JP2018091756A - Device and method for estimation of vehicle weight - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a device and a method for estimating a vehicle weight which can reduce estimation errors caused by influences of noise, for example, thereby estimating the weight of a vehicle rapidly and accurately.SOLUTION: A vehicle weight estimation apparatus 30 includes a position sensor 24, a rotational speed sensor 25, a vehicle speed sensor 26, an acceleration sensor 27, a controller 28, and a vehicle weight calculator 31. The vehicle weight calculator 31 includes a parameter acquisition unit 32, a temporary estimation unit 35, an estimation unit 33, and a maintenance unit 34. When a temporary estimation value Mx(k) is out of a preset range Ra, the maintenance unit 34 maintains, as the weight of a vehicle 10, an in-range estimated value mx(k-1) estimated by the estimation unit 33 on the basis of a parameter when a temporarily estimated value Mx(k-1) in the range Ra is temporarily estimated.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a vehicle weight estimation device and a vehicle weight estimation method, and more particularly to a vehicle weight estimation device and a vehicle weight estimation method for estimating the weight of a vehicle with high accuracy.

  When a value obtained by smoothing the estimated vehicle weight deviates from a preset range, an apparatus that corrects the value so as to fall within the range has been proposed (for example, see Patent Document 1). This apparatus performs high-precision estimation by using, as a range, an upper limit value based on the vehicle weight maximum value and a lower limit value based on the vehicle weight minimum value.

JP 2004-301576 A

  By the way, when the smoothed value is out of the preset range, the parameter used for estimation has a large error due to the influence of noise or the like.

  However, in the above apparatus, even if a large error occurs in the parameter, the vehicle weight is estimated based on the parameter, and the smoothing process is performed including the estimated value. Therefore, even if the estimation error is reduced by the correction, the corrected value has a large estimation error and is not highly accurate, and the calculation time required to converge to the true value is also long.

  The present invention has been made in view of the above, and an object of the present invention is to reduce the estimation error due to the influence of noise or the like, and to estimate the weight of the vehicle quickly and with high accuracy, and the vehicle weight. It is to provide an estimation method.

  The vehicle weight estimation apparatus of the present invention that achieves the above object includes a parameter acquisition unit that acquires a parameter that changes during travel of the vehicle, the parameter acquired by the parameter acquisition unit, and the parameter based on the parameter. Temporary estimation means for outputting a temporary estimated value obtained by temporarily estimating the weight of the vehicle, and using the smoothing process based on the parameter and the previous value estimated before acquiring the parameter and the parameter. An estimation unit that outputs an estimated value obtained by estimating the weight of the vehicle; and the temporary estimated value is input, and if the temporary estimated value is out of a preset range, the temporary estimated value is used as the weight of the vehicle. Before temporarily estimating the estimated value within the range estimated by the estimating means based on the parameter when the temporary estimated value within the range is temporarily estimated. And maintaining means for lifting, it is characterized in that it comprises a.

  The vehicle weight estimation method of the present invention that achieves the above object obtains a parameter that changes during travel of the vehicle, provisionally estimates a temporary estimated value as the weight of the vehicle based on the parameter, and this temporary estimated value Is out of the preset range, as the weight of the vehicle, before temporarily estimating the temporary estimated value, the parameter when the temporary estimated value within the range is temporarily estimated and the parameter This is a method characterized by maintaining an in-range estimated value estimated using a smoothing process based on a previous value estimated before acquisition.

  According to the present invention, when the temporary estimated value temporarily estimated based on the parameter is out of the range, the estimated value within the range estimated using the smoothing process when the temporary estimated value falls within the range as the weight of the vehicle. maintain. Therefore, it is possible to maintain a constant value as the weight of the vehicle, excluding parameters that can be regarded as having a large error. This is advantageous for reducing the estimation error due to the influence of noise or the like and for increasing the convergence speed to the true value by eliminating the error in the smoothing process, and can estimate the weight of the vehicle quickly and with high accuracy. .

It is explanatory drawing which illustrates 1st embodiment of the vehicle weight estimation apparatus of this invention. It is a block diagram which illustrates the control device of FIG. It is a block diagram which illustrates the vehicle weight calculating part of FIG. It is a flowchart which illustrates 1st embodiment of the vehicle weight estimation method of this invention. It is a relationship diagram which illustrates the relationship between an engine speed and fuel injection quantity, and an engine torque. It is a relationship diagram which illustrates the relationship with a temporary estimated value, an estimated value, a true value, and a range. It is a block diagram which illustrates the vehicle weight calculating part of 2nd embodiment of the vehicle weight estimation apparatus of this invention. It is a relationship diagram which illustrates the relationship with a temporary estimated value, an estimated value, a true value, and a range.

  Embodiments of a vehicle weight estimation device and a vehicle weight estimation method of the present invention will be described below.

  A vehicle weight estimation device 30 of the first embodiment illustrated in FIGS. 1 to 3 is a device that is mounted on the vehicle 10 and estimates the weight of the vehicle 10 based on parameters that change during the traveling of the vehicle 10. . In the following, a symbol accompanied by x and y is a variable, which indicates a detected value or an estimated value of a sensor or the like, and a symbol appended with k indicates a time series, and this k is “1” for each sampling period ts. "Increases by steps.

