Vehicle weight estimation method, storage medium and vehicle
By combining vehicle driving data and superstructure operating data, and using longitudinal dynamics and motor characteristic models to calculate vehicle weight reliability and perform weighted fusion, the problem of inaccurate vehicle weight estimation in existing technologies is solved, and higher accuracy and reliability of vehicle weight estimation are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SANY SPECIAL PURPOSE VEHICLE CO LTD
- Filing Date
- 2026-01-28
- Publication Date
- 2026-06-12
AI Technical Summary
Existing vehicle weight estimation methods rely on a single measurement, which leads to inaccurate calculations and abnormal situations such as jumps during transitional operating conditions.
By combining vehicle driving data and superstructure operating data, the confidence level of vehicle weight is calculated using longitudinal dynamics model and superstructure motor characteristic model, and then weighted and fused to establish the final vehicle weight estimate.
This improves the accuracy and reliability of vehicle weight estimation, ensuring the accuracy of calculation results under various operating conditions.
Smart Images
Figure CN122192482A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of vehicle parameter measurement technology, and in particular to vehicle weight estimation methods, storage media, and vehicles. Background Technology
[0002] New energy vehicles represent a crucial direction for the green transformation and upgrading of the global automotive industry. New energy vehicles have also been introduced in the special-purpose vehicle sector, such as new energy forklifts. Due to the operating environment of these new energy special-purpose vehicles, the weight of electric vehicles needs to be estimated. The task of the estimation system is to calculate the optimal vehicle weight under the current operating conditions based on the current vehicle operating status and different vehicle weight calculation models.
[0003] Current technologies typically estimate vehicle weight based solely on the current vehicle condition. For example, they might obtain the motor speed and torque of the mixing drum motor in an electric vehicle; determine the characteristic value of the mixing drum motor based on the motor torque; and then determine the vehicle's load capacity based on the motor speed, the correspondence between a preset speed and a characteristic threshold, and the characteristic value of the torque. Alternatively, they might estimate the vehicle weight solely based on the vehicle's longitudinal dynamic parameters. However, this approach suffers from limited information sources, incomplete coverage of operating conditions, and poor calculation performance under transitional conditions, such as when the mixing drum is not rotating or when the vehicle is shaking during movement. This can lead to abrupt changes and inaccurate estimations. Summary of the Invention
[0004] The main objective of this invention is to propose a vehicle weight estimation method, which aims to solve the technical problems of inaccurate estimation caused by the single source of measurement information and incomplete coverage of operating conditions in the existing technology, resulting in abnormal situations such as jumps.
[0005] To achieve the above objectives, this invention proposes a vehicle weight estimation method, which includes: Calculate the first estimated vehicle weight based on the vehicle's driving data; Calculate the second estimated vehicle weight based on the vehicle's superstructure operating data; Based on the longitudinal dynamics model, a first confidence level is calculated using the driving data; Based on the characteristic model of the superstructure motor, the second confidence level is calculated according to the superstructure operating condition data; Weighted fusion parameters are established based on the first confidence level and the second confidence level; The final vehicle weight estimate is determined based on the weighted fusion parameters, the first vehicle weight estimate, and the second vehicle weight estimate.
[0006] In one embodiment, calculating the first confidence level based on the driving data using the longitudinal dynamics model includes: Excitation parameters and stability parameters are determined based on the driving data, wherein the excitation parameters are used to determine whether the vehicle's acceleration satisfies the confidence condition of the longitudinal dynamics model, and the stability parameters are used to determine whether the vehicle's signal noise satisfies the confidence condition of the longitudinal dynamics model. The first confidence level is determined based on the excitation parameter, the weighting coefficient of the excitation parameter, the stability parameter, and the weighting coefficient of the stability parameter.
[0007] In one embodiment, after the step of determining the excitation parameters and stability parameters based on the driving data, the method further includes: Obtain the excitation value and excitation duration from the excitation parameters, and obtain the acceleration jitter value from the stability parameters; The weighting coefficients of the incentive parameters are determined based on the incentive value and the incentive duration. The weighting coefficient of the stability parameter is determined based on the acceleration jitter value.
[0008] In one embodiment, calculating the second confidence level based on the superstructure motor characteristic model and the superstructure operating condition data includes: Determine the speed and power parameters based on the aforementioned upper structure operating condition data; The second confidence level is determined based on the rotational speed parameter, the weighting coefficient of the rotational speed parameter, the power parameter, and the weighting coefficient of the power parameter.
