for the new energy vehicle controller to execute core control strategies such as drive torque distribution, regenerative braking, vehicle stability control, and adaptive cruise control. The real-time performance and accuracy of vehicle weight estimation directly determine the quality of vehicle control performance and the boundaries of driving safety. Therefore, developing low-cost, high-real-time, and high-precision vehicle weight estimation methods has become an important research and development direction in the field of new energy vehicle control.

[0003] Currently, vehicle weight estimation methods used in the new energy vehicle field mainly fall into two mainstream technical paths. One is based on six-axis inertial sensors, which collect vehicle motion attitude data such as longitudinal and lateral acceleration, pitch angle, and roll angle, and combine them with the vehicle's longitudinal dynamics model to complete the fitting estimation of the vehicle's mass. The other is through the vehicle controller (VCU) to collect measurement data from multiple sources such as tire pressure monitoring, suspension displacement sensors, and on-board millimeter-wave radar, and after data preprocessing and fusion calculation, the data is transmitted to the vehicle microcontroller unit (MCU) to execute the estimation output of vehicle weight parameters.

[0004] All of the aforementioned existing technical solutions have inherent limitations that are difficult to address simultaneously. Estimation schemes based on six-axis inertial sensors require additional high-precision inertial measurement hardware, significantly increasing the overall vehicle hardware cost and assembly complexity, which is detrimental to the large-scale mass production and promotion of the vehicle. Estimation schemes based on multi-sensor fusion require the transmission, synchronous calibration, and fusion processing of multi-source data across control units, resulting in significant communication and computational delays. This fails to meet the real-time requirements under complex operating conditions. Furthermore, the inherent measurement errors of multiple sensors are prone to superposition and amplification, and data adaptability is poor under extreme conditions, making it difficult to consistently guarantee the accuracy of vehicle weight estimation and hindering further improvements in the overall control performance of new energy vehicles. Summary of the Invention

[0005] Based on this, the purpose of the present invention is to provide a sensorless MCU load estimation method and system to solve the problems of significant communication and computation delays and high estimation costs in the existing technology during the estimation process.

[0006] The first aspect of the present invention proposes: A sensorless MCU load estimation method, wherein the method includes: Based on the principle of longitudinal force balance of the vehicle and the operating characteristics of the permanent magnet synchronous motor, an initial mathematical model of the longitudinal dynamics of the whole vehicle is constructed. The first derivative of both sides of the equation of the initial mathematical model of the longitudinal dynamics of the whole vehicle is calculated with respect to time t to eliminate the derivative terms related to fixed resistance with a rate of change of less than a preset threshold with time. The core solution equation for vehicle weight estimation is obtained, which only includes the vehicle mass, the rate of change of vehicle acceleration, the rate of change of slope angle, and the rate of change of wheel-end driving force. The MCU collects motor operating parameters and vehicle driving basic parameters from the vehicle's CAN bus in real time, and calculates the input parameters corresponding to the core solution equation for vehicle weight estimation based on the collected parameters. The validity of the time-domain signals corresponding to the collected parameters is determined to filter out continuous sampling data that meet the preset valid operating conditions. The filtered effective sampled time-domain signals are subjected to filtering and noise reduction processing to perform dual-dimensional phase calibration compensation for the signal phase lag caused by filtering and the phase difference between the traction force signal and the acceleration signal caused by the deformation of the non-rigid structure of the vehicle, so as to obtain the phase-aligned target sampled data. The target sampled data is imported into a preset recursive least squares iterative model with an adaptive forgetting factor to complete the recursive iterative calculation of the vehicle weight estimate and output the real-time vehicle weight estimate result.

[0007] The beneficial effects of this invention are as follows: This solution constructs a dynamic model based on the principle of longitudinal force balance of the vehicle and the operating characteristics of the permanent magnet synchronous motor. After eliminating low-rate-of-change fixed resistance related terms through first-order differentiation, a simplified core solution equation for vehicle weight estimation is obtained, which significantly reduces the amount of computation and effectively solves the pain point of excessive communication and computation latency in traditional solutions. The input parameter calculation is completed by collecting parameters from the vehicle's existing MCU and CAN bus, without the need for additional high-precision sensing equipment, which significantly reduces the hardware and deployment costs of vehicle weight estimation. Through working condition validity screening, filtering and noise reduction, and dual-dimensional phase calibration compensation, the interference of signal phase deviation on estimation accuracy is eliminated, ensuring the reliability of input data. Combined with the recursive least squares method with adaptive forgetting factor to complete iterative calculation, while ensuring the real-time performance of the estimation, the convergence speed and dynamic working condition adaptability of the results are improved, and high-precision real-time vehicle weight estimation results can be stably output.

[0008] Furthermore, the steps for constructing the initial mathematical model of the vehicle's longitudinal dynamics include: Using the vehicle wheel-end driving force as the input term on the left side of the equation, and the vehicle acceleration traction force, slope gravity component, rolling resistance, and air resistance as the sub-terms on the right side of the equation, a corresponding initial mathematical model of the vehicle's longitudinal dynamics is constructed. Among them, the wheel-end driving force is calculated by the motor output torque, the transmission system speed ratio, and the wheel radius. The air resistance coefficient and frontal area of ​​the air resistance term are preset fixed values, and the rolling resistance coefficient of the rolling resistance term is a preset fixed value.

[0009] Furthermore, the step of taking the first derivative of both sides of the initial mathematical model of the vehicle's longitudinal dynamics with respect to time t and eliminating the derivative terms related to fixed resistance whose rate of change with time is lower than a preset threshold includes: Take the first derivative of both sides of the equation of the initial mathematical model of longitudinal dynamics of the whole vehicle with respect to time t, so as to calculate the time change rate of each component. The rolling resistance derivative and air resistance derivative terms whose time change rate is lower than the preset proportional threshold of the vehicle acceleration change rate are removed, and the corresponding vehicle mass-related terms, slope gravity component derivative terms, and wheel-end driving force derivative terms are retained to obtain the core solution equation for vehicle weight estimation.

[0010] Furthermore, the step of determining the validity of the time-domain signal corresponding to the collected parameters and filtering out continuous sampling data that meet the preset valid operating conditions includes: The preset effective operating conditions are: the vehicle is in pure driving condition, the accelerator pedal opening is within the preset rapid acceleration or rapid deceleration driving opening range, the vehicle speed is within the preset effective speed range, the absolute value of the vehicle acceleration is within the preset effective acceleration range, and the parameters do not jump abnormally. The continuously sampled time-domain signal is judged frame by frame to retain the sampling data that continuously meets the valid operating conditions and whose sampling duration exceeds the preset minimum valid duration.