  As illustrated in FIG. 1, in the vehicle 10 on which the vehicle weight estimation device 30 is mounted, a cab 12 is disposed on the front side of the chassis 11, and a body 13 is disposed on the rear side of the chassis 11. .

  An engine 14, a clutch 15, a transmission 16, a propeller shaft 17, and a differential gear 18 are installed in the chassis 11. The rotational power of the engine 14 is transmitted to the transmission 16 via the clutch 15. The rotational power changed by the transmission 16 is transmitted to the differential gear 18 through the propeller shaft 17 and distributed as a driving force to the pair of driving wheels 19 as rear wheels. A torque converter may be interposed between the engine 14 and the clutch 15.

  The control device 20 is electrically connected to the engine 14, the clutch 15, the transmission 16, and various sensors via a signal line indicated by a one-dot chain line. As various sensors, the driver's cab 12 is provided with an accelerator opening sensor 22 for detecting the accelerator opening Ax from the depression amount of the accelerator pedal 21 and a position sensor 24 for detecting the lever position Px of the shift lever 23. The chassis 11 is provided with a rotation speed sensor 25 that detects a rotation speed Nx of a crankshaft (not shown) of the engine 14, a vehicle speed sensor 26, and an acceleration sensor 27.

The control device 20 is hardware that includes a CPU that performs various information processing, an internal storage device that can read and write programs and information processing results used for performing the various information processing, and various interfaces.

  As illustrated in FIG. 2, the control device 20 includes a control unit 28 that controls the engine 14, the clutch 15, and the transmission 16, and a vehicle weight calculation unit 31 that calculates the weight of the vehicle 10 as functional elements. doing. In this embodiment, each functional element is stored as a program in the internal storage device, but each functional element may be configured by individual hardware.

  The vehicle weight estimation apparatus 30 according to the present invention includes a position sensor 24, a rotation speed sensor 25, a vehicle speed sensor 26, an acceleration sensor 27, a control unit 28, and a vehicle weight calculation unit 31. And the vehicle weight calculation part 31 functions as a parameter acquisition means, an estimation means, a temporary estimation means, and a maintenance means, and the detection value of those sensors and the calculation result of a calculation part are input, each detection value and calculation result The result calculated based on is output as an output value mx.

  The control unit 28 is a functional element that functions as part of the parameter acquisition unit. In this embodiment, the control unit 28 acquires the fuel injection amount Qx in the engine 14 every sampling period ts. Since the fuel injection amount Qx is proportional to the injection time (drive pulse) of an injector (not shown) of the engine 14, it is obtained from the total value of the injection time.

  The control unit 28 receives the accelerator opening Ax detected by the accelerator opening sensor 22, and calculates a reference injection time based on the accelerator opening Ax. Next, the control unit 28 is added based on whether or not an in-vehicle device mounted on the vehicle 10 is driven by the engine 14 and whether or not an exhaust gas purification device that purifies exhaust gas discharged from the engine 14 is regenerated. The injection time is calculated. When the fuel injection amount based on this additional injection time is injected, the driving force of the in-vehicle device is supplemented or the exhaust gas purification device is regenerated. Next, the control unit 28 calculates the fuel injection amount Qx based on the total value of the reference injection time and the additional injection time for each sampling period ts.

  The controller 28 is not limited to this configuration as long as it can acquire the fuel injection amount Qx actually injected in the engine 14. The control unit 28 may calculate the reference injection time from the internal pressure, volumetric efficiency, and required air-fuel ratio of an intake manifold (not shown), or from the intake air amount and the engine speed Nx. Examples of the in-vehicle device include an air compressor and a motor generator. Examples of the exhaust gas purification device include a collection filter that collects particulate matter in the exhaust gas.

  The position sensor 24 is a device that functions as part of the parameter acquisition means, and detects the lever position Px requested by the driver by electrically detecting the position of the shift lever 23 operated by the driver of the vehicle 10. To do. The position sensor 24 detects the gear ratio ix of the transmission 16 corresponding to the lever position Px at every sampling period ts. Examples of the lever position Px include a parking position (P position), a reverse position (R position), a neutral position (N position), and a forward position (D range). As the forward position, for example, a plurality of stages of 1st to 6th speeds are set. Each forward position is set with a gear ratio ix that decreases as the number of gears increases from the first speed. When the transmission 16 is composed of an AMT, instead of the position sensor 24, one having a function of reading the gear position of the transmission 16 controlled by the control unit 28 may be used. Further, when the gear ratio ix of the transmission 16 is acquired from the control signal of the control unit 28, the gear ratio ix can also be obtained based on the vehicle speed vx and the engine speed Nx.

