Vehicle weight estimation system

Abstract

This invention provides a vehicle weight estimation system that can estimate vehicle weight without limiting the vehicle's driving conditions or the type of transmission. [Solution] The vehicle weight estimation system comprises an acquisition unit that acquires motion data including the longitudinal acceleration of the vehicle, and an estimation unit that estimates the vehicle weight by performing linear fitting using the successive least squares method from the acquired motion data. For example, the estimation unit estimates the vehicle weight using the longitudinal acceleration of the vehicle as the target variable.

Description

Technical Field

[0001] The present disclosure relates to a vehicle weight estimation system.

Background Art

[0002] A method for estimating the vehicle weight from the acceleration of a vehicle is known.

[0003] For example, Patent Document 1 discloses a vehicle weight estimation device including an acceleration acquisition unit that acquires the acceleration in the longitudinal direction of the vehicle, a torque acquisition unit that acquires the driving torque of the vehicle, a differential value acquisition unit that acquires an acceleration differential value that is a differential value of the acceleration and a driving torque differential value that is a differential value of the driving torque, and a vehicle weight estimation unit that estimates the vehicle weight based on the differential equation of the equation of motion using the acceleration differential value and the driving torque differential value.

[0004] Also, for example, in a vehicle equipped with an Automated Manual Transmission (AMT), there is a vehicle weight estimation method that estimates the vehicle weight using the driving force of the engine, the acceleration of the vehicle due to the driving force of the engine, and the acceleration (running resistance) of the vehicle at the time of clutch disconnection (coasting state).

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] By the way, the vehicle weight estimation unit described in Patent Document 1 estimates the vehicle weight based on the acceleration in the longitudinal direction of the vehicle and the driving torque of the vehicle. Thus, when estimating the vehicle weight, it is necessary to run the vehicle under predetermined conditions.

[0007] Furthermore, the above-described method for estimating vehicle weight uses the acceleration of the vehicle when the clutch is disengaged (free-running state), which has the problem of requiring the vehicle to be limited to a specific driving state and also requiring the transmission to be limited to one that can put the vehicle into a free-running state.

[0008] The purpose of this disclosure is to provide a vehicle weight estimation system that can estimate vehicle weight without limiting the vehicle's driving conditions or the type of transmission. [Means for solving the problem]

[0009] To achieve the above objectives, the vehicle weight estimation system in this disclosure is: An acquisition unit that acquires motion data including the longitudinal acceleration of the vehicle, An estimation unit estimates the vehicle weight by performing linear fitting using the successive least squares method from the acquired motion data, It is equipped with. [Effects of the Invention]

[0010] According to this disclosure, the vehicle weight can be estimated without limiting the vehicle's driving conditions or the type of transmission. [Brief explanation of the drawing]

[0011] [Figure 1] Figure 1 is an explanatory diagram of the vehicle weight estimation system in an embodiment of the present disclosure. [Figure 2] Figure 2 is a block diagram showing the functions of the vehicle weight estimation system in the embodiment of this disclosure. [Figure 3] Figure 3 is a diagram showing the relationship between the tire driving force and the longitudinal acceleration of the vehicle in an embodiment of the present disclosure. [Figure 4] Figure 4 is a flowchart showing an example of the operation of the vehicle weight estimation system in the embodiment of this disclosure. [Figure 5]Figure 5 shows a comparison of the fitting results when noise is applied only to the dependent variable and when noise is applied to the independent variables other than the dependent variable. [Figure 6] Figure 6 shows a comparison of the tire driving force values ​​corresponding to the acceleration calculated before the offset and the tire driving force values ​​corresponding to the acceleration calculated after the offset. [Modes for carrying out the invention]

[0012] The embodiments of this disclosure will be described below with reference to the drawings. Figure 1 is an explanatory diagram of a vehicle weight estimation system in an embodiment of the present disclosure. Figure 1 depicts a vehicle 1 traveling on an uphill road. In the following description, "vertical direction" refers to the direction parallel to the vertical center plane of vehicle 1. The vertical direction may also be referred to as the "x direction".

[0013] In this embodiment, the vehicle weight estimation system 100S is applied to a vehicle 1 powered by an engine, but the vehicle weight estimation system in this disclosure is not limited to engines and may be applied to vehicles powered by motors.

