Approximate estimation method for whole vehicle mass of new energy vehicle

Abstract

The invention belongs to the field of vehicle related parameter estimation, and particularly discloses a new energy vehicle whole vehicle mass approximate estimation method which comprises the following steps that vehicle parameters of a vehicle and real-time driving data in the driving process are obtained, and the real-time driving data comprise the vehicle speed, the acceleration and the motor output torque; the fixed parameters and the real-time driving data are substituted into a preset calculation formula, and a current whole vehicle mass estimation value is obtained through calculation; wherein the preset calculation formula is determined on the basis of a vehicle longitudinal kinetic equation, and corresponding parameter items in the equation are subjected to numeralization by adopting approximate parameters; a Kalman filtering algorithm is adopted to carry out iterative updating on the current whole vehicle mass estimation value, and a stable whole vehicle mass estimation value is obtained; and according to a pre-established corresponding relation table between the actual vehicle weight and the estimated vehicle weight of the vehicle, mapping or interpolation processing is carried out on the stable vehicle mass estimated value, and a final vehicle mass approximate value is obtained. The total mass of the current vehicle can be accurately judged.

Description

Technical Field

[0001] This application belongs to the field of vehicle-related parameter estimation, and more specifically, relates to a method for approximate estimation of the overall mass of a new energy vehicle. Background Technology

[0002] In recent years, the number of driverless sanitation vehicles has been increasing, such as sweepers and garbage trucks. During operation, the vehicle's weight changes in real time. Firstly, during sweeping operations, wastewater, mud, sand, stones, or bricks are sucked into the vehicle's wastewater tank. Relying solely on a water level sensor in the tank to calculate the vehicle's weight is inaccurate, and the calculated result often differs significantly from the actual weight. Secondly, driverless garbage trucks lack overall vehicle weight data; the fullness of the garbage bin is inferred from the number of operations each time garbage is delivered. This method of judgment is inaccurate and severely impacts operational efficiency. Additionally, while some vehicles on the market use sensors to determine vehicle weight, their effectiveness is poor, they require regular calibration, and their accuracy rate is low.

[0003] Therefore, it is necessary to develop a method to accurately determine the total mass of driverless sanitation vehicles. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this application is to provide a method for approximate estimation of the total mass of new energy vehicles, which can accurately determine the total mass of the current vehicle.

[0005] To achieve the above objectives, in a first aspect, this application provides a method for approximate estimation of the overall mass of new energy vehicles, applicable to unmanned sanitation vehicles, comprising the following steps: S10, acquire vehicle parameters and real-time driving data during driving; among which, vehicle parameters include fixed parameters inherent in vehicle design and approximate parameters preset based on engineering experience, and real-time driving data includes vehicle speed, acceleration and motor output torque. S20, Substitute the fixed parameters and real-time driving data into a predetermined calculation formula to calculate the current estimated value of the vehicle mass; wherein, the predetermined calculation formula is determined based on the vehicle longitudinal dynamics equation and the corresponding parameter terms in the equation are numerically quantified using the approximate parameters. S30, the Kalman filter algorithm is used to iteratively update the current vehicle mass estimate to obtain a stable vehicle mass estimate; S40, based on the pre-established correspondence table between the actual vehicle weight and the estimated vehicle weight, the stable estimated vehicle weight is mapped or interpolated to obtain the final approximate vehicle weight.

[0006] The approximate estimation method for the total mass of new energy vehicles provided in this application has the following advantages: The method constructs a core calculation model based on the vehicle's longitudinal dynamic equations and simplifies the calculation formula by introducing approximate parameters preset by engineering experience, so that estimation can be initiated using existing fixed parameters and real-time driving data without the need to install additional weighing sensors; In addition, the Kalman filter algorithm is used to iteratively correct and converge the errors in the single calculation of the simplified model, and the final mapping is performed by combining a lookup table pre-calibrated for the vehicle model. This allows for accurate determination of the total mass of unmanned sanitation vehicles without relying on external sensors or requiring complex online parameter identification.

[0007] As a further preferred embodiment, in step S10, the fixed parameters include the vehicle's rear axle final reduction ratio, tire rolling radius, and vehicle frontal area.

[0008] As a further preferred embodiment, in step S10, the approximate parameters include the mechanical efficiency of the transmission system, the rotational mass conversion factor, the ramp angle, the air density, the air resistance coefficient, and the rolling resistance coefficient.

