A locomotive tractive effort upper limit clipping method

Abstract

A locomotive traction force upper limit limiting method, according to the environmental temperature, weather state and track object state, the adhesion coefficient is adaptively adjusted by using the direct reasoning calculation method to obtain the adhesion coefficient setting value, so that the maximum traction force limit of the locomotive can change in real time with the change of the road section road condition, and the locomotive traction is performed as much as possible without wheel pair idling. When the wheel pair idling cannot be avoided, the non-linear mathematical expression is used to calculate the idling risk value according to the creep rate change rate and the creep degree, to judge whether the wheel pair of the locomotive idles, and to decide whether to perform the idling traction force control on the locomotive traction force after the upper limit limiting control according to the idling judgment result, to realize the comprehensive judgment of multiple factors, so that the idling judgment and the idling traction force control are more comprehensive and accurate.

Description

[0001] This invention patent application is a divisional application. The original application number is 202310472883.5, the application date is April 27, 2023, and the invention title is "An Adhesion Control System for Electric Locomotives". Technical Field

[0002] This invention belongs to the field of locomotive traction control technology, and in particular relates to a method for limiting the upper limit of locomotive traction force. Background Technology

[0003] Locomotive (train) operation is achieved through the interaction between wheels and rails. Only when effective adhesion between wheels and rails is ensured can the power of the traction motor be further utilized. Wheel-rail adhesion characteristics are not only related to the locomotive itself and the wheel and rail materials, but also to a series of uncertain factors that change with time and space, such as track conditions and rail surface cleanliness. If the traction force during locomotive operation exceeds the available adhesion between wheels and rails, the excess traction force will accelerate wheel spinning, rapidly increasing the relative sliding speed and quickly reducing the available adhesion. This will cause wear and even damage to the wheels and rails, increasing railway maintenance costs and threatening the safe operation of the locomotive. Because locomotive operating conditions are highly variable, changes in driver operation or deterioration of track conditions during traction can lead to slippage that cannot be completely avoided. Currently, domestic AC / DC locomotives mainly employ a combined correction method for slippage and anti-skid control. This method first assesses wheel acceleration; if acceleration exceeds a certain threshold, it indicates severe slippage, and the driving torque of the moving wheels is rapidly and deeply reduced, thus decreasing the locomotive's traction. If wheel acceleration does not exceed the threshold, creep speed is assessed; if creep speed exceeds the threshold, the driving torque is adjusted significantly; otherwise, it is considered normal operation. The current combined correction method uses two or more individual threshold conditions to determine slippage. When slippage has not occurred, it cannot comprehensively assess the risk of slippage; when slippage has already occurred, it cannot comprehensively assess the degree of slippage. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing a method for limiting the upper limit of locomotive traction force, according to formula...

[0005]

[0006] The locomotive traction force is subject to upper limit control; where F1 is the locomotive traction force before the upper limit is limited, F2 is the locomotive traction force after the upper limit is limited, and P... μ For calculating the adhesive weight of the locomotive, μ j ·P μ It is the maximum traction force limit.

[0007] Calculate the adhesion coefficient μ j in accordance with

[0008]

[0009] The calculation is performed, where V is the locomotive speed, a1, a2, a3, a4, and a5 are empirical formula parameters for calculating the adhesion coefficient, and ξ is the adhesion coefficient tuning value output by the adhesion coefficient expert control model.

[0010] The adhesion coefficient expert control model infers the adhesion coefficient tuning value ξ based on real-time input environmental temperature, weather conditions, and track-mounted object conditions. The method is as follows:

[0011] Step 1: If the weather condition is snowing or light rain, and the object on the track is snow, let ξ0 equal b0; if the weather condition is moderate or heavy rain, and the object on the track is snow, let ξ0 equal b1; if the weather condition is no rain or snow, and the object on the track is snow, let ξ0 equal b2; if the weather condition is snowing or light rain, and the object on the track is fallen leaves, let ξ0 equal b3; if the weather condition is snowing or light rain, and the object on the track is dust, let ξ0 equal b4; if the weather condition is moderate or heavy rain... When the object on the track is fallen leaves, let ξ0 equal b5; when the weather is no rain or snow and the object on the track is fallen leaves, let ξ0 equal b6; when the weather is moderate or heavy rain and the object on the track is dust, let ξ0 equal b7; when the weather is snowing or light rain and the object on the track is clean, let ξ0 equal b8; when the weather is no rain or snow and the object on the track is dust, let ξ0 equal b9; when the weather is moderate or heavy rain and the object on the track is clean, let ξ0 equal b... 10 When the weather conditions are no rain or snow and the track surface is clean, let ξ0 equal 1; b0 must be satisfied. <b1<b2<b3<b4≤b5<b6<b7≤b8≤b9<b 10 ;

[0012] The second step is to adjust the initial adhesion coefficient ξ0 and the measured ambient temperature C. 11 Calculate the adhesion coefficient tuning value ξ.

[0013]

[0014] Among them, the ambient temperature measurement value C 11 The unit is ℃, and the output range is -15℃ to +50℃.

[0015] The locomotive wheelset is judged to have idling based on the creep rate of change and creep degree. Based on the idling judgment result, it is determined whether to apply idling traction force control to the locomotive traction force after upper limit and width control. The method for judging whether the locomotive wheelset has idled is that when the idling risk value E is greater than or equal to 1, the locomotive wheelset has idled. The idling risk value E is calculated according to the formula...

[0016]

[0017] The calculation is performed, where x1 is the rate of change of creep, θ1 is the threshold for the rate of change of creep; x2 is the creep, θ2 is the creep threshold; γ1 and γ2 are nonlinear weighted control factors, and γ1≥10 and γ2≥10. The rate of change of creep x1 and the creep x2 are both non-negative values.

[0018] Idle traction control is achieved by controlling the idle traction control ratio θ. The idle traction control ratio θ is the ratio between the locomotive traction force output by the idle traction control module and the input locomotive traction force, and 0 ≤ θ ≤ 1. The idle traction control process of the idle traction control module is as follows:

[0019] Process I, the process of reducing idling traction, begins when the idling risk value E is greater than or equal to 1 and continues to increase, and ends when the idling risk value E changes from continuously increasing to starting to continuously decreasing; in Process I, the control θ begins to decrease with a slope d1, and the value of θ at the end of Process I is the minimum maintenance value; the minimum maintenance value of θ is not less than 0.

[0020] Process II, the process of maintaining the minimum traction force during idling, starts from the end of Process I and ends when the idling risk value E is less than 1; during Process II, the idling risk value E continuously decreases, and the control θ is equal to the minimum maintenance value;

[0021] Process III, the idling traction recovery process, begins at the end of Process II and ends when θ increases to equal 1. In Process III, the idling traction control module controls θ to increase at a slope d2 until θ equals 1. The rate of decrease of slope d1 is greater than the rate of increase of slope d2.

[0022] In the idling traction control process II of the idling traction control module, if the idling risk value E changes from continuously decreasing to continuously increasing, then return to process I for idling traction control; in the idling traction control process III of the idling traction control module, if the idling risk value E increases again to greater than or equal to 1, then return to process I for idling traction control.

[0023] The locomotive traction force upper limit limiting method is implemented by an electric locomotive adhesion control system comprising an adhesion coefficient expert control model, a traction force limiting self-tuning module, and an idle traction force control module. The adhesion coefficient expert control model takes ambient temperature, weather conditions, and track surface conditions as inputs and outputs an adhesion coefficient setpoint. The traction force limiting self-tuning module tunes the adhesion coefficient empirical calculation model based on the adhesion coefficient setpoint and outputs the upper limit limiting control applied to the input locomotive traction force. The idle traction force control module determines whether the locomotive wheelset is idle based on the creep rate of change and creep degree, and decides whether to apply idle traction force control to the locomotive traction force after the upper limit limiting control based on the idle determination result.

[0024] The electric locomotive adhesion control system also includes a climate track surface monitoring module; the climate track surface monitoring module includes an ambient temperature measurement unit, a weather condition measurement unit, and a track surface image acquisition and recognition unit; the ambient temperature measurement unit measures and outputs the current ambient temperature, the weather condition measurement unit measures and outputs the current weather condition, and the track surface image acquisition and recognition unit acquires real-time track surface images for recognition processing and outputs the current track attachment status.