[0014] Figure 2 is a block diagram showing the functions of the vehicle weight estimation system in the embodiment of this disclosure. The vehicle weight estimation system 100S comprises a control unit 100 and a storage unit 110. The storage unit 110 is a ROM (Read Only Memory) that stores the computer program for realizing the vehicle weight estimation system 100S, and a RAM (Random Access Memory) that serves as the working area for the control unit 100. It also includes interfaces such as an AD converter, a DA converter, I / O ports, and CAN. The ROM may be a storage device such as an HDD (Hard Disk Drive) or SSD (Solid State Drive) that stores the OS (Operating System), application programs, and various information referenced when the application programs are executed.

[0015] The control unit 100 is a processor such as a CPU (Central Processing Unit) or GPU (Graphics Processing Unit) of the vehicle weight estimation system 100S, and functions as the acquisition unit 101, calculation unit 102, and estimation unit 103 by executing a program stored in the memory unit 110. Note that the control unit 100 is not limited to being composed of a single device. The control unit 100 may be implemented by, for example, multiple processors or memory or other computing resources. In this case, each part constituting the control unit 100 is implemented by at least one of the multiple different processors executing a program.

[0016] In Figure 2, the ECU20 and the vehicle weight estimation system 100S are configured as separate devices, but they may also be configured as a single device.

[0017] Vehicle 1 is equipped with a vehicle speed sensor 10 and an ECU 20. The vehicle speed sensor 10 counts the engine rotation speed (vehicle speed pulse signal) at regular time intervals. The vehicle speed sensor 10 transmits the engine rotation speed to the ECU 20. The engine rotation speed transmitted to the ECU 20 is stored as motion data in the ECU 20's memory unit (not shown).

[0018] The ECU20 calculates the vehicle speed based on the engine rotational speed read from the memory unit. The ECU20 also calculates the engine torque (or motor torque if the power source is a motor) based on the engine rotational speed. Furthermore, the ECU20 calculates the engine-driven wheel torque based on the engine torque. The ECU20 transmits the vehicle speed and engine-driven wheel torque as motion data to the vehicle weight estimation system 100S.

[0019] (Acquisition part 101) The acquisition unit 101 acquires vehicle speed and engine drive wheel torque from the ECU 20. The vehicle speed and engine drive wheel torque are stored as motion data in the storage unit 110.

[0020] (Calculation unit 102) The calculation unit 102 reads the vehicle speed as the motion data from the storage unit 110, and calculates the longitudinal acceleration v x (dot) of the vehicle 1. The calculated acceleration v x (dot) is stored in the storage unit 110 as the motion data. The acceleration v x (dot) corresponds to the "motion data" of the present disclosure. In the following description, the longitudinal acceleration of the vehicle 1 may be represented by "a x ". Note that the ECU 20 may calculate the longitudinal acceleration v x (dot) of the vehicle 1, and the acquisition unit 101 may acquire the acceleration v x (dot) from the ECU 20.

[0021] The calculation unit 102 reads the engine driving wheel torque as the motion data from the storage unit 110, and based on the read engine driving wheel torque T w , the tire dynamic radius r w , the equivalent inertia mass Δm, and the acceleration v x (dot), calculates the driving force F of the tire. The calculated driving force F is stored in the storage unit 110 as the motion data. Note that the ECU 20 may calculate the driving force F, and the acquisition unit 101 may acquire the driving force F from the ECU 20.

[0022] (Equation of motion in the longitudinal direction of the vehicle) The equation of motion in the longitudinal direction (x direction) of the vehicle 1 can be expressed by the following formula (1). [Equation number] Here, m is the vehicle weight [kg], Δm is the equivalent inertia mass [kg], v x is the vehicle speed [m / s], T w is the engine driving wheel torque [Nm], T f is the engine driving wheel friction torque [Nm], r w is the tire dynamic radius [m], ρ is the air density [kg / m 3 , C d is the resistance coefficient [-], A f is the frontal projected area [m 2 , and g is the gravitational acceleration [m / s2 ], μ r is the rolling resistance coefficient [-], β is the road gradient angle [rad], F b This is the braking force [N].

[0023] Furthermore, β<<1, F b Setting = 0 and rearranging equation (1) above, the longitudinal equation of motion for vehicle 1 can be expressed as equation (2) below.

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[0024] Furthermore, equation (2) above can be expressed as equation (3) below.

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[0025] In equation (3), the first term on the right-hand side is the change in the longitudinal motion of vehicle 1, and the longitudinal acceleration a of vehicle 1. x This is expressed as the product of the parameter (vehicle weight m). Furthermore, the second term on the right-hand side is the air resistance acting on vehicle 1, and the squared vehicle speed v is used. x 2 and the parameter ((1 / 2)*ρ*C d *A f This is expressed as the product of ) and . Also, the third term on the right side of equation (3) above is expressed as the rolling resistance, and the parameter (m*g*μ r It is represented as ).