[0011] Furthermore, the step of performing dual-dimensional phase calibration compensation for the signal phase lag caused by filtering and the phase difference between the traction force signal and the acceleration signal caused by the non-rigid structural deformation of the vehicle includes: First-dimensional filter phase lag compensation: Based on the signal phase displacement corresponding to different filter window lengths obtained from the previous calibration, the filtered signal is phase-advanced and compensated. The compensation amount is positively correlated with the filter window length. The second dimension is non-rigid structure phase difference compensation: Based on the phase difference calibration MAP table of the traction force signal and acceleration signal corresponding to the deformation of the vehicle transmission system and the frame under different loads and accelerations obtained from the previous bench calibration, the phase difference compensation amount matching the current working condition is queried in real time, and the traction force signal and acceleration signal are phase aligned.

[0012] Furthermore, the step of importing the target sampled data into a preset recursive least squares iterative model with an adaptive forgetting factor to complete the recursive iterative calculation of the vehicle mass estimate includes: Based on the core equation for vehicle weight estimation, a recursive formula for estimation using the recursive least squares method is constructed accordingly. The forgetting factor in the recursive formula is adjusted in real time based on the fluctuation rate of vehicle acceleration and the duration of continuous stable operation. Specifically, the forgetting factor is decreased when the operation is continuously stable, and increased when the operation changes abruptly.

[0013] Furthermore, the step of outputting the real-time vehicle weight estimation result includes: During the recursive iterative calculation process, the relative deviation rate of the estimated vehicle mass output between two adjacent iterations is calculated in real time. When the relative deviation rate is continuously lower than the preset deviation threshold and the number of iterations exceeds the preset minimum number of iterations, the iteration is determined to be converged, the current estimated vehicle weight is locked and output as the real-time vehicle weight estimation result; when the relative deviation rate exceeds the preset deviation threshold, the recursive iterative calculation continues to be performed. The expression for the core equation for vehicle weight estimation is as follows:

[0014] in, Ft For wheel-end driving force, μ _ roll The rolling resistance coefficient, The angle between the ramps, C _ w The air drag coefficient, For windward area, V Let g represent the vehicle speed, a represent the acceleration due to gravity, and m represent the vehicle's own weight.

[0015] The second aspect of the present invention proposes: A sensorless MCU load estimation system, wherein the system comprises: The model building module is used to construct an initial mathematical model of the vehicle's longitudinal dynamics based on the principle of vehicle longitudinal force balance and the operating characteristics of permanent magnet synchronous motors; the first derivative of both sides of the equation of the initial mathematical model of the vehicle's longitudinal dynamics is calculated with respect to time t, and the derivative terms related to fixed resistance with a rate of change of less than a preset threshold with time are eliminated to obtain the core solution equation for vehicle weight estimation. The data acquisition module is used to collect motor operating parameters and vehicle driving basic parameters from the vehicle CAN bus in real time through the MCU, and calculate the input parameters corresponding to the core solution equation for vehicle weight estimation based on the collected parameters. The working condition screening module is used to determine the validity of the working condition of the time-domain signal corresponding to the collected parameters and screen out the continuous sampling data that meets the preset valid working condition conditions. The signal processing module is used to perform filtering and noise reduction processing on the filtered effective sampled time-domain signals. It performs two-dimensional phase calibration compensation for the signal phase lag caused by filtering and the phase difference between the traction force signal and the acceleration signal caused by the deformation of the non-rigid structure of the vehicle, so as to obtain the phase-aligned target sampled data. The iterative calculation module is used to import the target sampling data into a preset recursive least squares iterative model with an adaptive forgetting factor, complete the recursive iterative calculation of the vehicle weight estimate, and output the real-time vehicle weight estimate result.

[0016] Furthermore, the model building module is specifically used for: Using the vehicle wheel-end driving force as the input term on the left side of the equation, and the vehicle acceleration traction force, slope gravity component, rolling resistance, and air resistance as the sub-terms on the right side of the equation, a corresponding initial mathematical model of the vehicle's longitudinal dynamics is constructed. Among them, the wheel-end driving force is calculated by the motor output torque, the transmission system speed ratio, and the wheel radius. The air resistance coefficient and frontal area of ​​the air resistance term are preset fixed values, and the rolling resistance coefficient of the rolling resistance term is a preset fixed value.

[0017] Furthermore, the model building module is specifically used for: Take the first derivative of both sides of the equation of the initial mathematical model of longitudinal dynamics of the whole vehicle with respect to time t, so as to calculate the time change rate of each component. The rolling resistance derivative and air resistance derivative terms whose time change rate is lower than the preset proportional threshold of the vehicle acceleration change rate are removed, and the corresponding vehicle mass-related terms, slope gravity component derivative terms, and wheel-end driving force derivative terms are retained to obtain the core solution equation for vehicle weight estimation.

[0018] Furthermore, the working condition screening module is specifically used for: The preset effective operating conditions are: the vehicle is in pure driving condition, the accelerator pedal opening is within the preset rapid acceleration or rapid deceleration driving opening range, the vehicle speed is within the preset effective speed range, the absolute value of the vehicle acceleration is within the preset effective acceleration range, and the parameters do not jump abnormally. The continuously sampled time-domain signal is judged frame by frame to retain the sampling data that continuously meets the valid operating conditions and whose sampling duration exceeds the preset minimum valid duration.

[0019] Furthermore, the signal processing module is specifically used for: First-dimensional filter phase lag compensation: Based on the signal phase displacement corresponding to different filter window lengths obtained from the previous calibration, the filtered signal is phase-advanced and compensated. The compensation amount is positively correlated with the filter window length. The second dimension is non-rigid structure phase difference compensation: Based on the phase difference calibration MAP table of the traction force signal and acceleration signal corresponding to the deformation of the vehicle transmission system and the frame under different loads and accelerations obtained from the previous bench calibration, the phase difference compensation amount matching the current working condition is queried in real time, and the traction force signal and acceleration signal are phase aligned.

[0020] Furthermore, the iterative calculation module is specifically used for: Based on the core equation for vehicle weight estimation, a recursive formula for estimation using the recursive least squares method is constructed accordingly. The forgetting factor in the recursive formula is adjusted in real time based on the fluctuation rate of vehicle acceleration and the duration of continuous stable operation. Specifically, the forgetting factor is decreased when the operation is continuously stable, and increased when the operation changes abruptly.

