Vehicle mass estimation method and vehicle mass estimation system

Abstract

In the present invention, the longitudinal acceleration of a vehicle is detected by a longitudinal acceleration sensor, and by inputting the driving force of the vehicle to a vehicle model having an equation representing the relationship of the longitudinal acceleration, the driving force, the vehicle mass, and acceleration error that occurs in the longitudinal acceleration sensor, the equation having the vehicle mass and the acceleration error as state variables, the estimated value of the longitudinal acceleration assumed to be detected by the longitudinal acceleration sensor and prior estimated values of the state variables are calculated. The estimated values of the state variables are calculated on the basis of the prior estimated values, the estimated value of the longitudinal acceleration, and the difference between the estimated value of the longitudinal acceleration and the detected value of the longitudinal acceleration detected by the longitudinal acceleration sensor, and the state variables in the equation are updated by the estimated values of the state variables.

Description

Vehicle mass estimation method and vehicle mass estimation system

[0001] The present invention relates to a vehicle mass estimation method and a vehicle mass estimation system.

[0002] JP 4926258B discloses a technique for calculating rotational acceleration from the rotational speed of a vehicle's wheels, and estimating vehicle mass as a regression coefficient when linear regression of rotational acceleration and driving force is performed based on a Kalman filter from the vehicle's driving force and rotational angular velocity in accordance with the equation of motion of vehicle driving force = tire rotational acceleration x vehicle mass. JP 4583028B also discloses a similar technique.

[0003] However, in the case of the above-mentioned prior art, the road gradient during travel is estimated using the same method as for estimating the vehicle mass, and control is performed to reduce the effect of the road gradient on the estimation of the vehicle mass, so the estimation of the vehicle mass and the estimation of the road gradient interfere with each other, reducing the accuracy of the estimation of the vehicle mass. Furthermore, in the case of the above-mentioned prior art, the differential of the speed must be used as the acceleration, which increases the noise components contained in the speed sensor, which also reduces the accuracy of the estimation of the vehicle mass.

[0004] Therefore, an object of the present invention is to provide a vehicle mass estimation method and a vehicle mass estimation system that improve the accuracy of estimating vehicle mass.

[0005] According to one aspect of the present invention, the longitudinal acceleration of a vehicle is detected by a longitudinal acceleration sensor, and the driving force is input to a vehicle model having an equation expressing the relationship between the longitudinal acceleration, the driving force of the vehicle, the vehicle mass, and an acceleration error occurring in the longitudinal acceleration sensor, the equation having the vehicle mass and the acceleration error as state variables, thereby calculating an estimated value of the longitudinal acceleration expected to be detected by the longitudinal acceleration sensor and a priori estimated value of the state variable.The estimated value of the state variable is then calculated based on the prior estimated value and the difference between the estimated value of the longitudinal acceleration and the detected value of the longitudinal acceleration detected by the longitudinal acceleration sensor.Furthermore, the state variable in the equation is updated using the estimated value of the state variable.

[0006] FIG. 1 is a block diagram showing the basic configuration of an electric vehicle system to which the vehicle mass estimation method of this embodiment is applied. FIG. 2 is a flowchart showing the flow of processing performed by a motor controller. FIG. 3 is a diagram showing an example of an accelerator opening-torque table. FIG. 4 is a block diagram for explaining the vehicle mass estimation processing. FIG. 5 is a block diagram for explaining an input correction unit. FIG. 6 is a block diagram for explaining a mass estimation calculation unit. FIG. 7 is a time chart when the vehicle mass estimation method of the comparative example is applied. FIG. 8 is a time chart when the vehicle mass estimation method of this embodiment is applied.

[0007] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0008] [System Configuration of Electric Vehicle 10] Figure 1 is a block diagram showing the basic configuration of an electric vehicle system to which the vehicle mass estimation method of this embodiment is applied. The electric vehicle 10 is a vehicle that has a motor 18 as part or all of the vehicle's drive source and can run using the driving force of the motor 18, and includes electric vehicles and hybrid vehicles. Note that the electric vehicle 10 shown in Figure 1 is a 2WD vehicle using the motor 18 as its drive source, but the invention can also be applied to 2WD vehicles using an engine as their drive source, and 4WD vehicles using either the motor 18 or an engine as their drive source.

[0009] Digital signals indicating vehicle conditions such as the longitudinal acceleration of the vehicle, vehicle speed V, accelerator opening APO, rotor phase α of motor 18, and three-phase AC currents iu, iv, and iw of motor 18 are input to motor controller 12 (control device). Based on the input signals, motor controller 12 generates PWM signals tu, tv, and tw for controlling motor 18. In addition, motor controller 12 generates drive signals for inverter 16 in accordance with the generated PWM signals tu, tv, and tw.

