Trailer rolling resistance measuring and calculating method and system based on gradient and acceleration correction

Abstract

The invention discloses a trailer rolling resistance measuring and calculating method and system based on gradient and acceleration correction, and belongs to the field of tire road surface action contact surface mechanics. Whether the number of times that the real-time longitudinal acceleration is larger than the preset maximum acceleration exceeds a set first threshold value or the number of times that the real-time angular velocity is larger than the preset maximum angular velocity exceeds a second threshold value occurs, and if not, data fusion is conducted on the real-time vehicle speed, the real-time longitudinal acceleration and the real-time angular velocity through a Kalman filter, and the final road gradient is obtained; if yes, correcting the real-time longitudinal acceleration in combination with the real-time angular velocity to obtain a corrected acceleration, and performing data fusion on the real-time vehicle speed, the corrected acceleration and the real-time angular velocity by using a complementary filter to obtain a final road gradient; and calculating the rolling resistance of the unpowered trailer based on the final road gradient. The road gradient is calculated by selecting a proper algorithm according to the road condition, and then the accurate rolling resistance is obtained.

Description

Technical Field

[0001] The present application relates to the field of tire-road contact surface mechanics, and specifically to a method and system for calculating trailer rolling resistance based on slope and acceleration correction. Background Art

[0002] Vehicle transportation is one of the largest energy consumption sectors in the world. Studies have shown that air resistance Fair and rolling resistance Fr are the main sources of energy consumption in vehicle operation. The study of tire rolling resistance Fr and vehicle air resistance Fair has always been one of the key issues in the field of vehicle research, but it is subject to harsh test conditions and equipment requirements. The accurate measurement of tire rolling resistance Fr and vehicle air resistance Fair in actual road conditions is still a problem to be solved.

[0003] At present, the rolling resistance Fr of a vehicle cannot be measured directly. It is greatly affected by tire parameters and the measurement environment, such as tire type, tire pressure, ambient temperature, road surface characteristics, and vehicle speed. Currently, most studies on rolling resistance Fr are conducted using indirect measurement methods such as finite element analysis, vehicle sliding method, and laboratory roller measurement.

[0004] For ordinary passenger vehicles, Figure 1 The mechanical schematic diagram of the vehicle model is shown in FIG. 1 , and the longitudinal force balance equation of the vehicle is shown in the following formula (1):

[0005]

[0006] Where m is the vehicle mass, v x and v y are the vehicle longitudinal velocity and the vehicle lateral velocity respectively, r is the vehicle yaw rate, F xii and F yii They represent the longitudinal tire force and the lateral tire force respectively, σ is the front wheel turning angle of the vehicle, and F gr 、F cr , and F rr , which are the gravity component along the slope, the air resistance Fair, and the rolling resistance Fr.

[0007] It can be seen from the above formula (1) that the rolling resistance Fr is closely related to the road slope. When the vehicle is on a ramp, the value measured by the longitudinal acceleration acquisition device is not only the rate of change of the vehicle's longitudinal speed at this time, but also includes the acceleration signal generated by the gravity component of the ramp. The rolling resistance Fr is several orders of magnitude smaller than the vertical load of the measured component (including the test tire and the measurement system). When the vehicle runs on a non-horizontal road, the gravity component of the measured component will couple with the rolling resistance Fr. At this time, the rolling resistance Fr output by the measurement system actually contains the real rolling resistance Fr and the gravity component of the measured component. Therefore, accurate estimation of the road slope is very important for accurate measurement of the rolling resistance Fr.

[0008] Therefore, the vehicle kinematic model can be obtained as shown in the following formula (2):

[0009]

[0010] Among them, a is the measured value of the longitudinal acceleration sensor, g is the acceleration of gravity, θ is the road slope, and v is the vehicle speed.

[0011] It can be seen from the above formula (2) that the slope of the road where the vehicle is currently located can be calculated by the vehicle's own speed and longitudinal acceleration. Only after the road slope is calculated can the rolling resistance Fr of the vehicle test wheel be accurately calculated. Obviously, the above method is only applicable to a rolling resistance test trailer (referred to as trailer) with its own power system. For example, the trailer is used to measure its rolling resistance Fr. If the trailer has its own power system, the speed collection device of the trailer's own power system is used to collect speed information. In addition, a longitudinal acceleration collection device and an angular velocity collection device are installed on the trailer to collect longitudinal acceleration and angular velocity information respectively. The measurement system combines the trailer's speed, longitudinal acceleration, and angular velocity, and uses the existing data fusion algorithm for processing to obtain the road slope, and the rolling resistance Fr of the test wheel is obtained according to the road slope. The commonly used data fusion algorithms include Kalman filter algorithm, etc.