[0025] The electric locomotive adhesion control system also includes a locomotive speed measurement and adjustment module (or device) for measuring and adjusting locomotive speed parameters such as creep, creep rate of change, locomotive wheelset speed rate of change, and locomotive speed. Specifically, it periodically collects the locomotive wheel rotation speed, locomotive radar speed, and onboard satellite positioning system speed to calculate the locomotive speed, creep, creep rate of change, and locomotive wheelset speed rate of change. The locomotive speed measurement and adjustment module includes a speed adjustment calculation unit, a locomotive wheel rotation speed acquisition unit, a locomotive radar speed acquisition unit, and an onboard satellite positioning system speed acquisition unit. The locomotive wheel rotation speed acquisition unit periodically collects the locomotive wheel rotation speed V(h), the locomotive radar speed acquisition unit periodically collects the locomotive radar speed W(h), and the onboard satellite positioning system speed acquisition unit periodically collects the onboard satellite positioning system speed U(k) and positioning status information X(k). The period for collecting the onboard satellite positioning system speed and positioning status information is T. U The period for collecting the locomotive radar speed and locomotive wheel rotation speed is T. V ;T U Greater than T VThe speed adjustment calculation unit uses the vehicle-mounted satellite positioning system's speed U(k) and positioning status information X(k) to adjust and calculate the parameters of the wheel / vehicle speed ratio adjustment model and the locomotive radar speed adjustment model, or it uses the radar synchronization adjustment speed output by the locomotive radar speed adjustment model to adjust and calculate the parameters of the wheel / vehicle speed ratio adjustment model. The wheel / vehicle speed ratio adjustment model outputs the locomotive speed, creep, creep rate of change, and locomotive wheelset speed rate of change. Iterative calculations are used to adjust and calculate the parameters of the wheel / vehicle speed ratio adjustment model and the locomotive radar speed adjustment model. The iteration calculation period is the same as the acquisition period T of the vehicle-mounted satellite positioning system's speed acquisition unit. U same.

[0026] The locomotive speed measurement and adjustment module periodically reads the locomotive wheel rotation speed V(k) and locomotive radar speed W(k) collected at the synchronous acquisition time point and performs iterative calculations, where k is the current iteration calculation generation.

[0027] During the k-th iteration, the locomotive speed measurement and adjustment module determines whether the speed of the onboard satellite positioning system is valid. If the speed of the onboard satellite positioning system is valid, the current wheel / vehicle speed adjustment coefficient P is adjusted. V (k) According to formula

[0028]

[0029] The tuning is performed, where U(k) is the speed of the vehicle-mounted satellite positioning system most recently collected, and V(k) is the locomotive wheel rotation speed collected at the synchronous collection time point of U(k).

[0030] When the vehicle-mounted satellite positioning system is determined to have a valid speed, the current radar speed ratio coefficient P W (k) According to formula

[0031]

[0032] Tuning is performed, where W(k) is the locomotive radar speed acquired at the synchronous acquisition time point U(k). When the onboard satellite positioning system speed is valid, the radar speed adjustment coefficient P... W equals P W (k).

[0033] When the vehicle-mounted satellite positioning system speed is determined to be invalid, the current radar speed ratio coefficient is obtained by fitting the previous radar speed ratio coefficient. The method is to perform the following calculations on m points (k-1, P...). W (k-1)), (k-2,P W (k-2)), ..., (km,P) W (km) is used to perform linear fitting to obtain the first-order fitting line for the radar velocity ratio. Points (k, P) on the first-order fitting line for the radar velocity ratio are taken.W * The value P on (k) W * (k) represents the current radar velocity ratio coefficient P. W (k). P W (k-1), P W (k-2), ..., P W (km) represents the m radar speed ratio coefficients obtained from the tuning calculations during the first m iterations of the locomotive speed measurement and adjustment module. Radar synchronization adjustment speed W * (k) According to formula

[0034]

[0035] Calculations are performed; the current wheel / vehicle speed adjustment coefficient is calculated according to the formula.

[0036]

[0037] Perform the adjustment.

[0038] Wheel / vehicle speed ratio coefficient U V (k) is obtained by fitting the wheel / vehicle speed adjustment coefficient for m points (k, P). V (k)), (k-1,P V (k-1)), (k-2,P V (k-2)), ..., (k-m+1, P V (k-m+1) is used to perform linear fitting to obtain the first-order fitted line of the wheel / vehicle speed adjustment coefficient. The point (k, U) on the first-order fitted line of the wheel / vehicle speed adjustment coefficient is taken. V The value U on (k)) V (k) is the wheel / vehicle speed ratio coefficient U V (k). P V (k-1), P V (k-2), ..., P V (k-m+1) represents the m-1 wheel / vehicle speed adjustment coefficients obtained from the tuning calculations during the first m-1 iterations of the locomotive speed measurement and adjustment module. m is an integer greater than or equal to 3.

[0039] Current locomotive speed V C (h) According to formula

[0040]

[0041] Perform calculations, calculation period and sampling period T V Same. Take the locomotive speed V as the current locomotive speed V. C (h).

[0042] According to the formula

[0043]

[0044] Calculate the current creep degree x2(h), calculation period and sampling period T V Similarly, take the creep degree x2 as equal to the current creep degree x2(h).

[0045] The rate of change of creep x1 is calculated according to the formula

[0046]

[0047] Perform calculations, calculation period and sampling period T V Same. x2(h-1) is the previous sampling period T. V The current creep degree obtained when calculating creep degree.

[0048] The τth locomotive wheel rotation speed acquisition time before the sampling time of the vehicle-mounted satellite positioning system's speed U(k) is the synchronous acquisition time point of U(k), where τ is the number of hysteresis intervals. The locomotive wheel rotation speed acquired at this point is V(k). Similarly, the τth locomotive radar speed acquisition time before the sampling time of the vehicle-mounted satellite positioning system's speed U(k) is the synchronous acquisition time point of U(k). The synchronous acquisition time points of the locomotive radar speed W(k) and the locomotive wheel rotation speed V(k) are consistent. The number of hysteresis intervals τ is the acquisition period T converted from the time lag between the acquisition time of the vehicle-mounted satellite positioning system's speed and the acquisition times of the locomotive wheel rotation speed and the locomotive radar speed. V The multiple value. When the vehicle-mounted satellite positioning system's speed U(k) is invalid, its sampling time still exists, meaning the synchronous acquisition time point of U(k) still exists. When the condition is met...

[0049] When the vehicle-mounted satellite positioning system speed has been valid for the most recent m1 consecutive checks, the delay interval period τ is calculated, where m1 ≥ 10; ε is the acceleration change threshold greater than 0. Specifically, the value of ε can be... to Choose from within the range of values. Let be the average acceleration of the locomotive at startup. In the above formula, β(k) is β(ki) when i equals 0, which is the most recent rate of change of locomotive acceleration; β(ki) when i equals 1, 2, ..., m1-1 is the most recent m1-1 rate of change of locomotive acceleration.

[0050] The locomotive acceleration change rate is according to the formula

[0051]

[0052] The calculation is performed; where α(k) is the locomotive acceleration collected most recently, and α(k-1) is the locomotive acceleration collected previously.

[0053] Locomotive acceleration is measured and collected by an accelerometer. Alternatively, locomotive acceleration is calculated according to the formula...

[0054]

[0055] The calculation is performed; where U(k-1) is the speed of the vehicle-mounted satellite positioning system in the previous acquisition of U(k).

[0056] The method for calculating the number of hysteresis intervals τ is as follows: Let the parameter to be optimized be the number of hysteresis intervals τ. * and radar speed proportionality coefficient p W * The number of delay intervals is τ. * At that time, the locomotive wheel rotation speed collected at the synchronous acquisition time point corresponding to U(ki) is V. * (ki), the locomotive radar speed acquired at the synchronous acquisition time point corresponding to U(ki) is W. * (ki), that is, the locomotive wheel rotation speed and locomotive radar speed collected at the synchronous acquisition time point corresponding to U(k) are V respectively. * (k), W * (ki), the locomotive wheel rotation speed and locomotive radar speed collected at the synchronous acquisition time point corresponding to U(k-1) are V respectively. * (k-1), W * (k-1), the locomotive wheel rotation speed and locomotive radar speed collected at the synchronous acquisition time point corresponding to U(k-2) are V respectively. * (k-2), W * (k-2), and so on. The minimum optimization objective function is...

[0057]

[0058] Take the number of delay intervals τ that satisfy the optimal value (i.e., Q is at its minimum) of Q. * Let τ be the number of delay interval periods; τ * The value range is greater than 0 and less than 2 / T V integers, p W * The value range is greater than or equal to 0.8 and less than or equal to 1.2.

[0059] In the locomotive speed measurement and adjustment module, the sampled locomotive wheel rotation speed is filtered to obtain the collected locomotive wheel rotation speed; the sampled locomotive radar speed is filtered to obtain the collected locomotive radar speed; and the sampled vehicle-mounted satellite positioning system speed is filtered to obtain the collected vehicle-mounted satellite positioning system speed. Before collecting the first vehicle-mounted satellite positioning system speed, let...

[0060]

[0061] Where i = 1, 2, ..., m-1.