[0010] The inverter 16 converts the direct current supplied from the battery 14 into alternating current by turning on / off two switching elements (e.g., power semiconductor elements such as IGBTs and MOS-FETs) provided for each phase, and supplies the desired current to the motor 18.

[0011] The motor 18 (three-phase AC motor) generates driving force using the AC current supplied from the inverter 16, and transmits the driving force to left and right drive wheels 24 a, 24 b via a reducer 20 and a drive shaft 22. When the motor 18 rotates along with the drive wheels 24 a, 24 b while the vehicle is running, it generates regenerative driving force, thereby recovering the vehicle's kinetic energy as electrical energy. In this case, the inverter 16 converts the AC current generated during regenerative operation of the motor 18 into DC current and supplies it to the battery 14.

[0012] The current sensor 26 detects three-phase AC currents iu, iv, and iw flowing through the motor 18. However, since the sum of the three-phase AC currents iu, iv, and iw is zero, the currents of any two phases may be detected and the current of the remaining phase may be calculated.

[0013] The rotation sensor 28 is, for example, a resolver or an encoder, and detects the rotor phase α of the motor 18 .

[0014] 2 is a flowchart showing the flow of processing performed by the motor controller 12. The processing from S201 to S206 is constantly executed at regular intervals while the electric vehicle system is running.

[0015] In the input process of S201, signals required for the control calculations described below are input to the motor controller 12. Here, the longitudinal acceleration (m / s ２ ), vehicle speed V (km / h), accelerator opening APO (%), and rotor phase α (rad) of the motor 18. Also input are the rotation speed N (rpm) of the motor 18, three-phase AC currents iu, iv, and iw flowing through the motor 18, and the DC voltage value Vdc (V) of the battery 14.

[0016] Vehicle longitudinal acceleration sensor value a [m / s ２ ] (detected value) is acquired by a longitudinal acceleration sensor (not shown) that detects longitudinal acceleration occurring in the vehicle.

[0017] The vehicle speed V (km / h) is acquired from a vehicle speed sensor (not shown) or another controller via communication. Alternatively, the motor controller 12 obtains the vehicle speed V (m / s) by multiplying the motor angular velocity detection value ωm by the tire dynamic radius r and dividing the result by the gear ratio of the final gear, and then multiplying the result by 3600 / 1000 for unit conversion to obtain the vehicle speed V (km / h).

[0018] The accelerator opening APO (%) is obtained from an accelerator opening sensor (not shown) or is obtained by communication from another controller (not shown) such as a vehicle controller.

[0019] The rotor phase α (rad) of the motor 18 is acquired from the rotation sensor 28. The motor angular velocity detection value ωm, which is the mechanical angular velocity of the motor 18, is obtained by dividing the rotor angular velocity ω (electrical angle) by the number p of pole pairs of the motor 18. The rotation speed N (rpm) of the motor 18 is obtained by multiplying the obtained motor angular velocity detection value ωm by 60 / (2π). The rotor angular velocity ω is obtained by differentiating the rotor phase α.

[0020] The three-phase AC currents iu, iv, and iw (A) flowing through the motor 18 are acquired from a current sensor 26 .

[0021] The DC voltage value Vdc (V) is detected by a voltage sensor (not shown) provided on a DC power supply line between the battery 14 and the inverter 16. Note that the DC voltage value Vdc (V) may also be detected from a signal related to the power supply voltage value transmitted from a battery controller (not shown).

[0022] In the basic target torque calculation process of S202, the motor controller 12 calculates the basic motor torque command value Tm based on the accelerator opening APO and the motor angular velocity detection value ωm using the accelerator opening-torque table shown in FIG. １ ＊ Set.

[0023] In the vibration suppression control process of S203, the basic motor torque command value Tm calculated in S202 １ ＊and the motor angular velocity detection value ωm are input, and the final motor torque command value Tm is calculated to suppress torque transmission system vibration (torsional vibration of the drive shaft, etc.) without sacrificing the response of the drive shaft torque. ２ ＊ Calculate.

[0024] In the vehicle mass estimation process in S204, the final motor torque command value Tm calculated in S203 is used. ２ ＊ and the longitudinal acceleration sensor value a, the vehicle mass M is estimated. The vehicle mass estimation process will be described in detail later.

[0025] In the current command value calculation process of S205, the motor controller 12 calculates the final motor torque command value Tm ２ ＊ , the motor angular velocity detection value ωm, and the d-axis current command value id based on the DC voltage value Vdc. ＊ , q-axis current command value iq ＊ For example, the final motor torque command value Tm ２ ＊ , the rotation speed N of the motor 18, the DC voltage value Vdc, and the d-axis current command value id ＊ and the q-axis current command value iq ＊ A table is prepared in advance that defines the relationship between the d-axis current command value id and the ＊ , q-axis current command value iq ＊ Ask for.

