Abnormal noise generation risk prediction device, abnormal noise generation risk prediction method and damper

Abstract

To predict generation of abnormal noise.SOLUTION: An abnormal noise generation risk prediction device 20 includes: an acquisition part 21 for acquiring a vehicle behavior prediction value which is a prediction of a behavior of a vehicle, a road surface situation which is an estimation of a situation of a road surface where the vehicle travels, and a fluid temperature which is a temperature of a working fluid inside a damper 1; a damper input prediction part 25 (prediction input calculation part) for calculating a prediction input which is an input to the damper 1 predicted from the vehicle behavior prediction value and the road surface situation; an abnormal noise distribution output part 27 for outputting abnormal sound distribution which is abnormal noise generation probability distribution based on a fluid temperature according to the pattern of the input to the damper 1; and an abnormal noise generation risk prediction part 28 for predicting an abnormal noise generation risk which is an actual abnormal noise generation risk generated by the damper 1 from a relation with the prediction input for the abnormal noise distribution.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present disclosure relates to an abnormal noise occurrence risk prediction device that predicts abnormal noise generated by a damper mounted on a vehicle, an abnormal noise occurrence risk prediction method, and a damper. [Background technology]

[0002] Patent Document 1 discloses a method of detecting a force input to a wheel and predicting interior noise (abnormal noise) using the detected force and the damping characteristics of a semi-active damper. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2005-234040 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the abnormal noise predicted by the conventional technology is based on the input value of the force currently being generated, and the technical viewpoint is to "reduce the abnormal noise currently being generated." Therefore, it does not suppress the occurrence of the abnormal noise itself in advance.

[0005] An object of one embodiment of the present invention is to provide an abnormal noise occurrence risk prediction device, an abnormal noise occurrence risk prediction method, and a damper that are capable of predicting the occurrence of abnormal noise. [Means for solving the problem]

[0006] One embodiment of the present invention is an abnormal sound occurrence risk prediction device that predicts abnormal sound generated by a damper attached to a vehicle, and is characterized by having an acquisition unit that acquires a vehicle behavior prediction value that is a prediction of the behavior of the vehicle, road surface conditions that are an estimate of the condition of the road surface on which the vehicle is traveling, and a fluid temperature that is the temperature of the working fluid inside the damper, a predicted input calculation unit that calculates a predicted input that is an input to the damper predicted from the vehicle behavior prediction value and the road surface conditions, an abnormal sound distribution output unit that outputs an abnormal sound occurrence probability distribution of the abnormal sound based on the fluid temperature according to a pattern of the input to the damper, and an abnormal sound occurrence risk prediction unit that predicts the abnormal sound occurrence risk, which is the actual risk of occurrence of the abnormal sound generated by the damper, from the relationship between the predicted input and the abnormal sound distribution.

[0007] One embodiment of the present invention is an abnormal noise occurrence risk prediction method for predicting abnormal noise generated by a damper attached to a vehicle, the method comprising: an acquisition step for acquiring a vehicle behavior predicted value which is a prediction of the behavior of the vehicle, road surface conditions which are an estimate of the conditions of the road surface on which the vehicle is traveling, and a fluid temperature which is the temperature of the working fluid inside the damper; a predicted input calculation step for calculating a predicted input which is an input to the damper predicted from the vehicle behavior predicted value and the road surface conditions after the acquisition step; an abnormal noise distribution output step for outputting an abnormal noise distribution which is an abnormal noise occurrence probability distribution based on the fluid temperature according to each pattern of input to the damper; and an abnormal noise occurrence risk prediction step for predicting an abnormal noise occurrence risk which is the actual risk of occurrence of the abnormal noise generated by the damper from a relationship between the predicted input and the abnormal noise distribution after the predicted input calculation step and the abnormal noise distribution output step.

[0008] One embodiment of the present invention is a damper that is mounted on a vehicle and has an adjustable damping force, the damper having a bottomed cylindrical cylinder filled with working fluid, a piston that divides the inside of the cylinder into a first chamber and a second chamber, a damping force adjustment unit that adjusts the damping force by adjusting the flow of the working fluid, and a control unit that controls an adjustment amount of the damping force, the control unit having an acquisition unit that acquires a vehicle behavior predicted value that is a prediction of a state of the behavior of the vehicle, a road surface condition that is an estimate of the condition of the road surface on which the vehicle travels, and a fluid temperature that is the temperature of the working fluid, and The damping force adjusting unit has a predicted input calculation unit that calculates a predicted input, which is an input to the damper predicted from the road surface conditions; an abnormal sound distribution output unit that outputs an abnormal sound distribution, which is a distribution of the probability of abnormal sound occurrence based on the fluid temperature according to each pattern of the input to the damper; an abnormal sound occurrence risk prediction unit that predicts the abnormal sound occurrence risk, which is the actual risk of abnormal sound occurring from the damper, based on the relationship between the predicted input and the abnormal sound distribution; and a control amount calculation unit that calculates a control amount of the damping force adjustment unit using at least the abnormal sound occurrence risk as an input value so as to reduce the abnormal sound occurrence risk. Effect of the Invention