[0009] As a further preferred embodiment, in step S20, the longitudinal dynamic equation of the vehicle is:

[0010] in Indicates the actual driving force of the vehicle; This indicates the motor's output torque; Indicates the gear ratio of the current gear in the transmission; Indicates the rear axle final drive ratio of the vehicle; Indicates the mechanical efficiency of the transmission system; Indicates the tire's rolling radius;

[0011] in Indicates the vehicle's acceleration resistance; This indicates the conversion factor for the rotational mass of a vehicle. The curb weight of a vehicle; The acceleration of a vehicle;

[0012] in, Indicates rolling resistance; Indicates the rolling resistance coefficient; Represents gravitational acceleration; Indicates the slope angle of a road;

[0013] in, Indicates the vehicle's slope resistance; Represents gravitational acceleration; Indicates the slope angle of a road;

[0014] in, Indicates air resistance; Indicates the air drag coefficient; Indicates the windward area; air density; The relative speed between the vehicle and the air.

[0015] As a further preferred embodiment, in step S20, the predetermined calculation formula is:

[0016] In the formula, This is the current estimated vehicle weight. This refers to the output torque of the motor. This is the rear axle main reduction ratio of the vehicle. The tire's rolling radius; The vehicle's frontal area; The vehicle speed is mentioned; It is acceleration.

[0017] As a further preferred option, step S30 specifically includes: S31, Set the initial vehicle mass estimate. and the initial estimation error ; S32, Set the measurement error for each measurement. ; S33, Calculate the Kalman coefficient for the k-th measurement when the vehicle accelerates. ; S34, calculate the current vehicle mass estimate for the kth time according to step S20. ; S35, according to the formula Calculate the estimated vehicle mass after the k-th iteration; S36, according to formula Update the estimation error for the kth iteration; S37, Repeat steps S33 to S36 until the estimated vehicle weight is reached. Once the value stabilizes, it is taken as the estimated stable vehicle mass.

[0018] As a further preferred embodiment, step S40, the step of pre-establishing the correspondence table, specifically includes: On a straight road, obtain the first actual vehicle weight of the vehicle in an unloaded state. ; The vehicle was loaded with a preset mass increment at each step, and the actual vehicle weight was obtained after each loading. , ,in This is the loading sequence number. The quality of each load; For each of the aforementioned actual vehicle weights Perform steps S10 to S30 to obtain the corresponding stable estimated vehicle mass value. ; Pairs and Record the data to form the corresponding relationship table.

[0019] As a further preferred embodiment, in step S40, if the stable estimated vehicle weight is located between two adjacent estimated vehicle weights in the corresponding relationship table, then the final approximate vehicle weight is calculated using linear interpolation.

[0020] As a further preferred embodiment, in step S10, the real-time driving data is acquired via the vehicle's CAN bus.

[0021] Secondly, this application provides a system for approximate estimation of the overall mass of a new energy vehicle, comprising the steps of implementing the method described in any one of the above, including: The data acquisition module is used to acquire vehicle parameters and real-time driving data during the driving process. The vehicle parameters include fixed parameters inherent in the vehicle design and approximate parameters preset based on engineering experience. The real-time driving data includes vehicle speed, acceleration, and motor output torque. The vehicle mass estimation calculation module is used to substitute the fixed parameters and real-time driving data into a predetermined calculation formula to calculate the current vehicle mass estimation value; wherein, the predetermined calculation formula is determined based on the vehicle longitudinal dynamics equation and the corresponding parameter terms in the equation are numerically quantified using the approximate parameters. The iterative update module is used to iteratively update the current vehicle mass estimate using a Kalman filter algorithm to obtain a stable vehicle mass estimate. The data processing module is used to map or interpolate the stable estimated vehicle weight value according to a pre-established correspondence table between the actual vehicle weight and the estimated vehicle weight, so as to obtain the final approximate value of the vehicle weight.

[0022] It is understandable that the beneficial effects of the second aspect mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0023] Figure 1 This is a flowchart of the approximate estimation method for the overall weight of new energy vehicles provided in this application; Figure 2 This is a flowchart of a method for approximate estimation of the overall weight of new energy vehicles provided in an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0025] like Figure 1 As shown, this application provides a method for approximate estimation of the overall weight of new energy vehicles, applicable to unmanned sanitation vehicles, including steps S10 to S40, detailed below: Step S10: Obtain vehicle parameters and real-time driving data during the driving process.