[0021] Furthermore, the iterative calculation module is specifically used for: During the recursive iterative calculation process, the relative deviation rate of the estimated vehicle mass output between two adjacent iterations is calculated in real time. When the relative deviation rate is continuously lower than the preset deviation threshold and the number of iterations exceeds the preset minimum number of iterations, the iteration is determined to be converged, the current estimated vehicle weight is locked and output as the real-time vehicle weight estimation result; when the relative deviation rate exceeds the preset deviation threshold, the recursive iterative calculation continues to be performed. The expression for the core equation for vehicle weight estimation is as follows:

[0022] in, Ft For wheel-end driving force, μ _ roll The rolling resistance coefficient, The angle between the ramps, C _ w The air drag coefficient, For windward area, V Let g represent the vehicle speed, a represent the acceleration due to gravity, and m represent the vehicle's own weight.

[0023] The third aspect of the present invention proposes: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the sensorless MCU load estimation method as described above.

[0024] The fourth aspect of the present invention proposes: A computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the sensorless MCU load estimation method as described above.

[0025] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0026] Figure 1 A flowchart of a sensorless MCU load estimation method provided in the first embodiment of the present invention; Figure 2 The structural block diagram of the sensorless MCU load estimation system provided in the third embodiment of the present invention is shown.

[0027] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0028] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0029] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0031] Please see Figure 1The image shows a sensorless MCU load estimation method provided in the first embodiment of the present invention. The sensorless MCU load estimation method provided in this embodiment can eliminate the interference of signal phase deviation on estimation accuracy through working condition validity screening, filtering and noise reduction and dual-dimensional phase calibration compensation, and ensure the reliability of input data. Combined with the recursive least squares method with adaptive forgetting factor to complete iterative calculation, while ensuring the real-time estimation, it improves the convergence speed and dynamic working condition adaptability of the results, and can stably output high-precision real-time vehicle weight estimation results.

[0032] Specifically, this embodiment provides: A sensorless MCU load estimation method, wherein the method includes: Step S10: Based on the principle of longitudinal force balance of the vehicle and the operating characteristics of the permanent magnet synchronous motor, an initial mathematical model of the longitudinal dynamics of the whole vehicle is constructed, and the first derivative of both sides of the equation of the initial mathematical model of the longitudinal dynamics of the whole vehicle is calculated with respect to time t to eliminate the fixed resistance related derivative terms whose rate of change with time is lower than a preset threshold. The core solution equation for vehicle weight estimation is obtained, which only includes the vehicle mass, the rate of change of vehicle acceleration, the rate of change of slope angle, and the rate of change of wheel end driving force. It's important to note that traditional vehicle weight estimation directly uses the original longitudinal dynamics model, which includes several interference terms such as rolling resistance, air resistance, and slope resistance, which are difficult to measure accurately in real time. The estimation errors of these interference terms are directly transmitted to the vehicle weight solution, leading to significant estimation bias. This step first constructs an initial longitudinal dynamics model of the entire vehicle based on the vehicle's longitudinal force balance and the precise torque output characteristics of the permanent magnet synchronous motor. The precise torque output by the motor is used as the calculation benchmark for the wheel-end driving force, eliminating the need for external force sensors. Then, through mathematical transformation by taking the first derivative of both sides of the equation, the original static force balance equation is converted into a dynamic rate of change equation. Since the rate of change of fixed resistance terms such as rolling resistance and air resistance under normal driving conditions is much lower than the rate of change of vehicle acceleration and wheel-end driving force, their corresponding derivative terms can be eliminated. The final solution equation only includes the vehicle mass, the rate of change of acceleration, the rate of change of slope angle, and the rate of change of wheel-end driving force. This mathematical simplification completely eliminates the interference of parameters such as rolling resistance coefficient and air resistance coefficient, which are difficult to measure accurately, on vehicle weight estimation. It simplifies the complex equation with multiple unknowns into a solvable equation with the vehicle mass as the only core unknown, thereby improving the accuracy of vehicle weight estimation from a theoretical perspective. At the same time, it significantly reduces the complexity of subsequent calculations and perfectly matches the limited computing power of the vehicle MCU.

[0033] Step S20: The MCU collects the motor operating parameters and the vehicle driving basic parameters of the vehicle CAN bus in real time, and calculates the input parameters corresponding to the core solution equation for vehicle weight estimation based on the collected parameters. It's important to note that this step is the data source stage of the entire method. Its core function is to provide accurate and real-time input parameters for the core equations. The entire data collection is completed using the vehicle's existing hardware, without the need for additional sensors. Specifically, motor operating parameters are collected in real-time by the permanent magnet synchronous motor's MCU, including core parameters such as motor output torque, motor speed, torque constant, and rotor position. These parameters are the core basis for calculating the wheel-end driving force and the rate of change of driving force. The torque control accuracy of the permanent magnet synchronous motor can reach within ±5%, providing high-fidelity power input data for vehicle weight estimation. Basic vehicle driving parameters are acquired in real-time via the vehicle's CAN bus, including vehicle speed, accelerator pedal opening, brake pedal status, vehicle acceleration, slope angle, and vehicle attitude. These parameters are the core basis for calculating the rate of change of acceleration and the rate of change of slope angle. After collecting the raw parameters, the MCU calculates the corresponding input parameters in real-time according to the requirements of the core equations, including the rate of change of wheel-end driving force, the rate of change of vehicle acceleration, and the rate of change of slope angle, providing standardized input for subsequent estimation calculations.

[0034] Step S30: Determine the validity of the operating conditions of the time-domain signals corresponding to the collected parameters in order to filter out continuous sampling data that meet the preset valid operating conditions. It's important to note that the core equation for vehicle weight estimation is applicable only under conditions of rapid acceleration or deceleration. Only under these conditions does the force balance relationship of the longitudinal dynamics model hold strictly. However, in actual vehicle operation, most conditions are invalid, including braking, coasting, idling, frequent throttle fluctuations, and off-road bumps. The parameters fluctuate greatly under these conditions, disrupting the force balance. Directly using these conditions for calculation would lead to estimation results that deviate significantly from the true values. This step uses preset valid operating conditions to evaluate the continuously sampled time-domain signal frame by frame, retaining only the sampling data that meets all valid operating conditions and has the required continuous valid duration. This completely eliminates interference data from invalid operating conditions, ensuring that the data input to subsequent calculations fully complies with the applicable premise of the core equation, providing a fundamental guarantee for estimation accuracy.