[0009] According to one embodiment of the present invention, it is possible to provide a damper that prevents the occurrence of abnormal noise itself. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating a damper mounted on an automobile. [Diagram 2] FIG. 2 is a block diagram showing a configuration of a damper in FIG. [Diagram 3] FIG. 3 is a block diagram showing an abnormal sound occurrence risk prediction device in FIG. 2. [Figure 4] 4 is a block diagram showing an example of a vehicle behavior predicting unit in FIG. 3. [Diagram 5] 5 is a characteristic diagram showing a change over time in a value obtained by dividing the acceleration in the operating direction of the damper by the vehicle speed. FIG. [Figure 6] FIG. 4 is an explanatory diagram showing an example of the relationship between driving data and the occurrence of abnormal noise. [Figure 7] FIG. 11 is an explanatory diagram showing an example of an abnormal sound occurrence probability distribution. [Figure 8] 11 is an explanatory diagram showing an example of calculating an abnormal noise occurrence risk based on an overlap area between a frequency component output by a damper input prediction unit and an abnormal noise occurrence probability distribution; FIG. [Figure 9] 11 is a flowchart showing an abnormal noise occurrence risk prediction process. [Figure 10] FIG. 11 is an explanatory diagram showing an example of a prediction of the magnitude of a 10 Hz frequency component of a displacement input to a damper. [Figure 11] FIG. 11 is an explanatory diagram showing an example of an abnormal noise occurrence probability distribution with respect to the magnitude of a 10 Hz frequency component. [Figure 12] FIG. 11 is an explanatory diagram showing an example of calculating the risk of abnormal sound occurrence based on the overlap area between the predicted distribution of the magnitude of the 10 Hz frequency component and the abnormal sound occurrence probability distribution. [Figure 13] 1 is a characteristic diagram showing the predicted risk of abnormal noise occurrence and changes in the control value at that time. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An abnormal sound generation risk prediction device and an abnormal sound generation risk prediction method according to an embodiment of the present invention will be described in detail below with reference to the accompanying drawings, taking as an example a case in which the abnormal sound generation risk prediction device and the abnormal sound generation risk prediction method are applied to a four-wheeled automobile.

[0012] 1 and 2 show a damper 1. The damper 1 has a cylindrical cylinder 2 with a bottom that is filled with working fluid, a piston 3 that divides the inside of the cylinder 2 into a first chamber and a second chamber, a damping force adjustment unit 4 that adjusts the damping force by adjusting the flow of the working fluid, and a controller 11 that controls the adjustment amount of the damping force.

[0013] The cylinder 2 and the piston 3 constitute, for example, a hydraulic shock absorber. The cylinder 2, the piston 3 and the damping force adjustment section 4 constitute a damping force adjustable shock absorber (hereinafter referred to as a variable damper 5). The variable damper 5 is provided between a vehicle body B and a wheel W of the vehicle in a parallel relationship with a suspension spring 6 (hereinafter referred to as a spring 6). The variable damper 5 and the spring 6 constitute a suspension device 7. The suspension device 7 is provided between the vehicle body B and the wheel W of the vehicle.

[0014] 1 shows a case where one set of suspension devices 7 is provided between the vehicle body B and the wheels W. However, a total of four sets of suspension devices 7 are provided, for example, individually and independently between the four wheels W and the vehicle body B, and only one of these sets is shown diagrammatically in FIG.

[0015] Here, the variable damper 5 of the suspension device 7 is configured using a hydraulic shock absorber of a damping force adjustment type interposed between the vehicle body B and the wheels W. This variable damper 5 is provided with a damping force adjustment unit 4 (variable damping force actuator) consisting of a damping force adjustment valve or the like in order to continuously adjust the characteristics of the generated damping force (i.e., damping force characteristics) from hard characteristics (hard characteristics) to soft characteristics (soft characteristics). Note that the damping force adjustment unit 4 does not necessarily have to be configured to continuously adjust the damping force characteristics, and may be capable of adjusting the damping force in a plurality of stages, for example, two or more stages. Also, the variable damper 5 may be of a pressure control type or a flow rate control type. The variable damper 5 may be of a type that controls viscosity, such as a magnetorheological fluid or an electrorheological fluid.

[0016] The controller 11 constitutes a control unit that controls the adjustment amount of the damping force. The controller 11 is constituted by, for example, a microcomputer. The controller 11 is connected to, for example, a CAN10 (Controller Area Network), which is a line network required for data communication. The controller 11 acquires various measurement data related to the vehicle behavior, road surface conditions, and damper oil temperature through the CAN10. At this time, the measurement data includes, for example, the vehicle speed, accelerator operation amount, brake operation amount, steering operation amount, gear position, vehicle vertical acceleration, damper vertical acceleration, vehicle pitch angle, and air temperature. Therefore, these measurement data are input to the controller 11. The output side of the controller 11 is connected to the damping force adjustment unit 4 of the variable damper 5.