[0009] In one embodiment, after determining the speed parameters and power parameters based on the superstructure operating condition data, the method further includes: Obtain the error value from the speed parameter, and obtain the power fluctuation value from the power parameter; The weighting coefficients for the rotational speed parameters are assigned based on the error values; The power parameters are assigned weighting coefficients based on the power jitter value.
[0010] In one embodiment, the driving data includes the vehicle's acceleration, air resistance, driving force, gradient, and wheel rolling resistance coefficient; the calculation of the first estimated vehicle weight based on the vehicle's driving data includes: The vehicle's driving data is subjected to Kalman filtering and noise smoothing, and the vehicle's unloaded mass is obtained. The vehicle's cargo capacity is calculated based on the acceleration, air resistance, driving force, gradient, rolling resistance coefficient of the wheels, and empty mass. The estimated weight of the first vehicle is calculated based on the cargo weight and the unloaded weight.
[0011] In one embodiment, the superstructure operating data includes the current power and current speed of the superstructure motor; The calculation of the second vehicle weight estimate based on the vehicle's superstructure operating data includes: Measure the sample vehicle weight of multiple sample vehicles and the ratio of the set rotational speed to the sample power at the sample vehicle weight; The sample vehicle weight, the set rotation speed, and the sample power ratio of multiple sample vehicles are fitted to obtain the sample fitting curve; The second estimated vehicle weight is calculated based on the fitted curve, the current power, and the current rotational speed.
[0012] In one embodiment, establishing the weighted fusion parameters based on the first confidence level and the second confidence level includes: The weight ratio of the first confidence level and / or the second confidence level is calculated based on the driving data; Verify the weight normalization of the weight ratio, and establish weighted fusion parameters based on the verification results.
[0013] In addition, to solve the above problems, the present invention also proposes a computer-readable storage medium storing a vehicle weight estimation program, which, when executed by a processor, implements the steps of the vehicle weight estimation method described above.
[0014] Furthermore, to address the aforementioned problems, the present invention also proposes a vehicle comprising: The controller includes a processor and a memory, the memory storing a computer program, and when the processor executes the computer program, it performs the vehicle weight estimation method as described above.
[0015] The vehicle weight estimation method provided by this invention simultaneously measures the driving data of the vehicle during driving and the working condition data of the superstructure during driving, thereby obtaining the corresponding vehicle weight estimates under the two conditions. After calculating the confidence level of the two vehicle weight estimates using a mathematical model, the two vehicle weight estimates are weighted and fused to achieve adaptive allocation of weight ratio, thereby obtaining the final vehicle weight estimate, which effectively improves the calculation accuracy and reliability. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.
[0017] Figure 1This is a flowchart illustrating a vehicle weight estimation method provided in an embodiment of the present invention.
[0018] Figure 2 This is a flowchart illustrating a vehicle weight estimation method provided in another embodiment of the present invention.
[0019] Figure 3 This is a flowchart illustrating a vehicle weight estimation method provided in another embodiment of the present invention.
[0020] Figure 4 This is a flowchart illustrating a vehicle weight estimation method provided in another embodiment of the present invention.
[0021] Figure 5 The diagram shown is a structural block diagram of a vehicle provided in an embodiment of this application.
[0022] Attached icon number 10. Vehicle; 101. Processor; 102. Memory; 103. Input device; 104. Output device.
[0023] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.
[0025] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly.
[0026] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the use of "and / or" or "and / or" throughout the text includes three parallel solutions. For example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.
[0027] This invention proposes a vehicle weight estimation method. The vehicle system first collects driving data and superstructure operating data through sensors installed on the vehicle and the CAN bus.
[0028] In the initial stage of setting the algorithm for the final vehicle weight estimate, a large number of experimental analyses are required to establish a longitudinal dynamics model and a superstructure motor characteristic model, respectively. The first confidence level of the longitudinal dynamics model is verified by driving data, and the second confidence level of the superstructure motor characteristic model is verified by superstructure operating condition data.
[0029] Specifically, driving data includes, but is not limited to, vehicle speed, acceleration, motor drive torque, motor power, and gradient. Each type of parameter from the driving data is substituted into the longitudinal dynamics model. Similarly, the superstructure operating condition data may include, but is not limited to, the current power and torque of the superstructure motor (mixing tank drive motor), the set speed of the mixing tank, and the current speed. Each type of parameter from the superstructure operating condition data is substituted into the superstructure motor feature model to obtain the optimal range values. Then, real-vehicle verification is conducted to further adjust the confidence level values for greater accuracy. In the real-vehicle verification process, all parameters of the sample vehicles are known.