[0026] Furthermore, equation (3) above can be expressed as equation (4) below.

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[0027] In equation (4), the driving force F, which is the left side of equation (3) above, is expressed by the variable y. Also, the first term on the right side of equation (3) is expressed as the product of the parameter θ2 and the variable x2. Also, the second term on the right side of equation (3) is expressed as the product of the parameter θ1 and the variable x1. Also, the third term on the right side of equation (3) is expressed by the parameter θ0.

[0028] The estimation unit 103 estimates the vehicle weight m, which is the parameter θ2 in the equation of motion expressed in equation (4) above, by performing linear fitting using the successive least squares method.

[0029] As a sequential estimation method, the sequential least squares method, expressed by equation (5) below, is used.

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[0030] Figure 3 is a diagram showing the relationship between the tire driving force and the longitudinal acceleration of the vehicle in an embodiment of the present disclosure. In Figure 3, the vertical axis represents the tire driving force F [N], and the horizontal axis represents the acceleration a x [m / s 2 Figure 3 shows the acceleration a. x The corresponding tire's driving force F value is indicated by a circle.

[0031] Figure 3 shows a straight line. The straight line is represented by multiple circular marks (acceleration a). x This is the straight line obtained when linear fitting was performed using the successive least squares method with the corresponding value of the driving force F. The slope of the straight line is the acceleration a x This shows the increase in vehicle weight m, which is the increase in driving force F relative to the increase in m.

[0032] (Operation of the vehicle weight estimation system 100S) Next, an example of the operation of the vehicle weight estimation system 100S in this embodiment will be described with reference to Figure 4. Figure 4 is a flowchart showing an example of the operation of the vehicle weight estimation system in the embodiment of this disclosure. This flow starts after the vehicle 1 has been driven. Before and after the start of this flow, motion data (vehicle speed, engine drive wheel torque) is acquired, the acquired motion data is stored in the storage unit 110, and the calculation unit 102 calculates the acceleration v from the motion data. x (Dot) and driving force F are calculated. The calculated acceleration v x The (dot) and driving force F are stored in the memory unit 110. The motion data is associated with numbers k(=1) to k(=n) indicating the order in which they were acquired. The memory unit 110 also stores the initial values ​​of each parameter (θ0, θ1, θ2). As mentioned above, parameter θ2 corresponds to the vehicle weight m.

[0033] First, in step S100, the estimation unit 103 initializes the number k (k=0).

[0034] Next, in step S110, the estimation unit 103 adds 1 to the number k (k = k + 1).

[0035] Next, in step S120, the estimation unit 103 calculates the motion data of number k (driving force F, acceleration a x The data (including) is read from the memory unit 110.

[0036] Next, in step S130, the estimation unit 103 reads out the parameters (θ0, θ1, θ2) of number (k-1) from the storage unit.

[0037] Next, in step S140, the estimation unit 103 calculates each parameter (θ0, θ1, θ2) of number k.

[0038] Next, in step S150, the estimation unit 103 stores each parameter (θ0, θ1, θ2) of number k in the storage unit 110.

[0039] Next, in step S160, the estimation unit 103 determines whether the number k is n or not (k=n?). If the number k is n (step S160: YES), the process proceeds to step S170. If the number k is not n (step S160: NO), the process returns to before step S110.

[0040] Next, in step S170, the estimation unit 103 estimates the vehicle weight m by performing linear fitting using the successive least squares method on n parameters θ2. After that, this flow ends.

[0041] The vehicle weight estimation system 100S in the above embodiment includes an acquisition unit 101 that acquires motion data including the longitudinal acceleration of the vehicle 1, and an estimation unit 103 that estimates the vehicle weight m by performing linear fitting using the successive least squares method from the acquired motion data.

[0042] With the above configuration, if the driving force of the tires in relation to the longitudinal acceleration of vehicle 1 is known as motion data, the vehicle weight m can be estimated. Therefore, it is possible to estimate the vehicle weight m without limiting the vehicle's driving conditions.