[0062] The beneficial effects of this invention are as follows: Besides locomotive speed, the main factors affecting the adhesion coefficient include the condition of the rail surface and the surrounding environment. The input to the adhesion coefficient expert control model includes environmental temperature, weather conditions, and track surface conditions—major factors affecting the adhesion coefficient besides locomotive speed. A direct reasoning calculation method is used to obtain the adhesion coefficient tuning value. This tuning value is then used to tune the parameters of the empirical adhesion coefficient calculation model, which reflects the influence of locomotive speed. This allows the system to adaptively adjust the adhesion coefficient based on actual road condition data such as weather severity and track contamination. Furthermore, with the joint adjustment of the adhesion coefficient expert control model, the system can, based on an empirical formula for calculating the adhesion coefficient derived from extensive experimental data, consider both the actual operating section and changing road conditions. This allows the maximum traction limit of the locomotive to change in real time with changes in road conditions, facilitating locomotive traction as much as possible without wheelset slippage. When adhesion conditions worsen, even with upper limit and width restrictions, the locomotive traction force (wheel circumferential tangential force) on the wheel axle still exceeds the wheel-rail adhesion force, making wheelset slippage unavoidable. To quickly restore normal locomotive traction, this invention employs a nonlinear mathematical model to calculate the slippage risk value. It integrates multiple individual threshold judgment conditions for traditional wheelset slippage and weighted judgment conditions when none of the individual threshold conditions are met into a single whole, simplifying the judgment criteria. Even when none of the individual threshold conditions are met, it quantifies and weights multiple factors to achieve a comprehensive judgment, making slippage judgment more comprehensive and accurate. The selection of the nonlinear mathematical model minimizes the possibility of misjudgment by the weighted judgment conditions when none of the individual threshold conditions are met. Furthermore, the magnitude of the weighted judgment conditions and the relative magnitude of each weighted item can be set and adjusted through parameters, making this nonlinear mathematical model-normalized locomotive wheelset slippage judgment method applicable to different locomotive types and operating conditions. Attached Figure Description

[0063] Figure 1 A schematic diagram of the adhesion control system for an electric locomotive;

[0064] Figure 2 A method for direct inference calculation of the adhesion coefficient expert control model;

[0065] Figure 3 This diagram illustrates the idling traction control module when the locomotive wheelset experiences idling. Figure 1 ;

[0066] Figure 4 This diagram illustrates the idling traction control module when the locomotive wheelset experiences idling. Figure 2 ;

[0067] Figure 5 A schematic diagram of the locomotive speed measurement and adjustment module;

[0068] Figure 6 Flowchart of locomotive speed adjustment method;

[0069] Figure 7 A schematic diagram of an embodiment of the first-order fitting line for the radar velocity ratio coefficient;

[0070] Figure 8 A schematic diagram of an example of a first-order fitting line for the wheel / vehicle speed ratio coefficient;

[0071] Figure 9 A flowchart for calculating the number of delay intervals;

[0072] Figure 10 A schematic diagram showing the speed acquisition delay, locomotive acceleration, and locomotive acceleration change rate of the vehicle-mounted satellite positioning system;

[0073] Figure 11 A schematic diagram showing the time points at which the locomotive wheel rotation speed and locomotive radar speed are synchronously collected, representing the speed of the vehicle-mounted satellite positioning system. Detailed Implementation

[0074] The present invention will be further described below with reference to the accompanying drawings.

[0075] Figure 1 This is a schematic diagram of the adhesion control system for an electric locomotive, including an adhesion coefficient expert control model 10, a traction force limiting self-tuning module 11, a slip traction force control module 12, a locomotive speed measurement and adjustment module 13, and a climate and track surface monitoring module 14. F1 represents the locomotive traction force output from the locomotive speed controller, ξ is the adhesion coefficient setting value output from the adhesion coefficient expert control model, and the traction force limiting self-tuning module tunes the empirically calculated adhesion coefficient model based on the adhesion coefficient setting value. Specifically...

[0076]

[0077] In equation (1), V is the locomotive speed, μ jThis is the calculated adhesion coefficient output from the empirical model for calculating the adhesion coefficient. a1, a2, a3, a4, and a5 are parameters from the empirical formula for calculating the adhesion coefficient, and their values ​​are related to the electric locomotive model. For example, for various domestically produced electric locomotives, a1 = 0.24, a2 = 12, a3 = 100, a4 = 8, and a5 = 0 respectively; for the 6K type electric locomotive, a1 = 0.189, a2 = 8.86, a3 = 44, a4 ​​= 1, and a5 = 0 respectively; for the 8G type electric locomotive, a1 = 0.28, a2 = 4, a3 = 50, a4 = 6, and a5 = -0.0006 respectively; and so on. The unit of locomotive speed V is km / h.

[0078] The traction force limiting self-tuning module simultaneously uses the calculated adhesion coefficient output by the adhesion coefficient empirical calculation model to perform upper limit control on the locomotive traction force F1 before the upper limit limit, i.e.

[0079]

[0080] In equation (2), P μ The calculated adhesive weight of the locomotive is a constant for a given electric locomotive model; μ j ·P μ F1 is the maximum traction force limit; F2 is the locomotive traction force after the upper limit is set. μ The units for the traction forces F1 and F2 of each locomotive are kN; when needed, the traction force can also be converted into the corresponding torque.

[0081] Figure 1 In this embodiment, the climate orbital surface monitoring module includes an ambient temperature measurement unit, a weather condition measurement unit, and an orbital surface image acquisition and recognition unit. 11 The ambient temperature is measured and output by the ambient temperature measurement unit, with an output range of -15℃ to +50℃; when the ambient temperature is below -15℃, let C... 11 Equal to -15℃; when the ambient temperature is above +50℃, let C 11 Equal to +50℃. C 12 The weather conditions measured and output by the weather state measurement unit include five categories: snow, light rain, moderate rain, heavy rain, and no snow or rain. Fog conditions without rain are categorized under light rain. 13 The track surface image acquisition and recognition unit collects, identifies, and outputs track surface conditions, including snow, fallen leaves, dust, and cleanliness, totaling four track surface conditions. The ambient temperature measurement unit measures and outputs the ambient temperature, the weather condition measurement unit measures and outputs the weather condition, and the track surface image acquisition and recognition unit collects, identifies, and outputs the track surface conditions—all of which are standard techniques in this field.

[0082] In the embodiment, the adhesion coefficient expert control model infers the adhesion coefficient tuning value ξ based on the real-time input of ambient temperature, weather conditions, and track surface conditions during locomotive operation, as described in Method 1. Figure 2 As shown, the specific ones are:

[0083] Reasoning 1: Based on the ambient temperature and weather conditions, the weather environmental factor value ξ1 is derived. Specifically, when the ambient temperature is less than 0℃ and the weather condition is not rainless or snowless, ξ1 = b. 11 When the ambient temperature is greater than or equal to 0℃ and the weather is light rain or snow, ξ1=b 12 When the ambient temperature is greater than or equal to 0℃ and the weather condition is moderate rain, ξ1=b 13 When the ambient temperature is greater than or equal to 0℃ and the weather is heavy rain, ξ1=b 14 When the ambient temperature is below 0℃ and the weather is dry (no rain or snow), ξ1 = b 15 When the ambient temperature is greater than or equal to 0℃ and the weather conditions are rainless or snowless, ξ1=b 16 In reasoning 1, it is required that b is satisfied. 11 12 13 14 15 16 The specific value is determined based on the locomotive's operating status and expert experience. For example, one possible combination of values ​​is b. 11 =0.3, b 12 =0.4, b 13 =0.65, b 14 =0.7, b 15 =0.9, b 16 =1.

[0084] Reasoning 2: Based on the state of the orbital coating, the orbital coating factor value ξ2 is obtained. Specifically, when the orbital coating state is snow cover, ξ2 = b. 21 When the orbital landing state is fallen leaves, ξ2=b 22 When the orbital object is dust, ξ2=b 23 When the track surface is clean, ξ2 = b 24 In reasoning 2, it is required that b is satisfied. 21 22 23 24 The specific value is determined based on the locomotive's operating status and expert experience. For example, one possible combination of values ​​is b. 21 =0.5, b 22 =0.6, b 23 =0.8, b 24 =1.

[0085] ​​​​​​​​Reasoning 3: Based on the weather environment factor ξ1 and the orbital attachment factor ξ2, the adhesion coefficient tuning value ξ is calculated, that is, ξ=ξ1·ξ2.

[0086] Method 2 for obtaining the adhesion coefficient tuning value ξ by reasoning about the real-time input of ambient temperature, weather conditions and track surface conditions during locomotive operation using the adhesion coefficient expert control model is as follows: First, the initial adhesion coefficient tuning value ξ0 is obtained by reasoning based on Table 1.