[0017] The controller 11 also has a storage unit 11A including a ROM, a RAM, a non-volatile memory, etc. The storage unit 11A of the controller 11 stores various programs, information (vehicle information), data, etc. for controlling the variable damper 5. The storage unit 11A also stores a program for processing an abnormal noise occurrence risk prediction shown in FIG. 9. The controller 11 estimates the sprung velocity and relative velocity (piston velocity) as vehicle state quantities based on information such as vehicle behavior. The controller 11 determines the force to be generated by the variable damper 5 (force generating mechanism) of the suspension device 7 based on the estimated vehicle state quantities, and outputs the control signal (command current) to the damping force adjusting unit 4 of the suspension device 7.

[0018] As shown in FIG. 2, the controller 11 includes an abnormal sound generation risk prediction device 20 and a control amount calculation unit 12. The abnormal sound generation risk prediction device 20 predicts abnormal sound generated by a damper mounted on a vehicle by executing a program for abnormal sound generation risk prediction processing shown in FIG. 9. The control amount calculation unit 12 calculates the control amount of the damping force adjustment unit 4 so as to reduce the abnormal sound generation risk, using at least the abnormal sound generation risk as an input value. Specifically, the abnormal sound generation risk prediction device 20 calculates the abnormal sound generation risk based on measurement data during vehicle travel. When the value of the abnormal sound generation risk is equal to or greater than a predetermined threshold, the control amount calculation unit 12 calculates the control amount of the variable damper 5 (semi-ac damper) so as to reduce the abnormal sound generation risk. On the other hand, when the value of the abnormal sound generation risk is smaller than the predetermined threshold, the control amount calculation unit 12 calculates the control amount of the variable damper 5 so as to generate a damping force according to the vehicle state amount. In this case, the control amount calculation unit 12 may acquire data related to vehicle behavior, etc., and calculate the control amount of the damping force adjustment unit 4 for improving ride comfort and stabilizing vehicle behavior based on this data. The damping force adjuster 4 mounted on the variable damper 5 adjusts the flow rate of the working fluid based on the calculated control amount, thereby varying the damping force of the variable damper 5 .

[0019] Next, a specific configuration of the abnormal noise occurrence risk prediction device 20 will be described with reference to FIG.

[0020] 3 shows the overall configuration of the abnormal sound generation risk prediction device 20. The abnormal sound generation risk prediction device 20 acquires various data sensed from time to time, such as the vehicle speed while the vehicle is traveling, the vehicle vertical acceleration, the amount of driving operation of the accelerator and brake pedals, etc., the acceleration in the operating direction of the damper 1, and air temperature. The abnormal sound generation risk prediction device 20 uses these data to predict the vehicle behavior and road surface conditions, and estimates the oil temperature of the damper. The abnormal sound generation risk prediction device 20 includes an acquisition unit 21, a damper input prediction unit 25, and an abnormal sound generation risk calculation unit 26. The acquisition unit 21 is composed of a vehicle behavior prediction unit 22, a road surface condition prediction unit 23, and a temperature estimation unit 24.

[0021] The vehicle behavior prediction unit 22 predicts a change in the vehicle speed several seconds later based on the current vehicle speed and information on the driving operation amount. At this time, the vehicle speed several seconds later is the vehicle behavior prediction value. FIG. 4 shows an example of a prediction method of the vehicle behavior prediction unit 22. The vehicle behavior prediction unit 22 shown in FIG. 4 is illustrated as being applied to an electric vehicle. In this case, the vehicle behavior prediction unit 22 includes a motor torque prediction unit 22A, a power transmission prediction unit 22B, a propulsive force prediction unit 22C, a vehicle acceleration calculation unit 22D, and a vehicle speed prediction unit 22E. The motor torque prediction unit 22A predicts the motor torque from the depression amount of the accelerator pedal, taking into account the inverter characteristics, the torque characteristics of the motor, and the like. The power transmission prediction unit 22B predicts the power transmitted from the motor torque to the tires, taking into account the gear ratio, the inertia of the tires, and the like. The propulsive force prediction unit 22C calculates the direction of the tires from the steering angle, and predicts the forward propulsive force from the power output from the power transmission prediction unit 22B, taking into account the friction coefficient between the tires and the road surface, the direction of the tires, and the like. The vehicle acceleration calculation unit 22D calculates the vehicle acceleration in the traveling direction from the predicted forward thrust force and the pitch angle of the vehicle using a physical model that takes into account the effects of the vehicle weight and road surface gradient. The vehicle speed prediction unit 22E predicts the vehicle speed several seconds later by adding the vehicle speed based on the calculated vehicle acceleration to the current vehicle speed. The vehicle behavior prediction unit 22 is not limited to being applied to electric vehicles, but may also be applied to vehicles powered by various internal combustion engines (gasoline engines, diesel engines, etc.).

[0022] The road surface condition prediction unit 23 divides the acceleration in the operating direction of the damper 1 (damper acceleration) by the current vehicle speed, and stores the time series data of this divided value. The time series data of the divided value obtained by dividing the acceleration by the vehicle speed corresponds to the time change of the input due to the unevenness of the road surface.