  The vehicle speed sensor 26 is a device that functions as part of the parameter acquisition means, reads a pulse signal proportional to the rotational speed of the propeller shaft 17, and calculates the vehicle speed vx for each sampling period ts by a vehicle speed calculation process (not shown) of the control device 20. It is a sensor to acquire. Since the vehicle speed sensor 26 acquires the vehicle speed vx based on the pulse signal proportional to the rotation speed, the acquired vehicle speed vx is not negative but has a value of zero or more. As the vehicle speed sensor 26, a sensor that acquires the vehicle speed vx from the rotational speed of an output shaft (not shown) of the transmission 16, the drive wheel 19, the driven wheel, or the like may be used. In addition, when using the sensor which acquires vehicle speed vx from rotational speeds, such as a driving wheel 19 and a driven wheel, it is good to acquire each rotational speed of a pair of left and right wheels, and let the average value be the vehicle speed vx.

  The acceleration sensor 27 is a device that functions as a part of the parameter acquisition unit, and operates with an acceleration component accompanying a speed change in the front-rear direction of the vehicle 10 and a gravitational acceleration component accompanying a posture change of the vehicle 10. The acceleration sensor 27 is a sensor that acquires an acceleration component parallel to the road surface obtained by combining them, that is, an acceleration Gx in the front-rear direction of the vehicle 10 at each sampling period ts. Examples of the acceleration sensor 27 include a mechanical displacement measurement method, an optical method, and a semiconductor method.

  As illustrated in FIG. 3, in this embodiment, the vehicle weight calculation unit 31 includes a parameter acquisition unit 32, a temporary estimation unit 35, an estimation unit 33, and a maintenance unit 34 as functional elements. Each functional element of the vehicle weight calculation unit 31 is stored as a program in the internal storage device, but each functional element may be configured by individual hardware.

  The parameter acquisition unit 32 functions as a parameter acquisition unit, and the detection values of the control unit 28 and each sensor are input. The parameter acquisition unit 32 is a functional element that outputs the first parameter Φx and the second parameter Φy to the temporary estimation unit 35 and the estimation unit 33 as parameters that change during travel of the vehicle 10 at each sampling period ts. The parameter acquisition unit 32 includes a first parameter calculation block 32a, a second parameter calculation block 32b, and an engine torque calculation block 32c.

  The first parameter calculation block 32a is a functional element that receives the acceleration Gx and calculates the first parameter Φx. The second parameter calculation block 32b is a functional element that receives the vehicle speed vx, the gear ratio ix of the transmission 16, and the engine torque Te and calculates the second parameter Φy. The engine torque calculation block 32c is a functional element that receives the engine rotation speed Nx and the fuel injection amount Qx and calculates the engine torque Te actually output from the engine 14.

  The temporary estimation unit 35 functions as temporary estimation means, and the first parameter Φx and the second parameter Φy acquired by the parameter acquisition unit 32 are input, and the temporary estimation value Mx (k) temporarily estimated is maintained by the maintenance unit 34. Is a functional element to output to The temporary estimation unit 35 includes product blocks.

  The estimation unit 33 functions as an estimation unit and receives the first parameter Φx and the second parameter Φy and based on them and the previous value mx (k−1) estimated immediately before in the sampling period ts. The functional element that outputs the estimated value mx (k) estimated using the smoothing process. The estimation part 33 is comprised from the RLS estimation block. The RLS estimation block updates variables in the estimation calculation every sampling period ts. That is, the RLS estimation block stores the previous value mx (k−1), the covariance matrix P (k−1), and the gain K (k−1) calculated by the RLS algorithm when a new parameter is input. It is in the state that was done.

  The maintenance unit 34 functions as a maintenance unit, and is interposed between the parameter acquisition unit 32 and the estimation unit 33. The maintenance unit 34 is a functional element that inputs the parameter acquired by the parameter acquisition unit 32 to the estimation unit 33 based on the determination result of the temporary estimated value Mx (k) for each sampling period ts. Specifically, the maintaining unit 34 receives each parameter and the temporary estimated value Mx (k), and based on the temporary estimated value Mx (k), the maintaining unit 34 calculates the first parameter Φx and the second parameter Φy. Select whether to output to. The maintenance unit 34 includes a check block 34a and an assertion block 34b.

  The check block 34a is a functional element that receives the temporary estimated value Mx (k) output from the temporary estimating unit 35 and determines whether or not the temporary estimated value Mx (k) is within a preset range Ra. is there. Specifically, when the temporary estimated value Mx (k) falls within the range Ra, the check block 34a transmits “1” (a signal indicating true) as a binary signal. On the other hand, when the temporary estimated value Mx (k) is out of the range, “0” (signal indicating false) is transmitted as a binary signal.

  The assertion block 34b receives each parameter and the binary signal transmitted from the check block 34a, and determines whether to output each parameter to the estimation unit 33 based on the binary signal for each sampling period ts. Is a functional element. Specifically, the assertion block 34b permits the input of the first parameter Φx and the second parameter Φy to the estimation unit 33 when the binary signal is “1”. On the other hand, when the binary signal is “0”, the input of the first parameter Φx and the second parameter Φy to the estimation unit 33 is prohibited.

  Next, the estimation calculation of the estimated value mx (k) by the estimation unit 33 of this embodiment will be described. Based on each parameter and the previous value mx (k−1), the estimation unit 33 considers the equation of motion of the vehicle 10 in the longitudinal direction as a transfer function, and uses an adaptive algorithm as a smoothing process to estimate the value mx ( k-1) is estimated. As an adaptive algorithm, an RLS algorithm (sequential least squares algorithm) is used.