[0087] Table 1

[0088] Snow or light rain Moderate or heavy rain No rain or snow snow <![CDATA[b0]]> <![CDATA[b1]]> <![CDATA[b2]]> Fallen leaves <![CDATA[b3]]> <![CDATA[b5]]> <![CDATA[b6]]> dust <![CDATA[b4]]> <![CDATA[b7]]> <![CDATA[b9]]> Clean <![CDATA[b8]]> <![CDATA[b 10 ]]> 1

[0089] The specific reasoning based on Table 1 is as follows: When the weather condition is snow or light rain, and the orbital object is snow, let ξ0 equal b0; when the weather condition is moderate or heavy rain, and the orbital object is snow, let ξ0 equal b1; when the weather condition is no rain or snow, and the orbital object is snow, let ξ0 equal b2; when the weather condition is snow or light rain, and the orbital object is fallen leaves, let ξ0 equal b3; when the weather condition is snow or light rain, and the orbital object is dust, let ξ0 equal b4; when the weather condition is moderate rain... If the weather is raining heavily and the track is covered with fallen leaves, let ξ0 equal b5; if the weather is dry and there is no rain or snow, and the track is covered with fallen leaves, let ξ0 equal b6; if the weather is moderate or heavy rain, and the track is covered with dust, let ξ0 equal b7; if the weather is snowing or light rain, and the track is covered with clean material, let ξ0 equal b8; if the weather is dry and there is no rain or snow, and the track is covered with dust, let ξ0 equal b9; if the weather is moderate or heavy rain, and the track is covered with clean material, let ξ0 equal b... 10 Assuming the weather conditions are dry (no rain or snow) and the track surface is clean, let ξ0 equal 1. The condition b0 must be satisfied. <b1<b2<b3<b4≤b5<b6<b7≤b8≤b9<b 10 For example, the values ​​are 0.4, 0.45, 0.5, 0.6, 0.7, 0.7, 0.8, 0.85, 0.9, 0.9, and 0.95 respectively.

[0090] The second step is to adjust the initial adhesion coefficient ξ0 and the measured ambient temperature C. 11 Calculate the adhesion coefficient tuning value ξ.

[0091]

[0092] The idling traction control module uses an established nonlinear mathematical model to calculate the idling risk value E, which is calculated according to the formula...

[0093]

[0094] Calculations are performed. In equation (4), x1 is the creep rate of change, θ1 is the creep rate of change threshold; x2 is the creep, θ2 is the creep threshold; γ1 and γ2 are nonlinear weighted control factors, and γ1≥10 and γ2≥10. The creep rate of change x1 and the creep degree x2 are both non-negative values. The idling judgment condition is that when E≥1, the locomotive (train) wheelset is judged to have idled. Combining equation (4) and the idling judgment condition, the idling judgment logic obtained by decomposition is: there are 3 situations (or one of the 3 conditions) that can be judged as idling, namely, ① when the creep rate of change x1 is greater than or equal to the threshold θ1; ② or, when the creep degree x2 is greater than or equal to the threshold θ2; ③ or, when the creep rate of change x1 is less than the threshold θ1 and the creep degree x2 is less than the threshold θ2 and the idling risk value E is greater than or equal to 1. The first two conditions, ① and ②, are individual threshold conditions. Either x1 ≥ θ1 or x2 ≥ θ2 satisfies E > 1, thus meeting the conditions for idling judgment. Condition ③ is a weighted judgment condition when neither of the individual threshold conditions is met. The larger the values ​​of γ1 and γ2, the greater the influence of the individual threshold judgment, and the smaller the effect of the weighted judgment in condition ③. For example, if γ1 and γ2 are both equal to 100, and x1 / θ1 and x2 / θ2 are both equal to 0.84, then the idling risk value E is 0.957, which does not meet the idling judgment condition; if x1 / θ1 and x2 / θ2 are both equal to 0.85, then the idling risk value E is 1.002, which meets the idling judgment condition. When the values ​​of γ1 and γ2 are relatively small, the weighting effect of condition ③ is greater. For example, when both γ1 and γ2 are equal to 10, then when both x1 / θ1 and x2 / θ2 are equal to 0.7, the idle risk value E is equal to 1.002, satisfying the idle judgment condition. The relative magnitudes of the nonlinear weighting control factors γ1 and γ2 are used to determine the relative effects between weighting terms, without affecting the judgment conditions for each individual term exceeding the threshold. The larger the value of either γ1 or γ2, the smaller the weighting effect of the corresponding judgment term; conversely, the smaller the value of either γ1 or γ2, the greater the weighting effect of the corresponding judgment term. For example, if γ1 is small and γ2 is large, then in the calculation of the idle risk value E in condition ③, the term x1 / θ1 plays a greater role in the weighted calculation than the term x2 / θ2, but the effects of the individual threshold conditions ① and ② remain unchanged. As long as either ① or ② reaches or exceeds the threshold, the idle judgment condition is still satisfied.

[0095] The idling risk value E or according to the formula

[0096]

[0097] Calculations are performed. In equation (5), x1 is the rate of change of creep, θ1 is the threshold of the rate of change of creep; x2 is the creep, θ2 is the creep threshold; x3 is the rate of change of locomotive wheelset speed, θ3 is the threshold of the rate of change of wheelset speed; γ1, γ2, and γ3 are nonlinear weighted control factors, and γ1≥10, γ2≥10, and γ3≥10. The rates of change of creep x1, creep x2, and locomotive wheelset speed x3 are all non-negative values. The condition for judging idling is that when E≥1, it is judged that the locomotive (train) wheelset has idled. Combining equation (5) and the idling judgment condition, the decomposed idling judgment logic is as follows: There are four situations (or one of the four conditions must be met) that can be judged as idling, namely: ① when the creep rate of change x1 is greater than or equal to the threshold θ1; ② or when the creep x2 is greater than or equal to the threshold θ2; ③ or when the locomotive wheel speed rate of change x3 is greater than or equal to the threshold θ3; ④ or when the creep rate of change x1 is less than the threshold θ1, the creep x2 is less than the threshold θ2, the locomotive wheel speed rate of change x3 is less than the threshold θ3, and the idling risk value E is greater than or equal to 1. The first three conditions ①②③ are single-item threshold conditions, that is, when a single item satisfies x1≥θ1, or x2≥θ2, or x3≥θ3, E is greater than or equal to 1, which satisfies the idling judgment condition. Condition ④ is a weighted judgment condition when none of the individual threshold conditions are met; the larger the values ​​of γ1, γ2, and γ3 are, the greater the influence of the individual threshold judgment and the smaller the effect of the weighted judgment; for example, when γ1, γ2, and γ3 are all 100, if x1 / θ1, x2 / θ2, and x3 / θ3 are all equal to 0.76, then the idle risk value E is equal to 0.99, which does not meet the idle judgment condition; if x1 / θ1, x2 / θ2, and x3 / θ3 are all equal to 0.77, then the idle risk value E is equal to 1.04, which meets the idle judgment condition. When the values ​​of γ1, γ2, and γ3 are relatively small, the weighting effect of condition ④ is greater. For example, when γ1, γ2, and γ3 are all 10, x1 / θ1 and x2 / θ2 are both equal to 0.7, and x3 / θ3 is equal to 0, which also satisfies the idle judgment condition. Or, for example, when x1 / θ1, x2 / θ2, and x3 / θ3 are all equal to 0.53, the idle risk value E is equal to 1.01, which satisfies the idle judgment condition. The relative magnitudes of the nonlinear weighted control factors β1, γ2, and γ3 are used to determine the relative magnitudes of the weighting effects between the weighting terms and do not affect the judgment conditions for each individual term exceeding the threshold. The larger the value of any one of γ1, γ2, and γ3, the smaller the weighting effect of the corresponding judgment term; conversely, the smaller the value of any one of γ1, γ2, and γ3, the greater the weighting effect of the corresponding judgment term. For example, if γ1 is small and γ2 and γ3 are large, then in the calculation of the idle risk value E in condition ④, the single term x1 / θ1 plays a greater role in the weighted calculation than terms x2 / θ2 and x3 / θ3. However, the role of the single threshold conditions ①②③ remains unchanged. As long as any one of ①②③ reaches or exceeds the threshold, the idle judgment condition is still satisfied.

[0098] The aforementioned values ​​for θ2 range from 0.005 to 0.05; θ1 ranges from 0.0001 / s to 0.005 / s; and θ3 ranges from 3 m / s. 2 ~30m / s 2 Between. The units of x1, x2, and x3 are the same as the units of θ1, θ2, and θ3, respectively.