[0012] A non-rigid connection is adopted between the towing vehicle and the trailer. The speed of the towing vehicle cannot be strictly equal to the speed of the trailer. When the speed of the towing vehicle changes, there is a lag in the speed change of the trailer, especially when the road conditions change suddenly. For example, when the front and rear vehicles are driving from flat ground to a ramp, or the front vehicle has driven down the top of the ramp but the rear vehicle is still on the uphill side of the ramp, or the front vehicle has driven down the ramp but the rear vehicle is still on the downhill side of the ramp, the trailer will not only be unable to keep up with the speed change of the towing vehicle, but will also produce yaw and vibration due to the sudden change in road conditions.

[0013] In summary, when using an unpowered trailer to measure the rolling resistance Fr, it is necessary to accurately measure the road slope. Only based on the accurate road slope data can the accurate rolling resistance Fr be accurately calculated. Summary of the invention

[0014] The present application provides a method and system for calculating the rolling resistance of a trailer based on slope and acceleration correction, which can solve the technical problem of inaccurate calculation of road slope and rolling resistance in the prior art.

[0015] In a first aspect, an embodiment of the present application provides a method for calculating the rolling resistance of a trailer based on slope and acceleration correction, the method comprising:

[0016] Collect the real-time speed of the towing vehicle, the real-time longitudinal acceleration of the trailer, and the real-time angular velocity of the trailer; determine whether the number of times the real-time longitudinal acceleration is greater than the preset maximum acceleration exceeds a first threshold or the number of times the real-time angular velocity is greater than the preset maximum angular velocity exceeds a second threshold within a preset time period, if so, determine that the current road condition is urgent, if not, determine that the current road condition is smooth;

[0017] Under smooth road conditions, the Kalman filter is used to fuse the real-time vehicle speed, real-time longitudinal acceleration, and real-time angular velocity to obtain the final road slope;

[0018] Under urgent road conditions, the real-time longitudinal acceleration is corrected in combination with the real-time angular velocity to obtain the corrected acceleration, and the real-time vehicle speed, corrected acceleration, and real-time angular velocity are fused using a complementary filter to obtain the final road slope.

[0019] Calculate the rolling resistance of the unpowered trailer based on the final road grade.

[0020] In combination with the first aspect, in one implementation, the use of the Kalman filter algorithm to fuse the real-time vehicle speed, the real-time longitudinal acceleration, and the real-time angular velocity to obtain the final road slope specifically includes the following steps:

[0021] According to the real-time vehicle speed and the real-time longitudinal acceleration, the initial road slope is obtained;

[0022] The initial road slope is used as a measurement value of the Kalman filter and combined with the real-time angular velocity to obtain the final road slope.

[0023] In combination with the first aspect, in one implementation, the initial road slope is calculated using the following formula:

[0024]

[0025] Among them, a represents the real-time longitudinal acceleration, g represents the acceleration due to gravity, θ represents the initial road slope, and ν represents the real-time vehicle speed.

[0026] In combination with the first aspect, in one implementation, the final road slope is calculated using the following formula:

[0027]

[0028] Where k represents the time, represents the posterior estimate of the final road slope output by the k Kalman filter at the current time, represents the prior estimate of the final road slope output by the Kalman filter at the current time k, K k represents the gain coefficient of the Kalman filter at the current moment, θ represents the initial road slope, H represents the conversion matrix from state quantity to measurement value, and its size is 1. represents the prior estimate variance of the Kalman filter at the current time k, R represents the measurement noise covariance, P k represents the posterior estimation variance of the Kalman filter at the current time k, Q represents the state transition noise covariance, A represents the state transfer matrix, B represents the control matrix, and u k-1 represents the real-time angular velocity at the previous moment k-1, and I represents the unit matrix.

[0029] In combination with the first aspect, in one implementation, the step of correcting the real-time longitudinal acceleration in combination with the real-time angular velocity to obtain the corrected acceleration specifically includes the following steps:

[0030] According to the preset acceleration threshold range, it is judged whether the real-time longitudinal acceleration exceeds the acceleration threshold range; if the real-time longitudinal acceleration is greater than the upper limit of the acceleration threshold range, the upper limit is used as the preliminary correction acceleration; if the real-time longitudinal acceleration is less than the lower limit of the acceleration threshold range, the lower limit is used as the preliminary correction acceleration to achieve the amplitude limitation of the longitudinal acceleration;

[0031] Calculate the slope threshold according to the real-time angular velocity, and determine whether the speed change rate of the corrected acceleration exceeds the slope threshold. If not, continue to use the default strategy to limit the slope of the real-time longitudinal acceleration using the real-time angular velocity to obtain the corrected acceleration; if not, switch to the upgraded strategy to limit the slope of the real-time longitudinal acceleration using the real-time angular velocity to obtain the corrected acceleration;

[0032] The default strategy is to control the change rate of the corrected acceleration not to exceed the change rate of the real-time angular velocity; the upgraded strategy is to control the change rate of the corrected acceleration not to exceed the slope threshold.

