Vehicle mass estimation method and vehicle mass estimation device

Abstract

This vehicle mass estimation method includes: detecting an acceleration generated in a vehicle in the front-rear direction; setting, on the basis of the state of the vehicle, a value of a forgetting factor for past data to be forgotten; varying the value of the forgetting factor according to the elapse of time; and estimating a vehicle mass by recursive computation based on an recursive least square method using the forgetting factor on the basis of a vehicle model including the vehicle mass, the driving force of the vehicle, and a detected value of the acceleration.

Description

Vehicle mass estimation method and vehicle mass estimation device

[0001] The present invention relates to a method for estimating vehicle mass and a vehicle mass estimation apparatus.

[0002] Methods or apparatuses for estimating the mass of a vehicle are known (see, for example, Patent Document 1). Patent Document 1 describes a vehicle mass estimation apparatus that uses an update adjustment parameter, which is obtained by adding the ratio of past errors to the current error to a stabilization parameter, to sequentially estimate the mass using the update adjustment parameter.

[0003] Japanese Patent Publication No. 2011-180085

[0004] In the vehicle mass estimation device described in Patent Document 1, the constants of the update adjustment parameters are set so that the estimation responsiveness is always constant. Therefore, if the constants are changed to improve the estimation speed, the fluctuation of the estimated value in the steady state becomes large, and if the constants are changed to reduce the fluctuation of the estimated value in the steady state, the estimation speed becomes slow. For this reason, it is difficult to achieve both suppression of fluctuations in the estimated value in the steady state and securing the estimation response speed.

[0005] The object of the present invention is to provide a vehicle mass estimation method and a vehicle mass estimation device that can achieve both suppression of fluctuations in estimated values ​​in a steady state and securing a fast estimation response speed.

[0006] One aspect of the present invention involves setting a value for a forgetting coefficient, which is a coefficient for forgetting past data, based on the state of the vehicle, changing the value of the forgetting coefficient over time, and estimating the vehicle mass by sequential calculation using the sequential least squares method based on a vehicle model including the vehicle mass, the driving force of the vehicle, and the detected values ​​of the acceleration.

[0007] This figure shows the schematic configuration of the vehicle according to the embodiment. This is a flowchart of the control performed by the controller. This figure shows the relationship between the accelerator operation amount, the rotational speed of the electric motor, and the basic torque. This is a control block diagram of the controller. This is a control block diagram of the vehicle mass estimation unit of the controller. This is a control block diagram of the estimation variable calculation unit of the controller. This is a control block diagram of the forgetting coefficient calculation unit of the controller. This is a control block diagram of the mass calculation unit of the controller. This is a time chart showing the changes in the estimated vehicle mass and the forgetting coefficient when the forgetting coefficient is a fixed value. This is a time chart showing the changes in the estimated vehicle mass and the forgetting coefficient when the forgetting coefficient is variable. This is a time chart of the forgetting coefficient reset process.

[0008] Embodiments of the present invention will be described below with reference to the drawings. In the following, the driver's operation to request braking force using a braking force request operation means such as an accelerator pedal will be referred to as "accelerator operation," and the amount of this operation will be referred to as "accelerator operation amount." Furthermore, braking force refers to driving force and braking force, and is generated by driving torque and braking torque. Regenerative torque is also included in braking torque. In the control program, driving torque and braking torque are expressed by the positive and negative values ​​of torque, so torque may sometimes be described as a negative value.

[0009] [Overall Configuration] Figure 1 shows the schematic configuration of vehicle 1. Vehicle 1 is equipped with a braking / driving force generating device 2, a control device 3, sensors 4A to 4F, an ignition switch 4G, and a door switch 4H.

[0010] The braking and driving force generating device 2 comprises an inverter 21, a battery 22, an electric motor 23, a reduction gear 24, and wheels 25.