[0023] The road surface condition prediction unit 23 applies an autoregressive model to the time series data of the divided values ​​to estimate the model parameters (regression coefficients and the magnitude of variance of white noise). Using the estimated autoregressive model, it extrapolates in the time axis direction and predicts the change in the road surface input several seconds later. At this time, the predicted road surface input is predicted as a probability distribution with variance.

[0024] FIG. 5 shows an example of the prediction result of the road surface condition prediction unit 23. The road surface condition prediction unit 23 obtains the damper displacement by integrating the acceleration in the operating direction of the damper 1, and monitors the value obtained by dividing it by the current vehicle speed from moment to moment. The road surface condition prediction unit 23 regards the values ​​from a certain past time ti-N to the current time ti as time series data of the road surface input. The road surface condition prediction unit 23 applies an autoregressive model to this time series data, and estimates the parameters of the regression model. This enables the road surface condition prediction unit 23 to predict the road surface input several seconds later (ti+1). At this time, since the prediction is made with temporal uncertainty, the value several seconds later (ti+1) is calculated by subtracting the average ti+1 Let the standard deviation be σ ti+1 This is the probability distribution.

[0025] The temperature estimation unit 24 calculates the amount of heat applied to the damper 1 from the history of the speed input of the damper 1, and estimates the temperature change of the damper 1 with the air temperature as the boundary condition.

[0026] The damper input prediction unit 25 is a predicted input calculation unit that calculates a predicted input, which is an input to the damper 1 predicted from the vehicle behavior prediction value and the road surface condition. Specifically, the damper input prediction unit 25 calculates a displacement input in the operating direction of the damper 1 by multiplying the predicted road surface condition by the predicted vehicle speed. Since the prediction result of the road surface condition has a probability distribution, the damper input prediction unit 25 calculates the displacement input using values ​​of several points on the distribution. The damper input prediction unit 25 creates several patterns of time series data including predictions by combining the prediction results of several points with time series data of past displacement inputs. By performing a short-time FFT on each time series data, multiple patterns of prediction results of frequency components are obtained. The damper input prediction unit 25 assumes that the prediction values ​​of several points at a predetermined feature frequency (for example, 5 Hz, 7 Hz, 10 Hz, etc.) follow a normal distribution, estimates parameters of the distribution, and outputs the distribution parameters.

[0027] The feature frequency is an input frequency for any driving pattern. There are multiple feature frequencies for each driving pattern. Therefore, the distribution parameter is a feature of the abnormal noise frequency distribution of the damper 1 at a certain input frequency and a certain fluid temperature.

[0028] As a result, the damper input prediction unit 25 models the time series data (time history) of the input to the damper 1 on the road surface corresponding to the vehicle speed using an autoregressive moving average model, and calculates the upper or lower limit of the average and variance of the prediction of the input to the damper 1 as a predicted input. As a result, the damper input prediction unit 25 outputs a distribution (probability distribution) of the magnitude of a predetermined frequency component (feature frequency component).

[0029] The abnormal sound occurrence risk calculation section 26 includes an abnormal sound occurrence distribution output section 27 and an abnormal sound occurrence risk prediction section 28.

[0030] First, the abnormal sound distribution output unit 27 prepares in advance an abnormal sound occurrence probability distribution for the magnitude of frequency components at a certain frequency among the displacement inputs input to the damper 1. The abnormal sound distribution output unit 27 has a plurality of abnormal sound occurrence probability distributions according to the fluid temperature of the damper 1. The damper oil temperature estimated by the temperature estimation unit 24 is input to the abnormal sound distribution output unit 27. The abnormal sound distribution output unit 27 outputs an abnormal sound distribution which is an abnormal sound occurrence probability distribution based on the fluid temperature (damper oil temperature) according to the pattern of the input to the damper 1.

[0031] That is, the abnormal sound distribution output unit 27 has a map of abnormal sound occurrence probability distribution in which the probability of abnormal sound occurrence corresponding to each pattern of the input to the damper 1 in each predetermined range of damper oil temperature corresponds as an abnormal sound distribution in the form of an average and an upper or lower limit of the variance. The abnormal sound distribution output unit 27 outputs the abnormal sound distribution of a pattern corresponding to the oil temperature and the input to the damper 1 from the map.

[0032] Figure 6 is an example of driving data that is the basis for creating an abnormal noise occurrence probability distribution. The graph shown in the upper part of Fig. 6 is a plot of the change in the magnitude of the frequency component at a certain frequency (10 Hz in this figure) obtained by performing a short-time FFT on the time-series data of the displacement input to the damper 1 while the vehicle is traveling. The graph shown in the lower part of Fig. 6 is time-series data recorded at the timing corresponding to the upper graph, with the state where abnormal noise occurs being marked as "1" and the state where it does not occur being marked as "0."

[0033] Figure 7 shows an example of the abnormal noise occurrence probability distribution estimated based on driving data. The probability distribution is estimated in the following way.