[0033] In combination with the first aspect, in one implementation, the slope threshold is calculated using the following formula:

[0034] Δa limit =(g[sin(θ′+δ′)-sinθ′]+gsin(e)cos(θ′+δ′))·h·dt

[0035] Among them, Δa limit represents the slope threshold, g represents the gravitational acceleration, θ′ represents the optimal estimated value of the road slope in the previous time window, δ′ represents the integral value of the real-time angular velocity in the set time window, e represents the integral error value of the real-time angular velocity in the set time window, and dt is the operating step size of the controller;

[0036] The speed change rate of the modified acceleration is calculated using the following formula:

[0037]

[0038] Among them, s k represents the rate of change of the corrected acceleration at the current moment k, a(k) represents the corrected acceleration before the update at the current moment k, a(k-1) represents the corrected acceleration after the update at the previous moment k-1, a(k-2) represents the corrected acceleration after the update at the previous moment k-2, and t represents time.

[0039] In combination with the first aspect, in one implementation, the upgrade strategy uses the following formula to calculate the final corrected acceleration:

[0040] a(k)′=a(k-1)+sign(Δa k -Δa k-1 )·Δa k-1 ·Δa limit

[0041] Where, represents the corrected acceleration after the current time k is updated, Δa k represents the rate of change of the corrected acceleration at the current moment k, Δa k represents the rate of change of the corrected acceleration at the current moment k, Δa k-1 represents the rate of change of the corrected acceleration at the previous moment k-1, Δa limit represents the slope threshold, and h represents a custom positive constant.

[0042] In combination with the first aspect, in one implementation, the method of using a complementary filter to fuse the real-time vehicle speed, the corrected acceleration, and the real-time angular velocity to obtain the final road slope specifically includes the following steps:

[0043] The cutoff frequency of the complementary filter is obtained according to the change rate of the road slope output by the complementary filter, and the final road slope output by the complementary filter is adjusted by adjusting the cutoff frequency of the complementary filter.

[0044] In combination with the first aspect, in one implementation, the cutoff frequency is calculated using the following formula:

[0045]

[0046] Among them, f c represents the cutoff frequency of the complementary filter, represents the minimum cutoff frequency threshold of the complementary filter, γ represents the custom adjustment parameter, and θ fused (k-1) represents the final road slope output by the complementary filter at time k-1, θ fused (k-2) represents the final road slope output by the complementary filter at time k-2, and t represents time;

[0047] The final road slope is calculated using the following formula:

[0048]

[0049] Among them, F(θ) represents the final road slope estimation result in the frequency domain, F(θ) represents the road slope estimation result of the low-frequency part in the frequency domain, and F(θ H ) represents the road slope estimation result of the high frequency part in the frequency domain, H L Represents a low-frequency filter, H G Represents a high-frequency filter with an integral process, F(θ a ) represents the road slope estimation result obtained by filtering the final road slope calculated based on the acceleration correction by a low-pass filter, F(u) represents the road slope estimation result obtained by filtering the real-time angular velocity by a high-pass filter, β represents the regularization parameter, represents the normalized frequency, f represents the sampling frequency of the complementary filter, and f c Represents the cutoff frequency of the complementary filter.

[0050] In a second aspect, an embodiment of the present application provides a trailer rolling resistance calculation system based on slope and acceleration correction, the system comprising:

[0051] A vehicle speed collection device, which is arranged on the towing vehicle and is used to collect the real-time vehicle speed of the towing vehicle;

[0052] A longitudinal acceleration acquisition device, which is arranged on the trailer and used to acquire the real-time longitudinal acceleration of the trailer; an angular velocity acquisition device, which is arranged on the trailer and used to acquire the real-time angular velocity of the trailer;

[0053] A road slope calculation module, which is used to determine whether the number of times the real-time longitudinal acceleration is greater than the preset maximum acceleration exceeds a set first threshold or the number of times the real-time angular velocity is greater than the preset maximum angular velocity exceeds a second threshold within a preset time period; if not, it is determined that the current road condition is smooth; if so, it is determined that the current road condition is rapid; under smooth road conditions, the real-time vehicle speed, the real-time longitudinal acceleration, and the real-time angular velocity are fused using a Kalman filter to obtain a final road slope; under rapid road conditions, the real-time longitudinal acceleration is corrected in combination with the real-time angular velocity to obtain a corrected acceleration, and the real-time vehicle speed, the corrected acceleration, and the real-time angular velocity are fused using a complementary filter to obtain a final road slope;

[0054] The rolling resistance calculation module is used to calculate the rolling resistance of the unpowered trailer based on the final road slope.