[0099] In equation (4), each of the two terms and in equation (5), each term includes the following:

[0100] e = γ(ρ) -1) (6) shows a function term of the form where ρ represents x1 / θ1, x2 / θ2, and x3 / θ3, and γ represents γ1, γ2, and γ3, respectively. When 0 ≤ ρ < 1, i.e., when the value to be judged is less than the corresponding threshold, the closer the value to the threshold, the greater the impact of its change on the function term. For example, comparing x1 and θ1, the closer x1 is to θ1, the smaller the change in x1, the larger the change in e (i.e., the corresponding judgment term). This characteristic amplifies the effect of changes in the values ​​to be judged (i.e., x1, x2, x3) near the threshold, making it more sensitive near the threshold. Conversely, when the value to be judged is far from the threshold, the sensitivity is reduced to minimize the possibility of misjudgment of the weighted judgment condition when none of the individual threshold conditions are met.

[0101] The nonlinear mathematical models (4) and (5) for calculating the risk value of wheel slippage E both contain the creep rate of change and creep term. Creep is the relative difference between the locomotive wheelset speed and the locomotive speed. Its value directly reflects how far the locomotive wheelset is from wheel slippage, or how far it has already slipped. The creep rate of change is the speed at which creep changes. This value is related to both the locomotive wheelset speed rate of change and the locomotive speed rate of change. The larger the value, the higher the risk of wheel slippage. Equation (5) also contains the locomotive wheelset speed rate of change term. This term is similar to the creep rate of change. The larger the value, the higher the risk of wheel slippage. However, the locomotive wheelset speed rate of change is unrelated to the locomotive speed change. The inclusion of this term can help predict the occurrence of wheel slippage when the locomotive speed is high. When calculating the idling risk value E, you can choose either formula (4) or formula (5) as needed. When choosing formula (5), since the effects of the creep rate of change and the locomotive wheel speed rate of change are similar, the magnitudes of γ1 and γ3 should be taken into consideration.

[0102] The nonlinear mathematical model for calculating the idling risk value E, namely Equation (4) or Equation (5), and the corresponding idling judgment conditions, integrate the traditional single-threshold judgment conditions for multiple wheelsets idling and the weighted judgment conditions when none of the single-threshold conditions are met into a whole, simplifying the judgment basis. Furthermore, when none of the single-threshold conditions are met, multiple factors are quantified and weighted to achieve a comprehensive judgment of multiple factors, making the idling judgment more comprehensive and accurate. The selection of the nonlinear mathematical model can minimize the possibility of misjudgment by the weighted judgment conditions when none of the single-threshold conditions are met. At the same time, the magnitude of the weighted judgment conditions can be set and adjusted by parameters, and the relative magnitude of each weighted term can also be set and adjusted by parameters, making the locomotive wheelset idling judgment method normalized by this nonlinear mathematical model applicable to different locomotive types and operating conditions.

[0103] Figure 3 This diagram illustrates the idling traction control module when the locomotive wheelset experiences idling. Figure 1 The idling traction control ratio θ is the ratio between the locomotive traction force output by the idling traction control module and the input locomotive traction force. In other words, the idling traction control ratio θ is the ratio between the locomotive traction force F3 after idling traction control and the locomotive traction force F2 before idling traction control. F3 and the input traction force F2 satisfy the following condition:

[0104] The relationship between F3=θ·F2 0≤θ≤1 (7) is as follows. Figure 3 Before t1, the idling risk value E is less than 1, the locomotive wheelset does not idle, and the idling traction control ratio θ is equal to 1. The idling traction control process of the idling traction control module is as follows:

[0105] Process I, the process of decreasing traction force during idling; starting from when the idling risk value E is greater than or equal to 1 and continues to increase, until the idling risk value E changes from continuously increasing to beginning to decrease, that is, from... Figure 3 The process begins at time t1 and ends at time t2. During process I, the idling traction control module controls θ to decrease at a slope d1, and the value of θ at the end of process I is the minimum maintenance value. The minimum maintenance value of θ is not less than 0.

[0106] Process II involves maintaining the minimum idling traction force value; starting from the end of Process I, the idling risk value E continuously decreases until it falls below 1, at which point the process ends. Figure 3 The process begins at time t2 and ends at time t3; during process II, the idling traction control module controls θ to be equal to the minimum maintenance value.

[0107] Process III, the traction recovery process during idling; it begins at the end of Process II and ends when θ increases to equal 1, i.e., from... Figure 3The process begins at time t3 and ends at time t4. In process III, the idling traction control module controls θ to increase at a slope of d2 until θ equals 1.

[0108] When the idling risk value E increases from less than 1 to greater than or equal to 1, the condition that the idling risk value E is greater than or equal to 1 and continues to increase is met. When θ equals 1 and the idling risk value E is continuously less than 1, the idling traction control module does not implement idling traction control.

[0109] Figure 4 This diagram illustrates the idling traction control module when the locomotive wheelset experiences idling. Figure 2 In process II, if the idling risk value E changes from continuously decreasing to continuously increasing, then return to process I for idling traction control; if... Figure 4 In process t5, the idling risk value E changes from continuously decreasing to continuously increasing, and the idling traction control module immediately returns from process II to process I. In process III, if the idling risk value E increases again to a level greater than or equal to 1, it returns to process I for idling traction control; if... Figure 4 At time t6, the idling risk value E increases again to greater than or equal to 1, and the idling traction control module immediately returns from process III to process I.

[0110] The rate of descent of slope d1 is chosen between 0.3 / s and 2 / s. For example, if the rate of descent of slope d1 is chosen to be 0.5 / s, then θ will decrease by 50% in 1 second, which could be a decrease from 100% to 50% in 1 second, or a decrease from 80% to 30% in 1 second, and so on. The rate of rise of slope d2 is chosen between 0.05 / s and 0.5 / s. For example, if the rate of rise of slope d2 is chosen to be 0.2 / s, then θ will increase by 20% in 1 second, which could be an increase from 40% to 60% in 1 second, or an increase from 50% to 70% in 1 second, and so on. When determining d1 and d2, the absolute value of the rate of descent of slope d1 should be greater than the absolute value of the rate of rise of slope d2.

[0111] Currently, commonly used combined correction methods in China employ a fixed torque unloading strategy regardless of the degree of idling, failing to consider the wheel-rail adhesion state during unloading. This leads to several problems: first, insufficient unloading depth results in incomplete suppression of idling; second, excessive unloading depth causes locomotive traction loss; and third, unloading only stops when acceleration or creep rate falls below a set threshold, potentially resulting in excessive unloading depth. The idling traction control module of this invention controls idling traction based on an idling risk value that incorporates multi-factor comprehensive judgment. Both the degree and process of locomotive traction reduction are controlled by the idling risk value, reflecting the wheel-rail adhesion state. This effectively avoids situations where insufficient unloading depth leads to incomplete suppression of idling, or excessive unloading depth results in locomotive traction loss. Unloading stops when the idling risk value changes from increasing to decreasing, further mitigating the consequences of excessive unloading depth. The non-linear characteristics of the idling risk value allow judgments with higher risks to have a more significant control effect.

[0112] Figure 5 This is a schematic diagram of a locomotive speed measurement and adjustment module, or locomotive speed measurement and adjustment device, used to measure and adjust locomotive speed parameters such as locomotive creep, creep rate of change, locomotive wheelset speed rate of change, and locomotive speed. The locomotive wheel rotation speed acquisition unit 101 outputs the acquired locomotive wheel rotation speed V(h) (including V(k)) to the speed adjustment calculation unit 104; the locomotive radar speed acquisition unit 103 outputs the acquired locomotive radar speed W(h) (including W(k)) to the speed adjustment calculation unit 104; the on-board satellite positioning system speed acquisition unit 102 acquires and outputs the on-board satellite positioning system speed U(k) and positioning status information X(k) to the speed adjustment calculation unit 104; the speed adjustment calculation unit 104, based on the input information, adjusts and calculates the wheel / vehicle speed ratio adjustment model parameters and the locomotive radar speed adjustment model parameters, and outputs the locomotive speed, creep, creep rate of change, and locomotive wheelset speed rate of change. Specifically, the combination switch SW1 in the speed adjustment calculation unit 104 is controlled by the positioning status information X(k) input from terminal 5; when it is determined that the vehicle-mounted satellite positioning system speed is valid based on X(k), terminal 1 of the combination switch SW1 is connected to terminals 2 and 3, and the parameters of the wheel / vehicle speed ratio adjustment model and the locomotive radar speed adjustment model are adjusted by the vehicle-mounted satellite positioning system speed U(k); terminal 4 is left floating, and the radar synchronization adjustment speed W output by the locomotive radar speed adjustment model is... * (k) Not used at this time, i.e., W *(k) has no effect at this time. When it is determined that the vehicle-mounted satellite positioning system speed is invalid based on X(k), terminal 4 of control SW1 is connected to terminal 2. The locomotive radar speed adjustment model recursively derives the parameters of the locomotive radar speed adjustment model according to the given method. The locomotive radar speed adjustment model adjusts the locomotive radar speed value W(k) at the synchronous acquisition time point in the locomotive radar speed value W(h) to obtain the radar synchronous adjustment speed W. * (k), speed W is adjusted synchronously by radar. * (k) To tune the parameters of the wheel / vehicle speed ratio adjustment model; terminals 1 and 3 are suspended, meaning that the vehicle-mounted satellite positioning system speed U(k) is not used (or is invalid) at this time, and the parameters of the locomotive radar speed adjustment model are not tuned by external signals. The wheel / vehicle speed ratio adjustment model is adjusted and calculated based on the input locomotive wheel rotation speed V(k) and locomotive radar speed W(k), and outputs the locomotive speed V to the traction limit self-tuning module and the locomotive speed related quantities C2 to the idle traction control module. Among them, when the idle risk value E is calculated according to formula (4), the locomotive speed related quantities C2 include the creep change rate x1 and creep x2; when the idle risk value E is calculated according to formula (5), the locomotive speed related quantities C2 include the creep change rate x1, creep x2 and locomotive wheel speed change rate x3. Figure 5 The combination switch SW1 in the diagram is a schematic switch, which means that the direction of the signal flow is controlled according to X(k). In digital control, it is usually implemented by program branching.