[0035] Step S40: Perform filtering and noise reduction processing on the filtered effective sampled time domain signals to perform dual-dimensional phase calibration compensation for the signal phase lag caused by filtering and the phase difference between the traction force signal and the acceleration signal caused by the deformation of the non-rigid structure of the vehicle, so as to obtain the phase-aligned target sampled data. It should be noted that the effective sampled signal inevitably contains high-frequency noise from road bumps, motor torque fluctuations, and CAN bus transmission. Low-pass filtering is necessary for noise reduction to ensure signal smoothness. However, filtering inevitably introduces signal phase lag, and the amount of lag varies for different signals, leading to phase misalignment between the driving force and acceleration signals that should be synchronized. Secondly, the vehicle is not an ideal rigid structure. Torsional clearances in the transmission system and elastic deformation of the frame and suspension cause the wheel-end driving force output by the motor to be delayed before being converted into actual vehicle acceleration, creating an inherent phase difference between the traction and acceleration signals—a deviation completely ignored by traditional theoretical models. This step addresses these two types of phase deviations by performing two-dimensional phase calibration compensation: the first dimension compensates for the phase lag caused by filtering by using the correspondence between the previously calibrated filtering window and phase displacement to perform advance compensation on the filtered signal; the second dimension compensates for the inherent phase difference caused by the non-rigid structure by using a phase difference MAP table calibrated on the bench to match the compensation amount under the current operating conditions in real time, aligning the two signals in phase. After dual-dimensional compensation, the final target sampling data achieved strict time synchronization of the driving force change rate, acceleration change rate, and slope change rate signals, completely eliminating the estimation deviation caused by phase misalignment, and ensuring that the input data of the core solution equation fully meets the time synchronization requirements of the theoretical model.

[0036] Step S50: The target sampling data is imported into a preset recursive least squares iterative model with an adaptive forgetting factor to complete the recursive iterative calculation of the vehicle weight estimate and output the real-time vehicle weight estimate result.

[0037] It's worth noting that traditional least squares methods require storing all sampled data for batch calculations, placing extremely high demands on the storage and computing power of the MCU, which cannot meet the requirements of real-time vehicle computing. In contrast, recursive least squares (RLS) does not require storing all data; each iteration only needs to input new sampled data to update the vehicle weight estimate, resulting in extremely low computing power consumption and perfectly adapting to the operating environment of vehicle MCUs. Furthermore, this method introduces an adaptive forgetting factor into the recursive model, addressing the pain point that a fixed forgetting factor cannot simultaneously ensure steady-state convergence accuracy and dynamic tracking performance: when the vehicle load changes abruptly (such as during loading and unloading), the algorithm can quickly track the new vehicle weight value; when the vehicle is in a stable driving condition, the algorithm can quickly converge to an accurate vehicle weight value, avoiding fluctuations in the results. Finally, through iterative calculation and convergence judgment rules, stable and reliable real-time vehicle weight estimation results are output, which can be directly used for vehicle control scenarios such as energy management strategy optimization for new energy vehicles, hill start assist control, anti-lock braking system (ABS) parameter adaptation, and overload warning for freight vehicles, completing the entire closed loop from data collection to result implementation.

[0038] Second Embodiment Furthermore, the steps for constructing the initial mathematical model of the vehicle's longitudinal dynamics include: Using the vehicle wheel-end driving force as the input term on the left side of the equation, and the vehicle acceleration traction force, slope gravity component, rolling resistance, and air resistance as the sub-terms on the right side of the equation, a corresponding initial mathematical model of the vehicle's longitudinal dynamics is constructed. Among them, the wheel-end driving force is calculated by the motor output torque, the transmission system speed ratio, and the wheel radius. The air resistance coefficient and frontal area of ​​the air resistance term are preset fixed values, and the rolling resistance coefficient of the rolling resistance term is a preset fixed value.

[0039] It's important to note that during longitudinal movement, the driving force output from the wheels ultimately overcomes the traction required for acceleration, the gravitational component of the slope, the rolling resistance between the tires and the ground, and air resistance. This is the fundamental principle of longitudinal force balance and the core framework of the initial mathematical model. The wheel-end driving force on the left side of the equation is the sole source of power for the vehicle's longitudinal motion and the core input term of the entire model. The four terms on the right side of the equation cover all the core resistance and power consumption terms during vehicle movement, ensuring that the initial model fully reflects the actual force situation during longitudinal movement and providing a complete theoretical foundation for subsequent mathematical simplification.

[0040] This section clarifies the calculation methods and value rules for the core parameters in the model, which are also the key to achieving "sensorless" estimation: the wheel-end driving force does not require additional force sensors for measurement, but is precisely calculated by combining the motor output torque collected by the motor MCU with three fixed vehicle parameters: transmission system speed ratio and wheel radius. The permanent magnet synchronous motor has high torque control accuracy, and the parameters have been calibrated at the vehicle factory, ensuring stable and reliable values, thus guaranteeing the accuracy of the model input terms from the source. The air drag coefficient, frontal area, and rolling resistance coefficient are all fixed calibration parameters for the vehicle and will not change after the vehicle is finalized. Therefore, they are set as preset fixed values, providing a premise for subsequent simplification of differentiation. Specifically, the drag terms corresponding to the fixed parameters have extremely low time change rates under normal driving conditions and can be directly eliminated after differentiation, without significantly affecting the vehicle weight solution.

[0041] Furthermore, the step of taking the first derivative of both sides of the initial mathematical model of the vehicle's longitudinal dynamics with respect to time t and eliminating the derivative terms related to fixed resistance whose rate of change with time is lower than a preset threshold includes: Take the first derivative of both sides of the equation of the initial mathematical model of longitudinal dynamics of the whole vehicle with respect to time t, so as to calculate the time change rate of each component. The rolling resistance derivative and air resistance derivative terms whose time change rate is lower than the preset proportional threshold of the vehicle acceleration change rate are removed, and the corresponding vehicle mass-related terms, slope gravity component derivative terms, and wheel-end driving force derivative terms are retained to obtain the core solution equation for vehicle weight estimation.