The equation of motion in the front-rear direction of the vehicle 10 is represented by the following formula (1). In Formula (1), vx ′ is a differential value obtained by differentiating the vehicle speed vx with respect to time, Tw is a driving torque transmitted to the driving wheels 19, rw is a wheel diameter of the driving wheels 19, and Δmx is a mass corresponding to a rotating portion described later. , B represents a constant, g represents a gravitational acceleration, and μ represents a rolling resistance coefficient. The constant B is a constant obtained by multiplying “0.5”, the air density ρ, the front projected area Af of the vehicle 10, and the air resistance coefficient Cd. The wheel diameter rw, the constant B, and the rolling resistance coefficient μ are obtained as values unique to the vehicle 10.

When the above formula (1) is modified, the weight mx of the vehicle 10 is expressed by the following formula (2).

The above equation (2) can be regarded as a transfer function in which the first parameter Φx is an input value, the second parameter Φy is an output value, and the estimated value mx is a variable. Therefore, the transfer function (Φy (k) = Φx (k) · mx (k)) is self-adapted according to the RLS algorithm to estimate the estimated value mx (k). The estimated value mx (k) is expressed by the following mathematical formulas (3) to (5). In the following equation, mx (k−1) is the previous value that was estimated previously in the sampling period ts, K (k) is the gain calculated by the RLS algorithm, and P (k) is the common value. The variance matrix, I is the unit matrix, and “ ^T ” is the transposed matrix.

  If the initial value P (0) of the covariance matrix P (k) is determined, the parameter Φx is used to calculate the covariance matrix P (k) based on the above formula (5) and the gain based on the formula (4). K (k) can be calculated respectively. That is, every time the first parameter Φx and the second parameter Φy are newly obtained, the covariance matrix P (k) and the gain K (k) are newly updated. And based on these and numerical formula (3), the estimated value mx (k) can be calculated by a method of correcting the previous value mx (k−1) estimated immediately before.

  The initial value P (0) is represented by the product of the constant α and the unit matrix I. As the constant α, a value of about 1000 is usually used, but when the noise is large, the constant α may be set small. This constant α is determined by the magnitude of noise. Further, as the initial value m (0), for example, the vehicle weight when the vehicle is empty excluding the driver and the load, the total vehicle weight when the vehicle is fully loaded, or an average value of the estimated value mx (k) may be used.

When the covariance matrix P (k) increases, the estimated value mx (k) moves away from the true value, and when the covariance matrix P (k) converges small, the estimated value mx (k) approaches the true value.

  As described above, the estimated value mx (k) can be sequentially smoothed by estimating the estimated value mx (k) using an adaptive algorithm, considering the above formula (2) as a transfer function. This is advantageous for speeding up convergence to a true value and improving robustness against noise, disturbance, or a change in statistical properties of detection values of each sensor, and can reduce estimation errors. Accordingly, the weight of the vehicle 10 can be estimated with high accuracy.

  In this embodiment, by using the RLS algorithm of the adaptive algorithms, the estimated value mx (k) can be obtained from the above formulas (3) to (5). This is advantageous for online estimation, and the estimated value mx (k) can be calculated in real time. Further, it is advantageous for ensuring the responsiveness of the estimation of the weight of the vehicle 10 as compared with the method of removing noise from the detection value acquired by each sensor using a low-pass filter.

  In addition, it is only necessary to update the previous value mx (k−1), the covariance matrix P (k−1), and the gain K (k−1) for each sampling period ts, and store them in the internal storage device of the control device 20. The numerical value to be made can be minimized. Therefore, the memory required for estimation is larger than the method based on offline estimation (batch processing estimation) in which an infinite storage area must be secured in the internal storage device of the control device 20 for an uncertain traveling period of the vehicle 10. This is advantageous for capacity reduction. The offline estimation mentioned here can be exemplified by a batch process least square method, a method of calculating an average value of all estimated values mx (0) to mx (k), and the like.

  Furthermore, the estimated value mx (k) can be calculated for each sampling period ts by using the RLS algorithm. This is advantageous for estimation in real time as compared with a method of estimating when the state of the vehicle 10 (for example, the gear ratio ix or the driving torque Tw) changes or when traveling a predetermined distance.

  Next, the vehicle weight estimation method of the present invention will be described as each function of the vehicle weight calculation unit 31 with reference to the flowchart of FIG. The following vehicle weight estimation method is started when the control device 20 of the vehicle 10 is energized, and is repeatedly performed every sampling cycle ts to estimate the weight of the vehicle 10 in real time. That is, the process from the start to the return is processed with one sampling period ts. Then, the control device 20 ends when a power failure occurs.

  When starting, the vehicle weight calculation unit 31 acquires parameters that change during the traveling of the vehicle 10 by the function of the parameter acquisition unit 32 (S110). The parameters are a first parameter Φx and a second parameter Φy.