[0011] The inverter 21 converts the DC current input from the battery 22 into AC current and outputs it to the electric motor 23 based on commands input from the control device 3. The inverter 21 also converts the AC current supplied from the electric motor 23 into DC current and inputs it to the battery 22 based on commands input from the control device 3. The inverter 21 is composed of, for example, two pairs of switching elements (power semiconductor elements such as IGBTs and MOS-FETs) for each phase of the electric motor 23, and by turning the switching elements on and off, it converts the DC current supplied from the battery 22 into AC or vice versa, and supplies the desired current to the electric motor 23.

[0012] The battery 22 supplies power to drive the electric motor 23 and charges the regenerative power of the electric motor 23.

[0013] The electric motor 23 generates driving force using alternating current supplied from the inverter 21 and transmits this driving force to the drive wheels via a power transmission system including a reduction gear 24 and a drive shaft 26. Furthermore, when the motor rotates along with the drive wheels while the vehicle 1 is in motion, it generates regenerative braking force, thereby recovering the kinetic energy of the vehicle 1 as electrical energy.

[0014] The reduction gear 24 includes a transmission and a differential gear, and reduces the output of the electric motor 23 before transmitting it to the wheels 25.

[0015] The control device 3 includes an accelerator position sensor 4A as an accelerator operation amount sensor, a wheel rotation speed sensor 4B, an acceleration sensor 4C, a rotation sensor 4D, a current sensor 4E, a voltage sensor 4F, an ignition switch 4G, a door switch 4H, and a controller 5. The control device 3 also serves as a vehicle mass estimation device for estimating the mass of the vehicle 1 (hereinafter referred to as vehicle mass).

[0016] The accelerator position sensor 4A detects the amount of accelerator operation of the vehicle 1. The accelerator position sensor 4A is composed of, for example, a pedal stroke sensor and detects the amount of operation of the accelerator pedal, which is used as a driving force request operation means, as the amount of accelerator operation.

[0017] The wheel rotation speed sensor 4B detects the rotation speed of each wheel 25, that is, the rotation speed of the drive wheels and the rotation speed of the driven wheels.

[0018] The acceleration sensor 4C is mounted on the vehicle 1 and detects the longitudinal acceleration occurring in the vehicle 1.

[0019] The rotation sensor 4D is composed of, for example, a resolver and an encoder, and detects the rotor phase of the electric motor 23.

[0020] The current sensor 4E detects the current of each phase of the electric motor 23.

[0021] The voltage sensor 4F detects the DC voltage of the DC power line between the inverter 21 and the battery 22.

[0022] Ignition switch 4G is a switch for starting the system of vehicle 1. When ignition switch 4G is turned on, vehicle 1 becomes ready to drive.

[0023] Door switch 4H is a sensor that detects the open / closed state of the door of vehicle 1. Door switch 4H is ON when the door is closed and OFF when the door is open.

[0024] The controller 5 controls the braking and driving torque generated by the electric motor 23 based on the accelerator pedal input and the rotational speed of the electric motor 23. The controller 5 also functions as a computing device that performs calculations to estimate the vehicle mass.

[0025] [Controller Control Flowchart] Figure 2 is a flowchart of the control performed by the controller 5. The control routines shown in the flowchart are pre-programmed and installed in the controller 5. The controller 5 repeatedly executes the following control routines according to the program, for example, in calculation cycles of about 10 milliseconds.

[0026] In step S1 of FIG. 2, the controller 5 acquires the outputs of sensors 4A to 4F, ignition switch 4G, and door switch 4H, that is, the accelerator operation amount, the rotational speed of wheel 25, the acceleration of vehicle 1 in the longitudinal direction, the rotor phase of electric motor 23, the current of electric motor 23, the DC voltage between inverter 21 and battery 22, the on / off state of ignition switch 4G, and the open / closed state of the doors of vehicle 1. Further, the controller 5 acquires the speed of vehicle 1 (hereinafter referred to as vehicle speed) output from an external controller such as a speed meter or a brake controller through communication. As an input process, the controller 5 differentiates the rotor phase of electric motor 23 to calculate the rotor angular velocity which is an electrical angle, and divides the rotor angular velocity by the number of pole pairs of electric motor 23 to calculate the rotational speed which is the mechanical angular velocity of electric motor 23. [[ID=I]]