[0026] The vehicle parameters include fixed parameters inherent in the vehicle design and approximate parameters preset based on engineering experience. Real-time driving data includes vehicle speed, acceleration, and motor output torque.

[0027] This step, by acquiring the vehicle's inherent characteristic parameters and real-time operating data, provides the necessary input data foundation for subsequent mass estimation based on the vehicle's longitudinal dynamics principles, ensuring that the calculation model possesses the required vehicle specificity and real-time performance under operating conditions.

[0028] Step S20: Substitute the fixed parameters and real-time driving data into the predetermined calculation formula to calculate the current estimated value of the vehicle mass.

[0029] The predetermined calculation formula is based on the vehicle longitudinal dynamics equation and is determined by numericalizing the corresponding parameter terms in the equation using approximate parameters.

[0030] This step uses a pre-defined, simplified calculation formula that incorporates engineering experience parameters to directly calculate the quality estimate using readily available fixed parameters and real-time data. This avoids the need for complex and difficult-to-obtain dynamic environmental parameters for each estimation, thus enabling fast and feasible single-shot estimation in the vehicle controller.

[0031] Step S30: The Kalman filter algorithm is used to iteratively update the current vehicle mass estimate to obtain a stable vehicle mass estimate.

[0032] This step introduces the Kalman filter algorithm, using the single estimate with errors obtained in step S20 as the observation value for iterative filtering. This effectively corrects the errors step by step, allowing the estimated value to converge continuously as the vehicle accelerates, ultimately yielding a stable and reliable estimate of the vehicle mass.

[0033] Step S40: Based on the pre-established correspondence table between the actual vehicle weight and the estimated vehicle weight, the stable estimated vehicle weight is mapped or interpolated to obtain the final approximate vehicle weight.

[0034] This step utilizes a pre-calibrated table of correspondence between actual weight and estimated value for a specific vehicle model to perform secondary correction or interpolation on the filtered and stabilized estimated value. This allows the stable estimated value output by the algorithm to be mapped to a result that is closer to the actual total mass of the vehicle, thereby further improving the accuracy and practicality of the final output result.

[0035] The approximate estimation method for the total mass of new energy vehicles provided in this application has the following advantages: The method constructs a core calculation model based on the vehicle's longitudinal dynamic equations and simplifies the calculation formula by introducing approximate parameters preset by engineering experience, so that estimation can be initiated using existing fixed parameters and real-time driving data without the need to install additional weighing sensors; In addition, the Kalman filter algorithm is used to iteratively correct and converge the errors in the single calculation of the simplified model, and the final mapping is performed by combining a lookup table pre-calibrated for the vehicle model. This allows for accurate determination of the total mass of unmanned sanitation vehicles without relying on external sensors or requiring complex online parameter identification.

[0036] In one embodiment, the technical solution to achieve the above objective can be as follows: The basic principle of this embodiment is to use the Kalman filter method, based on the vehicle's longitudinal dynamics equations, to perform an iterative calculation for each acceleration. As the number of iterations increases, the vehicle mass parameter will continuously converge to near its true value, and ultimately, the estimated vehicle mass can be close to the true value within a certain error range. This algorithm is implemented in software within the VCU (Vehicle Controller Unit), and the specific technical solution is as follows (e.g.) Figure 2 (as shown) Extract the vehicle's static parameters and, based on the vehicle's longitudinal dynamics equations, Determine the specific parameters of the vehicle, including:

[0037] : Indicates the actual driving force of the vehicle; Real-time output torque of the motor; The gear ratio of the current gear in the transmission; : Rear axle final reduction ratio of the vehicle; Mechanical efficiency of the transmission system; Tire rolling radius.

[0038] Indicates the vehicle's acceleration resistance; This indicates the conversion factor for the rotational mass of a vehicle. The curb weight of a vehicle; The acceleration of a car.

[0039]

[0040] Rolling resistance; Rolling resistance coefficient; The curb weight of a vehicle; Gravitational acceleration; The slope angle of a road.

[0041]

[0042] Indicates the vehicle's slope resistance; The curb weight of a vehicle; Gravitational acceleration; The slope angle of a road.

[0043]

[0044] Air resistance; Air drag coefficient; Windward area; air density; The relative speed between the vehicle and the air.