[0113] In this embodiment of the locomotive speed measurement and adjustment device, the acquisition period T of the locomotive wheel rotation speed acquisition unit is... V The acquisition period T of the vehicle-mounted satellite positioning system's speed acquisition unit is 32ms. UThe value is 1 second, and m equals 4. When outputting the locomotive wheel rotation speed V(h) and the locomotive radar speed W(h), the corresponding speed acquisition unit has already performed corresponding filtering processing according to the specific situation in the speed sampling and data processing stage; for example, if the locomotive wheel rotation speed V(h) is sampled using a pulse speed sensor (encoder), the jitter interference of the pulse edge and the high-frequency interference in the pulse transmission process are filtered out accordingly; if the locomotive wheel rotation speed V(h) and the locomotive radar speed W(h) are directly output as analog or digital quantities, low-pass filtering, smoothing filtering, Kalman filtering, and other filtering methods can be used alone or in combination to filter out high-frequency interference, random interference, white noise interference, etc. The vehicle-mounted satellite positioning system speed acquisition unit includes one or more GNSS (Global Navigation Satellite System) receiving terminals, such as GPS, BeiDou, Galileo, and GLONASS system receiving terminals, and a corresponding receiving and processing module. The receiving and processing module receives information such as the number of satellites currently calculating the position, ground velocity (vehicle-mounted satellite positioning system speed), and the validity of the positioning status from one or more receiving terminals. Alternatively, it may also receive information such as longitude, latitude, UTC time, and altitude from one or more receiving terminals and calculate the vehicle-mounted satellite positioning system speed accordingly. The technical means employed in the locomotive wheel rotation speed acquisition unit, locomotive radar speed acquisition unit, and vehicle-mounted satellite positioning system speed acquisition unit are conventional techniques in this field.

[0114] Figure 6 This is a flowchart illustrating the locomotive speed adjustment method in a locomotive speed measurement and adjustment device. It involves setting and calculating the wheel / vehicle speed ratio adjustment model parameters, the locomotive radar speed adjustment model parameters, and the wheel / vehicle speed ratio coefficient and various locomotive speed-related quantities. The iterative calculation cycle is the same as the acquisition cycle of the vehicle-mounted satellite positioning system speed acquisition unit. The specific steps for each iterative calculation are as follows:

[0115] Step 1, read the calculation results of the kth iteration (equivalent to kT). U The data from the vehicle-mounted satellite positioning system at the sampling time includes the vehicle-mounted satellite positioning system speed U(k) and positioning status information X(k);

[0116] Step 2: Read the locomotive wheel rotation speed V(k) and locomotive radar speed W(k) collected at the synchronous acquisition time point of the vehicle-mounted satellite positioning system speed U(k);

[0117] Step 3: Determine if the vehicle-mounted satellite positioning system speed is valid; if the vehicle-mounted satellite positioning system speed is valid, proceed to step 4; if the vehicle-mounted satellite positioning system speed is invalid, proceed to step 5.

[0118] Step 4: Adjust the model parameters based on the wheel / vehicle speed ratio and the locomotive radar speed, i.e., according to formula...

[0119]

[0120] Adjust the current wheel / vehicle speed adjustment coefficient P V (k) and radar velocity ratio coefficient P W (k); Let the radar velocity adjustment coefficient P W equals P W (k), proceed to step 6;

[0121] Step 5: Calculate and adjust the parameters of the locomotive radar speed adjustment model, that is, for m points (k-1, P... W (k-1)), (k-2,P W (k-2)), ..., (km,P) W (km) is used to perform linear fitting to obtain the first-order fitting line for the radar velocity ratio. Points (k, P) on the first-order fitting line for the radar velocity ratio are taken. W * The value P on (k) W * (k) represents the current radar velocity ratio coefficient P. W (k). Let the radar speed adjustment coefficient P W equals P W (k), according to formula

[0122]

[0123] Calculate the radar synchronization adjustment speed W * (k); according to W * (k) Adjust the wheel / vehicle speed ratio model parameters, i.e., according to the formula

[0124]

[0125] Adjust the current wheel / vehicle speed adjustment coefficient P V (k); Proceed to step 6;

[0126] Step 6: Calculate the wheel / car speed ratio coefficient and calculate the speed-related quantities for each locomotive. Calculate the wheel / car speed ratio coefficient U. V (k) There are 2 implementation examples; calculate the wheel / vehicle speed ratio coefficient U. V Example 1 of (k), according to formula

[0127]

[0128] Calculate the wheel / vehicle speed ratio coefficient U V (k). Calculate the wheel / vehicle speed ratio coefficient U. V(k) Example 2, for m points (k, P) V (k)), (k-1,P V (k-1)), ..., (k-m+1, P V (k-m+1) is used to perform linear fitting to obtain the first-order fitted line of the wheel / vehicle speed adjustment coefficient. The point (k, U) on the first-order fitted line of the wheel / vehicle speed adjustment coefficient is taken. V The value of (k)U V (k) is the wheel / vehicle speed ratio coefficient.

[0129] In the example, m equals 4. Figure 7 This is a schematic diagram of an embodiment of the first-order fitting line for the radar velocity ratio coefficient. Figure 7 In the middle, the four "+" points from left to right represent points (k-4, P). W (k-4)), (k-3,P W (k-3)), (k-2,P) W (k-2)), (k-1,P W (k-1)), the point "o" on the first-order fitting line of the radar velocity ratio coefficient is the point (k, P). W * (k)). Figure 8 A schematic diagram of an example of a first-order fitted straight line for wheel / vehicle speed adjustment coefficients. Figure 8 In the middle, the four "+" points from left to right represent points (k-3, P). V (k-3)), (k-2,P) V (k-2)), (k-1,P V (k-1)), (k,P) V (k)), the point "o" on the first-order fitted line of the wheel / vehicle speed adjustment coefficient is the point (k, U). V (k)). Figure 7 , Figure 8 For illustrative purposes, the coefficient values ​​of the four "+" points are not actual data. To make the illustration clearer, the errors are intentionally marked as larger, and the slope of the first-order fitted line is also intentionally marked as larger.

[0130] The positioning status information X(k) includes whether the positioning status is valid or invalid, and the number of satellites currently using the position calculation. In step 3 of the locomotive speed adjustment method, the method for determining whether the vehicle-mounted satellite positioning system speed is valid is as follows: if the positioning status in the positioning status information X(k) is valid, the vehicle-mounted satellite positioning system speed is valid; otherwise, the vehicle-mounted satellite positioning system speed is invalid. Alternatively, the method for determining whether the vehicle-mounted satellite positioning system speed is valid is as follows: if the positioning status in both positioning status information X(k) and X(k-1) is valid, the vehicle-mounted satellite positioning system speed is valid; otherwise, the vehicle-mounted satellite positioning system speed is invalid. Another method for determining whether the vehicle-mounted satellite positioning system speed is valid is as follows: if the positioning status in the positioning status information X(k) is valid, and the number of satellites currently using the position calculation in the positioning status information X(k) is greater than or equal to δ, the vehicle-mounted satellite positioning system speed is valid; otherwise, the vehicle-mounted satellite positioning system speed is invalid. One method to determine the validity of the vehicle-mounted satellite positioning system speed is as follows: The vehicle-mounted satellite positioning system speed is valid when both the positioning status information X(k) and X(k-1) show valid positioning, and the number of satellites currently calculating the location in both X(k) and X(k-1) is greater than or equal to δ. Otherwise, the vehicle-mounted satellite positioning system speed is invalid. X(k-1) is the vehicle-mounted satellite positioning system data read at time k-1 during the previous iteration calculation. In this embodiment, the vehicle-mounted satellite positioning system speed acquisition unit includes a GPS system receiving terminal and a corresponding receiving and processing module. When the method for determining the validity of the vehicle-mounted satellite positioning system speed uses the latter two of the aforementioned four methods, and the number of satellites currently calculating the location in the positioning status information X(k) is required, δ should be greater than or equal to 4, with a preferred value of 5.