[0026] In the current control of S206, the motor controller 12 controls the d-axis current id and the q-axis current iq by the d-axis current command value id calculated in S205. ＊ and the q-axis current command value iq ＊ To this end, first, the d-axis current id and the q-axis current iq are calculated based on the three-phase AC currents iu, iv, and iw input in S201 and the rotor phase α of the motor 18. Next, the d-axis current command value id ＊ and the d-axis current id, the d-axis voltage command value vd is calculated, and the q-axis current command value iq ＊and the q-axis current iq, a q-axis voltage command value vq is calculated. Note that a decoupling voltage required to cancel out the interference voltage between the d- and q-axis orthogonal coordinate axes may be added to the calculated d-axis voltage command value vd and q-axis voltage command value vq.

[0027] Next, three-phase AC voltage command values ​​vu, vv, and vw are calculated from the d-axis voltage command value vd, the q-axis voltage command value vq, and the rotor phase α of the motor 18. Then, PWM signals tu(%), tv(%), and tw(%) are calculated from the calculated three-phase AC voltage command values ​​vu, vv, and vw and the DC voltage value Vdc. The PWM signals tu, tv, and tw thus calculated are used to open and close the switching elements of the inverter 16, thereby controlling the motor 18 to the final motor torque command value Tm ２ ＊ The motor can be driven at a desired torque indicated by the arrow.

[0028] [Vehicle Model] The vehicle model of this embodiment will be described below. The equation of the vehicle model is shown in equation (1).

[0029] where a is the longitudinal acceleration sensor value, M is the vehicle mass, u is the driving force, and e is the acceleration error.

[0030] The longitudinal acceleration sensor is a sensor that can detect longitudinal acceleration occurring in the vehicle, and therefore the longitudinal acceleration sensor value also includes a gravitational acceleration component due to the gradient.

[0031] The acceleration error e includes an error in the installation of the longitudinal acceleration sensor and an error in the acceleration caused by the tilt of the vehicle due to loading.

[0032] Here, when the transformation of equation (2) is applied to equation (1), equation (3) is obtained.

[0033] When equation (3) is discretized, equation (4) is obtained.

[0034] Reciprocal M of vehicle mass M ｉｎｖ To estimate the acceleration error e, the state variable x is set to the inverse M of the vehicle mass M. ｉｎｖWhen the acceleration error e is taken as the acceleration error, and the observed value y is taken as the longitudinal acceleration sensor value a, the state equation becomes equation (5) and the observation equation becomes equation (6).

[0035] Furthermore, the mean is 0 and the variance σ ｗ ２ The system noise w is a normal white noise with mean 0 and variance σ ｖ ２ Considering the observation noise v, which is normal white noise, equation (5) becomes equation (7), and equation (6) becomes equation (8).

[0036] However, in equation (7), equations (9) and (10) hold, and in equation (8), equation (11) holds.

[0037] [Vehicle Mass Estimation Process] Fig. 4 is a block diagram for explaining the vehicle mass estimation process. The motor controller 12 (Fig. 1) includes an input correction unit (S401) and a mass estimation calculation unit (S402).

[0038] The input correction unit (S401) calculates the vehicle longitudinal acceleration sensor value, the final motor torque command value Tm ２ ＊ , driving wheel speed ω ｗ＿ｄｒｉｖｅ , driven wheel speed ω ｗ＿ｄｒｉｖｅｎ , air resistance / rolling resistance F ｌｏａｄ The vehicle longitudinal acceleration sensor value (corrected) a and the driving force u, which are input signals to the mass estimation calculation unit (S402), are calculated based on the above. The details of the input correction unit (S401) will be described with reference to FIG.

[0039] The mass estimation calculation unit (S402) estimates the vehicle mass M and acceleration error e defined in equation (1) based on the driving force u and the vehicle longitudinal acceleration sensor value (after correction) a. Details of the mass estimation calculation unit (S402) will be explained using FIG. 6.

[0040] <Input Correction Unit> Fig. 5 is a block diagram for explaining the input correction unit. The input correction unit (S401 in Fig. 4) executes the following steps S501 to S511.

[0041] In S501, the input correction unit calculates the driving wheel speed ω ｗ＿ｄｒｉｖｅ Approximate differentiation is performed to obtain the driving wheel angular acceleration ω ・ ｗ＿ｄｒｉｖｅ Calculate.

[0042] In S502, the input correction unit calculates the driving wheel angular acceleration ω ・ ｗ＿ｄｒｉｖｅ Wheel inertia J ｗ , motor / differential inertia (motor / differential gear inertia) J ｍｅ , wheel radius R ａ The driving force equivalent of the drive wheel inertia is calculated by multiplying the drive wheel inertia by a gain configured as follows:

[0043] In S503, the input correction unit calculates the driven wheel speed ω ｗ＿ｄｒｉｖｅｎ Approximate differentiation is performed to obtain the driven wheel angular acceleration ω ・ ｗ＿ｄｒｉｖｅｎ Calculate.