[0045] Some parameters are difficult to obtain precise values ​​for, and approximate values ​​are usually used in engineering. One such parameter is the tire rolling radius. Typically smaller than the static radius, and taken as 98% of the static radius, the mechanical efficiency of the transmission system in a pure electric vehicle. The value is 0.93. Pure electric vehicles do not have a transmission; this is the gear ratio of the current gear in the transmission. Value 1, rotational mass conversion factor The value was 1.07. This test was conducted on a straight road with a slope angle of 1.07. Approximately 0, gravitational acceleration The value is 9.8, representing the air density. The approximate value is 1.2 kg / m³, the air drag coefficient. The rolling resistance coefficient is approximately taken as 1.05. Taking an approximate value of 0.01, substituting the above data into the formula yields: For a specific car model, , and These are fixed parameters. , and This information can be obtained via the CAN bus. After each acceleration, a specific vehicle mass value can be calculated using this formula; however, this value has errors stemming from the various approximations mentioned above. Currently, the method to eliminate these errors primarily involves four steps, each of which will be explained in detail below.

[0046] Step 1: On a straight road, the test vehicle is weighed beforehand to obtain its current actual weight. ; Step 2: Estimate the vehicle weight using Kalman filtering. The specific method is as follows: (1) Set the estimated total mass of the current vehicle as (This estimated vehicle weight is not related to the actual weight in the first step; it can be an estimate based on your own visual experience.) (2) Set the current estimation error as ; (3) Set the measurement error of the vehicle mass for each test as follows: This measurement error is assumed to be the same each time; (4) Calculate the Kalman coefficient for the kth measurement according to the formula: ; (5) After the vehicle accelerates once, the total mass of the vehicle obtained during this acceleration process can be calculated using the above formula for calculating m. And according to the formula Calculate the k-th estimated mass; (6) According to the formula Calculate the estimation error for the kth time; (7) During each acceleration process when the vehicle presses the accelerator, the estimated mass of the vehicle is calculated once per second (steps 4, 5, and 6). After multiple accelerations and iterative calculations (repeatedly calculating steps 4, 5, and 6), the estimated mass can be obtained. Once the value stabilizes (generally satisfied after about 10 iterations), the actual mass can be obtained. The corresponding estimated mass is ; Step 3: By loading the vehicle with different weights, repeating steps 1 and 2, different actual results can be obtained. The estimated mass corresponding to the vehicle's weight. The mass loaded each time can be calculated as follows: = (Vehicle fully loaded – Vehicle empty) / 10. This provides a table showing the correspondence between the actual vehicle weight and the estimated vehicle weight under varying load conditions, from empty to overloaded.

[0047]

[0048] Step 4: Use the lookup table above to approximate the total mass of the vehicle, and perform linear interpolation on the data outside the lookup table.

[0049] This embodiment, through actual testing, ensures that the error between the estimated and actual vehicle mass is within 5%, and requires no additional devices such as sensors. For a specific vehicle model, only one data collection is needed before market launch, deploying the corresponding software algorithm into the VCU. The vehicle mass can be calculated in real time during each start-up and acceleration. For driverless sanitation vehicles, knowing the vehicle mass is equivalent to knowing the operational status, whether to stop work, whether to return for water replenishment, whether to continue loading garbage, etc. This improves operational efficiency and reduces the risk of malfunctions due to overloading.

[0050] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for approximate estimation of the overall weight of new energy vehicles, applied to unmanned sanitation vehicles, characterized in that, Includes the following steps: S10, acquire vehicle parameters and real-time driving data during driving; among which, vehicle parameters include fixed parameters inherent in vehicle design and approximate parameters preset based on engineering experience, and real-time driving data includes vehicle speed, acceleration and motor output torque. S20, Substitute the fixed parameters and real-time driving data into a predetermined calculation formula to calculate the current estimated value of the vehicle mass; wherein, the predetermined calculation formula is determined based on the vehicle longitudinal dynamics equation and the corresponding parameter terms in the equation are numerically quantified using the approximate parameters. S30, the Kalman filter algorithm is used to iteratively update the current vehicle mass estimate to obtain a stable vehicle mass estimate; S40, based on the pre-established correspondence table between the actual vehicle weight and the estimated vehicle weight, the stable estimated vehicle weight is mapped or interpolated to obtain the final approximate vehicle weight.

2. The method for approximate estimation of the overall weight of new energy vehicles as described in claim 1, characterized in that, In step S10, the fixed parameters include the vehicle's rear axle final reduction ratio, tire rolling radius, and vehicle frontal area.