[0030] Please see Figure 1 The vehicle weight estimation method includes the following steps: Step S10: Calculate the first estimated vehicle weight based on the vehicle's driving data; The driving data also includes the vehicle's acceleration, air resistance, driving force, gradient, and wheel rolling resistance coefficient. After the driving data is collected, Kalman filtering and noise smoothing are performed on the vehicle's driving data to ensure accuracy in subsequent calculations.
[0031] Based on the longitudinal dynamic equation, the collected parameters are substituted into the calculation to obtain the first estimated vehicle weight M. dynThe longitudinal dynamic equation satisfies the following relationship: ; Where M1 is the vehicle's cargo mass, M0 is the vehicle's total mass, and F... t As the driving force, F w For air resistance, F r λ is the rolling resistance coefficient, α is the slope value, λ is the inertia coefficient, a is the acceleration, and g is the gravitational acceleration.
[0032] After calculating and obtaining the vehicle's cargo mass M1, the estimated weight of the first vehicle is M. dyn The sum of the cargo mass M1 and the vehicle mass M0, i.e., the formula for calculating the estimated first vehicle weight, satisfies the following relationship: .
[0033] Step S20: Calculate the second vehicle weight estimate based on the vehicle's superstructure operating data; Before calculating the estimated weight of the second vehicle based on the upper structure's operating data, a database needs to be established, which stores fitted curves: .
[0034] Among them, M y P represents the total mass of the sample vehicles. y ω represents the sample power of the motor mounted on the sample vehicle. y The sample rotational speed of the motor installed on the sample vehicle.
[0035] The operating data for the superstructure may include, but is not limited to, the current power and torque of the superstructure motor (mixing tank drive motor), the set speed and current speed of the mixing tank, etc. The current power and current speed of the superstructure motor are then substituted into the P-value of the fitted curve. y and ω y In this way, the estimated weight M of the second vehicle can be calculated. pwr .
[0036] Specifically, before establishing the database, the total mass of a large number of sample vehicles is sampled. After obtaining the total mass of the sample vehicles, the superstructure motors of the sample vehicles are turned on, and sample rotational speeds and sample power of the superstructure motors are collected. Each sample rotational speed corresponds to a sample power. When the number of collected samples is sufficient, the samples are fitted into sample curves. When the number of sample vehicle types is sufficient, all sample curves are stored in the database. Furthermore, when multiple fitted curves of the same type of sample vehicles are collected, the fitted curves of multiple sample vehicles of the same type can be fitted again to further improve the calculation accuracy.
[0037] To improve search speed, a corresponding MAP table is generated based on sample vehicles with different total masses. When calculating the second vehicle weight estimate, it is only necessary to look up the corresponding total mass in the MAP table based on the vehicle's current speed and power, and use the total mass as the second vehicle weight estimate.
[0038] Step S30: Calculate the first confidence level based on the longitudinal dynamics model and driving data; Specifically, a longitudinal dynamics model is established based on simulation software. This model is used to simulate the vehicle's driving process. Taking acceleration as an example, the acceleration is input into the longitudinal dynamics model, which then simulates the driving process and calculates and outputs the first confidence level.
[0039] The first vehicle weight estimate is obtained based on the longitudinal dynamics equation. The higher the first confidence level, the more accurate the first vehicle weight estimate is, and the closer it is to the actual vehicle weight.
[0040] To improve the first confidence level, optionally, the acceleration of the input longitudinal dynamic model should satisfy the following condition: (1) The acceleration reaches the preset upper limit threshold, that is, the acceleration is large enough. When calculating the first confidence level, the greater the acceleration, the greater the first confidence level, and the more accurate the estimated first vehicle weight data. By setting the preset upper limit threshold, the phenomenon that the tires may slip when the acceleration is too large, which may lead to inaccurate calculation of the first confidence level can be avoided.
[0041] (2) The rate of change of the acceleration signal is within the preset range and the duration is the first preset duration. That is, within the first preset duration, the rate of change of the acceleration signal is small, indicating that the acceleration signal is stable and does not jitter. This reduces the probability that the acceleration signal will fluctuate violently due to road bumps and affect the accuracy of the first confidence level, thereby improving the accuracy of the first confidence level.