[0044] In S504, the input correction unit calculates the driven wheel angular acceleration ω ・ ｗ＿ｄｒｉｖｅｎ Wheel inertia J ｗ , wheel radius R ａ The driving force equivalent of the driven wheel inertia is calculated by multiplying the driving force by a gain composed of the following:

[0045] In S505, the input correction unit adds the driving force equivalent value of the driving wheel inertia output in S502 to the driving force equivalent value of the driven wheel inertia output in S504 to calculate the driving force equivalent value of the driving wheel / driven wheel inertia.

[0046] In S506, the input correction unit calculates the air resistance / rolling resistance F ｌｏａｄ A low-pass filter is applied to adjust the phase of the air resistance / rolling resistance F ｌｏａｄ is calculated using equation (12) using the vehicle speed V, and the parameter A ０ , A １ , A ２ The design value or the value identified by experiment is used.

[0047] In S507, the input correction unit corrects the final motor torque command value Tm ２＊ A low-pass filter process is performed on the signal for phase adjustment.

[0048] In S508, the input correction unit calculates the final motor torque command value Tm ２ ＊ The gain K, which is the efficiency of the drive source and the drive force transmission system, ｅ Multiply by.

[0049] In S509, the input correction unit calculates the gear ratio N ａｌ and wheel radius R ａ By multiplying the gain composed of the above, the unit is converted into the dimension of driving force.

[0050] In S510, the input correction unit calculates the driving force u by subtracting the output of S505 and the output of S506 from the output of S509.

[0051] In S511, the input correction unit performs low-pass filtering for phase adjustment on the vehicle longitudinal acceleration sensor value, and calculates a vehicle longitudinal acceleration sensor value (corrected) a.

[0052] The time constants τ used in the filter processes of S501, S503, S506, S507, and S511 are all set to the same value to align the phases.

[0053] <Mass Estimation Calculation Unit> Fig. 6 is a block diagram for explaining the mass estimation calculation unit. The mass estimation calculation unit (S402 in Fig. 4) executes S601 to S605 shown below.

[0054] In S601, the mass estimation calculation unit processes the equations (5) and (6) (or the equations (7) and (8)) to calculate the state variable x ｋ The prior estimate of x^ ｋ｜ｋ－１ and calculate the estimated value a^ of the longitudinal acceleration that is expected to be detected by the vehicle longitudinal acceleration sensor. ｋ｜ｋ－１ is the state variable x in equations (5) and (7). ｋ＋１ The estimated value a^ of the longitudinal acceleration detected by the vehicle longitudinal acceleration sensor is calculated using the observed value y ｋ This becomes:

[0055] In S602, the mass estimation calculation unit outputs the difference obtained by subtracting the estimated value a^ of the longitudinal acceleration detected by the vehicle longitudinal acceleration sensor from the vehicle longitudinal acceleration sensor value (after correction) a.

[0056] In S603, the mass estimation calculation unit applies a correction gain K Ｋ By multiplying by ｋ The prior estimate of x^ ｋ｜ｋ－１ A correction amount for correcting the above is calculated.

[0057] Correction gain K Ｋ When the Kalman filter algorithm is used, it is found by successively calculating the equations (13), (14), and (15).

[0058] where P is the error covariance matrix, K Ｋ is the Kalman gain, Q is the covariance matrix for the system noise, and R is the covariance matrix for the observation noise. Ｋ is updated within the algorithm using the driving force u as an input value, but Q and R are set arbitrarily, so they are set taking into consideration noise contained in the sensors used for the input and observed values ​​and any errors that may occur. The initial value of P is also set taking into consideration the magnitude of the initial estimation error.

[0059] A covariance matrix is ​​a matrix of covariances between vector elements. The larger the covariance, the larger the error involved in the state transition and observation. In this case, the autocovariance (the diagonal components of the covariance matrix) is important among the covariances. This is because the acceleration error e, which is a state variable, and the reciprocal M of the vehicle mass M ｉｎｖ are independent of each other. That is, Q is given by equation (16) and R is given by equation (17).

[0060] Here, q ｅ is the autocovariance of the acceleration error e, q Ｍｉｎｖ is the autocovariance of the vehicle mass M, r ａ is the autocovariance of the longitudinal acceleration sensor value a. ｅ , q Ｍｉｎｖ , and rａ is estimated by experiment or prior knowledge. For example, Q depends on the sampling rate, and R depends on the noise characteristics of the sensor.

[0061] In addition, the correction gain K Ｋ When the algorithm of the recursive least squares method with forgetting factor is used, it can be obtained by sequentially calculating the equations (18), (19), and (20).

[0062] where λ is the forgetting factor.