[0043] Conventional methods for estimating vehicle weight have been limited to estimating the vehicle weight immediately after starting, because acceleration tends to decrease at high speeds, leading to a greater relative increase in noise and a deterioration of the signal-to-noise ratio. Furthermore, conventional methods that estimate vehicle weight using driving force divided by acceleration require consideration of the effect of division by zero in the low acceleration range. In contrast, the least squares method in the vehicle weight estimation system 100S eliminates the effect of noise and eliminates the need for division by zero, making it possible to estimate vehicle weight m even when the vehicle is traveling at high speeds. In other words, it is possible to estimate vehicle weight m without limiting the vehicle's travel conditions.

[0044] Furthermore, in the vehicle weight estimation system 100S of this embodiment, the estimation unit 103 estimates the vehicle weight m as a parameter in the equation of motion representing the longitudinal motion of the vehicle 1. The parameter includes the air resistance acting on the vehicle and the rolling resistance acting on the vehicle. In conventional vehicle weight estimation methods, it is necessary to change the parameter when the air resistance changes. In contrast, the vehicle weight estimation system 100S does not require prior measurement of parameters such as the air resistance and rolling resistance acting on the vehicle 1, making it possible to easily estimate the vehicle weight m.

[0045] (modified version) Next, a modified example of the vehicle weight estimation system 100S of this embodiment will be described. In the description of the modified example, the configurations that differ from the above embodiment will be mainly described, and the configurations that are the same as the above embodiment will not be described.

[0046] In the equation of motion in the above embodiment, y (driving force F) was used as the objective variable, as shown in equation (7).

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[0047] To estimate the vehicle weight m, the longitudinal acceleration a of vehicle 1 is used. x , vehicle speed v x Therefore, the driving force F calculated from the engine torque (or motor torque) is required. x Since it is calculated from the vehicle speed pulse signal, the noise is low. Also, since engine torque is estimated from engine rotational speed, etc., the noise is not high. In the case of a motor, the torque is determined by the current, so the noise is also low in this case. On the other hand, acceleration a x When using acceleration sensor values, typical acceleration sensors detect mechanical displacement using capacitance, making it unavoidable that vibrations during operation will introduce noise. The drawback of the least squares method (including successive methods) is that it can only consider the effect of noise on the target variable.

[0048] Figure 5 shows a comparison of fitting results when noise is introduced to the dependent variable and when noise is introduced to the independent variables other than the dependent variable. In Figure 5, the vertical axis shows the dependent variable and the horizontal axis shows the independent variables. In Figure 5, the true value is marked with a circle, the value when noise is introduced vertically is marked with a square, the value when noise is introduced horizontally is marked with a triangle, the result of fitting the square marks is shown with a dotted line, and the result of fitting the triangle marks is shown with a dashed line. The dotted line, which is the result of fitting the square marks, matches the circle marks, which are the true values, indicating that the noise was processed correctly. The dashed line, which is the result of fitting the triangle marks, does not process correctly and the slope becomes flatter. Since the slope is the vehicle weight, when there is noise in the independent variables, the estimated vehicle weight will be calculated to be lower.

[0049] As shown in equation (7), the explanatory variable (longitudinal acceleration a of vehicle 1) x When using acceleration sensor values, the results are affected by acceleration sensor noise, requiring a strong low-pass filter in preprocessing. However, in noisy environments, the vehicle weight tends to be underestimated. Consequently, there is a problem of reduced accuracy in estimating the vehicle weight m.

[0050] In contrast, in the equation of motion for the modified example, the longitudinal acceleration a of vehicle 1 is expressed in the following equation (8): x The dependent variable is .

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[0051] Therefore, in the modified example, as shown in equation (8), the objective variable in the equation of motion is the longitudinal acceleration a of the vehicle 1. x This allows us to suppress the decrease in the accuracy of calculating each parameter (θ0, θ1, θ2) on the right-hand side of the equation of motion. Consequently, the longitudinal acceleration a of vehicle 1 x Even when using an acceleration sensor 30 (see Figure 2, also called an inertial sensor) to measure the vehicle weight m, it is possible to suppress the decrease in the accuracy of the vehicle weight estimation.

[0052] In the modified example, equation (8) of motion is replaced with equation (9).

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[0053] In equation (9), the acceleration a is the left-hand side of equation (8) above. x Let this be expressed by the variable y. Also, the first term on the right-hand side of equation (8) is expressed as the product of the parameter θ2 and the variable x2. Also, the second term on the right-hand side of equation (8) is expressed as the product of the parameter θ1 and the variable x1. Also, the third term on the right-hand side of equation (8) is expressed by the parameter θ0.

[0054] In the modified example, the vehicle weight m is expressed as the reciprocal of the parameter θ2, as shown in equation (10).