[0027] In step S2, the controller 5 performs a basic torque setting process for setting the basic torque. The basic torque is the torque required by the driver of vehicle 1 through the operation of the accelerator pedal. For example, as shown in FIG. 3, the controller 5 refers to a map defining the relationship between the accelerator operation amount, the rotational speed of electric motor 23, and the basic torque, and sets the torque obtained from the map as the basic torque. In FIG. 3, the bottommost line indicates the basic torque when the accelerator operation amount is zero, and the upper lines indicate the basic torque when the accelerator operation amount is large.

[0028] In step S3 of FIG. 2, the controller 5 performs a target torque setting process for setting the target torque based on the basic torque.

[0029] In step S4, the controller 5 estimates the vehicle mass. Details of the vehicle mass estimation process will be described later.

[0030] In step S5, the controller 5 performs a motor control process for controlling electric motor 23 based on the target torque, the rotational speed of electric motor 23, the rotor phase of electric motor 23, and the DC voltage between inverter 21 and battery 22.

[0031] [Controller Configuration and Mass Estimation Method] Figure 4 is a control block diagram of the controller 5. The controller 5 is composed of a microcomputer equipped with, for example, a processing unit with a processor such as a CPU (Central Processing Unit) or GPU (Central Graphics Processing Unit), a storage unit 51 such as ROM (Read Only Memory) and RAM (Random Access Memory), and an input / output unit such as an input / output interface. The controller 5 is electrically or communicatively connected to an accelerator position sensor 4A, a wheel rotation speed sensor 4B, an acceleration sensor 4C, a rotation sensor 4D, a current sensor 4E, a voltage sensor 4F, an ignition switch 4G, a door switch 4H, and an inverter 21.

[0032] The controller 5 includes a storage unit 51, a basic torque setting unit 52, a target torque setting unit 53, a vehicle mass estimation unit 54, and a motor control unit 55.

[0033] The memory unit 51 stores computer programs for operating the controller 5, various maps used for control, and various parameter values. Examples of maps stored in the memory unit 51 include a map defining the relationship between the accelerator pedal input and the rotational speed and basic torque of the electric motor 23. Examples of parameter values ​​stored in the memory unit 51 include the initial value λ of the forgetting coefficient. ｓｔａｒｔ and upper limit λ ｅｎｄ The filter has a time constant τ and various threshold values.

[0034] The basic torque setting unit 52 is the part that performs the basic torque setting process, and sets the basic torque based on the accelerator operation amount and vehicle speed.

[0035] The target torque setting unit 53 is the part that performs the target torque setting process and sets the target torque based on the basic torque. In this embodiment, the target torque setting unit 53 performs vibration damping processing as part of the target torque setting process. To this end, the target torque setting unit 53 includes, for example, a transmission model that models the characteristics from the output torque of the electric motor 23 to the torsional angular velocity of the drive shaft 26, and a feedback model that feeds back the torsional angular velocity of the drive shaft 26 output from the transmission model. As a result, the target torque setting unit 53 sets a target torque that suppresses vibrations of the power transmission system (such as torsional vibrations of the drive shaft 26) without sacrificing the response of the drive shaft torque by reflecting the feedback in the basic torque.

[0036] Figure 5 is a control block diagram of the vehicle mass estimation unit 54. The vehicle mass estimation unit 54 is the part that performs the vehicle mass estimation process. Based on the detected values ​​of the vehicle model 543A (see Figure 8) including the vehicle mass, the driving force of the vehicle 1, and the acceleration, the vehicle mass estimation unit 54 estimates the acceleration error and the vehicle mass by sequential calculation using the sequential least squares method with a forgetting coefficient, which is a coefficient for exponentially forgetting past data.