  Specifically, the parameter acquisition unit 32 acquires the parameters from the detection values detected by the control unit 28 and each sensor. First, the control unit 28 sets the fuel injection amount Qx, the position sensor 24 sets the gear ratio ix of the transmission 16, the rotation speed sensor 25 sets the engine rotation speed Nx, the vehicle speed sensor 26 sets the vehicle speed vx, and the acceleration sensor 27 sets the acceleration Gx. Get each.

Next, the first parameter calculation block 32a calculates the first parameter Φx shown in the following mathematical formula (6) by each block.

  The acceleration Gx is an acceleration component parallel to the road surface obtained by combining the acceleration component accompanying the speed change in the longitudinal direction of the vehicle 10 and the gravitational acceleration component accompanying the posture change of the vehicle 10 as described above. That is, the acceleration Gx is a value obtained by adding the differential value vx ′ and the gravitational acceleration component g · sin β. Therefore, Formula (6) is synonymous with the numerator of Formula (2) above.

  As a variable of the first parameter Φx, a gravitational acceleration component based on a differential value vx ′ of the vehicle speed vx and a road gradient on which the vehicle 10 is traveling, instead of the acceleration Gx, as shown in the above equation (2). g · sin β may also be used. In this case, instead of the acceleration sensor 27, a vehicle speed sensor 26 and a gradient sensor that acquires a road surface gradient on which the vehicle 10 is traveling and a functional element that calculates the road surface gradient may be used.

  Next, the engine torque calculation block 32c calculates an actual engine torque Te output from the engine 14 based on the fuel injection amount Qx and the engine rotation speed Nx.

  As illustrated in FIG. 5, the engine torque Te output from the engine 14 has a positive relationship with respect to each of the engine rotation speed Nx and the fuel injection amount Qx, the engine rotation speed Nx is high, and the fuel injection amount Qx. The larger the is, the larger it becomes. This map data is obtained in advance by experiments and tests and stored in the engine torque calculation block 32c, which is a data block.

  In this embodiment, the engine torque Te is calculated from the relationship between the engine speed Nx and the fuel injection amount Qx. However, the accelerator opening Ax acquired by the accelerator opening sensor 22 may be used instead of the fuel injection amount Qx. Other acquisition methods may be used.

Next, the second parameter calculation block 32b calculates the drive torque Tw transmitted to the drive wheels 19 using the following formula (7). In Equation (7), if indicates the gear ratio of the differential gear 18 and η indicates the transmission efficiency that varies depending on the gear ratio. The drive torque Tw may be obtained using a torque sensor or may be obtained by other methods.

  Next, the second parameter calculation block 32b calculates the rotation portion equivalent mass Δmx by the lookup table block 32d.

  The rotating portion equivalent mass Δmx is a value determined according to the gear ratio ix which is a variable. The look-up table block 32d is set with a plurality of rotation portion equivalent masses Δmx for each gear ratio ix, and selects one corresponding to the gear ratio ix. The rotating part equivalent mass Δmx may be calculated from the relationship between the vehicle weight when empty, the gear ratio ix, and a predetermined coefficient.

Next, the second parameter calculation block 32b calculates a second parameter Φy shown in the following mathematical formula (8) by each block.

  In this embodiment, the second parameter Φy is expressed by the above mathematical formula (8), but the friction torque Tf acting on the engine 14, the clutch 15, the transmission 16, the differential gear 18, and the like may be taken into consideration. In this case, the value obtained by subtracting the friction torque Tf from the drive torque Tw may be divided by the wheel diameter rw of the drive wheel 19. Considering the friction torque Tf, it is advantageous for improving the estimation accuracy.

  When each parameter is acquired as described above, the vehicle weight calculation unit 31 calculates the temporary estimated value Mx (k) by the function of the temporary estimating unit 35 (S120). Specifically, the temporary estimation unit 35 divides the second parameter Φy by the first parameter Φx by using only the equation of motion of the vehicle 10 in the front-rear direction shown in the mathematical formula (2), and calculates the temporary estimated value Mx (k ) Is temporarily estimated.

  Next, the vehicle weight calculation unit 31 determines whether or not the temporary estimated value Mx (k) is within the preset range Ra by the function of the check block 34a of the maintenance unit 34 (S130).

  As illustrated in FIG. 6, the range Ra indicates a region between the minimum value Mx (min) and the maximum value Mx (max), and an error is caused by the influence of noise or the like of the temporary estimated value Mx (k). It is set to a range where the size can be determined. In FIG. 6, the slope indicates weight and m indicates the true value.

  The minimum value Mx (min) is set based on the minimum weight min of the vehicle 10, and the maximum value Mx (max) is set based on the maximum weight max. In this embodiment, the minimum weight min indicates the vehicle weight that is the weight of the vehicle 10 when it is empty, that is, the weight of the main body (chassis 11, cab 12, body 13) of the vehicle 10 (including the engine 14 and the like). ing. The vehicle weight may include fuel, oil, cooling water, spare tires, tools, and the like. The maximum weight max indicates the total weight of the vehicle, that is, the total weight of the maximum boarding capacity and the weight of the load with the maximum loading capacity, in addition to the vehicle weight.