[0063] In S604, the mass estimation calculation unit calculates the state variable x ｋ The prior estimate of x^ ｋ｜ｋ－１ is corrected with the output value of S603, the state variable x ｋ The estimated value of x^ ｋ｜ｋ Here, the state variable x ｋ The component of the acceleration error e and the vehicle mass M are ｉｎｖ Therefore, the estimated value of the vehicle mass M can be calculated using equation (2).

[0064] In S605, the mass estimation calculation unit calculates the state variable x ｋ The estimated value of x^ ｋ｜ｋ Then, the past value of the state variable x is sampled, and the process returns to S601. ｋ The estimated value of x^ ｋ｜ｋ The past value of is the state variable x in equations (5)-(8). ｋ This becomes:

[0065] By sequentially repeating the processes of S601 to S605, the state variable x ｋ The estimated value of x^ ｋ｜ｋ By updating the above, it is possible to calculate an accurate estimate of the vehicle mass M.

[0066] [Time Chart] Fig. 7 is a time chart when the vehicle mass estimation method of the comparative example is applied, and Fig. 8 is a time chart when the vehicle mass estimation method of this embodiment is applied.

[0067] The time charts shown in Figures 7 and 8 are based on the assumption that the vehicle starts traveling on a flat road at time t0, enters a slope from the flat road at time t2 after time t0, and the true value of the vehicle mass M is changed at time t4. The vehicle speed is also assumed to reach a predetermined speed at time t1 immediately after time t0, and thereafter travels at a constant speed on flat roads and slopes. Here, the final target torque shown in Figures 7 and 8 is the final motor torque command value Tm commanded to the motor 18 when the vehicle to be used in this embodiment is a vehicle that travels using the motor 18. ２ ＊ In the case of a vehicle that runs on an engine, this corresponds to an engine torque command value that is issued to the engine.

[0068] In the comparative example, the road gradient and vehicle mass M are estimated individually from the acceleration obtained by differentiating the vehicle speed and the driving force information. In FIG. 7, in order to make the calculation in the comparative example always possible, the vehicle speed and the final target torque (final motor torque command value Tm ２ ＊ ) is superimposed with a pulsating component of a predetermined amplitude and period, but in actual situations, calculations are possible when the vehicle speed changes.

[0069] As shown in FIG. 7, between time t1 and time t2, the vehicle travels at a constant speed (excluding pulsation components), and no error occurs between the true mass value and the estimated mass value.

[0070] At time t2, the vehicle enters a slope from a flat road, causing a change in gradient. In the comparative example, because the vehicle mass M and gradient are estimated separately from the speed and driving force information as described above, interference occurs between the calculation processes for the mass estimate and the gradient estimate when the gradient changes, causing the accuracy of the mass estimate to deteriorate and deviate from the true mass value. Similarly, the accuracy of the gradient estimate also deteriorates and deviates from the true gradient value.

[0071] Thereafter, at time t3 after the gradient has become constant, the estimated mass value converges to the true mass value, and the estimated gradient value also converges to the true gradient value.

[0072] After time t4, as the true mass value changes like a step function, the estimated mass value also changes like a first-order response and converges to the true mass value over time. Also, as the true mass value changes like a step function at time t4, the estimated gradient temporarily deviates from the true gradient value due to interference with the calculation process, but it quickly converges to the true gradient value.

[0073] On the other hand, in this embodiment, the state variable x is calculated from the longitudinal acceleration detected by the longitudinal acceleration sensor and the driving force information. ｋ In FIG. 8, the final target torque (final motor torque command value Tm ２ ＊ ) as described above, but in actual situations, the final motor torque command value Tm ２ ＊ When the value changes, the operation becomes possible.

[0074] As shown in FIG. 8, between time t1 and time t2, the vehicle travels at a constant speed (excluding pulsation components), no error occurs between the true mass value and the estimated mass value, and the acceleration error e is also constant.

[0075] At time t2, the vehicle enters a slope from a flat road, causing a change in gradient. In this embodiment, the gradient is reflected in the longitudinal acceleration sensor value a. In this embodiment, the vehicle mass M is estimated from the longitudinal acceleration sensor value a and the driving force u. Therefore, there is no need to estimate the vehicle mass M and gradient separately, as in the comparative example. This eliminates the risk of interference between the calculation processes for the mass estimate and the gradient estimate when the gradient changes, resulting in a deterioration in accuracy. Furthermore, there is no need to calculate acceleration using the derivative of the speed sensor value, as in the comparative example. This eliminates the risk of noise components contained in the speed sensor being amplified, resulting in a deterioration in the accuracy of the mass estimate. This allows for accurate estimation of mass.

[0076] Thereafter, between time t3 and time t4 when the gradient becomes constant, there is no deviation between the estimated mass value and the true mass value.