[0037] The vehicle mass estimation unit 54 includes an estimation variable calculation unit 541, a forgetting coefficient calculation unit 542, and a mass calculation unit 543.

[0038] Figure 6 is a control block diagram of the estimation variable calculation unit 541. The estimation variable calculation unit 541 calculates the driving force of the vehicle 1 and the filtered acceleration detection value as estimation variables used to estimate the vehicle mass. The estimation variable calculation unit 541 includes low-pass filters 541A to 541C, approximate differentiators 541D and 541E, multipliers 541F to 541I, and a subtractor 541J.

[0039] The estimation variable calculation unit 541 performs a low-pass filter process on the acceleration detection value using a low-pass filter 541A for phase adjustment, and calculates the acceleration detection value after filtering. The estimation variable calculation unit 541 also calculates the driving force equivalent value of the target torque, the driving force equivalent value of the drive wheel inertia, the driving force equivalent value of the driven wheel inertia, and air resistance / rolling resistance.

[0040] Specifically, the estimation variable calculation unit 541 performs a low-pass filter process on the target torque using a low-pass filter 541B for phase adjustment, and then calculates the driving force equivalent value of the target torque by multiplying the gain Ke, which is the efficiency of the drive source and power transmission system, with a multiplier 541F, and the gain consisting of the gear ratio Na of the reduction gear 24 and the wheel radius Ra with a multiplier 541G.

[0041] The estimation variable calculation unit 541 calculates the drive wheel rotation speed ω using the approximate differentiator 541D. ｄｒｉｖｅ The driving force equivalent value of the driving wheel inertia is calculated by multiplying the drive wheel angular acceleration obtained by approximate differentiation with a gain consisting of the inertia Jw of the wheel 25, the inertia Jme of the electric motor 23 and the reduction gear 24, the gear ratio Nal of the reduction gear 24, and the wheel radius Ra using the multiplier 541H.

[0042] The estimation variable calculation unit 541 calculates the driven wheel rotation speed ω using the approximate differentiator 541E. ｄｒｉｖｅｎ The driving force equivalent value of the driven wheel inertia is calculated by multiplying the driven wheel angular acceleration obtained by approximate differentiation by a gain consisting of the inertia Jw of the wheel 25 and the wheel radius Ra using the multiplier 541I.

[0043] The estimation variable calculation unit 541 calculates the vehicle speed as V and the air resistance / rolling resistance as F. ｌｏａｄ F ｌｏａｄ =A0+A1×V+A2×V ２ After calculating the air resistance / rolling resistance using the formula, a low-pass filter 541C is used to apply a low-pass filter to the air resistance / rolling resistance for phase adjustment. Parameters A0, A1, and A2 are either design values ​​or values ​​identified experimentally.

[0044] Subsequently, the estimation variable calculation unit 541 calculates the driving force by subtracting the driving force equivalent value of the drive wheel inert shuttle, the driving force equivalent value of the driven wheel inert shuttle, and the air resistance / rolling resistance after the low-pass filter from the target torque's driving force equivalent value using the subtractor 541J. Note that the time constant τ used in the estimation variable calculation unit 541 is the same value in order to align the phases.

[0045] FIG. 7 is a control block diagram of the forgetting coefficient calculation unit 542. The forgetting coefficient calculation unit 542 sets the value of the forgetting coefficient based on the state of the vehicle 1 and changes the value of the forgetting coefficient according to the passage of time. In the case of this embodiment, the forgetting coefficient calculation unit � ｓｔａｒｔ resets the forgetting coefficient to the initial value λ when a predetermined change occurs in the state of the vehicle 1, such as when the ignition switch 4G changes from OFF to ON or when the door of the vehicle 1 is closed after being opened. That is, the forgetting coefficient calculation unit 542 sets the value of the forgetting coefficient to a preset minimum value based on the state of the vehicle 1, and then increases the forgetting coefficient from the minimum value to a value with an upper limit of 1. Further, the forgetting coefficient calculation unit 542 changes the forgetting coefficient based on the elapsed time from the start of estimation of the vehicle mass.