  A preset difference Δma is provided between the minimum value Mx (min) and the minimum weight min and the maximum value Mx (max) and the maximum weight max, and the range Ra is set between the minimum weight min and the maximum weight max. It is larger than the range between. That is, the minimum value Mx (min) is a value obtained by subtracting the difference Δma from the minimum weight min, and the maximum value Mx (max) is a value obtained by adding the difference Δma to the maximum weight max.

  The difference Δma is set in advance based on an estimation error between the output value mx and the true value m of the vehicle weight estimation device 30 through experiments and tests. As described above, in the estimation calculation of this embodiment, the estimated value mx (k) can be quickly converged to the true value m by using the RLS algorithm. On the other hand, an estimation error may occur until convergence. The difference Δma is set to the average value of the estimation errors.

If it is determined in this step that the temporary estimated value Mx (k) is within the range Ra, the vehicle weight calculation unit 31 estimates the first parameter Φx and the second parameter Φy by the function of the assertion block 34b of the maintenance unit 34. Input to the unit 33. Specifically, in the assertion block 34b, when the temporary estimated value Mx (k) falls within the range Ra, “1” that is a binary signal transmitted from the check block 34a is input, and the first parameter Φx and The second parameter Φy is output to the estimation unit 33.

  At the same time, the vehicle weight calculation unit 31 permits the output of the estimated value mx (k) by the function of the switch block 34 c of the maintenance unit 34. Specifically, in the switch block 34c, “1” that is a binary signal transmitted from the check block 34a is input, and the switch is switched to the output of the estimated value mx (k).

  Next, the vehicle weight calculation unit 31 estimates the estimated value mx (k) by the above-described estimation method of the estimation unit 33 (S140). Next, the vehicle weight calculator 31 outputs the input estimated value mx (k) as an output value mx (S150). Then return to the start.

  On the other hand, if it is determined in the above step that the temporary estimated value Mx (k) is out of the range Ra, the vehicle weight calculation unit 31 uses the function of the assertion block 34b of the maintenance unit 34 to set the first parameter Φx and the second parameter Φy. Prohibit output. Specifically, in the assertion block 34b, when the temporary estimated value Mx (k) is out of the range Ra, a binary signal “0” transmitted from the check block 34a is input, and the first parameter Φx and The output of the second parameter Φy is prohibited.

  That is, since the vehicle weight calculation unit 31 is prohibited from inputting the first parameter Φx and the second parameter Φy to the estimation unit 33, the previous value mx is assumed as the in-range estimated value in which the sampling period ts is delayed by one. (K-1) is output as an output value mx (S160). Then go back to the start.

  According to the estimation method described above, when the temporary estimated value Mx (k) is out of the range Ra, the temporary estimated value Mx () within the range Ra before the temporary estimated value Mx (k) is temporarily estimated. The previous value mx (k−1) is output as the weight of the vehicle 10 as the in-range estimated value estimated based on the parameter when k−1) is temporarily estimated.

  As illustrated in FIG. 6, the temporary estimated values Mx (2), Mx (5), and Mx (6) are out of the range Ra. At this time, as the estimated values mx (2), mx (5), and mx (6), the previous values mx (1) and mx (4) are output as output values mx as in-range estimated values. These temporary estimated values Mx (2), Mx (5), and Mx (6) are considered to have a large parameter error, and are excluded from the smoothing process in the estimation unit 33.

  As described above, when the temporary estimated value Mx (k) temporarily estimated based on the parameters is out of the range Ra, smoothing is performed when the temporary estimated value Mx (k−1) falls within the range Ra as the weight of the vehicle 10. The previous value mx (k−1) is output as the in-range estimated value estimated using the processing. That is, when it can be considered that the error of the parameter is large, the parameter is excluded, and the previous value mx (k−1) is output, so that a constant value can be held as the weight of the vehicle 10. This is advantageous for reducing the estimation error due to the influence of noise or the like and for increasing the convergence speed to the true value m by eliminating the error in the smoothing process, and estimating the weight of the vehicle 10 quickly and with high accuracy. Can do.

  Moreover, the calculation load of the vehicle weight calculating part 31 can be restrained low by using the system which compares temporary estimated value Mx (k) and the range Ra. Furthermore, it is advantageous for ensuring responsiveness as compared with a method in which a noise removal process such as a low-pass filter is performed on parameters that change variously depending on the type of vehicle 10 and the running state.

  In particular, in this embodiment, the check block 34a and the assertion block 34b of the maintaining unit 34 function as an assertion check for the estimation calculation process of the estimated value mx (k). That is, when the temporary estimated value Mx (k) is out of the range Ra, the first parameter Φx and the second parameter Φy are regarded as negated and are not input to the estimating unit 33. On the other hand, when the temporary estimated value Mx (k) falls within the range Ra, the first parameter Φx and the second parameter Φy are regarded as asserted and input to the estimating unit 33. As a result, when each parameter is valid, the estimation unit 33 estimates the estimated value mx (k), and when each parameter is invalid, estimation by the estimation unit 33 can be prohibited.