[0034] For each fixed time period Δt in the driving data, a data set is collected of the average value of the frequency component magnitude and the average value of the presence or absence of abnormal noise during that time. If the average value of the presence or absence of abnormal noise is 0.5 or greater, it is recorded as "1" (abnormal noise occurs), and if it is less than that, it is recorded as "0." The magnitude of the frequency component is divided into several intervals, and the number of cases in which abnormal noise occurrence is "1" in that interval is divided by the total number of cases to determine the probability of 0 to 1, and a histogram as shown in Figure 7 is created for the magnitude of the frequency component. From this histogram, the histogram density estimation method is used to determine the probability distribution of abnormal noise occurrence.

[0035] An abnormal sound occurrence risk prediction unit 28 calculates the overlap area between the abnormal sound occurrence probability distribution (abnormal sound distribution) output by the abnormal sound distribution output unit 27 and the distribution of the magnitude of the frequency components output by the damper input prediction unit 25, and outputs it as an abnormal sound occurrence risk. Here, the abnormal sound occurrence probability distribution with respect to the magnitude of the frequency components is created, for example, by the following method.

[0036] A driving simulation to predict the occurrence of abnormal noise is performed at a certain temperature with several road surface patterns and vehicle speeds, and a short-time FFT is performed on the time series data of the damper displacement input during driving to determine the time change in the magnitude of the component for each frequency. At the same time, based on the time series data that records the binary values ​​of whether or not abnormal noise occurs, the presence or absence of abnormal noise is tallied for the magnitude of the frequency components to calculate the probability of abnormal noise occurrence and create a histogram. A distribution function is obtained for this data using the histogram density estimation method, and this is used as the abnormal noise occurrence probability distribution function at a certain temperature. The above process is performed at several different oil temperatures to prepare an abnormal noise occurrence probability distribution for each temperature.

[0037] Fig. 8 shows an example of a method for calculating the abnormal noise occurrence risk by the abnormal noise occurrence risk prediction unit 28. The overlap area (shaded area in Fig. 8) between the abnormal noise occurrence probability distribution for the magnitude of specific frequency components prepared in advance for each temperature and the predicted distribution output by the damper input prediction unit is calculated. The abnormal noise occurrence risk prediction unit 28 outputs the calculated overlap area as the abnormal noise occurrence risk.

[0038] Next, the abnormal noise occurrence risk prediction process performed by the abnormal noise occurrence risk prediction device 20 will be described with reference to Fig. 3 and Fig. 9. Each step in the flow chart shown in Fig. 9 is represented by the letter "S" (for example, step 1 is represented by "S1").

[0039] S1 to S3 constitute the acquisition steps. In S1, the abnormal sound occurrence risk prediction device 20 acquires a vehicle behavior predicted value which is a prediction of the vehicle behavior. Specifically, the vehicle behavior prediction section 22 of the abnormal sound occurrence risk prediction device 20 predicts a change in vehicle speed several seconds later as the vehicle behavior predicted value from information on the current vehicle speed and driving operation amount.

[0040] In S2, the abnormal sound generation risk prediction device 20 acquires road surface conditions, which are estimates of the conditions of the road surface on which the vehicle is traveling. Specifically, the road surface condition prediction section 23 of the abnormal sound generation risk prediction device 20 divides the acceleration in the operation direction of the damper 1 by the current vehicle speed, and stores the time series data of this divided value as the road surface conditions.

[0041] In S3, the abnormal noise generation risk prediction device 20 acquires the fluid temperature, which is the temperature of the working fluid inside the damper 1. Specifically, the temperature estimation unit 24 of the abnormal noise generation risk prediction device 20 calculates the amount of heat applied to the damper 1 from the history of the speed input of the damper 1, and estimates the temperature change of the damper 1 with the air temperature as the boundary condition.

[0042] S4 is a predicted input calculation step. In S4, after the acquisition steps S1 to S3, the abnormal noise occurrence risk prediction device 20 calculates a predicted input, which is an input to the damper 1 predicted from the vehicle behavior predicted value and the road surface condition. Specifically, the damper input prediction unit 25 of the abnormal noise occurrence risk prediction device 20 multiplies the predicted road surface condition by the predicted vehicle speed to calculate a displacement input in the operating direction of the damper 1.

[0043] Since the predicted result of the road surface condition has a probability distribution, the damper input prediction unit 25 calculates the displacement input using the values ​​of several points on the distribution. The damper input prediction unit 25 creates several patterns of time series data including predictions by combining the predicted results of several points with time series data of past displacement inputs. By performing a short-time FFT on each time series data, multiple patterns of predicted results of frequency components are obtained. The damper input prediction unit 25 assumes that the predicted values ​​of several points at a certain frequency (for example, 5 Hz, 7 Hz, 10 Hz, etc.) follow a normal distribution, estimates the parameters of the distribution, and outputs the distribution parameters. As a result, the damper input prediction unit 25 outputs a distribution (probability distribution) of the magnitude of a predetermined frequency component (feature frequency component).