[0055] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:

[0056] Under smooth road conditions, the Kalman filter is used to fuse the real-time vehicle speed, real-time longitudinal acceleration, and real-time angular velocity to obtain the final road slope. Under rapid road conditions, the amplitude and slope of the acquired acceleration signal are dynamically limited according to the operation of different vehicles, and then the slope dynamic fusion algorithm is adopted to obtain the actual operating slope of the vehicle after complementary filtering, which solves the problem of poor slope estimation accuracy of the test vehicle under complex conditions. Finally, accurate rolling resistance is obtained based on the accurate road slope. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a mechanical schematic diagram of a vehicle model in the prior art;

[0058] Figure 2 This is a flow chart of an embodiment of a method for calculating the rolling resistance of a trailer based on slope and acceleration correction in this application.

[0059] Figure 3 This is a functional module diagram of an embodiment of a trailer rolling resistance measurement system based on slope and acceleration correction in the present application. DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0061] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0062] In a first aspect, an embodiment of the present application provides a method for calculating the rolling resistance of a trailer based on slope and acceleration correction.

[0063] In one embodiment, referring to Figure 2 , Figure 2 This is a flow chart of the first embodiment of the method for calculating the rolling resistance of a trailer based on slope and acceleration correction in this application. Figure 1 As shown, the method for calculating the rolling resistance of a trailer based on slope and acceleration correction includes:

[0064] Step S1, collecting the real-time vehicle speed of the towing vehicle, the real-time longitudinal acceleration of the trailer, and the real-time angular velocity of the trailer.

[0065] Step S2: determine whether the number of times the real-time longitudinal acceleration is greater than the preset maximum acceleration exceeds a first threshold or the number of times the real-time angular velocity is greater than the preset maximum angular velocity exceeds a second threshold within a preset time period:

[0066] If yes, go to step S4.

[0067] If not, go to step S3.

[0068] Step S3: determine that the current road condition is flat, and use the Kalman filter to fuse the real-time vehicle speed, real-time longitudinal acceleration, and real-time angular velocity to obtain the final road slope.

[0069] Step S4: determine that the current road condition is urgent, correct the real-time longitudinal acceleration in combination with the real-time angular velocity to obtain a corrected acceleration, and use a complementary filter to fuse the real-time vehicle speed, the corrected acceleration, and the real-time angular velocity to obtain a final road slope.

[0070] Step S5: Calculate the rolling resistance of the unpowered trailer based on the final road slope.

[0071] In this embodiment, under smooth road conditions, the Kalman filter is used to fuse the real-time vehicle speed, real-time longitudinal acceleration, and real-time angular velocity to obtain the final road slope. Under rapid road conditions, the amplitude and slope of the collected acceleration signal are dynamically limited according to the running conditions of different vehicles, and then the slope dynamic fusion algorithm is adopted to obtain the actual running slope of the vehicle after complementary filtering, which solves the problem of poor slope estimation accuracy of the test vehicle under complex conditions. Finally, the accurate test wheel rolling resistance is obtained based on the accurate road slope. The real-time vehicle speed, real-time longitudinal acceleration, and real-time angular velocity are collected using corresponding collection devices, generally sensors, such as using a gyroscope to collect angular velocity.

[0072] In summary, in order to solve the problem that a single sensor is easily affected by the environment and the equipment itself, this paper proposes an adaptive switching algorithm based on vehicle operating conditions. According to the different operating states of the vehicle, different algorithms are used to estimate the slope based on accelerometer and angular velocity meter data to improve the real-time slope estimation accuracy during the test.

[0073] Further, in one embodiment, the Kalman filter algorithm is used to fuse the real-time vehicle speed, the real-time longitudinal acceleration, and the real-time angular velocity to obtain the final road slope, which specifically includes the following steps:

[0074] The initial road slope is obtained according to the real-time vehicle speed and the real-time longitudinal acceleration.

[0075] The initial road slope is used as the measurement value of the Kalman filter and combined with the real-time angular velocity to obtain the above-mentioned final road slope.

[0076] The initial road slope is calculated using the following formula (1):

[0077]

[0078] Among them, a represents the real-time longitudinal acceleration, g represents the acceleration due to gravity, θ represents the initial road slope, and v represents the real-time vehicle speed.

[0079] The above-mentioned final road slope is calculated using the following formula (2):

[0080]

[0081] Where k represents the time, represents the posterior estimate of the final road slope output by the k Kalman filter at the current time, represents the prior estimate of the final road slope output by the Kalman filter at the current time k, K k represents the gain coefficient of the Kalman filter at the current moment, θ represents the initial road slope, H represents the conversion matrix from state quantity to measurement value, and its size is 1. represents the prior estimate variance of the Kalman filter at the current time k, R represents the measurement noise covariance, P k represents the posterior estimation variance of the Kalman filter at the current time k, Q represents the state transition noise covariance, A represents the state transfer matrix, B represents the control matrix, and u k-1 represents the real-time angular velocity at the previous moment k-1, and I represents the unit matrix.

[0082] In this embodiment, the vehicle state may change due to environmental changes or different driving habits during actual driving, thereby affecting the accuracy of slope estimation. The existing angular velocity sensor may accumulate errors during long-term operation, which results in the inability to obtain accurate slope estimation values ​​using the angular velocity sensor for a long time during vehicle driving. Although the acceleration sensor is subject to greater external interference, it has higher estimation accuracy over a longer period of time.