[0042] It should be noted that the initial longitudinal dynamics model is a static force balance equation, which includes multiple variables such as vehicle mass, rolling resistance coefficient, air resistance coefficient, and slope angle. While vehicle mass is the sole objective, the real-time values ​​of the other interfering variables are difficult to measure accurately, and direct solution would lead to significant errors. This step transforms the static force balance equation into a dynamic rate-of-change equation by taking the first derivative of both sides of the equation with respect to time t. This mathematical transformation converts the original absolute value problem into a rate-of-change problem, a crucial prerequisite for eliminating interfering terms. After differentiation, the left side of the equation becomes the time-dependent rate of change of the wheel-end driving force, while the right side becomes the rate of change of the acceleration traction force, the rate of change of the slope gravity component, the rate of change of rolling resistance, and the rate of change of air resistance, respectively. The rate of change of each component can be calculated using real-time collected parameters.

[0043] Under normal stable driving conditions, rolling resistance is mainly determined by the rolling resistance coefficient, vehicle weight, gravitational acceleration, and the cosine of the slope. Except for a small change in the slope angle, the other parameters are constant, resulting in an extremely low time rate of change of rolling resistance. Air resistance is mainly determined by the air resistance coefficient, frontal area, and the square of the vehicle speed. Under stable driving conditions, the rate of change of vehicle speed is much lower than the rate of change of vehicle acceleration; therefore, the time rate of change of air resistance is also much lower than the rate of change of acceleration. This step completely eliminates the derivative terms of rolling resistance and air resistance, whose rates of change are much lower than the rate of change of vehicle acceleration, by setting a preset proportional threshold. The impact of these terms on the vehicle weight solution is negligible and will not lead to significant estimation errors. After eliminating interference terms, only the vehicle mass-related terms, the gradient gravity component derivative terms, and the wheel-end driving force derivative terms are retained in the equation. The vehicle mass becomes the only core unknown in the equation, and the other variables can be obtained in real time through MCU and CAN bus data acquisition and calculation. Finally, a core equation that can be directly used to solve for vehicle weight is obtained. This simplification completely eliminates the influence of interference terms such as rolling resistance and air resistance, which are difficult to measure accurately, on vehicle weight estimation, thus theoretically improving the estimation accuracy significantly. At the same time, it simplifies complex multivariate equations into solvable equations with a single unknown, greatly reducing computational complexity and adapting to the limited computing power of onboard MCUs.

[0044] Furthermore, the step of determining the validity of the time-domain signal corresponding to the collected parameters and filtering out continuous sampling data that meet the preset valid operating conditions includes: The preset effective operating conditions are: the vehicle is in pure driving condition, the accelerator pedal opening is within the preset rapid acceleration or rapid deceleration driving opening range, the vehicle speed is within the preset effective speed range, the absolute value of the vehicle acceleration is within the preset effective acceleration range, and the parameters do not jump abnormally. The continuously sampled time-domain signal is judged frame by frame to retain the sampling data that continuously meets the valid operating conditions and whose sampling duration exceeds the preset minimum valid duration.

[0045] It should be noted that this section clarifies the quantitative criteria for determining effective operating conditions. Each condition corresponds to the applicable premise of the core solution equations, ensuring that the selected data fully meets the force balance requirements of the model. Specifically, "pure driving condition" ensures that the vehicle is only subjected to the driving force output by the motor, without additional resistance interference from the braking system, and that the longitudinal force balance relationship is valid. "Accelerator pedal opening is within the range of rapid acceleration or deceleration" and "Vehicle speed is within the effective vehicle speed range" eliminate creeping conditions with excessively low speeds and high-speed conditions with excessively high speeds. In low-speed conditions, the motor torque fluctuates greatly, and in high-speed conditions, the rate of change of air resistance increases; eliminating these conditions avoids errors caused by model simplification. "Absolute acceleration value is within the effective acceleration range" eliminates extreme conditions of rapid acceleration and deceleration, ensuring a stable rate of change of acceleration that meets the applicable conditions of the model. "No abnormal parameter jumps" eliminates abnormal jump data caused by CAN bus transmission faults and sensor faults, preventing junk data from entering the calculation process.

[0046] A single sampled data point meeting the valid operating condition conditions cannot guarantee the stability of the stress state and is highly susceptible to instantaneous interference from road bumps. Therefore, continuously sampled signals must meet the valid operating condition conditions frame by frame within a preset minimum valid duration to be considered valid sampled data. For example, if the preset minimum valid duration is 1 second and the sampling period is 10ms, then 100 consecutive frames of sampled data must meet the valid operating condition conditions before being retained for subsequent calculations. This continuous duration filtering further ensures the stability of the input data, eliminates invalid data with instantaneous fluctuations, and ensures that the data input to subsequent filtering and iteration stages can truly reflect the stress state under stable driving conditions, providing a fundamental guarantee for the accuracy and stability of vehicle weight estimation.

[0047] Furthermore, the step of performing dual-dimensional phase calibration compensation for the signal phase lag caused by filtering and the phase difference between the traction force signal and the acceleration signal caused by the non-rigid structural deformation of the vehicle includes: First-dimensional filter phase lag compensation: Based on the signal phase displacement corresponding to different filter window lengths obtained from the previous calibration, the filtered signal is phase-advanced and compensated. The compensation amount is positively correlated with the filter window length. The second dimension is non-rigid structure phase difference compensation: Based on the phase difference calibration MAP table of the traction force signal and acceleration signal corresponding to the deformation of the vehicle transmission system and the frame under different loads and accelerations obtained from the previous bench calibration, the phase difference compensation amount matching the current working condition is queried in real time, and the traction force signal and acceleration signal are phase aligned.

[0048] It should be noted that the high-frequency noise in the effective sampled signal needs to be reduced using methods such as moving average filtering and low-pass filtering. However, all filtering operations inevitably introduce phase lag, and the longer the filter window and the lower the cutoff frequency, the greater the phase lag. More importantly, the frequency characteristics of the wheel-end driving force signal, acceleration signal, and slope angle signal are different, resulting in different phase lags after the same filtering process. This can cause misalignment of signals that were originally time-synchronized, and directly substituting them into the core solution equation will lead to systematic deviations in the calculation results. In the early vehicle calibration phase, this step calibrates the signal phase displacement corresponding to different filter window lengths through bench tests, establishing the correspondence between the filter window and the phase compensation amount. During actual operation, based on the adopted filter window length, the corresponding phase lead compensation amount is matched to compensate the phase of the filtered signal, eliminating the phase lag caused by filtering and realigning the timing of different signals.