[0044] S5 is an abnormal sound distribution output step. In S5, the abnormal sound distribution output unit 27 outputs an abnormal sound distribution, which is an abnormal sound occurrence probability distribution based on the fluid temperature corresponding to each pattern of the input to the damper 1 after the acquisition steps S1 to S3. Specifically, the abnormal sound distribution output unit 27 has a map in which the abnormal sound occurrence probability corresponding to each pattern of the input to the damper 1 in each predetermined range of oil temperature corresponds to an abnormal sound distribution in the form of an average and an upper or lower limit of the variance. Therefore, the abnormal sound distribution output unit 27 outputs an abnormal sound distribution of a pattern corresponding to the oil temperature and the input to the damper 1 from the map.

[0045] S6 is an abnormal sound occurrence risk prediction step. In S6, after S4, which is the predicted input calculation step, and S5, which is the abnormal sound distribution output step, the abnormal sound occurrence risk prediction unit 28 predicts the abnormal sound occurrence risk, which is the actual occurrence risk of abnormal sound generated by the damper 1, from the relationship between the predicted input and the abnormal sound distribution. Specifically, the abnormal sound occurrence risk prediction unit 28 calculates the overlap area between the abnormal sound occurrence probability distribution output by the abnormal sound distribution output unit 27 and the distribution of the magnitude of the frequency components output by the damper input prediction unit 25, and outputs it as the abnormal sound occurrence risk.

[0046] Next, an example of calculation of the risk of abnormal noise occurrence by the abnormal noise occurrence risk prediction device 20 and an example of control of the variable damper 5 based on the calculation will be described with reference to Figs.

[0047] FIG. 10 is a prediction example of the magnitude of the 10 Hz frequency component of the displacement input to the damper 1 at ti+1. FIG. 11 is an example of the abnormal sound occurrence probability distribution with respect to the magnitude of the 10 Hz frequency component. At this time, the abnormal sound occurrence risk is calculated as the overlap (shaded area in FIG. 12) of the predicted distribution of the magnitude of the 10 Hz frequency component and the abnormal sound occurrence probability distribution, as shown in FIG. 12. In the calculation example shown in FIG. 12, the abnormal sound occurrence risk value is 0.35 (35%). Based on this abnormal sound occurrence risk value, damping force control of the variable damper 5, which is a semi-active damper, is performed. For example, when the abnormal sound occurrence risk exceeds a preset threshold value of 0.2, the control amount calculation unit 12 outputs a control command to suppress the abnormal sound occurrence.

[0048] FIG. 13 is an example of a graph showing the predicted risk of abnormal noise generation and the change in the control value at that time. The graph shown in the upper part of FIG. 13 shows the time change of the prediction result of the risk of abnormal noise generation. The graph shown in the lower part of FIG. 13 shows the current value of the solenoid coil of the variable damper 5 as the control value. The controller 11 can control the magnitude of the damping force of the variable damper 5 by controlling the current value of the solenoid coil of the damping force adjustment unit 4. In FIG. 13, when the risk of abnormal noise generation exceeding the threshold value of 0.2 is predicted, the controller 11 reduces the current value of the solenoid coil to reduce the change in the damping force of the variable damper 5. In this way, the controller 11 suppresses the generation of abnormal noise from the variable damper 5. In the example shown in FIG. 13, the control of fixing the current value of the solenoid coil to a predetermined value is performed as the control to suppress the generation of abnormal noise, but the control method to suppress the generation of abnormal noise is not limited to this. The threshold value of the risk of abnormal noise generation is not limited to 0.2, and may be smaller than 0.2 or larger than 0.2. The abnormal noise occurrence risk threshold is appropriately set taking into consideration the actual circumstances under which abnormal noise occurs in a vehicle.

[0049] Thus, the abnormal sound occurrence risk prediction device 20 of this embodiment has an acquisition unit 21 that acquires a vehicle behavior predicted value, which is a prediction of the vehicle behavior, road surface conditions, which are an estimate of the condition of the road surface on which the vehicle is traveling, and a fluid temperature, which is the temperature of the working fluid inside the damper 1, a damper input prediction unit 25 (predicted input calculation unit) that calculates a predicted input, which is an input to the damper 1 predicted from the vehicle behavior predicted value and the road surface conditions, an abnormal sound distribution output unit 27 that outputs an abnormal sound occurrence probability distribution based on the fluid temperature according to the pattern of the input to the damper 1, and an abnormal sound occurrence risk prediction unit 28 that predicts the abnormal sound occurrence risk, which is the actual risk of occurrence of abnormal sound generated by the damper, from the relationship between the predicted input and the abnormal sound distribution.

[0050] This allows the abnormal noise occurrence risk prediction unit 28 to predict the risk of abnormal noise occurrence based on the vehicle behavior prediction value, road surface conditions, and the fluid temperature (oil temperature) of the working fluid. Therefore, when the risk of abnormal noise occurrence is high, the damper 1 can be controlled, for example, to reduce the change in damping force. As a result, the occurrence of abnormal noise from the damper 1 can be suppressed.