  As described above, when the smoothing process is performed using the RLS algorithm based on the first parameter Φx, the second parameter Φy, and the previous value mx (k−1), the estimated value mx (k ) Is advantageous in reducing the estimation error when performing the smoothing process, and the weight of the vehicle 10 can be estimated with high accuracy. Further, it is advantageous for increasing the convergence speed to the true value m when the estimated value mx (k) is smoothed, and the estimated value mx (k) can be quickly converged to the true value m.

  In this embodiment, since the range Ra is set based on the minimum weight min and the maximum weight max of the vehicle 10, the temporary estimated value Mx (k) is a value that is not practically possible as the weight of the vehicle 10. It can be determined whether or not. This is advantageous for reducing the estimation error when the error increases due to the influence of noise or the like.

  Further, by setting the range Ra based on the minimum weight min and the maximum weight max that are set in advance in the vehicle 10, it is possible to simplify the setting of the threshold value, and based on the state of the vehicle 10, the traveling state, and the like. There is no need to tune.

  When the true value m exists in the vicinity of the minimum weight min or the maximum weight max, the estimated value mx (( The update frequency of k) may be reduced. When the true value m is the minimum weight min or the maximum weight max, the measured value has a true value m ± error. Therefore, in this embodiment, by providing the difference Δma based on the estimation error, the range Ra is expanded to a range lighter than the minimum weight min and heavier than the maximum weight max. This is advantageous for suppressing a decrease in the update frequency of the estimated value mx (k) when the true value m exists in the vicinity of the minimum weight min or the maximum weight max, and the estimation accuracy can be improved.

  Thus, the range Ra may be a range in which the temporary estimated value Mx (k) having a large error due to the influence of noise or the like can be removed. For the range Ra, the maximum value Mx (max) may be heavier or the minimum value Mx (min) may be heavier depending on the vehicle 10, for example, the minimum value Mx (min) and the minimum value Mx (min) The maximum value Mx (max) may be obtained.

  In this embodiment, the temporary estimated value Mx (k) is calculated using only the equation of motion in the front-rear direction of the vehicle 10 expressed by the above formula (2) based on the parameter, and the estimated value mx (k) is smoothed. Calculate using. Thereby, an assertion check can be simply performed for the smoothing process.

Thus, the temporary estimated value Mx (k) is only required to be calculated by a simple method, and is not limited to the method using only the equation of motion in the front-rear direction of the vehicle 10 represented by the above formula (2). For example, when the vehicle 10 is equipped with an air suspension, a method based on the vertical change of the vehicle 10 may be used. Further, a method based on torque input to the transmission before and after the shift and the amount of change in the rotational speed output from the transmission may be used. Further, a value obtained by adding the weight of the vehicle 13 at the time of empty to the value acquired by the weight sensor such as the load cell as the weight of the body 13 according to the change in the loading amount may be used as the temporary estimated value Mx.

  The vehicle weight estimation apparatus 30 according to the second embodiment illustrated in FIGS. 7 and 8 is different from the first embodiment in that an error check is performed by the maintenance unit 34.

  As illustrated in FIG. 7, the maintaining unit 34 includes comparison blocks 34c and 34d, a counter block 34e, and a display block 34f.

  The comparison block 34c is a functional element that checks whether or not the binary signal output from the check block 34a is “0”. The comparison block 34d is a functional element that checks whether or not the binary signal output from the check block 34a is “1”.

  The counter block 34e is a functional element that receives the binary signal output from the comparison block 34c and outputs an error signal based on the count number of the binary signal. The counter block 34e is a functional element whose count number is reset by the binary signal output from the comparison block 34d.

  Specifically, the counter block 34e counts when the binary signal output from the comparison block 34c is “1”, that is, when the binary signal output from the check block 34a is “0”. The counter block 34e is set with an upper limit value, and outputs an error signal when the count number reaches the upper limit value. Accordingly, the counter block 34e outputs an error signal when the number of times that the temporary estimated value mx (k-1) has deviated from the range Ra has reached the upper limit value continuously. The upper limit value is set to a value that can determine the failure of each sensor and the parameter acquisition unit 32, and is obtained in advance through experiments and tests.

  As illustrated in FIG. 8, the temporary estimated value Mx (5) to the temporary estimated value Mx (8) are continuously out of the range Ra. When the upper limit value of the counter block 34e is set to “4”, if it is found that the temporary estimated value Mx (8) is out of the range Ra, an error is displayed on the display block 34f.

  As described above, in this embodiment, an error is displayed when the number of times that the temporary estimated value Mx (k) has deviated from the range Ra is continuously generated, so that a failure of each sensor can be determined. This is advantageous for early detection of failure of each sensor, and early repair is possible by notifying the driver of the error.

  In the above-described embodiment, the vehicle 10 is described as an example of a large vehicle such as a truck. However, the vehicle weight estimation device 30 of the present invention can be applied to a bus, a normal vehicle, and a tow vehicle (tractor). The type is not limited. That is, as a power source of the vehicle 10, a motor generator can be exemplified in addition to the engine 14.