[0077] After time t4, as the true mass value changes like a step function, the estimated mass value also changes like a first-order response and converges to the true mass value over time. Also in this embodiment, the true mass value changes like a step function at time t4, causing the acceleration error e to fluctuate temporarily due to interference with the calculation process, but it quickly converges to a constant value.

[0078] [Effects of this embodiment] The vehicle mass estimation method of this embodiment detects the longitudinal acceleration (longitudinal acceleration sensor value a) of a vehicle (electric vehicle 10) using a longitudinal acceleration sensor (not shown), and calculates the relationship between the longitudinal acceleration (longitudinal acceleration sensor value a), the vehicle driving force u, the vehicle mass M, and the acceleration error e generated in the longitudinal acceleration sensor (not shown) using equations (equations (5) and (6), or equations (7) and (8)). ｋ By inputting the driving force u into a vehicle model (S601) having an equation, an estimated value a^ of longitudinal acceleration that is assumed to be detected by a longitudinal acceleration sensor (not shown) and a state variable x ｋ The prior estimate of x^ ｋ｜ｋ－１ and calculate the prior estimate x^ ｋ｜ｋ－１ and the difference between the estimated value a^ of the longitudinal acceleration and the detected value of the longitudinal acceleration (longitudinal acceleration sensor value a) detected by the longitudinal acceleration sensor, and the state variable x ｋ The estimated value of x^ ｋ｜ｋ and calculate the state variable x ｋ The estimated value of x^ ｋ｜ｋ The state variable x in the equation ｋ Update.

[0079] By the above method, the state variable x ｋ The estimated value of x^ ｋ｜ｋ The estimated value of the vehicle mass M can be calculated by extracting the component of the vehicle mass M from the above method. ｋ｜ｋ－１ and a state variable x based on the difference between the estimated value a^ of the longitudinal acceleration that is assumed to be detected by a longitudinal acceleration sensor (not shown) and the detected value of the longitudinal acceleration (longitudinal acceleration sensor value a) detected by the longitudinal acceleration sensor. ｋ The estimated value of x^ ｋ｜ｋ and calculate the state variable x ｋThe estimated value of x^ ｋ｜ｋ The state variable x in the equations ((5) and (6), or (7) and (8)) is ｋ , the modeling error of the vehicle model (S601) can be reduced, and the state variable x ｋ The estimated value of x^ ｋ｜ｋ It is possible to accurately estimate the vehicle mass M included in the equation (1). Furthermore, because the vehicle mass M is estimated from the longitudinal acceleration sensor value a and the driving force u, there is no need to estimate the vehicle mass M and the gradient separately, and there is no concern that interference will occur in the calculation process of the mass estimation value and the gradient estimation value when the vehicle mass or the gradient changes, resulting in a deterioration in accuracy. Furthermore, because there is no need to calculate acceleration using the derivative of the speed sensor value, there is no concern that noise components included in the speed sensor will be increased, resulting in a deterioration in the accuracy of the mass estimation value. This allows the vehicle mass M to be estimated with high accuracy.

[0080] In this embodiment, the driving force u is sequentially input to the vehicle model (S601) to obtain the estimated value a^ of the longitudinal acceleration and the preliminary estimated value x^ ｋ｜ｋ－１ and sequentially calculate the difference (between the estimated value a^ of the longitudinal acceleration detected by the longitudinal acceleration sensor (not shown) and the detected value of the longitudinal acceleration detected by the longitudinal acceleration sensor (longitudinal acceleration sensor value a)) based on the sequentially calculated estimated value a^ of the longitudinal acceleration and the detected value of the longitudinal acceleration (longitudinal acceleration sensor value a), and sequentially calculate the prior estimated value x^ ｋ｜ｋ－１ and the state variable x based on the difference. ｋ The estimated value of x^ ｋ｜ｋ The state variables x ｋ The estimated value of x^ ｋ｜ｋ The state variable x in the vehicle model (S601) is calculated by ｋ is updated sequentially.

[0081] By using the above method, the modeling error in the vehicle model (S601) can be successively reduced, and the vehicle mass M can be estimated with high accuracy.

[0082] In this embodiment, the state variable x ｋ is the acceleration error e and the inverse M of the vehicle mass M ｉｎｖ Set to.

[0083] The above method eliminates the need for linear approximation of the vehicle mass M, thereby reducing the computational load.

[0084] In this embodiment, the prior estimate x^ ｋ｜ｋ－１ and a correction gain K Ｋ and the state variable x ｋ The estimated value of x^ ｋ｜ｋ is calculated sequentially, and the correction gain K Ｋ is calculated sequentially through the Kalman filter algorithm (Equations (13)-(15)) using the driving force u as an input value.

[0085] With the above method, the Kalman filter is a sequential operation and does not require data storage, thereby reducing the storage capacity of the system.

[0086] In this embodiment, the Kalman filter calculates the component (q Ｍｉｎｖ ) and the component (q ｅ ), and a system noise (Q) including a component (q Ｍｉｎｖ ) and the component (q ｅ ) and are set separately.