[0083] In order to solve the problem that a single sensor is easily affected by the environment and equipment, this paper uses accelerometer and angular velocity meter for slope fusion estimation to improve the accuracy of slope estimation.

[0084] The vehicle motion state condition is set as follows: to count whether the number of times the real-time longitudinal acceleration is greater than the set threshold or the number of times the real-time angular velocity is greater than the set threshold exceeds 10 times in any 0.5 second time window. If the condition is met, it means that the slope changes greatly, and the algorithm switch will be triggered.

[0085] When the vehicle is running in a stable state, because the road slope and vehicle running state are good, the speed change is small, so there is no need to correct the real-time acceleration, and the real-time longitudinal acceleration and real-time angular velocity are directly used to fuse and estimate the final road slope. Specifically, a slope estimation Kalman filter based on acceleration and angular velocity is constructed. The initial road slope calculated by the acceleration of formula (1) is used as the measurement value in the Kalman filter, and the conversion matrix H from the state variable to the measurement value in the Kalman filter is 1.

[0086] Furthermore, in one embodiment, the above-mentioned correction of the real-time longitudinal acceleration in combination with the real-time angular velocity to obtain the corrected acceleration specifically includes the following steps:

[0087] According to the pre-set acceleration threshold range, determine whether the real-time longitudinal acceleration exceeds the acceleration threshold range. If the real-time longitudinal acceleration is greater than the upper limit of the acceleration threshold range, the upper limit is used as the preliminary correction acceleration. If the real-time longitudinal acceleration is less than the lower line of the acceleration threshold range, the lower line is used as the preliminary correction acceleration to achieve amplitude limitation of the longitudinal acceleration.

[0088] The slope threshold is calculated according to the real-time angular velocity, and it is determined whether the speed change rate of the modified acceleration exceeds the slope threshold. If not, the default strategy is continued, and the slope of the real-time longitudinal acceleration is limited by the real-time angular velocity to obtain the modified acceleration. If not, the upgraded strategy is switched to, and the slope of the real-time longitudinal acceleration is limited by the real-time angular velocity to obtain the modified acceleration.

[0089] The above default strategy is to control the change rate of the corrected acceleration not to exceed the change rate of the real-time angular velocity. The above upgrade strategy is to control the change rate of the corrected acceleration not to exceed the slope threshold.

[0090] In this embodiment, when the vehicle's operating state changes significantly, the longitudinal acceleration is corrected for amplitude using angular velocity, and a slope correction scheme is used to limit the change in the slope of the longitudinal acceleration. The corrected longitudinal acceleration data is used using a dynamic complementary filtering algorithm with a faster operating speed to obtain a more accurate road slope estimation result.

[0091] Acceleration correction is used to deal with complex working conditions of traction vehicles during actual operation, such as the acceleration process of traction vehicles, rapid deceleration process, and gear shifting process. In general, the data collected by the acceleration sensor varies within a certain range, but when the traction vehicle accelerates and stops suddenly, the longitudinal acceleration sensor will generate a large pulse signal. We set the maximum and minimum range of the acceleration signal based on the driving data of the traction vehicle. The basic logical idea is as follows:

[0092] when a senx >a max ,y=a max ;

[0093] when a senx min ,y=a min ;

[0094] when a min ≤a senx ≤a max ,y=a senx ;

[0095] Among them, a senx is the signal to be processed, y is the signal after amplitude limitation, a min and a max Represents the minimum and maximum limiting amplitude of the signal.

[0096] Due to the complexity of the test conditions, simply using amplitude limitation will cause serious distortion of the initial signal. Therefore, slope limitation is performed on the basis of amplitude limitation. In actual operation, the range of amplitude limitation can be appropriately relaxed to better retain the effective components in the original signal and avoid data distortion caused by too small amplitude limitation.

[0097] ​The angular velocity collected by the gyroscope is integrated within this time window to obtain the angle change in this short time. This angle change is converted into a change limit for the measured value of the acceleration sensor within this time window, so as to achieve correction of the measured value of the acceleration sensor and reduce or eliminate the influence of the trailer's own longitudinal acceleration on slope recognition. For example, when going up and down a normal road, the slope changes slowly, and the slope change can be limited to a certain range. However, when going up and down a steep mountain road, the slope changes greatly in a short time. At this time, the limit on the slope should be released to allow the signal to respond quickly to the change of the slope.

[0098] Furthermore, in one embodiment, the slope threshold is calculated using the following formula (3):

[0099] Δa limit =(g[sin(θ′+δ′)-sinθ′]+gsin(e)cos(θ′+δ′))·h·dt (3)

[0100] Among them, Δa limit represents the slope threshold, g represents the gravitational acceleration, θ′ represents the optimal estimated value of the road slope in the previous time window, δ′ represents the integral value of the real-time angular velocity in the set time window, and e represents the integral error value of the real-time angular velocity in the set time window.

