ATO train control method for reducing energy consumption of train

Abstract

The application discloses an ATO train control method for reducing train energy consumption and relates to the technical field of train signal control. The method comprises the following steps: a vehicle-mounted ATO system calculates a current target speed CurSbi and a predicted target speed PreSbi; a target speed curve change model is identified according to a continuous three-period operation change rule of the ATO system; a dynamic P coefficient overshoot control protection check is performed on a PID controller according to the current target speed CurSbi and a current vehicle speed Vc, and if the overshoot protection is met, the P coefficient of the PID controller is adjusted; the vehicle-mounted ATO system calculates an expected output control instruction Rst0 of a current period according to the PID controller, and the control instruction Rst0 is optimized based on the identified target speed curve change model to obtain an output control instruction Rst1. The application identifies the target speed curve change model in front, optimizes and adjusts the current control instruction, reduces unnecessary traction and braking, and reduces train energy consumption.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of rail train signal control, in particular to an ATO train control method for reducing train energy consumption. BACKGROUND

[0002] ATO train automatic driving system (referred to as ATO or ATO system) works under the protection of automatic train protection system (ATP system), is a train automatic control system for realizing automatic train driving, accurate parking, automatic station operation, unmanned turnaround, automatic train operation adjustment and other functions, which can greatly reduce the labor intensity of drivers, and is the main guarantee for efficient, comfortable, punctual, accurate parking and energy-saving operation of the current high-speed and high-density urban rail transit system.

[0003] With the rapid development of urban rail transit, the power consumption of urban rail transit system is also increasing, and the energy consumption problem of urban rail transit system has attracted close attention from all walks of life. Especially with the continuous development of social economy and the continuous upgrading of consumption structure, people's demand for urban rail transit safety, reliability, convenience, comfort and economy is getting higher and higher, and it is more and more important to do a good job in energy saving and emission reduction of urban rail transit. SUMMARY

[0004] The purpose of the present application is to provide an ATO train control method for reducing train energy consumption, which identifies the change model of the front curve by comparing the trend of the ATO target speed control curve after time offset prediction with the current control stage, optimizes and adjusts the current control instruction according to the identified model, and reduces unnecessary traction and braking, so as to achieve the purpose of reducing train energy consumption.

[0005] The technical scheme adopted by the present application is as follows:

[0006] The present application is an ATO train control method for reducing train energy consumption, comprising the following steps:

[0007] The on-board ATO system calculates the current target speed CurSbi, obtains the estimated new position of the train after pushing the current position of the train to the running direction by a running distance PreDist, calculates the corresponding predicted target speed PreSbi at this position, and calculates the highest target speed MaxSbi allowed to be reached at the current position;

[0008] According to the continuous three-period running change rule of the ATO system of the current target speed CurSbi and the predicted target speed PreSbi, the change model of the target speed curve is identified;

[0009] According to the dynamic P coefficient overshoot control protection check of the PID controller according to the current target speed CurSbi and the current speed Vc, if the overshoot protection is met, the P coefficient of the PID controller is adjusted;

[0010] The vehicle-mounted ATO system calculates an expected output control instruction Rst0 for the current period according to a PID controller, optimizes the control instruction Rst0 based on the identified target speed curve change model, and obtains an output control instruction Rst1;

[0011] A highest target speed MaxSbi brake protection check is performed, and if the PID control calculation instruction corresponding to the highest target speed MaxSbi needs to output braking, the optimization revision of Rst0 in the above step is cleared.

[0012] Further, a time offset PreT is set, and a train current position advancing distance in a running direction is obtained based on a train current speed Vc: PreDist = Vc*PreT, that is, a train estimated new position is obtained, and the MA and the slope, static speed limit, and target stopping point information corresponding to the train estimated new position are combined to calculate an ATO system prediction target speed PreSbi of the position.

[0013] Further, in the ATO system for three consecutive operation periods, with continuous updating of the real position during train operation and continuous setting of the time offset PreT of the ATO system, the target speed prediction PreSbi is obtained, that is, the CurSbi and PreSbi target speed curves are obtained, and by comparing the change rules of the current target speed curve and the predicted target speed curve, the change model of the target curve tracking can be identified.