[0131] P in steps 4-6 V (k), or P when i equals 0 V (ki) represents the current wheel / vehicle speed adjustment coefficient. P is defined as follows: when i equals 1, 2, ..., m-1. V (k-1), P V (k-2), ..., P V (k-m+1) represent the wheel / vehicle speed adjustment coefficients obtained during the first m-1 iterations. P in steps 4-5... W (k), or P when i equals 0 W (ki) represents the current radar velocity ratio coefficient. P is defined as follows: when i equals 1, 2, ..., m. W (k-1), P W (k-2), ..., P W (km) represents the radar velocity ratio coefficients obtained during the first m iterations.

[0132] Step 6: Calculate the wheel / vehicle speed ratio coefficient U V In Example 1 of (k), μ V (k), μ V (k-1), ..., μ V (k-m+1) is related to P V (k-1), P V (k-2), ..., P V The corresponding variable weighting coefficients (k-m+1) satisfy the equation

[0133] The relationship between μ and μ, from largest to smallest. V (k), μ V (k-1), ..., μ V The value is taken from (k-m+1). For example, if m equals 4, μ V (k), μ V (k-1), μ V (k-2), μ V (h-3) is equal to 0.4, 0.3, 0.2, 0.1 respectively, or equal to 0.55, 0.27, 0.13, 0.05 respectively, and so on.

[0134] In step 6, the relevant quantities for each locomotive speed include locomotive speed V, creep rate of change x1, creep x2, and locomotive wheelset speed rate of change x3. Current locomotive speed V C (h) According to formula

[0135]

[0136] Perform calculations, calculation period and sampling period T V Same. V(h), V(k), W(h), W(k), U(k), W * (k), V C The unit of (h) is m / s; T V T U The unit is seconds (s). The locomotive speed V is taken as the current locomotive speed V. C (h); The unit of locomotive speed V is km / h. After converting the unit m / s to km / h, the value of locomotive speed V is equal to V0. C 3.6 times the (h) value.

[0137] U V (k) reflects the ratio between the locomotive wheelset speed and the locomotive speed, therefore the creep coefficient x2 can be calculated according to the formula...

[0138]

[0139] Perform calculations, calculation period and sampling period TU Same. Or, according to the formula.

[0140]

[0141] Calculate the current creep degree x2(h), calculation period and sampling period T V Similarly, take the creep degree x2 as equal to the current creep degree x2(h).

[0142] The rate of change of creep x1 is calculated according to the formula

[0143]

[0144] Perform calculations, calculation period and sampling period T U Same. U V (k-1) is the wheel / vehicle speed ratio coefficient obtained from the previous iterative calculation using the locomotive speed adjustment method. Alternatively, it can be calculated according to formula...

[0145]

[0146] Calculate the current rate of change of creep x1, the calculation period and the sampling period T. V Same. x2(h-1) is the previous sampling period T. V The current creep degree obtained when calculating creep degree.

[0147] According to the formula:

[0148]

[0149] Perform calculations, calculation period and sampling period T V Same. V(h-1) is the previous sample value of V(h).

[0150] The creep degree x2 and creep degree change rate x1 are calculated using equations (14) and (16). To ensure a rapid response when calculating the idling risk value E, it is recommended to use equation (5) to calculate the idling risk value E. When the creep degree x2 and creep degree change rate x1 are calculated using equations (15) and (17), equation (4) or equation (5) can be selected to calculate the idling risk value E as needed.

[0151] Figure 9 The flowchart illustrates a method for calculating the number of delay interval cycles in an embodiment of a locomotive speed measurement and adjustment device. The calculation cycle is the same as the acquisition cycle of the speed acquisition unit in the onboard satellite positioning system. This calculation can be performed before or after the iterative calculation of the locomotive speed adjustment method. Specifically:

[0152] Step ①: Obtain the current time, i.e., time k (i.e., kT). UThe rate of change of locomotive acceleration β(k) at the sampling time;

[0153] Step ②: Determine whether the conditions for calculating the number of delay interval periods are met. The equation is satisfied.

[0154] If the relationship is such that the speed of the vehicle-mounted satellite positioning system has been valid for the most recent m1 consecutive checks, proceed to step ③; otherwise, exit; m1 is greater than or equal to 10. The acceleration change threshold ε can be selected based on the acceleration capability of the locomotive (train) and in conjunction with experiments. The value of ε can be determined by... to Choose from within the range of values. This refers to the average acceleration of the locomotive (train) during startup. In the example, T... U Given a time interval of 1 second and m1 equals 20, the average acceleration of a locomotive from 0 to 200 meters can typically reach 0.4 m / s². 2 Then, the value of ε can be selected in the range of 0.4 to 2.4. For example, ε can be taken as 0.8. In equation (22), β(ki) when i equals 0 is the locomotive acceleration change rate β(k) at the current moment; β(ki) when i equals 1 is the locomotive acceleration change rate obtained when calculating the number of delay interval cycles in the previous calculation (i.e., iteratively calculating the wheel / vehicle speed ratio coefficient); and so on, β(ki) when i equals 1 to m1-1 are the locomotive acceleration change rates obtained when calculating the number of delay interval cycles in the previous m1-1 calculations. The most recent consecutive m1 judgments that the vehicle-mounted satellite positioning system speed is valid refers to the following Figure 6 In the iterative calculation of the locomotive speed adjustment method, in the most recent consecutive m1 iterations, step 3 determined that the speed of the on-board satellite positioning system was valid.

[0155] Step ③: Obtain the number of hysteresis intervals τ. The method is to set the parameter to be optimized as the number of hysteresis intervals τ. * and radar speed proportionality coefficient p W * ;τ * The value of is selected within the range that ensures the delay interval is no greater than 2 seconds, i.e., greater than 0 and less than 2 / T. V Integer; in the example, T V Equals 32ms, or 0.032s; 2 / T V Equals 62.5, therefore τ * The value of p is greater than 0 and less than or equal to 62. W * The value range is greater than or equal to 0.8 and less than or equal to 1.2. The parameter p to be optimized is... W * Only used in this optimization process. The number of delay interval periods is τ. *At that time, the locomotive wheel rotation speed collected at the synchronous acquisition time point corresponding to U(ki) is V. * (ki), the locomotive radar speed acquired at the synchronous acquisition time point corresponding to U(ki) is W. * (ki), the minimum optimization objective function is

[0156]

[0157] Optimization can employ various algorithms such as genetic algorithms and particle swarm optimization, selecting the number of delay intervals τ that satisfies the optimal (minimum) value Q. * Let τ be the number of delay intervals.

[0158] In step ①, obtain kT U The method for calculating the locomotive acceleration change rate β(k) at the sampling time is as follows:

[0159]

[0160] The calculation is performed, where α(k) is the currently collected locomotive acceleration, and α(k-1) is the previously collected locomotive acceleration. In this embodiment, the currently collected locomotive acceleration α(k) is calculated according to formula...

[0161]

[0162] The calculation is performed, where U(k) is the currently acquired vehicle satellite positioning system speed, and U(k-1) is the previously acquired vehicle satellite positioning system speed. The locomotive acceleration α(k) can also be measured using an accelerometer. The unit of α(k) is m / s². 2 The unit of β(k) is m / s. 3 .

[0163] Figure 10 This diagram illustrates the speed acquisition hysteresis, locomotive acceleration, and rate of change of locomotive acceleration for the vehicle-mounted satellite positioning system. Here, V(t) is the locomotive wheel rotation speed after V(h) is continuously converted, W(t) is the locomotive radar speed after W(h) is continuously converted, and U(t) is the satellite positioning system speed after U(k) is continuously converted; T τ The lag time between the vehicle-mounted satellite positioning system's speed acquisition time and the locomotive wheel rotation speed acquisition time is given by point k-7; points k-7 to k represent the various sampling times (k-7)T for the vehicle-mounted satellite positioning system's speed. U To kT U α(k) and β(k) are the locomotive acceleration and the rate of change of locomotive acceleration, respectively.