[0087] By using the above method, the estimation accuracy and the estimated speed can be individually designed for the vehicle mass M and the acceleration error e.

[0088] In this embodiment, the prior estimate x^ ｋ｜ｋ－１ and a correction gain K Ｋ and the state variable x ｋ The estimated value of x^ ｋ｜ｋ is calculated sequentially, and the correction gain K Ｋ is calculated sequentially through a recursive least squares algorithm with the driving force u as an input value and a forgetting factor (λ).

[0089] According to the above method, since the recursive least squares method is a recursive calculation, there is no need to store data, and therefore the storage capacity of the system can be reduced.

[0090] In this embodiment, the acceleration error e includes an offset component of the longitudinal acceleration sensor.

[0091] By using the above method, the vehicle mass M can be estimated with high accuracy, taking into account the effects of mounting errors of the longitudinal acceleration sensor and tilt due to loading.

[0092] In this embodiment, the driving force u is calculated based on a driving torque command value (for example, a final motor torque command value Tm ２ ＊ ) is calculated based on

[0093] By using the above method, it is possible to estimate the driving force u that actually acts on the vehicle without installing any additional sensors. Note that the driving torque command value is the engine torque command value in the case of a vehicle that uses an engine as a driving source. In the case of a vehicle that uses a motor 18 as a driving source, it is the motor torque command value (final motor torque command value Tm ２ ＊ ) refers to

[0094] In this embodiment, the driving force u is calculated based on a driving torque command value (for example, a final motor torque command value Tm ２ ＊ ) the driving force equivalent value of the inertia of the vehicle's wheels (drive wheels 24a, 24b, driven wheels), and the driving force equivalent value of the vehicle's running resistance (excluding gradient resistance).

[0095] By using the above method, the driving force u used in the estimation calculation of the vehicle mass M is corrected in advance for known disturbances of systems other than the driving source and driving force transmission system, thereby improving the accuracy of the estimated value of the vehicle mass M.

[0096] In this embodiment, the driving force u is calculated based on a driving torque command value (for example, a final motor torque command value Tm ２ ＊) is a gain K , which represents the efficiency (transmission efficiency) of the drive source (motor 18, engine) and the vehicle's drive force transmission system. ｅ (S508) is calculated based on the multiplied value.

[0097] By using the above method, the driving force u used in the calculation of the vehicle mass M is corrected in advance for known disturbances of the driving source and the driving force transmission system, thereby improving the accuracy of the estimated value of the vehicle mass M.

[0098] The vehicle mass estimation system of this embodiment includes a longitudinal acceleration sensor (not shown) for detecting longitudinal acceleration of the vehicle, a driving force calculation unit (input correction unit (S401)) for calculating a driving force u of the vehicle, and equations (formulas (5) and (6), or (7) and (8)) that represent the relationship between the longitudinal acceleration (longitudinal acceleration sensor value a), the driving force u, the vehicle mass M, and an acceleration error e generated in the longitudinal acceleration sensor, in which the vehicle mass M and the acceleration error e are converted into a state variable x ｋ The equation is expressed as follows: the estimated value a^ of longitudinal acceleration that is assumed to be detected by a longitudinal acceleration sensor (not shown) when a driving force u is input; and the state variable x ｋ The prior estimate of x^ ｋ｜ｋ－１ and a vehicle model (S601) for calculating the prior estimated value x^ ｋ｜ｋ－１ and the difference between the estimated value a^ of the longitudinal acceleration and the detected value of the longitudinal acceleration (longitudinal acceleration sensor value a) detected by a longitudinal acceleration sensor (not shown), based on the state variable x ｋ The estimated value of x^ ｋ｜ｋ and a state variable estimation unit (S604) that calculates the state variable x ｋ The estimated value of x^ ｋ｜ｋ The state variable x in the equation ｋ Update the vehicle mass estimation system.

[0099] With the above configuration, the state variable x ｋ The estimated value of x^ ｋ｜ｋ By extracting the component of the vehicle mass M from the vehicle model (S601), an estimated value of the vehicle mass M can be calculated. ｋ｜ｋ－１and a state variable x based on the difference between the estimated value a^ of the longitudinal acceleration that is assumed to be detected by a longitudinal acceleration sensor (not shown) and the detected value of the longitudinal acceleration (longitudinal acceleration sensor value a) detected by the longitudinal acceleration sensor. ｋ The estimated value of x^ ｋ｜ｋ and calculate the state variable x ｋ The estimated value of x^ ｋ｜ｋ The state variable x in the equations ((5) and (6), or (7) and (8)) is ｋ , the modeling error of the vehicle model (S601) can be reduced, and the state variable x ｋ The estimated value of x^ ｋ｜ｋ It is possible to accurately estimate the vehicle mass M included in the equation (1). Furthermore, because the vehicle mass M is estimated from the longitudinal acceleration sensor value a and the driving force u, there is no need to estimate the vehicle mass M and the gradient separately, and there is no concern that interference will occur in the calculation process of the mass estimation value and the gradient estimation value when the vehicle mass or the gradient changes, resulting in a deterioration in accuracy. Furthermore, because there is no need to calculate acceleration using the derivative of the speed sensor value, there is no concern that noise components included in the speed sensor will be increased, resulting in a deterioration in the accuracy of the mass estimation value. This allows the vehicle mass M to be estimated with high accuracy.