[0046] As shown in FIG. 7, the forgetting coefficient calculation unit 542 includes an OR calculator 542A, a selector 542B, a low-pass filter 542C, and an adder 542D.

[0047] When the ignition switch 4G changes from OFF to ON or when the door switch 4H changes from OFF to ON, that is, when the door of the vehicle 1 is closed after being opened, the forgetting coefficient calculation unit 542 outputs a reset signal of the forgetting coefficient to Hi by the OR calculator 542A, and when not, outputs the reset signal to Lo by the OR calculator 542A. Next, when the reset signal is Hi, the forgetting coefficient calculation unit 542 selects and outputs the initial value λ of the forgetting coefficient stored in advance by the selector 542B, and when the reset signal is Lo, selects and outputs the upper limit value λ of the forgetting coefficient by the selector 542B. ｓｔａｒｔ Thereafter, the forgetting coefficient calculation unit 542 performs a low-pass filter process with a time constant τ by the low-pass filter 542C on the difference between the initial value λ of the forgetting coefficient and the output value from the selector 542B, and then adds the output value from the selector 542B by the adder 542D. By doing so, the initial value λ is obtained at the time of ignition on or at the time of door closing operation, and thereafter, a forgetting coefficient that increases to the upper limit value λ with the time constant τ is output. Note that the initial value λ ｅｎｄ ｓｔａｒｔ ｌａｍｂｄａ ｓｔａｒｔ ｌａｍｂｄａ ｅｎｄ ｓｔａｒｔ ​​​​​​, upper limit λ ｅｎｄ , time constant τ ｌａｍｂｄａ Although the optimal response is derived experimentally, the forgetting coefficient is set in the range of 0 to 1 in principle, and therefore, in this invention as well, it is set to be variable in the range of 0 to 1.

[0048] Figure 8 is a control block diagram of the mass calculation unit 543. The mass calculation unit 543 estimates the vehicle mass by sequential calculation using the sequential least squares method with a forgetting coefficient, based on the vehicle model 543A, the driving force of the vehicle 1, and the detected acceleration values. Specifically, the mass calculation unit 543 estimates the longitudinal acceleration of the vehicle 1 based on the vehicle model 543A and the driving force of the vehicle 1, and then estimates the vehicle mass by correcting the vehicle model 543A based on the error between the estimated acceleration and the detected acceleration value, and the forgetting coefficient.

[0049] As shown in Figure 8, the mass calculation unit 543 includes a vehicle model 543A, a subtractor 543B, a multiplier 543C, and an adder 543D.

[0050] First, let's explain vehicle model 543A. Vehicle model 543A defines the relationship between the driving force of vehicle 1 and the acceleration of vehicle 1. Here, the equation of motion for vehicle 1 is expressed by the following equation (1), where a is the detected acceleration value of vehicle 1, M is the vehicle mass, u is the driving force of vehicle 1, and e is the acceleration error.

[0051]

[0052] Since the acceleration sensor 4C is a sensor that detects acceleration in the longitudinal direction occurring in the vehicle 1, the detected acceleration value also includes the gravitational acceleration component due to the gradient. The acceleration error includes the mounting error of the acceleration sensor 4C and the acceleration error caused by the tilt of the vehicle 1 due to the load.

[0053] Applying the transformation of equation (2) below to equation (1), we obtain equation (3) below.

[0054]

[0055]

[0056] Discretizing equation (3) yields equation (4) below.