[0042] During the calculation process, even if the acceleration value is appropriate, frequent acceleration and deceleration operations by the vehicle can easily lead to acceleration instability, resulting in inaccurate calculations of vehicle weight. Therefore, preferably, a time stamp can be introduced to minimize the need for "long acceleration" and "long deceleration" processes, thereby achieving a more balanced impact of various factors on the assessment of the first confidence level and improving accuracy. Specifically, before inputting the acceleration value into the longitudinal dynamics model, it is determined whether a first preset duration is greater than a set duration threshold, for example, 5 seconds. Only when the first preset duration is greater than the set duration threshold is the acceleration value input into the longitudinal dynamics model. That is, only when the acceleration duration is greater than 5 seconds is the acceleration signal used to calculate the first confidence level in the longitudinal dynamics model.
[0043] In this preferred embodiment, only acceleration signals that have been detected for a sufficiently long time and are stable are input into the longitudinal dynamic model, so that the first confidence level of the output can be more accurate.
[0044] Step S40: Based on the superstructure motor feature model, calculate the second confidence level according to the superstructure operating condition data; A superstructure motor model is established based on simulation software. This model is used to simulate the working process of the vehicle's superstructure motor. Taking acceleration as an example, the current speed of the superstructure motor is input into the superstructure motor model. The superstructure motor model simulates the working process and calculates and outputs the second confidence level.
[0045] The second vehicle weight estimate is based on the current speed and power of the motor in the superstructure. The higher the confidence level of the second estimate, the more accurate it is and the closer it is to the actual weight of the vehicle.
[0046] To improve the first confidence level, optionally, the acceleration of the input longitudinal dynamic model should satisfy the following condition: (1) Set an error threshold between the current speed and the set speed, that is, the current speed of the superstructure motor is consistent with the set speed. When calculating the second confidence level, the closer the current speed of the superstructure motor is to the set speed, the higher the second confidence level, and the more accurate the estimated second vehicle weight data. When the superstructure motor is in a transition process such as acceleration or deceleration, the current speed may deviate significantly from the set speed. By setting an error threshold, the accuracy of the second confidence level can be avoided.
[0047] (2) The signal change rate of the current power of the upper motor is continuously within the preset range and the duration is the second preset duration. That is, within the second preset duration, the signal change rate of the current power is small, which reduces the probability that the load change will affect the accuracy of the second confidence level due to uneven concrete mixing, sticking to the can, etc., thereby improving the accuracy of the second confidence level.
[0048] Therefore, only when a current speed sufficiently close to the set speed and a stable current power signal are detected are they input into the upper motor model, making the output second confidence level more accurate.
[0049] Step S50: Establish weighted fusion parameters based on the first confidence level and the second confidence level; Step S60: Determine the final vehicle weight estimate based on the weighted fusion parameters, the first vehicle weight estimate, and the second vehicle weight estimate.
[0050] Finally, a weighted fusion parameter is established based on the first confidence level and the second confidence level. That is, the confidence levels of the two estimation sources are calculated based on the real-time detected driving data and the superstructure working condition data, and adaptive weight allocation is performed accordingly. Finally, a high-precision and high-reliability vehicle weight estimation result under all working conditions is output through weighted fusion. In other words, the final vehicle weight estimate is determined based on the weighted fusion parameter, the first vehicle weight estimate, and the second vehicle weight estimate.
[0051] In one embodiment of the present invention, please refer to Figure 2 Step S50 includes: Step S51: Calculate the weight ratio of the first confidence level and / or the second confidence level based on the driving data; A higher first confidence level indicates greater reliability of the first vehicle weight estimate calculated based on the longitudinal dynamic equation; a higher second confidence level indicates greater reliability of the second vehicle weight estimate calculated based on the superstructure motor. Weights are assigned to the first and second confidence levels according to their values, i.e., the degree of confidence in the first and second vehicle weight estimates, to obtain the fusion parameters.
[0052] The fusion parameters specifically include the weight coefficients for the first confidence level and the second confidence level. Weights are assigned to the first and second confidence levels through adaptive weight calculation, specifically satisfying the following relationship: ; ; Among them, C dyn As the first confidence level, C pwr For the second confidence level, W dyn W is the weighting coefficient for the first confidence level. pwr These are the weighting coefficients for the second confidence level. After assigning the weights, the weights are normalized and verified, i.e., they satisfy... .
[0053] When the first confidence level is greater than the second confidence level, the first confidence level has a higher weight in the fusion parameters, and vice versa.
[0054] Step S52: Verify the weight normalization of the weight ratio and establish weighted fusion parameters based on the verification results.