[0014] Further, the target speed curve change model includes:

[0015] C-C model: the current is uniform speed, the front is predicted to be uniform speed, PreSbi is higher than CurSbi, but PreSbi is uniform speed, which also belongs to this model;

[0016] C-D model: the current is uniform speed, and the front is predicted to enter deceleration braking;

[0017] D-D model: the current is braking, and the front is predicted to be deceleration braking;

[0018] D-C model: the current is braking, and the front is predicted to enter uniform speed, PreSbi is higher than CurSbi, but PreSbi is uniform speed, which also belongs to this model;

[0019] D-S model: the current is braking, and the front is predicted to stop;

[0020] Wherein, C represents the uniform speed stage, D represents the deceleration stage, and S represents the stopping stage; in order to ensure the stability of the determination of the control stage of the target speed Sbi, the target speed Sbi needs to be in the uniform speed, deceleration or zero speed for three consecutive operation periods to be considered to enter the corresponding stage.

[0021] Further, the dynamic P coefficient overshoot control protection is specifically:

[0022] When the ATO system is in the cruise control stage and in the non-deceleration section, and the difference between the current vehicle speed Vc and the current target speed CurSbi is less than the "adjustment P coefficient vehicle speed and target speed difference threshold", the P coefficient of the PID controller is adjusted according to the "P coefficient adjustment coefficient".

[0023] Further, the optimization based on the target speed curve change model includes optimizing the control instruction Rst0, which is specifically:

[0024] C-C model: When the current vehicle speed is expected to run at a constant speed between the current target speed and the predicted target speed, that is, PreSbi >= CurSbi, as much as possible, the coasting control should be used, that is, the target speed curve is tracked between the allowed traction speed threshold and the coasting speed threshold; PreSbi < CurSbi and the current vehicle speed Vc > PreSbi, at this time, if Rst0 > 0, the optimized output Rst1 = 0, filtering the traction energy consumption, at this time, if Rst0 < 0 but the current vehicle speed Vc is lower than the maximum target speed MaxSbi, the optimized output Rst1 = 0 is also filtered, and a small section of braking deceleration is converted into coasting deceleration; in other scenarios, the output instruction Rst0 is maintained;

[0025] C-D model: It is predicted that the deceleration section will be entered in a period of time, at this time, it is necessary to calculate whether small braking or coasting should be applied according to the current vehicle speed Vc and PreSbi, the calculation formula of the small braking rate is:

[0026] DestV = CurSbi*a + PreSbi*b (1)

[0027] ErrorV = DestV - Vc (2)

[0028] BrakeMin = p*ErrorV + i*Xi + d*ErrorDiff + GradAcc (3)

[0029] Wherein, the coefficients a and b of formula (1) are the control coefficients of CurSbi and PreSbi, so as to obtain the speed difference ErrorV of the current vehicle speed Vc and PreSbi, and then the control result BrakeMin is calculated based on the PID controller; the setting of a and b coefficients is positively related to the vehicle braking response characteristics,

[0030] Under this model, if Rst0>=0, when BrakeMin>0, the optimization is Rst1=0; when BrakeMin<GradAcc, the optimization is Rst1=GradAcc; for other scenarios of Rst0>0, the optimization is Rst1=BrakeMin, and when Rst0<0, there is no optimization processing;

[0031] Under the D-D model: indicating continuous deceleration, this model needs to prevent the deviation of the current vehicle speed Vc and CurSbi from being too large when the early braking rate is large, and the subsequent secondary traction and braking, the purpose is to filter the energy consumption of traction + braking; when Vc>PreSbi and Rst0>0, the optimization is Rst1=0;

[0032] Under the D-C model: indicating that it has entered the end of the deceleration section, and the PID controller has a serious control overshoot problem in the large downhill deceleration section, in order to filter the secondary traction energy consumption caused by brake overshoot, the output brake rate Rst1 is optimized through the following formula:

[0033] BrkMin=(PreSbi-Vc) / TimeRateA (4)

[0034] Rst1=BrkMin+GradAcc (5)

[0035] Wherein, TimeRateA is the time range of deceleration section acceleration calculation;

[0036] In addition to the optimization scenarios involved in the above models, the remaining scenarios maintain the control instruction output result Rst0.