[0057]

[0058] And the reciprocal of the vehicle mass, M. ｉｎｖ To estimate the acceleration error e, the state variable x is set to the reciprocal of the vehicle mass M. ｉｎｖ By setting the acceleration error e and the observed value y as the acceleration detection value, the following equations (5) and (6) are obtained as the state equation and observation equation for the vehicle model 543A.

[0059]

[0060]

[0061] However, x ｋ and H ｋ These are given by equations (7) and (8) below.

[0062]

[0063]

[0064] Therefore, the mass calculation unit 543 calculates the pre-estimated value x of the state variable by performing calculations using equations (5) and (6) that represent the vehicle model 543A. ｋ|ｋ－１ Next, the mass calculation unit 543 subtracts the acceleration estimate from the filtered acceleration detection value using the subtractor 543B. After that, the mass calculation unit 543 applies a correction gain K to the output value of the subtractor 543B using the multiplier 543C. ｋ By multiplying by x, the prior estimate of the state variable is obtained. ｋ|ｋ－１ The correction amount to compensate for this is calculated. Correction gain K ｋ When using the successive least squares algorithm, this can be obtained by sequentially performing the following equations (9), (10), and (11).

[0065]

[0066]

[0067]

[0068] Here, P is the error covariance matrix, I is the identity matrix, and Kｋ P and K are the correction gain and λ is the forgetting coefficient. ｋ The value is updated through sequential calculation, but the forgetting coefficient is set arbitrarily, taking into account the noise contained in the sensors used for input and observed values, as well as the errors that occur. The initial value of P is also set taking into account the magnitude of the initial estimation error.

[0069] Subsequently, the mass calculation unit 543 uses the adder 543D to calculate the pre-estimated value x of the state variable. ｋ|ｋ－１ By adding the output value of the multiplier 543C to this, the estimated value of the state variable x ｋ|ｋ The following calculation is performed. The state variable is the reciprocal of the vehicle mass, M. ｉｎｖ Since it includes the acceleration error, the estimated value of the vehicle mass can be calculated using equation (2). Also, since the state variable includes the acceleration error, the estimated value of the acceleration error can be calculated. The mass calculation unit 543 calculates the estimated value of the state variable x ｋ|ｋ Past values ​​are sampled and reflected in vehicle model 543A.

[0070] Returning to Figure 4, the motor control unit 55 is the part that performs motor control processing, and controls the electric motor 23 based on the target torque, the rotational speed of the electric motor 23, the rotor phase of the electric motor 23, and the DC voltage between the inverter 21 and the battery 22.

[0071] For example, the motor control unit 55 refers to the current target value table stored in the memory unit 51 and determines the dq-axis current target value from the target torque, the rotational speed of the electric motor 23, and the DC voltage between the inverter 21 and the battery 22. Next, the motor control unit 55 calculates the dq-axis current value from the three-phase current value and the rotor phase of the electric motor 23. Then, the motor control unit 55 calculates the dq-axis voltage command value from the deviation between the dq-axis current target value and the dq-axis current value. Non-interference control may also be applied to this part. After that, the motor control unit 55 calculates the three-phase voltage command value from the dq-axis voltage command value and the rotor phase of the electric motor 23. The motor control unit 55 generates a PWM (Pulse Width Modulation) signal (on duty) from this three-phase voltage command value and the DC voltage value. The motor control unit 55 drives the electric motor 23 with the target torque by controlling the switching elements of the inverter 21 to open and close using this PWM signal.

[0072] [Effects] Figures 9 and 10 are time charts showing the changes in acceleration detection value, vehicle mass estimate, and forgetting coefficient. Figures 9 and 10 show the mass estimation results when the initial value of the mass estimate (shown by the solid line in the figure) is intentionally deviated from the initial value, and when the offset value of the acceleration sensor 4C changes at time t1, the forgetting coefficient is fixed and when the forgetting coefficient is variable.

[0073] As shown in Figure 9, when the forgetting coefficient is fixed, the estimated response of the vehicle mass is slow, and it takes some time after the start of driving for it to settle at a value close to the actual mass, as shown by the dashed line in the same figure.