[0055] The final fusion output determines the final vehicle weight estimate based on the weighted fusion parameters, the first vehicle weight estimate, and the second vehicle weight estimate. The specific final vehicle weight estimate is m. final The following relationship must be satisfied: ; When the weighting coefficient of the first confidence level is larger, the final vehicle weight estimate will place greater emphasis on the influence of the first vehicle weight estimate, using it as the primary reference. Conversely, when the weighting coefficient of the second confidence level is larger, the final vehicle weight estimate will place greater emphasis on the influence of the second vehicle weight estimate. This results in a final vehicle weight estimate that more closely reflects the actual weight of the vehicle, thus increasing its reliability.
[0056] In one embodiment of the present invention, please refer to Figure 3 Step S30 includes: Step S31: Determine the excitation parameters and stability parameters based on the driving data; As stated above, when calculating the first confidence level, it is necessary to ensure that the vehicle's acceleration is sufficiently large and the acceleration signal is sufficiently stable. The excitation parameters are used to verify whether the acceleration is large enough, while the stability parameters are used to verify whether the acceleration signal is sufficiently stable.
[0057] Specifically, the formulas for calculating the excitation parameters satisfy the following relationship: ; Among them, a threshold K is the incentive value. excite Here, is the excitation parameter, and 'a' is the acceleration.
[0058] The excitation value is a parameter that needs to be calibrated. Based on real-vehicle verification, the excitation value can be set with reference to experimental experience. Specifically, at the beginning of establishing the longitudinal dynamics model, the driving data collected from the sample vehicle is input into the longitudinal dynamics model to verify whether the final sample vehicle weight is consistent with the first estimated vehicle weight, thereby verifying whether the excitation value setting is reasonable. The magnitude of the excitation value is further adjusted through a large number of sample experiments.
[0059] In addition to verifying the first confidence level by inputting acceleration, vehicle speed, gradient, and other parameters can also be input into the longitudinal dynamics model for verification. By continuously improving the longitudinal dynamics model and selecting data that influences vehicle weight calculations, the first confidence level can be verified through multiple dimensions, further improving the calculation results based on longitudinal dynamics. Therefore, in practical applications, vehicle speed, gradient, and other parameters from driving data can be input into the longitudinal dynamics model to improve the accuracy of the output first confidence level.
[0060] Substitute the acceleration data from the driving data into the above formula to calculate the ratio of acceleration to excitation value. The greater the acceleration and the larger the excitation parameter, the higher the confidence level of the vehicle's current acceleration, the higher the first confidence level, and the more reliable the estimation result.
[0061] The formula for calculating the stability parameter satisfies the following relationship: ; Where, σa Let K be the variance of acceleration, ε be a constant coefficient, and K be a constant coefficient. stable These are stable parameters.
[0062] The variance of acceleration specifically refers to the standard deviation of acceleration collected within a time window. The size of this time window is determined by those skilled in the art based on the characteristics of the vehicle platform, or through expert experience calibration or machine learning training. In this embodiment, the past 2 seconds are used as the time window. The variance is calculated based on the acceleration collected within the past 2 seconds. The constant coefficient can be a very small constant, such as 0.01, to prevent the denominator from being zero. The larger the variance of acceleration, the more severe the acceleration jitter, the lower the stability parameter, and the lower the first confidence level. The smaller the variance of acceleration, the smoother the acceleration jitter, the larger the stability parameter, and the higher the first confidence level. The stability parameter ranges from (0, +∞).
[0063] Finally, when calculating the first confidence level, it is also necessary to assign weight coefficients to the excitation parameters and the stability parameters. These coefficients represent the importance of the "excitation parameters" and the "stability parameters" in the final calculation of the first confidence level.
[0064] Specifically, after step S31, the following steps are also included: Step S32: Obtain the excitation value and excitation duration from the excitation parameters, and obtain the acceleration jitter value from the stability parameters; Step S33: Determine the weighting coefficient of the excitation parameter based on the excitation value and the excitation duration; Step S34: Determine the weighting coefficient of the stability parameter based on the acceleration jitter value.
[0065] The weighting coefficient of the incentive parameter (hereinafter referred to as incentive weight) represents the degree of importance attached to the condition that "the vehicle must have sufficiently strong acceleration or deceleration" in the first confidence level. If the incentive weight is set to a large value, it is believed that the greater the acceleration, the more likely it is to affect the value of the first confidence level, that is, the greater the acceleration, the more accurate the final vehicle weight estimate will be.