[0164] Figure 11This diagram illustrates the synchronized data acquisition time points of the locomotive wheel rotation speed and locomotive radar speed for the vehicle-mounted satellite positioning system. The sampling time at which U(k) occurs (i.e., kT) is... U The sampling times of V(h-τ), V(h-τ+1), ..., V(h-3), V(h-2), V(h-1), V(h), etc., are the sampling times of the locomotive wheel rotation speed, respectively. For example, the time of V(h) is its sampling time hT. V Due to ionospheric delay and other factors, for the acquisition of locomotive speed (including speed from the onboard satellite positioning system and speed from the locomotive radar) and locomotive wheel rotation speed at the same moment, the acquisition time of the onboard satellite positioning system speed lags behind the acquisition time of the locomotive wheel rotation speed and locomotive radar speed, with a time lag value of T. τ The delay interval τ is the number of acquisition periods relative to the locomotive wheel rotation speed acquisition period T. V The number of cycles, i.e., the number of hysteresis interval cycles τ, is the acquisition period T converted from the time lag between the acquisition time of the vehicle-mounted satellite positioning system's speed and the acquisition time of the locomotive wheel rotation speed and the locomotive radar speed. V Multiple values. Figure 11 In the above, V(h-τ) is located at the sampling time (h-τ)T. V Let V(k) be the synchronous acquisition time point of the vehicle-mounted satellite positioning system speed U(k). Specifically, the τ-th locomotive wheel rotation speed acquisition time (which is also the locomotive radar speed acquisition time) before the vehicle-mounted satellite positioning system speed U(k) sampling time is the synchronous acquisition time point of the vehicle-mounted satellite positioning system speed U(k). The acquisition period and time of the locomotive radar speed and the locomotive wheel rotation speed are the same, and the mutual delay between them is negligible. Therefore, the sampling times of the locomotive radar speeds W(hW), W(h-τ+1), ..., W(h-3), W(h-2), W(h-1), and W(h) are the same as the sampling times of the locomotive wheel rotation speeds V(h-τ), V(h-τ+1), ..., V(h-3), V(h-2), V(h-1), and V(h), respectively. The sampling time (h-τ)T of V(h-τ) is... V The sampling time of W(h-τ) is also the synchronous acquisition time point of the vehicle-mounted satellite positioning system speed U(k). The vehicle radar speed W(h-τ) acquired at this point is W(k).

[0165] Similarly, with Figure 11 For example, when performing the optimization calculation of the number of delay interval periods τ, if τ *If V(h-1) equals 1, then the sampling point where V(h-1) is located is its corresponding synchronous acquisition time point, and its V * (k) equals V(h-1), W * (k) equals W(h-1); if τ * If V(h-2) equals 2, then the sampling point where V(h-2) is located is its corresponding synchronous acquisition time point, and its V * (k) equals V(h-2), W * (k) equals W(h-2); and so on. Note that, for example, τ * Equal to 1, V * (k) equals V(h-1), and V * (k-1) is not V(h-2); in the embodiment, the vehicle-mounted satellite positioning system samples the speed once, and the locomotive wheel rotation speed is sampled an average of 31.25 times. Therefore, if τ * Equal to 1, V * (k) equals V(h-1), then V * (k-1) could be V(h-32) or V(h-33).

[0166] Due to creep, especially wheel slippage, the locomotive wheelset speed does not match the actual locomotive speed. Furthermore, when determining whether wheel slippage has occurred and calculating creep rate and creep degree, it is necessary to measure both the locomotive wheelset speed and the locomotive speed separately; the wheelset speed cannot be used to represent the locomotive speed. Locomotive speed is commonly measured using radar or satellite positioning. Satellite positioning speed measurement uses satellite positioning to track the locomotive's speed and position in real time, transmitting this information to the locomotive control unit for processing to obtain the final locomotive speed. Satellite positioning speed measurement can overcome errors caused by wheel slippage and wheel slippage, but its accuracy is greatly affected by weather and terrain, and it cannot achieve 100% speed measurement. There is also data transmission delay, which varies depending on distance and ionospheric conditions, affecting the real-time performance of speed measurements. Radar speed measuring devices are generally installed under the locomotive, with the radar antenna at a certain angle to the ground. The radar waves are emitted in the direction of the vehicle. When the vehicle moves relative to the ground, the received radar waves will experience a frequency shift. This shift is determined based on the radar wavelength, frequency shift amount, and angle. By calculating data such as radar installation height, the locomotive speed can be obtained; however, the included angle... Data such as radar installation height may experience time-shift fluctuations, and road conditions vary, potentially affecting the accuracy of radar speed measurement. In the aforementioned locomotive speed adjustment device, when satellite positioning speed measurement is effective, the wheel / vehicle speed ratio adjustment model parameters and locomotive radar speed adjustment model parameters are adjusted and calculated using the satellite positioning speed measurement data. When satellite positioning speed measurement is ineffective, new locomotive radar speed adjustment model parameters are calculated based on previously adjusted parameters using a given expression or by employing a first-order linear fitting method. These adjusted radar speed adjustment model parameters are then used to adjust and calculate the wheel / vehicle speed ratio adjustment model parameters. Finally, based on the wheel / vehicle speed ratio adjustment model, various locomotive speed-related quantities, such as locomotive speed, creep rate of change, creep, and locomotive wheelset speed rate of change, are calculated. This method combines the high accuracy of satellite positioning speed measurement with the real-time performance and long-term operational capability of radar speed measurement, improving the accuracy and reliability of measuring various locomotive speed-related quantities. The locomotive speed adjustment method also uses the method of determining whether the locomotive is in a speed change state. If it is in a speed change state, the information obtained from radar speed measurement, satellite positioning speed measurement, and locomotive wheelset speed measurement after the locomotive speed change is collected. The satellite positioning data transmission time, i.e. the number of delay intervals, is optimized to obtain the accurate real-time satellite positioning data transmission delay time (i.e. the number of delay intervals). This further ensures the accuracy and reliability of the speed data calculated by the aforementioned locomotive speed adjustment method.

Claims

1. A method for limiting the upper limit of locomotive traction force, characterized in that, According to the formula The traction force of the locomotive is subject to upper limit and amplitude control; among which... F 1 represents the locomotive traction force before the upper limit and width restriction. F 2 represents the locomotive traction force after the upper limit and width restriction. P μ For calculating the adhesive weight of the locomotive, μ j • P μ This is the maximum traction force limit; Calculate the viscosity coefficient μ j in accordance with Calculations are performed, in which, V It is the speed of the locomotive. a 1. a 2. a 3. a 4. a 5 represents the parameters in the empirical formula for calculating the adhesion coefficient; ξ It is the adhesion coefficient tuning value output by the adhesion coefficient expert control model; The method for determining whether locomotive wheelsets are spinning is based on the rate of change of creep and creep degree. Based on the spinning determination result, it is decided whether to apply spinning traction control to the locomotive traction force after upper limit and width control. The method for determining whether locomotive wheelsets are spinning is when the spinning risk value... E When the value is greater than or equal to 1, the locomotive wheelset will experience wheel spin; wheel spin risk value E According to the formula Calculations are performed, in which, x 1 represents the rate of change in creep. θ 1 represents the threshold for the rate of change of creep. x 2 represents creep. θ 2 represents the creep threshold; γ 1. γ 2 is a non-linear weighted control factor, and γ 1 ≥ 10 γ 2 ≥ 10.

2. The locomotive traction force upper limit limiting method as described in claim 1, characterized in that, The adhesion coefficient expert control model infers the adhesion coefficient setpoint by considering real-time input environmental temperature, weather conditions, and track-attached conditions. ξ The method is: First, if the weather condition is snowing or light rain, and the track is covered in snow, then... ξ 0 equals b 0; When the weather condition is moderate or heavy rain, and the track loading condition is snow, then... ξ 0 equals b 1; When the weather conditions are no rain or snow, and the track surface is covered with snow, then... ξ 0 equals b 2; When the weather condition is snowing or light rain, and the object on the track is fallen leaves, then... ξ 0 equals b 3; When the weather condition is snowing or light rain, and the track surface is dusty, then... ξ 0 equals b 4; When the weather condition is moderate or heavy rain, and the object on the track is fallen leaves, then... ξ 0 equals b 5; When the weather conditions are no rain or snow, and the track-mounted object is fallen leaves, then... ξ 0 equals b 6; When the weather condition is moderate or heavy rain, and the track surface is dusty, then... ξ 0 equals b 7; When the weather conditions are snowing or light raining, and the track surface is clean, then... ξ 0 equals b 8; When the weather conditions are no rain or snow, and the track surface is dusty, then... ξ 0 equals b 9; When the weather condition is moderate or heavy rain, and the track surface is clean, then... ξ 0 equal b 10 When the weather conditions are no rain or snow, and the track surface is clean, then... ξ 0 Equals 1; Requires satisfying b 0< b 1< b 2< b 3< b 4≤ b 5< b 6< b 7≤ b 8≤ b 9< b 10 ; The second step is to adjust the value based on the initial adhesion coefficient. ξ 0 and ambient temperature measurement value C 11 Calculate the adhesion coefficient tuning value ξ , Among them, ambient temperature measurement value C 11 The unit is ℃, and the output range is -15℃ to +50℃.

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