[0100] Although the embodiments of the present invention have been described above, the above embodiments merely illustrate some of the application examples of the present invention, and it is not intended that the technical scope of the present invention be limited to the specific configurations of the above embodiments.

Claims

1. A vehicle mass estimation method comprising: detecting the longitudinal acceleration of a vehicle using a longitudinal acceleration sensor; inputting the driving force into a vehicle model having an equation representing the relationship between the longitudinal acceleration, the driving force of the vehicle, the vehicle mass, and an acceleration error generated in the longitudinal acceleration sensor, the equation having the vehicle mass and the acceleration error as state variables; calculating an estimate of the longitudinal acceleration that is expected to be detected by the longitudinal acceleration sensor and a prior estimate of the state variable; calculating an estimate of the state variable based on the prior estimate and the difference between the estimated value of the longitudinal acceleration and the detected value of the longitudinal acceleration detected by the longitudinal acceleration sensor; and updating the state variable in the equation using the estimate of the state variable.

2. A vehicle mass estimation method as described in claim 1, which comprises sequentially inputting the driving force into the vehicle model to sequentially calculate the estimated value of the longitudinal acceleration and the prior estimated value, sequentially calculating the difference based on the sequentially calculated estimated value of the longitudinal acceleration and the detected value of the longitudinal acceleration, sequentially calculating the estimated value of the state variable based on the sequentially calculated prior estimated value and the difference, and sequentially updating the state variable in the equation using the sequentially calculated estimated value of the state variable.

3. The vehicle mass estimation method according to claim 2, wherein the state variables are set to the acceleration error and the reciprocal of the vehicle mass.

4. A vehicle mass estimation method according to claim 2 or claim 3, wherein an estimated value of the state variable is calculated sequentially based on the prior estimated value and a value obtained by multiplying the difference by a correction gain, and the correction gain is calculated sequentially via a Kalman filter algorithm that uses the driving force as an input value.

5. A vehicle mass estimation method according to claim 4, wherein the Kalman filter includes system noise including a component related to the vehicle mass and a component related to the acceleration error, and the component related to the vehicle mass and the component related to the acceleration error are set separately.

6. A vehicle mass estimation method according to claim 2 or claim 3, wherein an estimated value of the state variable is calculated sequentially based on the prior estimated value and a value obtained by multiplying the difference by a correction gain, and the correction gain is calculated sequentially via a recursive least squares algorithm having a forgetting factor and using the driving force as an input value.

7. A vehicle mass estimation method according to any one of claims 1 to 3, wherein the acceleration error includes an offset component of the longitudinal acceleration sensor.

8. A vehicle mass estimation method according to any one of claims 1 to 3, wherein the driving force is calculated based on a driving torque command value issued to a driving source that drives the vehicle.

9. A vehicle mass estimation method according to any one of claims 1 to 3, wherein the driving force is calculated based on a driving force equivalent value of a driving torque command value issued to a driving source that drives the vehicle, a driving force equivalent value of the inertia of the vehicle's wheels, and a driving force equivalent value of the vehicle's running resistance.

10. A vehicle mass estimation method according to any one of claims 1 to 3, wherein the driving force is calculated based on a value obtained by multiplying a driving torque command value issued to a driving source that drives the vehicle by a gain that represents the efficiency of the driving source and the driving force transmission system of the vehicle.

11. A vehicle mass estimation system comprising: a longitudinal acceleration sensor that detects longitudinal acceleration of a vehicle; a driving force calculation unit that calculates a driving force of the vehicle; a vehicle model having an equation that represents the relationship between the longitudinal acceleration, the driving force, vehicle mass, and an acceleration error generated in the longitudinal acceleration sensor, the equation having the vehicle mass and the acceleration error as state variables, the vehicle model calculating an estimated value of the longitudinal acceleration that is expected to be detected by the longitudinal acceleration sensor when the driving force is input, and a priori estimated value of the state variable; and a state variable estimation unit that calculates an estimated value of the state variable based on the prior estimated value and the difference between the estimated value of the longitudinal acceleration and the detected value of the longitudinal acceleration detected by the longitudinal acceleration sensor, wherein the vehicle model updates the state variable in the equation using the estimated value of the state variable.

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