[0066] The weighting coefficients of the stability parameters (hereinafter referred to as stability weights) represent the degree of importance placed on the condition that "the acceleration signal must be stable and without jitter" in the first confidence level. If the stability weights are set too high, the longitudinal dynamics model will be very "sensitive" to acceleration fluctuations. As a result, even if the acceleration is large, if the road surface is bumpy and causes the acceleration signal to jump violently, the longitudinal dynamics model will easily lead to a decrease in the first confidence level due to data instability.
[0067] For example, the excitation weight is set to 0.6 and the stability weight to 0.4. This means that when judging the reliability of the longitudinal dynamics model, more emphasis is placed on whether the acceleration is large enough (accounting for 60% of the importance). Under this setting, as long as the acceleration is large enough, even if there are slight fluctuations in the acceleration jitter value, the longitudinal dynamics model can still obtain a high first confidence level.
[0068] Step S35: Determine the first confidence level based on the excitation parameters, the weighting coefficients of the excitation parameters, the stability parameters, and the weighting coefficients of the stability parameters.
[0069] The formula for calculating the longitudinal dynamic confidence level satisfies the following relationship: ; C dyn For the first confidence level, W excite As an incentive weight, W stable To stabilize the weights, and W excite +W stable =1.
[0070] It should be noted that when calculating the first confidence level, in addition to setting excitation and stability parameters based on the influencing factors in the longitudinal dynamics equations, it is also possible to set corresponding parameters such as air resistance, driving force, gradient, and wheel rolling resistance coefficient to assist in verifying the reliability of the first confidence level. Furthermore, by considering the acceleration duration and the time interval between two adjacent accelerations, it is possible to avoid the impact of abnormal situations such as excessively short acceleration times or frequent acceleration / deceleration on the reliability of the first confidence level.
[0071] Furthermore, when adding additional parameters, the weighting coefficients should be assigned according to their corresponding incentive values, setting appropriate and reasonable weight percentages to improve the reliability of the first confidence level.
[0072] In one embodiment of this application, please refer to Figure 4 Step S40 includes: Step S41: Determine the speed and power parameters based on the upper structure operating condition data; If the current speed of the superstructure motor deviates significantly from the set speed, it means that the superstructure motor is still in a speed regulation state rather than a stable speed state. Therefore, it is impossible to deduce the corresponding second vehicle weight estimate by substituting the current speed and current power into the fitted curve. Therefore, this embodiment sets a speed parameter to verify whether the deviation between the current speed and the set speed is too large.
[0073] Meanwhile, during the calculation of the second confidence level, even if the rotational speed is stable, if the output power or torque of the superstructure motor fluctuates greatly, it indicates that the load is unstable, such as due to concrete segregation or other disturbances. In this case, the estimated value is also unreliable. The power parameter is then used to verify whether the current power is sufficiently stable.
[0074] Specifically, the formula for calculating the rotational speed parameter satisfies the following relationship: ; Among them, K speed For rotational speed parameter, ω set : To set the rotational speed, ω actual ε is the current rotational speed, and ε is a constant coefficient.
[0075] The smaller the error between the set speed and the current speed, the closer the speed parameter is to 1, and the higher the second confidence level; the larger the error, the closer the speed parameter is to 0, and the lower the second confidence level.
[0076] The formulas for calculating power parameters satisfy the following relationship: ; K power For power parameters, σ p Let ε be the variance of the current power, and ε be a constant coefficient.
[0077] The variance of current power refers to the standard deviation of current power (or current torque) within a time window. The size of this time window is determined by those skilled in the art based on vehicle platform characteristics, or through expert experience calibration or machine learning training. In this embodiment, the past 2 seconds are used as an example for this time window, and the variance is calculated based on the current power collected within the past 2 seconds. The smaller the variance of current power, the more stable the power signal, the larger the power parameter, and the higher the second confidence level.
[0078] Finally, when calculating the second confidence level, it is also necessary to assign weight coefficients to the power parameter and the speed parameter, which represent the importance proportion of the "speed parameter" and "power parameter" in the final calculation of the second confidence level, respectively.
[0079] Specifically, after step S41, the following steps are also included: Step S42: Obtain the error value in the speed parameter and the power fluctuation value in the power parameter; Step S43: Assign weighting coefficients to the rotational speed parameters based on the error value; Step S44: Assign weighting coefficients to the power parameters based on the power jitter value.