[0101] The speed change rate of the above-mentioned modified acceleration is calculated using the following formula (4):

[0102]

[0103] Among them, s k represents the rate of change of the corrected acceleration at the current moment k, a(k) represents the corrected acceleration before the update at the current moment k, a(k-1) represents the corrected acceleration after the update at the previous moment k-1, a(k-2) represents the corrected acceleration after the update at the previous moment k-2, and t represents time.

[0104] Furthermore, in one embodiment, the above upgrade strategy uses the following formula (5) to calculate the final corrected acceleration:

[0105] a(k)′=a(k-1)+sign(Δa k -Δa k-1 )·Δa k-1 ·Δa limit (5)

[0106] Where, represents the corrected acceleration after the current time k is updated, Δa k represents the rate of change of the corrected acceleration at the current moment k, Δa k represents the rate of change of the corrected acceleration at the current moment k, Δak-1 represents the rate of change of the corrected acceleration at the previous moment k-1, Δa limit represents the slope threshold, and h represents a custom positive constant.

[0107] In this embodiment, if there is s k Exceed Δa limit , then update a(k).

[0108] Further, in one embodiment, the above-mentioned complementary filter is used to fuse the real-time vehicle speed, the corrected acceleration, and the real-time angular velocity to obtain the final road slope, which specifically includes the following steps:

[0109] The cutoff frequency of the complementary filter is obtained according to the change rate of the road slope output by the complementary filter, and the final road slope output by the complementary filter is adjusted by adjusting the cutoff frequency of the complementary filter.

[0110] Furthermore, in one embodiment, the cut-off frequency is calculated using the following formula (6):

[0111]

[0112] Among them, f c represents the cutoff frequency of the complementary filter, represents the minimum cutoff frequency threshold of the complementary filter, γ represents the custom adjustment parameter, and θ fused (k-1) represents the final road slope output by the complementary filter at time k-1, θ fused (k-2) represents the final road slope output by the complementary filter at time k-2, and t represents time.

[0113] The final road slope is calculated using the following formula (7):

[0114]

[0115] Among them, F(θ) represents the final road slope estimation result in the frequency domain, F(θ) represents the road slope estimation result of the low-frequency part in the frequency domain, and F(θ H ) represents the road slope estimation result of the high frequency part in the frequency domain, H L Represents a low-frequency filter, H G Represents a high-frequency filter with an integral process, F(θ a ) represents the road slope estimation result obtained by filtering the final road slope calculated based on the acceleration correction by a low-pass filter, F(u) represents the road slope estimation result obtained by filtering the real-time angular velocity by a high-pass filter, β represents the regularization parameter, represents the normalized frequency, f represents the sampling frequency of the complementary filter, and f cRepresents the cutoff frequency of the complementary filter.

[0116] In this embodiment, because it is difficult to obtain external noise signals (Q and R) in complex situations, and because of the influence of acceleration correction. There is a time lag characteristic, the use of complementary filters can significantly reduce the calculation burden of the sensor. Therefore, in the case of a sharp change in slope, acceleration correction and complementary filtering algorithms are used to estimate the slope. The total road slope is composed of the slope obtained by the low-pass filter of the accelerometer and the slope calculated by the high-pass filter of the angular velocity integral. f is the sampling frequency. Let f c It represents the cutoff frequency that divides the entire frequency domain into a low-frequency range and a high-frequency range, written relative to the normalized frequency.

[0117] In order to better adapt to the vehicle operating environment, we designed an adaptive complementary filter. The low-pass filter reduces the cutoff frequency f c Will reduce jitter, but increase lag.

[0118] It is difficult to design a suitable cutoff frequency in the actual process of the vehicle, because the vehicle is more sensitive to vibration at high speed and more sensitive to lag at low speed. This is why it is necessary to design an adaptive cutoff frequency. Updating the cutoff frequency according to the slope change when the vehicle is running can improve the accuracy of the algorithm.

[0119] In a second aspect, an embodiment of the present application also provides a trailer rolling resistance measurement system based on slope and acceleration correction.

[0120] In one embodiment, referring to Figure 3 , Figure 3 This is a functional module diagram of an embodiment of a trailer rolling resistance measurement system based on slope and acceleration correction in this application. Figure 3 As shown, the trailer rolling resistance measurement system based on slope and acceleration correction includes:

[0121] The vehicle speed collecting device 1 is arranged on the traction vehicle and is used for collecting the real-time vehicle speed of the traction vehicle.

[0122] The longitudinal acceleration acquisition device 2 is arranged on the trailer and used to acquire the real-time longitudinal acceleration of the trailer. The angular velocity acquisition device 3 is arranged on the trailer and used to acquire the real-time angular velocity of the trailer.