[0051] At this time, the vehicle behavior predicted value is the vehicle speed in the forward / reverse direction or a forward / reverse vehicle speed related value, which is its differential value. Therefore, the abnormal noise occurrence risk prediction device 20 can predict the risk of abnormal noise occurrence based on the vehicle speed, the acceleration in the forward / reverse direction, etc.

[0052] The road surface condition is calculated using the acceleration in the operation direction of the damper 1 as an input value, and is the change over time of the displacement input in the operation direction of the damper 1 due to unevenness of the road surface. Therefore, the abnormal noise generation risk prediction device 20 can predict the risk of abnormal noise generation based on the change over time of the displacement input in the operation direction of the damper 1.

[0053] The damper input prediction unit 25 (predicted input calculation unit) models the time history of the input to the damper 1 on the road surface corresponding to the front / rear vehicle speed related value using an autoregressive moving average model, and calculates the upper or lower limit of the average and variance of the prediction of the input to the damper 1 as a predicted input. This allows the damper input prediction unit 25 to output a probability distribution according to the vehicle speed and road surface conditions as a predicted input, which is an input to the damper 1.

[0054] The abnormal sound distribution output unit 27 has a map in which the probability of abnormal sound occurrence corresponding to each pattern of the input to the damper 1 in each predetermined range of fluid temperature corresponds to an abnormal sound distribution in the form of an average and an upper or lower limit of variance, and outputs from the map the abnormal sound distribution of a pattern corresponding to the fluid temperature and the input to the damper 1. As a result, the abnormal sound occurrence risk prediction unit 28 is able to find the overlap area between the abnormal sound occurrence probability distribution (abnormal sound distribution) output by the abnormal sound distribution output unit 27 and the distribution of the magnitude of the frequency components output by the damper input prediction unit 25, and output the abnormal sound occurrence risk based on this overlap area.

[0055] The damper 1 also has a cylindrical cylinder 2 with a bottom that is filled with working fluid, a piston 3 that divides the inside of the cylinder 2 into a first chamber and a second chamber, a damping force adjustment unit 4 that adjusts the damping force by adjusting the flow of the working fluid, and a controller 11 (control unit) that controls the adjustment amount of the damping force.

[0056] At this time, the controller 11 has an acquisition unit 21 that acquires a vehicle behavior prediction value that is a prediction of the state of the vehicle behavior, a road surface condition that is an estimate of the condition of the road surface on which the vehicle runs, and a fluid temperature that is the temperature of the working fluid, a damper input prediction unit 25 (predicted input calculation unit) that calculates a predicted input that is an input to the damper 1 predicted from the vehicle behavior prediction value and the road surface condition, an abnormal sound distribution output unit 27 that outputs an abnormal sound distribution that is an abnormal sound occurrence probability distribution based on the fluid temperature according to each pattern of the input to the damper 1, an abnormal sound occurrence risk prediction unit 28 that predicts an abnormal sound occurrence risk that is an actual occurrence risk of an abnormal sound generated by the damper 1 from the relationship between the abnormal sound distribution and the predicted input, and a control amount calculation unit 12 that calculates a control amount of the damping force adjustment unit 4 so that the abnormal sound occurrence risk is reduced using at least the abnormal sound occurrence risk as an input value. Therefore, when the abnormal sound occurrence risk is high, the controller 11 can control the damper 1 to, for example, reduce the change in the damping force, and can suppress the occurrence of abnormal sound from the damper 1.

[0057] In the above embodiment, the abnormal sound occurrence risk prediction unit 28 calculates the overlap area between the abnormal sound occurrence probability distribution at a single feature frequency (e.g., 10 Hz) and the distribution of the magnitude of the frequency component output by the damper input prediction unit 25, and outputs it as the abnormal sound occurrence risk. However, the present invention is not limited to this. For example, when there are two or more systems of means for estimating the road surface condition, when the damper input prediction unit 25 outputs it by regression, the predicted input distribution may differ depending on the system. In that case, the abnormal sound occurrence risk prediction unit may calculate the abnormal sound occurrence risk for each corresponding feature frequency and select the maximum value of these multiple abnormal sound occurrence risks. Also, the overlap area may be calculated for multiple feature frequencies, and the abnormal sound occurrence risk may be calculated by adding up these areas.

[0058] In the above embodiment, the controller 11 acquires vehicle behavior information including wheel speeds through the CAN 10, but the present invention is not limited to this. The controller 11 may acquire detection values ​​of various sensors directly from the various sensors, for example. The controller 11 may also acquire various information from other controllers, etc.

[0059] In the above embodiment, the force generating mechanism is configured as the variable damper 5 made of a semi-active damper. The present invention is not limited to this, and an active damper (hydraulic actuator) may be used as the force generating mechanism. In the above embodiment, the force generating mechanism is configured as the variable damper 5 made of a damping force adjustable hydraulic shock absorber. The present invention is not limited to this, and the force generating mechanism may be configured as an air suspension or the like in addition to a hydraulic shock absorber.