  In the embodiment described above, the estimation unit 33 does not define a condition for estimating the estimated value mx (k). However, the estimated value mx (k) may be estimated when the following condition is satisfied. . An example of the condition is when the differential value vx ′ of the vehicle speed vx is equal to or greater than a preset threshold value and the drive torque Tw is equal to or greater than the threshold value when the clutch 15 is completely engaged in a state where the brake is not activated. . In addition to comparing the temporary estimated value Mx (k) with the range Ra, the estimated value mx (k) is estimated by the RLS algorithm only when such a condition is satisfied, thereby improving the estimation accuracy. Can do.

In the above-described embodiment, the example in which the RLS algorithm is used as the estimation calculation of the estimated value mx (k) has been described. However, the estimation unit 33 only needs to be able to estimate the weight of the vehicle 10, and the estimation calculation is not limited thereto. . For example, instead of the RLS algorithm, an LMS algorithm or an NLMS algorithm may be used as an adaptive algorithm. Moreover, you may perform the averaging process which outputs the average value of all the estimated values mx (0)-estimated values mx (k) estimated. The averaging process is a specific pattern of the smoothing process. In any case, similarly to this embodiment, when the temporary estimated value Mx (k) is out of the range Ra, it is desirable to assume that the estimation error is large and prohibit the estimation.

  Moreover, although embodiment mentioned above demonstrated the example in which the vehicle weight estimation apparatus 30 was comprised from the vehicle weight calculating part 31, each sensor, etc., this invention is not limited to this. For example, the vehicle weight estimation device 30 may be configured by one sensor that functions as a parameter acquisition unit and a temporary estimation unit, and hardware that functions as an estimation unit and a maintenance unit.

  The maintenance unit 34 may be configured to prohibit the estimation unit 33 from outputting the estimated value mx (k) estimated by the estimation unit 33 in addition to prohibiting the estimation by the estimation unit 33. For example, when the binary signal from the check block 34a is “1”, the estimated value mx (k) is output, whereas when the binary signal is “0”, the previous value mx (k−1) is output. You may comprise so that it may output. However, when configured in this way, estimation in the estimation unit 33 is not prohibited as compared with the above-described embodiment.

DESCRIPTION OF SYMBOLS 10 Vehicle 24 Position sensor 25 Rotational speed sensor 26 Vehicle speed sensor 27 Acceleration sensor 28 Control part 30 Vehicle weight estimation apparatus 31 Vehicle weight calculating part 32 Parameter acquisition part 33 Estimation part 34 Maintenance part 35 Temporary estimation part (PHI) x First parameter (PHI) y Second parameter Mx (k) Temporary estimated value mx (k) Estimated value mx (k-1) Previous value (estimated value within range)

Claims (7)

Parameter acquisition means for acquiring parameters that change while the vehicle is running;
A parameter obtained by the parameter obtaining means is input, temporary estimation means for outputting a temporary estimated value obtained by temporarily estimating the weight of the vehicle based on the parameter; and
Estimating means for outputting the estimated value of the weight of the vehicle estimated using a smoothing process based on the parameter and the previous value estimated before acquiring the parameter and the parameter;
When the temporary estimated value is input and the temporary estimated value is out of the preset range, the temporary estimate that falls within the range is estimated as the weight of the vehicle before the temporary estimated value is temporarily estimated. A vehicle weight estimation device comprising: a maintaining unit that maintains an in-range estimated value estimated by the estimating unit based on the parameter when the value is temporarily estimated.   2. The configuration according to claim 1, wherein when the temporary estimated value is out of the range by the maintaining unit, the estimation by the estimating unit based on the parameter when the temporary estimated value is temporarily estimated is prohibited. Vehicle weight estimation device.   The vehicle weight estimation apparatus according to claim 1 or 2, wherein the range is set based on a minimum weight and a maximum weight of the vehicle. For each predetermined sampling period, the temporary estimation value is output by the temporary estimation unit based on the parameter acquired by the parameter acquisition unit,
When the temporary estimated value falls within the range by the maintaining means, the estimated value estimated by the estimating means is output based on the parameter and the previous value estimated immediately before in the sampling period. ,
4. The configuration according to claim 1, wherein when the temporary estimated value is out of the range, the previous value estimated immediately before in the sampling period is output as the in-range estimated value. 5. Vehicle weight estimation device.   The vehicle weight according to any one of claims 1 to 4, wherein the temporary estimation unit is configured to temporarily estimate the temporary estimated value using only the equation of motion in the front-rear direction of the vehicle based on the parameter. Estimating device.   Based on the parameter and the previous value, the estimating means considers the equation of motion in the longitudinal direction of the vehicle as a transfer function, and estimates the estimated value using an adaptive algorithm as the smoothing process. The vehicle weight estimation apparatus according to any one of claims 1 to 5. Get parameters that change while the vehicle is running,
Based on the parameter, temporarily estimate a temporary estimated value as the weight of the vehicle,
When the temporary estimated value deviates from the preset range, the parameter when the temporary estimated value within the range is temporarily estimated as the weight of the vehicle before the temporary estimated value is temporarily estimated. And an in-range estimated value estimated using a smoothing process based on a previous value estimated before acquiring the parameter, and a vehicle weight estimating method.