[0123] The road slope calculation module 4 is used to determine whether the number of times the real-time longitudinal acceleration is greater than the preset maximum acceleration exceeds the set first threshold or the number of times the real-time angular velocity is greater than the preset maximum angular velocity exceeds the second threshold within a preset time period. If not, it is determined that the current road condition is smooth. If so, it is determined that the current road condition is rapid. Under smooth road conditions, the Kalman filter is used to fuse the real-time vehicle speed, the real-time longitudinal acceleration, and the real-time angular velocity to obtain the final road slope. Under rapid road conditions, the real-time longitudinal acceleration is corrected in combination with the real-time angular velocity to obtain the corrected acceleration, and the complementary filter is used to fuse the real-time vehicle speed, the corrected acceleration, and the real-time angular velocity to obtain the final road slope.

[0124] The rolling resistance calculation module 5 is used to calculate the rolling resistance of the unpowered trailer based on the final road slope.

[0125] Among them, the functional implementation of each module in the above-mentioned trailer rolling resistance calculation system based on slope and acceleration correction corresponds to the steps in the above-mentioned trailer rolling resistance calculation method based on slope and acceleration correction embodiment, and its functions and implementation process will not be repeated here one by one.

[0126] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0127] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0128] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0129] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0130] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0131] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0132] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for calculating the rolling resistance of a trailer based on slope and acceleration correction, characterized in that: The method comprises: Collect the real-time speed of the towing vehicle, the real-time longitudinal acceleration of the trailer, and the real-time angular velocity of the trailer; determine whether the number of times the real-time longitudinal acceleration is greater than the preset maximum acceleration exceeds a first threshold or the number of times the real-time angular velocity is greater than the preset maximum angular velocity exceeds a second threshold within a preset time period, if so, determine that the current road condition is urgent, if not, determine that the current road condition is smooth; Under smooth road conditions, the Kalman filter is used to fuse the real-time vehicle speed, real-time longitudinal acceleration, and real-time angular velocity to obtain the final road slope; Under urgent road conditions, the real-time longitudinal acceleration is corrected in combination with the real-time angular velocity to obtain the corrected acceleration, and the real-time vehicle speed, corrected acceleration, and real-time angular velocity are fused using a complementary filter to obtain the final road slope. Calculate the rolling resistance of the unpowered trailer based on the final road grade.

2. The method for calculating the rolling resistance of a trailer based on slope and acceleration correction according to claim 1, characterized in that: The method of using the Kalman filter algorithm to fuse the real-time vehicle speed, the real-time longitudinal acceleration, and the real-time angular velocity to obtain the final road slope specifically includes the following steps: According to the real-time vehicle speed and the real-time longitudinal acceleration, the initial road slope is obtained; The initial road slope is used as a measurement value of the Kalman filter and combined with the real-time angular velocity to obtain the final road slope.

3. The method for calculating the rolling resistance of a trailer based on slope and acceleration correction according to claim 2, characterized in that: The initial road slope is calculated using the following formula: Among them, a represents the real-time longitudinal acceleration, g represents the acceleration due to gravity, θ represents the initial road slope, and v represents the real-time vehicle speed.

4. The method for calculating the rolling resistance of a trailer based on slope and acceleration correction according to claim 2, characterized in that: The final road slope is calculated using the following formula: Where k represents the time, represents the posterior estimate of the final road slope output by the k Kalman filter at the current time, represents the prior estimate of the final road slope output by the Kalman filter at the current time k, K k represents the gain coefficient of the Kalman filter at the current moment, θ represents the initial road slope, H represents the conversion matrix from state quantity to measurement value, and its size is 1. represents the prior estimate variance of the Kalman filter at the current time k, R represents the measurement noise covariance, P k represents the posterior estimation variance of the Kalman filter at the current time k, Q represents the state transition noise covariance, A represents the state transfer matrix, B represents the control matrix, and u k-1 represents the real-time angular velocity at the previous moment k-1, and I represents the unit matrix.

5. The method for calculating the rolling resistance of a trailer based on slope and acceleration correction according to claim 1, characterized in that: The method of correcting the real-time longitudinal acceleration in combination with the real-time angular velocity to obtain the corrected acceleration specifically includes the following steps: According to the preset acceleration threshold range, it is judged whether the real-time longitudinal acceleration exceeds the acceleration threshold range; if the real-time longitudinal acceleration is greater than the upper limit of the acceleration threshold range, the upper limit is used as the preliminary correction acceleration; if the real-time longitudinal acceleration is less than the lower limit of the acceleration threshold range, the lower limit is used as the preliminary correction acceleration to achieve the amplitude limitation of the longitudinal acceleration; Calculate the slope threshold according to the real-time angular velocity, determine whether the speed change rate of the modified acceleration exceeds the slope threshold, if not, continue to use the default strategy to limit the slope of the real-time longitudinal acceleration using the real-time angular velocity to obtain the modified acceleration; If not, switch to the upgrade strategy and use the real-time angular velocity to limit the slope of the real-time longitudinal acceleration to obtain the corrected acceleration; The default strategy is to control the change rate of the corrected acceleration not to exceed the change rate of the real-time angular velocity; The upgrade strategy is to control the change rate of the corrected acceleration not to exceed the slope threshold.