[0060] In the above embodiment, the vehicle behavior device used in a four-wheeled automobile has been described as an example. However, the present invention is not limited to this, and can also be applied to, for example, work vehicles, transport vehicles such as trucks and buses. [Explanation of symbols]

[0061] 1: damper, 2: cylinder, 3: piston, 4: damping force adjustment unit, 11: controller, 12: control amount calculation unit, 20: abnormal sound occurrence risk prediction device, 21: acquisition unit, 22: vehicle behavior prediction unit, 23: road surface condition prediction unit, 24: temperature estimation unit, 25: damper input prediction unit, 26: abnormal sound occurrence risk calculation unit, 27: abnormal sound distribution output unit, 28: abnormal sound occurrence risk prediction unit

Claims

1. An abnormal noise occurrence risk prediction device for predicting abnormal noise generated by a damper installed in a vehicle, comprising: A vehicle behavior prediction value which is a prediction of the behavior of the vehicle; Road conditions, which are estimates of the conditions of the road surface on which the vehicle is traveling; and A fluid temperature, which is the temperature of the working fluid inside the damper; An acquisition unit for acquiring a predicted input calculation unit that calculates a predicted input, which is an input to the damper predicted from the vehicle behavior predicted value and the road surface condition; an abnormal noise distribution output unit that outputs an abnormal noise distribution, which is an occurrence probability distribution of the abnormal noise based on the fluid temperature corresponding to a pattern of an input to the damper; an abnormal noise occurrence risk prediction unit that predicts an abnormal noise occurrence risk, which is an actual occurrence risk of the abnormal noise generated by the damper, based on a relationship between the abnormal noise distribution and the predicted input; The abnormal noise occurrence risk prediction device has the following features.

2. The vehicle behavior prediction value is A forward / rear vehicle speed related value is a vehicle speed in a forward / rear direction or a differential value thereof. The abnormal noise occurrence risk prediction device according to claim 1 .

3. The road surface condition is The acceleration in the actuation direction of the damper is calculated as an input value. A time change in a displacement input in the operating direction of the damper due to unevenness of the road surface. The abnormal noise occurrence risk prediction device according to claim 2 .

4. The predicted input calculation unit is modeling a time history of an input to the damper on the road surface corresponding to the value of the front / rear vehicle speed related value using an autoregressive moving average model, and calculating an upper limit or a lower limit of a mean and a variance of a prediction of the input to the damper as the predicted input; The abnormal noise occurrence risk prediction device according to claim 2 .

5. The abnormal sound distribution output unit is a map in which the probability of occurrence of the abnormal noise corresponding to each input pattern to the damper in each predetermined range of the fluid temperature is associated with an abnormal noise distribution in the form of an average and an upper or lower limit of a variance, outputting, from the map, an abnormal noise distribution pattern corresponding to the fluid temperature and the input to the damper; The abnormal noise occurrence risk prediction device according to claim 4.

6. 1. A method for predicting an abnormal noise occurrence risk that an abnormal noise will be generated by a damper mounted on a vehicle, comprising: A vehicle behavior prediction value which is a prediction of the behavior of the vehicle; Road conditions, which are estimates of the conditions of the road surface on which the vehicle is traveling; and A fluid temperature, which is the temperature of the working fluid inside the damper; and an acquisition step of acquiring a predicted input calculation step of calculating a predicted input, which is an input to the damper predicted from the vehicle behavior predicted value and the road surface condition, after the acquisition step; an abnormal noise distribution output step of outputting an abnormal noise distribution, which is an occurrence probability distribution of the abnormal noise based on the fluid temperature corresponding to each pattern of the input to the damper after the acquisition step; an abnormal noise occurrence risk prediction step of predicting an abnormal noise occurrence risk, which is an actual occurrence risk of the abnormal noise generated by the damper, based on a relationship between the predicted input and the abnormal noise distribution after the predicted input calculation step and the abnormal noise distribution output step; The abnormal noise occurrence risk prediction device has the following features.

7. A damper that is mounted on a vehicle and has an adjustable damping force, The damper is A cylindrical cylinder filled with a working fluid; a piston that divides the interior of the cylinder into a first chamber and a second chamber; a damping force adjusting unit that adjusts the damping force by adjusting the flow of the working fluid; A control unit that controls an adjustment amount of the damping force; having The control unit is A vehicle behavior prediction value which is a prediction of a state of the behavior of the vehicle; Road conditions, which are estimates of the conditions of the road surface on which the vehicle is traveling; and A fluid temperature which is the temperature of the working fluid; An acquisition unit for acquiring a predicted input calculation unit that calculates a predicted input, which is an input to the damper predicted from the vehicle behavior predicted value and the road surface condition; an abnormal noise distribution output unit that outputs an abnormal noise distribution, which is an occurrence probability distribution of abnormal noise based on the fluid temperature corresponding to each input pattern to the damper; an abnormal noise occurrence risk prediction unit that predicts an abnormal noise occurrence risk, which is an actual occurrence risk of the abnormal noise generated by the damper, based on a relationship between the abnormal noise distribution and the predicted input; a control amount calculation unit that calculates a control amount of the damping force adjustment unit using at least the abnormal noise generation risk as an input value so as to reduce the abnormal noise generation risk; having Damper.

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