[0080] The weighting coefficient of the speed parameter (hereinafter referred to as speed weight) represents the degree of importance placed on the condition that "the actual speed of the mixing tank must strictly follow the set speed" in the second confidence level. For example, if the value of the speed parameter is set too high, the superstructure motor characteristic model will have very "strict" requirements for the speed control accuracy. Only when the actual speed is almost identical to the set speed will the superstructure motor characteristic model consider the superstructure motor to be in a stable working state, which is more likely to increase the second confidence level. If there is a large deviation in speed, the superstructure motor characteristic model will consider the superstructure motor to be in a transition process of acceleration or deceleration, which is more likely to decrease the second confidence level.
[0081] The weighting coefficient of the power parameter (hereinafter referred to as power weight) represents the degree of importance attached to the condition that "the power / torque signal of the superstructure motor must be stable" in the second confidence level. If the value of the power weight is set too high, the superstructure motor characteristic model will be very "sensitive" to fluctuations in the power signal. Even if the current speed of the superstructure motor is controlled very precisely, if the power signal fluctuates drastically, the superstructure motor characteristic model will still consider the current load to be unstable, thus making it easier to affect the reduction of the second confidence level.
[0082] For example, the speed weight is calibrated to 0.6, and the power weight to 0.4. This means that when judging the reliability of the superstructure motor characteristic model, "precise speed control" (accounting for 60% of the importance) is given more weight. This is a prerequisite for ensuring that the superstructure motor operates on the preset fitting curve calibration curve. "Absolute stability of the power signal" (accounting for 40% of the importance) is relatively less important. Under this setting, as long as the mixing tank speed is controlled very precisely, even with slight fluctuations in power, the superstructure motor characteristic model can still obtain a high second confidence level.
[0083] Step S45: Determine the second confidence level based on the rotational speed parameter, the weighting coefficient of the rotational speed parameter, the power parameter, and the weighting coefficient of the power parameter.
[0084] The formula for calculating the confidence level of the upper structure under operating conditions satisfies the following relationship: ; C pwr For the second confidence level, W speed W is the weighting coefficient for the speed parameter (hereinafter referred to as speed weight). power W is the weighting coefficient for the power parameter (hereinafter referred to as power weight). speed +W power =1.
[0085] It should be noted that, in calculating the second confidence level, in addition to setting the speed and power parameters, corresponding parameters such as torque can also be set to help verify the confidence level of the second confidence level.
[0086] Furthermore, when adding additional parameters, the weighting coefficients should be assigned according to the stability of the current torque signal, setting appropriate and reasonable weight percentages to improve the reliability of the second confidence level.
[0087] In addition, to solve the above problems, the present invention also proposes a computer-readable storage medium storing an adaptive weighted fusion vehicle weight estimation program, which, when executed by a processor, implements the steps of the vehicle weight estimation method described above.
[0088] Computer-readable storage media may take the form of any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0089] In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program information. When the computer program information is run by a processor, it causes the processor to execute the steps in a vehicle weight estimation method according to various embodiments of this application.
[0090] Computer program products can be written in any combination of one or more programming languages to perform the operations of the embodiments of this application. The programming languages include object-oriented programming languages such as Java and C++, as well as conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.
[0091] The technical solution of this invention simultaneously measures the driving data of the vehicle during driving and the working condition data of the superstructure during driving, thereby obtaining the vehicle weight estimate corresponding to the two situations. After calculating the confidence level of the two vehicle weight estimates using a mathematical model, the two vehicle weight estimates are weighted and fused to achieve adaptive allocation of the weight ratio, thereby obtaining the final vehicle weight estimate, which effectively improves the calculation accuracy and reliability.
[0092] In addition, to solve the above problems, the present invention also proposes a vehicle, the vehicle including a controller, the controller including a processor and a memory, the memory storing a computer program, and the processor executing the computer program to perform the vehicle weight estimation method as described above.
[0093] like Figure 5 As shown, vehicle 10 includes one or more processors 101 and memory 102.
[0094] The processor 101 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the vehicle 10 to perform desired functions.
[0095] The memory 102 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 101 may execute the program instructions to implement a vehicle weight estimation method and / or other desired functions according to the various embodiments of this application described above.
[0096] In one example, vehicle 10 may also include input device 103 and output device 104, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).
[0097] When the vehicle is a standalone device, the input device 103 can be a communication network connector for receiving the collected input signals from the first device and the second device.
[0098] In addition, the input device 103 may also include, for example, a keyboard, a mouse, etc.
[0099] The output device 104 can output various information to the outside, including determined distance information, direction information, etc. The output device 104 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.