[0074] In contrast, as shown in Figure 10, when the forgetting coefficient is made variable, the estimated mass value moves to a value close to the actual mass in about one-tenth the time compared to when it is set to a fixed value. Furthermore, the fluctuation of the estimated mass value when the offset value of the acceleration sensor 4C changes is suppressed to the same extent as when the forgetting coefficient is set to a fixed value, as shown in Figure 9. As can be seen from these results, it is possible to shorten the estimation convergence time while maintaining robustness to disturbances at the same level as when the forgetting coefficient is set to a fixed value.

[0075] Figure 11 is a time chart of the forgetting coefficient reset process. When the ignition switch 4G is turned on from off, as at time t2 in Figure 11, or when the door of vehicle 1 is closed from an open state, as at time t3, it is assumed that the number of occupants and luggage inside vehicle 1 has increased or decreased during that time, so the value of the forgetting coefficient is reset at these timings. As a result, the forgetting coefficient is set to its minimum value, which speeds up the response of mass estimation when the vehicle mass changes due to an increase or decrease in the number of occupants and luggage.

[0076] According to the embodiment described above, the value of the forgetting coefficient for sequential calculation using the successive least squares method is set based on the state of the vehicle 1, and the value of the forgetting coefficient is changed according to the passage of time, thereby achieving both suppression of fluctuations in the estimated value in the steady state and securing the response speed of estimation. Furthermore, by repeating the sequential calculation, the estimated value x of the state variable of the vehicle model 543A is obtained. ｋ|ｋ By updating this data, the vehicle mass can be estimated with greater accuracy.

[0077] According to this embodiment, the forgetting coefficient is changed based on the elapsed time since the start of vehicle mass estimation. As a result, highly responsive estimation is possible immediately after the start of estimation, and by increasing the value of the forgetting coefficient over time, highly responsive estimation with little fluctuation in the steady state can be achieved.

[0078] According to this embodiment, the value of the forgetting coefficient is set to a preset minimum value based on the state of the vehicle 1, and then the forgetting coefficient is increased from the minimum value to a value with an upper limit of 1. Therefore, depending on the state of the vehicle 1, it is possible to suppress fluctuations in the estimated value in the steady state and ensure the response speed of the estimation at the same time.

[0079] According to the embodiment, when a predetermined change occurs in the state of the vehicle 1, such as when the ignition switch 4G is turned from off to on, or when the door of the vehicle 1 is closed from an open state, the forgetting coefficient is set to an initial value λ. ｓｔａｒｔ Since it resets to a certain value, the response speed of vehicle mass estimation can be improved when there is a high possibility that the vehicle mass is changing due to an increase or decrease in the number of occupants or luggage inside vehicle 1.

[0080] As described above, the best configurations, methods, etc., for carrying out the present invention are disclosed in the above description, but the present invention is not limited thereto. That is, although the present invention is mainly illustrated and described in relation to specific embodiments, those skilled in the art can make various modifications to the embodiments described above in terms of shape, material, quantity, and other detailed configurations without departing from the scope of the technical idea and objectives of the present invention. Furthermore, the descriptions of shapes, materials, etc. disclosed above are illustrative to facilitate understanding of the present invention and do not limit the present invention, so descriptions of components with some or all of those limitations removed are included in the present invention.

[0081] Vehicle 1 may be a front-wheel drive vehicle, a rear-wheel drive vehicle, or an all-wheel drive vehicle.

[0082] The braking and driving force generating device 2 may be equipped with an internal combustion engine, and the internal combustion engine may drive the wheels 25. Alternatively, the braking and driving force generating device 2 may drive a generator with the internal combustion engine to charge the battery 22, and then supply power from the battery 22 to the electric motor 23, thereby driving the wheels 25 with the electric motor 23, or the wheels 25 may be driven by both the electric motor 23 and the internal combustion engine. In other words, the vehicle 1 may be a so-called series hybrid vehicle or a parallel hybrid vehicle.