[0049] The vehicle is not an ideal rigid body. The reducer and drive shaft of the transmission system have torsional backlash and elastic deformation, and the frame and suspension also have elastic deformation. The wheel-end driving force output by the motor needs to be transmitted through the torsion of the transmission system and the deformation of the suspension before it can be finally converted into the vehicle's acceleration. This transmission process introduces a fixed phase delay, causing the change in traction force signal to precede the change in acceleration signal. The magnitude of the phase difference is directly related to the vehicle load and acceleration: the greater the load and the higher the acceleration, the greater the deformation of the transmission system and the frame, and the greater the phase difference. In the early calibration stage of the vehicle, this step uses bench tests to test the phase difference under different loads and accelerations, and establishes a corresponding two-dimensional calibration MAP table. During actual operation, based on the current estimated load and acceleration values, the corresponding phase difference compensation amount is queried and matched in real time to perform phase alignment processing on the traction force signal and acceleration signal, completely eliminating the inherent phase deviation caused by the non-rigid structure of the vehicle. After two-dimensional phase compensation, all input signals required for solving the core equations are strictly synchronized in time, ensuring that the variables on both sides of the equations correspond completely on the time axis, thus fundamentally eliminating the estimation error caused by phase misalignment.

[0050] Furthermore, the step of importing the target sampled data into a preset recursive least squares iterative model with an adaptive forgetting factor to complete the recursive iterative calculation of the vehicle mass estimate includes: Based on the core equation for vehicle weight estimation, a recursive formula for estimation using the recursive least squares method is constructed accordingly. The forgetting factor in the recursive formula is adjusted in real time based on the fluctuation rate of vehicle acceleration and the duration of continuous stable operation. Specifically, the forgetting factor is decreased when the operation is continuously stable, and increased when the operation changes abruptly.

[0051] It's important to note that the core idea of ​​Recursive Least Squares (RLS) is to achieve the optimal linear unbiased estimate of unknown parameters by minimizing the sum of squared errors. The calculation is performed recursively, requiring only new sampled data for each iteration to update the parameter estimate. This eliminates the need to store all historical data, resulting in extremely low computational and storage requirements, making it perfectly suited to the operating environment of automotive MCUs. This step is based on the simplified core equation for vehicle weight estimation. The vehicle mass is used as the unknown parameter to be estimated, and the rates of change of driving force, acceleration, and gradient are used as input observation vectors. The corresponding recursive formulas for RLS estimation are constructed, including parameter update formulas, gain matrix formulas, and covariance matrix update formulas, enabling iterative updates of the vehicle weight estimate.

[0052] The forgetting factor is a core parameter in recursive least squares, ranging from 0 to 1. Its function is to adjust the weighting of historical data and newly sampled data on the estimation results: a forgetting factor closer to 1 indicates that the algorithm relies more heavily on historical data, resulting in higher steady-state convergence accuracy, but slower tracking of parameter abrupt changes; a smaller forgetting factor indicates that the algorithm relies more heavily on newly sampled data, resulting in faster tracking of parameter abrupt changes, but lower steady-state convergence accuracy and greater fluctuations. Traditional methods using a fixed forgetting factor cannot simultaneously balance steady-state accuracy and dynamic tracking performance: if loading and unloading of a vehicle causes a step change in load, an algorithm with a fixed large forgetting factor will take a long time to track the new vehicle weight; if a fixed small forgetting factor is used, the estimation results under steady-state conditions will fluctuate significantly. This step employs an adaptive forgetting factor, which adjusts its value in real time based on the volatility of vehicle acceleration and the duration of stable operating conditions. When the operating conditions are stable, the forgetting factor value is reduced, allowing the algorithm to place greater emphasis on historical stable data, thereby improving the convergence accuracy of vehicle weight estimation and reducing result fluctuations. When the operating conditions undergo a step change and the acceleration volatility increases significantly, the forgetting factor value is increased, allowing the algorithm to place greater emphasis on newly sampled data and quickly track sudden changes in vehicle weight. This achieves the optimal balance between steady-state accuracy and dynamic tracking performance, perfectly adapting to various operating condition changes during vehicle operation.

[0053] Furthermore, the step of outputting the real-time vehicle weight estimation result includes: During the recursive iterative calculation process, the relative deviation rate of the estimated vehicle mass output between two adjacent iterations is calculated in real time. When the relative deviation rate is continuously lower than the preset deviation threshold and the number of iterations exceeds the preset minimum number of iterations, the iteration is determined to be converged, the current estimated vehicle weight is locked and output as the real-time vehicle weight estimation result; when the relative deviation rate exceeds the preset deviation threshold, the recursive iterative calculation continues to be performed. The expression for the core equation for vehicle weight estimation is as follows:

[0054] in, Ft For wheel-end driving force, μ _ roll The rolling resistance coefficient, The angle between the ramps, C _ w The air drag coefficient, For windward area, V Let g represent the vehicle speed, a represent the acceleration due to gravity, and m represent the vehicle's own weight.

[0055] It's important to note that during the iterative process of recursive least squares, the initial estimation results will fluctuate continuously with the input of new data. Directly outputting the result of each iteration would cause frequent jumps in vehicle weight values, making it unsuitable for in-vehicle control strategies. Therefore, the convergence state of the iteration must be determined using the relative deviation rate. The relative deviation rate is calculated as the absolute value of the difference between two consecutive estimations divided by the previous estimation value. This accurately reflects the degree of fluctuation in the estimation result; a smaller relative deviation rate indicates a more stable estimation result that is closer to the actual vehicle weight value.

[0056] This section clarifies the quantitative judgment rules for iterative convergence. Iterative convergence is determined and the final vehicle weight estimation result is output only when two core conditions are simultaneously met: first, the relative deviation rate is continuously lower than a preset deviation threshold, indicating that the estimation result has stabilized and is not fluctuating significantly; second, the number of iterations exceeds a preset minimum number of iterations, ensuring that the algorithm has completed sufficient sample learning and avoiding misjudgments caused by occasional low deviations. Only when both conditions are met will the current estimated value be locked as the real-time vehicle weight result output. If the relative deviation rate exceeds the threshold, it indicates that the estimation result is still fluctuating and has not reached convergence, and the recursive iteration continues, without outputting unstable results. This convergence judgment mechanism completely avoids frequent jumps in the estimation result, ensuring the stability and reliability of the output result, and can directly adapt to the needs of control scenarios such as vehicle energy management, hill start assist, and overload warning.