3. The method for approximate estimation of the overall weight of new energy vehicles as described in claim 1, characterized in that, In step S10, the approximate parameters include the mechanical efficiency of the transmission system, the rotational mass conversion factor, the ramp angle, the air density, the air resistance coefficient, and the rolling resistance coefficient.

4. The method for approximate estimation of the overall weight of new energy vehicles as described in claim 1, characterized in that, In step S20, the longitudinal dynamic equation of the vehicle is: in Indicates the actual driving force of the vehicle; This indicates the motor's output torque; Indicates the gear ratio of the current gear in the transmission; Indicates the rear axle final drive ratio of the vehicle; Indicates the mechanical efficiency of the transmission system; Indicates the tire's rolling radius; in Indicates the vehicle's acceleration resistance; This indicates the conversion factor for the rotational mass of a vehicle. The curb weight of a vehicle; The acceleration of a vehicle; in, Indicates rolling resistance; Indicates the rolling resistance coefficient; Represents gravitational acceleration; Indicates the slope angle of a road; in, Indicates the vehicle's slope resistance; Represents gravitational acceleration; Indicates the slope angle of a road; in, Indicates air resistance; Indicates the air drag coefficient; Indicates the windward area; air density; The relative speed between the vehicle and the air.

5. The method for approximate estimation of the overall weight of new energy vehicles as described in claim 1, characterized in that, In step S20, the predetermined calculation formula is: In the formula, This is the current estimated vehicle weight. This refers to the output torque of the motor. This is the rear axle main reduction ratio of the vehicle. The tire's rolling radius; The frontal area of ​​the vehicle; The vehicle speed is mentioned; It is acceleration.

6. The method for approximate estimation of the overall weight of new energy vehicles as described in claim 1, characterized in that, Step S30 is as follows: S31, Set the initial vehicle mass estimate. and the initial estimation error ; S32, Set the measurement error for each measurement. ; S33, Calculate the Kalman coefficient for the k-th measurement when the vehicle accelerates. ; S34, calculate the current vehicle mass estimate for the kth time according to step S20. ; S35, according to formula Calculate the estimated vehicle mass after the k-th iteration; S36, according to formula Update the estimation error for the kth iteration; S37, Repeat steps S33 to S36 until the estimated vehicle weight is reached. Once the value stabilizes, it is taken as the estimated stable vehicle mass.

7. The method for approximate estimation of the overall weight of new energy vehicles as described in claim 1, characterized in that, In step S40, the step of pre-establishing the correspondence table specifically includes: On a straight road, obtain the first actual vehicle weight of the vehicle in an unloaded state. ; The vehicle was loaded with a preset mass increment at each step, and the actual vehicle weight was obtained after each loading. , ,in This is the loading sequence number. The quality of each load; For each of the aforementioned actual vehicle weights Perform steps S10 to S30 to obtain the corresponding stable estimated vehicle mass value. ; Pairs and Record the data to form the corresponding relationship table.

8. The method for approximate estimation of the overall weight of new energy vehicles as described in claim 1, characterized in that, In step S40, if the stable estimated vehicle weight is located between two adjacent estimated vehicle weights in the corresponding relationship table, then the final approximate value of the vehicle weight is calculated using linear interpolation.

9. The method for approximate estimation of the overall weight of new energy vehicles as described in claim 1, characterized in that, In step S10, the real-time driving data is acquired via the vehicle's CAN bus.

10. A system for approximate estimation of the overall weight of a new energy vehicle, characterized in that, The steps for implementing the method according to any one of claims 1 to 9 include: The data acquisition module is used to acquire vehicle parameters and real-time driving data during the driving process. The vehicle parameters include fixed parameters inherent in the vehicle design and approximate parameters preset based on engineering experience. The real-time driving data includes vehicle speed, acceleration, and motor output torque. The vehicle mass estimation calculation module is used to substitute the fixed parameters and real-time driving data into a predetermined calculation formula to calculate the current vehicle mass estimation value; wherein, the predetermined calculation formula is determined based on the vehicle longitudinal dynamics equation and the corresponding parameter terms in the equation are numerically quantified using the approximate parameters. The iterative update module is used to iteratively update the current vehicle mass estimate using a Kalman filter algorithm to obtain a stable vehicle mass estimate. The data processing module is used to map or interpolate the stable estimated vehicle weight value according to a pre-established correspondence table between the actual vehicle weight and the estimated vehicle weight, so as to obtain the final approximate value of the vehicle weight.

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