6. The method for calculating the rolling resistance of a trailer based on slope and acceleration correction according to claim 5, characterized in that: The slope threshold is calculated using the following formula: Da limit =(g[sin(θ′+δ′)-sinθ′]+gsin(e)cos(θ′+δ′))·h·dt Among them, Δa limit represents the slope threshold, g represents the gravitational acceleration, θ′ represents the optimal estimated value of the road slope in the previous time window, δ′ represents the integral value of the real-time angular velocity in the set time window, e represents the integral error value of the real-time angular velocity in the set time window, and dt is the operating step size of the controller; The speed change rate of the modified acceleration is calculated using the following formula: Among them, s k represents the rate of change of the corrected acceleration at the current moment k, a(k) represents the corrected acceleration before the update at the current moment k, a(k-1) represents the corrected acceleration after the update at the previous moment k-1, a(k-2) represents the corrected acceleration after the update at the previous moment k-2, and t represents time.

7. The method for calculating the rolling resistance of a trailer based on slope and acceleration correction according to claim 5, characterized in that: The upgrade strategy uses the following formula to calculate the final corrected acceleration: a(k)′=a(k-1)+sign(Δa k -Δa k-1 )·Δa k-1 ·Δa limit Where, represents the corrected acceleration after the current time k is updated, Δa k represents the rate of change of the corrected acceleration at the current moment k, Δa k represents the rate of change of the corrected acceleration at the current moment k, Δa k-1 represents the rate of change of the corrected acceleration at the previous moment k-1, Δa limit represents the slope threshold, and h represents a custom positive constant.

8. The method for calculating the rolling resistance of a trailer based on slope and acceleration correction according to claim 1, characterized in that: The method of using a complementary filter to fuse the real-time vehicle speed, the corrected acceleration, and the real-time angular velocity to obtain the final road slope specifically includes the following steps: The cutoff frequency of the complementary filter is obtained according to the change rate of the road slope output by the complementary filter, and the final road slope output by the complementary filter is adjusted by adjusting the cutoff frequency of the complementary filter.

9. The method for calculating the rolling resistance of a trailer based on slope and acceleration correction according to claim 8, characterized in that: The cut-off frequency is calculated using the following formula: Among them, f c represents the cutoff frequency of the complementary filter, f cmin represents the minimum cutoff frequency threshold of the complementary filter, γ represents the custom adjustment parameter, and θ fused (k-1) represents the final road slope output by the complementary filter at time k-1, θ fused (k-2) represents the final road slope output by the complementary filter at time k-2, and t represents time; The final road slope is calculated using the following formula: Among them, F(θ) represents the final road slope estimation result in the frequency domain, F(θ) represents the road slope estimation result of the low-frequency part in the frequency domain, and F(θ H ) represents the road slope estimation result of the high frequency part in the frequency domain, H L Represents a low-frequency filter, H G Represents a high-frequency filter with an integral process, F(θ a ) represents the road slope estimation result obtained by filtering the final road slope calculated based on the acceleration correction by a low-pass filter, F(u) represents the road slope estimation result obtained by filtering the real-time angular velocity by a high-pass filter, β represents the regularization parameter, represents the normalized frequency, f represents the sampling frequency of the complementary filter, and f c Represents the cutoff frequency of the complementary filter.

10. A trailer rolling resistance calculation system based on slope and acceleration correction, characterized in that: The system comprises: A vehicle speed collection device, which is arranged on the traction vehicle and is used to collect the real-time vehicle speed of the traction vehicle; A longitudinal acceleration acquisition device, which is arranged on the trailer and used to acquire the real-time longitudinal acceleration of the trailer; an angular velocity acquisition device, which is arranged on the trailer and used to acquire the real-time angular velocity of the trailer; A road slope calculation module, which is used to determine whether the number of times the real-time longitudinal acceleration is greater than the preset maximum acceleration exceeds a set first threshold or the number of times the real-time angular velocity is greater than the preset maximum angular velocity exceeds a second threshold within a preset time period; if not, it is determined that the current road condition is smooth; if so, it is determined that the current road condition is rapid; under smooth road conditions, the real-time vehicle speed, the real-time longitudinal acceleration, and the real-time angular velocity are fused using a Kalman filter to obtain a final road slope; under rapid road conditions, the real-time longitudinal acceleration is corrected in combination with the real-time angular velocity to obtain a corrected acceleration, and the real-time vehicle speed, the corrected acceleration, and the real-time angular velocity are fused using a complementary filter to obtain a final road slope; The rolling resistance calculation module is used to calculate the rolling resistance of the unpowered trailer based on the final road slope.

Images