[0057] Please see Figure 2 The third embodiment of the present invention provides: A sensorless MCU load estimation system, wherein the system comprises: The model building module is used to construct an initial mathematical model of the vehicle's longitudinal dynamics based on the principle of vehicle longitudinal force balance and the operating characteristics of permanent magnet synchronous motors; the first derivative of both sides of the equation of the initial mathematical model of the vehicle's longitudinal dynamics is calculated with respect to time t, and the derivative terms related to fixed resistance with a rate of change of less than a preset threshold with time are eliminated to obtain the core solution equation for vehicle weight estimation. The data acquisition module is used to collect motor operating parameters and vehicle driving basic parameters from the vehicle CAN bus in real time through the MCU, and calculate the input parameters corresponding to the core solution equation for vehicle weight estimation based on the collected parameters. The working condition screening module is used to determine the validity of the working condition of the time-domain signal corresponding to the collected parameters and screen out the continuous sampling data that meets the preset valid working condition conditions. The signal processing module is used to perform filtering and noise reduction processing on the filtered effective sampled time-domain signals. It performs two-dimensional phase calibration compensation for the signal phase lag caused by filtering and the phase difference between the traction force signal and the acceleration signal caused by the deformation of the non-rigid structure of the vehicle, so as to obtain the phase-aligned target sampled data. The iterative calculation module is used to import the target sampling data into a preset recursive least squares iterative model with an adaptive forgetting factor, complete the recursive iterative calculation of the vehicle weight estimate, and output the real-time vehicle weight estimate result.

[0058] Furthermore, the model building module is specifically used for: Using the vehicle wheel-end driving force as the input term on the left side of the equation, and the vehicle acceleration traction force, slope gravity component, rolling resistance, and air resistance as the sub-terms on the right side of the equation, a corresponding initial mathematical model of the vehicle's longitudinal dynamics is constructed. Among them, the wheel-end driving force is calculated by the motor output torque, the transmission system speed ratio, and the wheel radius. The air resistance coefficient and frontal area of ​​the air resistance term are preset fixed values, and the rolling resistance coefficient of the rolling resistance term is a preset fixed value.

[0059] Furthermore, the model building module is specifically used for: Take the first derivative of both sides of the equation of the initial mathematical model of longitudinal dynamics of the whole vehicle with respect to time t, so as to calculate the time change rate of each component. The rolling resistance derivative and air resistance derivative terms whose time change rate is lower than the preset proportional threshold of the vehicle acceleration change rate are removed, and the corresponding vehicle mass-related terms, slope gravity component derivative terms, and wheel-end driving force derivative terms are retained to obtain the core solution equation for vehicle weight estimation.

[0060] Furthermore, the working condition screening module is specifically used for: The preset effective operating conditions are: the vehicle is in pure driving condition, the accelerator pedal opening is within the preset rapid acceleration or rapid deceleration driving opening range, the vehicle speed is within the preset effective speed range, the absolute value of the vehicle acceleration is within the preset effective acceleration range, and the parameters do not jump abnormally. The continuously sampled time-domain signal is judged frame by frame to retain the sampling data that continuously meets the valid operating conditions and whose sampling duration exceeds the preset minimum valid duration.

[0061] Furthermore, the signal processing module is specifically used for: First-dimensional filter phase lag compensation: Based on the signal phase displacement corresponding to different filter window lengths obtained from the previous calibration, the filtered signal is phase-advanced and compensated. The compensation amount is positively correlated with the filter window length. The second dimension is non-rigid structure phase difference compensation: Based on the phase difference calibration MAP table of the traction force signal and acceleration signal corresponding to the deformation of the vehicle transmission system and the frame under different loads and accelerations obtained from the previous bench calibration, the phase difference compensation amount matching the current working condition is queried in real time, and the traction force signal and acceleration signal are phase aligned.

[0062] Furthermore, the iterative calculation module is specifically used for: Based on the core equation for vehicle weight estimation, a recursive formula for estimation using the recursive least squares method is constructed accordingly. The forgetting factor in the recursive formula is adjusted in real time based on the fluctuation rate of vehicle acceleration and the duration of continuous stable operation. Specifically, the forgetting factor is decreased when the operation is continuously stable, and increased when the operation changes abruptly.

[0063] Furthermore, the iterative calculation module is specifically used for: During the recursive iterative calculation process, the relative deviation rate of the estimated vehicle mass output between two adjacent iterations is calculated in real time. When the relative deviation rate is continuously lower than the preset deviation threshold and the number of iterations exceeds the preset minimum number of iterations, the iteration is determined to be converged, the current estimated vehicle weight is locked and output as the real-time vehicle weight estimation result; when the relative deviation rate exceeds the preset deviation threshold, the recursive iterative calculation continues to be performed. The expression for the core equation for vehicle weight estimation is as follows:

[0064] in, Ft For wheel-end driving force, μ _ roll The rolling resistance coefficient, The angle between the ramps, C _ w The air drag coefficient, For windward area, V Let g represent the vehicle speed, a represent the acceleration due to gravity, and m represent the vehicle's own weight.

[0065] The fourth embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the sensorless MCU load estimation method as described above.

[0066] The fifth embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the sensorless MCU load estimation method as described above.

[0067] In summary, the sensorless MCU load estimation method and system provided by the above embodiments of the present invention can eliminate the interference of signal phase deviation on estimation accuracy through working condition validity screening, filtering and noise reduction, and dual-dimensional phase calibration compensation, thus ensuring the reliability of input data. Combined with the recursive least squares method with adaptive forgetting factor to complete iterative calculation, it can improve the convergence speed and dynamic working condition adaptability of the results while ensuring the real-time performance of the estimation, and can stably output high-precision real-time vehicle weight estimation results.

[0068] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0069] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0070] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0071] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0072] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0073] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A sensorless based MCU load estimation method, characterized in that, The method includes: Based on the principle of longitudinal force balance of the vehicle and the operating characteristics of the permanent magnet synchronous motor, an initial mathematical model of the longitudinal dynamics of the whole vehicle is constructed. The first derivative of both sides of the equation of the initial mathematical model of the longitudinal dynamics of the whole vehicle is calculated with respect to time t to eliminate the derivative terms related to fixed resistance with a rate of change of less than a preset threshold with time. The core solution equation for vehicle weight estimation is obtained, which only includes the vehicle mass, the rate of change of vehicle acceleration, the rate of change of slope angle, and the rate of change of wheel-end driving force. The MCU collects motor operating parameters and vehicle driving basic parameters from the vehicle's CAN bus in real time, and calculates the input parameters corresponding to the core solution equation for vehicle weight estimation based on the collected parameters. The validity of the time-domain signals corresponding to the collected parameters is determined to filter out continuous sampling data that meet the preset valid operating conditions. The filtered effective sampled time-domain signals are subjected to filtering and noise reduction processing to perform dual-dimensional phase calibration compensation for the signal phase lag caused by filtering and the phase difference between the traction force signal and the acceleration signal caused by the deformation of the non-rigid structure of the vehicle, so as to obtain the phase-aligned target sampled data. The target sampled data is imported into a preset recursive least squares iterative model with an adaptive forgetting factor to complete the recursive iterative calculation of the vehicle weight estimate and output the real-time vehicle weight estimate result.