[0100] Of course, for the sake of simplicity, Figure 5 Only some of the components of the vehicle 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the vehicle 10 may include any other suitable components depending on the specific application.
[0101] The above description is merely an exemplary embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention specification and drawings under the technical concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.
Claims
1. A method for estimating vehicle weight, characterized in that, The vehicle weight estimation method includes: Calculate the first estimated vehicle weight based on the vehicle's driving data; Calculate the second estimated vehicle weight based on the vehicle's superstructure operating data; Based on the longitudinal dynamics model, a first confidence level is calculated using the driving data; Based on the characteristic model of the superstructure motor, the second confidence level is calculated according to the superstructure operating condition data; Weighted fusion parameters are established based on the first confidence level and the second confidence level; The final vehicle weight estimate is determined based on the weighted fusion parameters, the first vehicle weight estimate, and the second vehicle weight estimate.
2. The vehicle weight estimation method as described in claim 1, characterized in that, The calculation of the first confidence level based on the longitudinal dynamics model and the driving data includes: Excitation parameters and stability parameters are determined based on the driving data, wherein the excitation parameters are used to determine whether the vehicle's acceleration satisfies the confidence condition of the longitudinal dynamics model, and the stability parameters are used to determine whether the vehicle's signal noise satisfies the confidence condition of the longitudinal dynamics model. The first confidence level is determined based on the excitation parameter, the weighting coefficient of the excitation parameter, the stability parameter, and the weighting coefficient of the stability parameter.
3. The vehicle weight estimation method as described in claim 2, characterized in that, After the step of determining the excitation parameters and stability parameters based on the driving data, the method further includes: Obtain the excitation value and excitation duration from the excitation parameters, and obtain the acceleration jitter value from the stability parameters; The weighting coefficients of the incentive parameters are determined based on the incentive value and the incentive duration. The weighting coefficient of the stability parameter is determined based on the acceleration jitter value.
4. The vehicle weight estimation method as described in claim 1, characterized in that, The calculation of the second confidence level based on the superstructure motor feature model and the superstructure operating condition data includes: Determine the speed and power parameters based on the aforementioned upper structure operating condition data; The second confidence level is determined based on the rotational speed parameter, the weighting coefficient of the rotational speed parameter, the power parameter, and the weighting coefficient of the power parameter.
5. The vehicle weight estimation method as described in claim 4, characterized in that, After the step of determining the speed parameters and power parameters based on the superstructure operating condition data, the method further includes: Obtain the error value from the speed parameter, and obtain the power fluctuation value from the power parameter; The weighting coefficients for the rotational speed parameters are assigned based on the error values; The power parameters are assigned weighting coefficients based on the power jitter value.
6. The vehicle weight estimation method as described in claim 1, characterized in that, The driving data includes the vehicle's acceleration, air resistance, driving force, gradient, and wheel rolling resistance coefficient; the calculation of the first estimated vehicle weight based on the vehicle's driving data includes: The vehicle's driving data is subjected to Kalman filtering and noise smoothing, and the vehicle's unloaded mass is obtained. The vehicle's cargo capacity is calculated based on the acceleration, air resistance, driving force, gradient, rolling resistance coefficient of the wheels, and empty mass. The estimated weight of the first vehicle is calculated based on the cargo weight and the unloaded weight.
7. The vehicle weight estimation method as described in claim 1, characterized in that, The superstructure operating data includes the current power and current speed of the superstructure motor; The calculation of the second vehicle weight estimate based on the vehicle's superstructure operating data includes: Measure the sample vehicle weight of multiple sample vehicles and the ratio of the set rotational speed to the sample power at the sample vehicle weight; The sample vehicle weight, the set rotation speed, and the sample power ratio of multiple sample vehicles are fitted to obtain the sample fitting curve; The second estimated vehicle weight is calculated based on the fitted curve, the current power, and the current rotational speed.
8. The vehicle weight estimation method as described in any one of claims 1 to 7, characterized in that, The step of establishing weighted fusion parameters based on the first confidence level and the second confidence level includes: The weight ratio of the first confidence level and / or the second confidence level is calculated based on the driving data; Verify the weight normalization of the weight ratio, and establish weighted fusion parameters based on the verification results.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a vehicle weight estimation program, which, when executed by a processor, implements the steps of the vehicle weight estimation method as described in any one of claims 1 to 8.
10. A vehicle, characterized in that, The vehicles include: A controller, comprising a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to perform the vehicle weight estimation method according to any one of claims 1 to 8.