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[0055] In the modified example, the estimation unit 103 estimates the parameter θ2 by performing linear fitting using the successive least squares method, and further estimates the vehicle weight m as the reciprocal of the parameter θ2.

[0056] In the modified vehicle weight estimation system 100S of this embodiment, the estimation unit 103 estimates the vehicle weight using the longitudinal acceleration of the vehicle 1 as the target variable. This minimizes the influence of the noisy acceleration sensor 30, thereby suppressing a decrease in the accuracy of the vehicle weight m estimation. Furthermore, it becomes possible to use the acceleration sensor 30 as the device for measuring the longitudinal acceleration of the vehicle 1. In addition, it becomes possible to eliminate the need for a low-pass filter to suppress the influence of noise.

[0057] Note that acceleration a x When using the value from the acceleration sensor 30, the acceleration sensor 30 determines its zero point at the time of factory shipment. The zero point of the acceleration sensor 30 is the zero point in the vehicle's unloaded state. When a body or cargo is loaded onto the vehicle 1, the vehicle 1 becomes nearly horizontal. In other words, the zero point of the acceleration sensor 30 is in a forward-tilted position, but in actual use, it is not zero but in an offset state, and a gravitational acceleration component is included. Therefore, using conventional methods results in a decrease in accuracy due to the effect of the offset. In contrast, the model used in the successive least squares method in the vehicle weight estimation system 100S has a term that represents the steady-state resistance force, so the offset of the acceleration sensor 30 is absorbed in that term, making it possible to suppress the decrease in accuracy due to the effect of the offset.

[0058] Figure 6 shows a comparison of the driving force values ​​corresponding to acceleration calculated before offset and the driving force values ​​corresponding to acceleration calculated after offset. Figure 6 shows the acceleration a before offset. x The corresponding value of the driving force F is indicated by a white circle, and the acceleration a after the offset is shown. x The corresponding value of the driving force F is indicated by a black circle.

[0059] As shown in Figure 6, a positional discrepancy occurs between the value of the driving force corresponding to the acceleration before the offset and the value of the driving force corresponding to the acceleration after the offset. However, the acceleration a before the offset xThe slope of the straight line obtained when linear fitting was performed using the successive least squares method with the corresponding value of the driving force F, and the acceleration a after offset. x There is no significant difference between the slope of the straight line obtained when linear fitting is performed using the successive least squares method with the corresponding value of the driving force F. In other words, since there is no significant difference in the slope of the vehicle weight m between before and after the offset of the acceleration sensor 30, there is no problem in the estimation of the vehicle weight m by the estimation unit 103 even if the acceleration sensor 30 is offset.

[0060] Furthermore, the embodiments described above are merely examples of how the Disclosure may be implemented, and the technical scope of the Disclosure should not be limited by them. In other words, the Disclosure can be implemented in various ways without departing from its essence or its main features. [Industrial applicability]

[0061] This disclosure is particularly useful for vehicles equipped with a vehicle weight estimation system that requires estimating vehicle weight without limiting the vehicle's driving conditions or the type of transmission. [Explanation of Symbols]

[0062] 1 vehicle 10. Vehicle speed sensor 20 ECU 30 Accelerometer 100 Control Unit 100S Vehicle Weight Estimation System 101 Acquisition Department 102 Calculation Unit 103 Estimation part 110 Storage section

Claims

1. An acquisition unit that acquires motion data including the longitudinal acceleration of the vehicle, An estimation unit estimates the vehicle weight by performing linear fitting using the successive least squares method from the acquired motion data, A vehicle weight estimation system equipped with the following features.

2. The estimation unit estimates the vehicle weight as a parameter in the equation of motion representing the longitudinal motion of the vehicle, The aforementioned parameters include the air resistance acting on the vehicle and the rolling resistance of the tires. The aforementioned equation of motion is expressed by equation (1): [Math 1] Here, F is the driving force of the tire [N], m is the vehicle weight [kg], a ｘ is the longitudinal acceleration of the vehicle [m / s ２ , ρ is the air density [kg / m ３ , C ｄ is the resistance coefficient [-], A ｆ is the frontal projected area [m ２ , v ｘ is the longitudinal vehicle speed [m / s], g is the gravitational acceleration [m / s ２ , μ ｒ is the rolling resistance coefficient [-]. The vehicle weight estimation system according to claim 1.

3. The estimation unit estimates the vehicle weight using the longitudinal acceleration of the vehicle as the target variable. The vehicle weight estimation system according to claim 1.

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