2. The sensorless based MCU load estimation method according to claim 1, wherein, The steps for constructing the initial mathematical model of the vehicle's longitudinal dynamics include: Using the vehicle wheel-end driving force as the input term on the left side of the equation, and the vehicle acceleration traction force, slope gravity component, rolling resistance, and air resistance as the sub-terms on the right side of the equation, a corresponding initial mathematical model of the vehicle's longitudinal dynamics is constructed. Among them, the wheel-end driving force is calculated by the motor output torque, the transmission system speed ratio, and the wheel radius. The air resistance coefficient and frontal area of ​​the air resistance term are preset fixed values, and the rolling resistance coefficient of the rolling resistance term is a preset fixed value.

3. The sensorless based MCU load estimation method of claim 1, wherein, The step of taking the first derivative of both sides of the initial mathematical model of the vehicle's longitudinal dynamics with respect to time t and eliminating derivative terms related to fixed resistance whose rate of change with time is lower than a preset threshold includes: Take the first derivative of both sides of the equation of the initial mathematical model of longitudinal dynamics of the whole vehicle with respect to time t, so as to calculate the time change rate of each component. The rolling resistance derivative and air resistance derivative terms whose time change rate is lower than the preset proportional threshold of the vehicle acceleration change rate are removed, and the corresponding vehicle mass-related terms, slope gravity component derivative terms, and wheel-end driving force derivative terms are retained to obtain the core solution equation for vehicle weight estimation.

4. The sensorless MCU load estimation method according to claim 1, characterized in that, The step of determining the validity of the time-domain signal corresponding to the collected parameters and filtering out continuous sampling data that meet the preset valid operating conditions includes: The preset effective operating conditions are: the vehicle is in pure driving condition, the accelerator pedal opening is within the preset rapid acceleration or rapid deceleration driving opening range, the vehicle speed is within the preset effective speed range, the absolute value of the vehicle acceleration is within the preset effective acceleration range, and the parameters do not jump abnormally. The continuously sampled time-domain signal is judged frame by frame to retain the sampling data that continuously meets the valid operating conditions and whose sampling duration exceeds the preset minimum valid duration.

5. The sensorless MCU load estimation method according to claim 1, characterized in that, The steps of performing dual-dimensional phase calibration compensation for the signal phase lag caused by filtering and the phase difference between the traction force signal and the acceleration signal caused by the non-rigid structural deformation of the vehicle include: First-dimensional filter phase lag compensation: Based on the signal phase displacement corresponding to different filter window lengths obtained from the previous calibration, the filtered signal is phase-advanced and compensated. The compensation amount is positively correlated with the filter window length. The second dimension is non-rigid structure phase difference compensation: Based on the phase difference calibration MAP table of the traction force signal and acceleration signal corresponding to the deformation of the vehicle transmission system and the frame under different loads and accelerations obtained from the previous bench calibration, the phase difference compensation amount matching the current working condition is queried in real time, and the traction force signal and acceleration signal are phase aligned.

6. The sensorless MCU load estimation method according to claim 1, characterized in that, The step of importing the target sampling data into a preset recursive least squares iterative model with an adaptive forgetting factor to complete the recursive iterative calculation of the vehicle mass estimate includes: Based on the core equation for vehicle weight estimation, a recursive formula for estimation using the recursive least squares method is constructed accordingly. The forgetting factor in the recursive formula is adjusted in real time based on the fluctuation rate of vehicle acceleration and the duration of continuous stable operation. Specifically, the forgetting factor is decreased when the operation is continuously stable, and increased when the operation changes abruptly.

7. The sensorless MCU load estimation method according to claim 1, characterized in that, The steps for outputting real-time vehicle weight estimation results include: During the recursive iterative calculation process, the relative deviation rate of the estimated vehicle mass output between two adjacent iterations is calculated in real time. When the relative deviation rate is continuously lower than the preset deviation threshold and the number of iterations exceeds the preset minimum number of iterations, the iteration is determined to be converged, the current estimated vehicle weight is locked and output as the real-time vehicle weight estimation result; when the relative deviation rate exceeds the preset deviation threshold, the recursive iterative calculation continues to be performed. The expression for the core equation for vehicle weight estimation is as follows: in, Ft For wheel-end driving force, μ _ roll The rolling resistance coefficient, The angle between the ramps, C _ w The air drag coefficient, For windward area, V Let g represent the vehicle speed, a represent the acceleration due to gravity, and m represent the vehicle's own weight.

8. A sensorless MCU load estimation system, characterized in that, The system includes: The model building module is used to construct an initial mathematical model of the vehicle's longitudinal dynamics based on the principle of vehicle longitudinal force balance and the operating characteristics of permanent magnet synchronous motors; the first derivative of both sides of the equation of the initial mathematical model of the vehicle's longitudinal dynamics is calculated with respect to time t, and the derivative terms related to fixed resistance with a rate of change of less than a preset threshold with time are eliminated to obtain the core solution equation for vehicle weight estimation. The data acquisition module is used to collect motor operating parameters and vehicle driving basic parameters from the vehicle CAN bus in real time through the MCU, and calculate the input parameters corresponding to the core solution equation for vehicle weight estimation based on the collected parameters. The working condition screening module is used to determine the validity of the working condition of the time-domain signal corresponding to the collected parameters and screen out the continuous sampling data that meets the preset valid working condition conditions. The signal processing module is used to perform filtering and noise reduction processing on the filtered effective sampled time-domain signals. It performs two-dimensional phase calibration compensation for the signal phase lag caused by filtering and the phase difference between the traction force signal and the acceleration signal caused by the deformation of the non-rigid structure of the vehicle, so as to obtain the phase-aligned target sampled data. The iterative calculation module is used to import the target sampling data into a preset recursive least squares iterative model with an adaptive forgetting factor, complete the recursive iterative calculation of the vehicle weight estimate, and output the real-time vehicle weight estimate result.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the sensorless MCU load estimation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the sensorless MCU load estimation method as described in any one of claims 1 to 7.

Images