[0083] In the control device 3, the driving force request operation means was configured as an accelerator pedal, and the accelerator operation amount sensor was configured as an accelerator position sensor 4A, but other configurations are also possible. For example, the driving force request operation means may be configured as an operation lever or operation dial, and the accelerator operation amount sensor may be configured as a sensor such as a stroke sensor or potentiometer that detects the amount of operation of these.

[0084] The controller 5 may obtain the accelerator input, the rotational speed of the wheels 25, the acceleration of the vehicle 1, the rotor phase of the electric motor 23, the current of the electric motor 23, the on / off state of the ignition switch 4G, and the open / closed state of the doors of the vehicle 1 from external controllers such as a vehicle controller, brake controller, and motor controller via communication. The controller 5 does not need to obtain the vehicle speed from an external controller; for example, it may calculate the vehicle speed by multiplying the rotational speed of the electric motor 23 by the radius r of the wheels 25 and dividing the result by the gear ratio Nal of the reduction gear 24.

[0085] The vehicle mass estimation unit 54 calculates the reciprocal of the vehicle mass M in the vehicle model 543A of the mass calculation unit 543. ｉｎｖ We used the state variable as the reciprocal of the vehicle mass M. ｉｎｖ Alternatively, the vehicle mass may be used as a state variable. The vehicle mass estimation unit 54 may estimate the vehicle mass using the detected driving force of the vehicle 1 if the driving force of the vehicle 1 can be detected by the sensor.

[0086] The forgetting coefficient calculation unit 542 may change the forgetting coefficient based on the distance traveled since the start of the estimation of the vehicle mass. For example, if the suspension stroke changes by more than a threshold while the vehicle 1 is stationary, the forgetting coefficient calculation unit 542 determines that a predetermined change has occurred in the state of the vehicle 1 and sets the forgetting coefficient to an initial value λ ｓｔａｒｔ You can reset it to that.

[0087] 1...Vehicle, 4C...Accelerometer (sensor), 4G...Ignition switch, 5...Controller (processing unit), 543A...Vehicle model

Claims

1. A vehicle mass estimation method comprising: detecting longitudinal acceleration occurring in a vehicle; setting a value for a forgetting coefficient, which is a coefficient for forgetting past data, based on the state of the vehicle; changing the value of the forgetting coefficient over time; and estimating the vehicle mass by sequential calculation using the sequential least squares method with respect to the forgetting coefficient, based on a vehicle model including the vehicle mass, the driving force of the vehicle, and the detected value of the acceleration.

2. A vehicle mass estimation method according to claim 1, wherein the forgetting coefficient is changed based on the elapsed time from the start of estimation of the vehicle mass or the distance traveled from the start of estimation.

3. A vehicle mass estimation method according to claim 1, wherein the value of the forgetting coefficient is set to a preset minimum value based on the state of the vehicle, and then the forgetting coefficient is increased from the minimum value to a value with an upper limit of 1.

4. A vehicle mass estimation method according to claim 1, wherein the forgetting coefficient is reset to an initial value when a predetermined change occurs in the state of the vehicle.

5. A vehicle mass estimation method according to claim 4, wherein a predetermined change in the state of the vehicle occurs when the ignition switch of the vehicle is turned from off to on, or when the door of the vehicle is turned from open to closed.

6. A vehicle mass propulsion device comprising: a sensor for detecting longitudinal acceleration occurring in a vehicle; and a calculation device that sets a value for a forgetting coefficient, which is a coefficient for exponentially forgetting past data, based on the state of the vehicle, changes the value of the forgetting coefficient over time, and estimates the vehicle mass by sequential calculation using the sequential least squares method with respect to the forgetting coefficient, based on a vehicle model including the vehicle mass, the driving force of the vehicle, and the detected value of the acceleration.

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