[0037] Further, the highest target speed MaxSbi is the highest target speed MaxSbi allowed to be reached under the current vehicle speed without considering the running level speed limit, and the PID control result RstMax calculated according to the highest target speed MaxSbi is that when the brake is not needed to be output, the ATO is allowed to be idling to reduce the brake to achieve energy saving; when RstMax needs to be output, the optimization control result is idling or a brake rate less than RstMax, which is not allowed, to prevent the ATO system from running at a speed risk.

[0038] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present application are:

[0039] The present application is an ATO train control method for reducing train energy consumption, which predicts the trend of the ATO target speed control curve after a fixed time offset and compares it with the current control stage to identify the change model of the front curve, optimizes and adjusts the current control instruction according to the identified model, reduces unnecessary traction and braking, and achieves the energy-saving purpose of reducing train energy consumption.

[0040] The application is an ATO control method for reducing energy consumption of a train. A train-mounted ATO system calculates a recommended speed of the train running according to received moving authority MA and current position information, slope, static speed limit, whether platform stopping is needed and energy-saving running and other factors, controls the train to run at the recommended speed, and automatically completes reasonable control of starting, accelerating, cruising, coasting, decelerating and stopping of the train. The application has the function of online calculating the ATO target speed curve after fixed time offset, and early identifies approaching deceleration area, exiting deceleration area or stopping and the like in front of running, and calculates reasonable control operation to be applied in combination with current running state, so as to achieve the purpose of energy saving. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained from these drawings without creative labor, wherein:

[0042] Figure 1 Fig. 1 describes the relationship between the current target speed curve and the predicted target speed curve;

[0043] Figure 2 Fig. 3 describes the identification types included in the target speed curve change model;

[0044] Figure 3 Fig. 4 describes the target speed curve optimization output characteristics under the C-C model;

[0045] Figure 4 Fig. 5 describes the target speed curve optimization output characteristics under the C-D model;

[0046] Figure 5 Fig. 6 describes the target speed curve optimization output characteristics under the D-D model;

[0047] Figure 6 Fig. 7 describes the target speed curve optimization output characteristics under the D-C model;

[0048] Figure 7 Fig. 8 describes the flowchart of the determination method of the ATO train energy-saving control strategy based on curve prediction provided by the application. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with the accompanying drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and are not used to limit the present application, that is, the described examples are only some of the embodiments of the present application, but not all the embodiments. The components of the embodiments of the present application generally described and shown in the accompanying drawings can be arranged and designed in various different configurations.

[0050] It should be noted that the terms "comprising", "containing" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0051] The features and performances of the present application will be further described in combination with the examples.

[0052] Example 1

[0053] The present application is an ATO train control method for reducing train energy consumption, the method flow chart is shown as Figure 7 The specific steps are as follows:

[0054] The on-board ATO system calculates the current target speed CurSbi, and obtains the estimated new position of the train by pushing the current position of the train in the running direction by a running distance PreDist, and calculates the corresponding predicted target speed PreSbi at this position, and calculates the highest target speed MaxSbi allowed to be reached at the current position.

[0055] Specifically, the time offset PreT is set to 3s-7s, and the time offset PreT in this embodiment is 5s. The running distance PreDist in the running direction of the train based on the current speed Vc of the train is obtained, that is, PreDist=Vc*PreT, so that the estimated new position of the train is obtained. The ATO system predicted target speed PreSbi at this position is calculated in combination with MA and the slope, static speed limit and target stopping point information corresponding to the estimated new position of the train.

[0056] The ATO system calculates the current target speed CurSbi according to the current train position.

[0057] According to the continuous three-period operation change rule of the ATO system of the current target speed CurSbi and the predicted target speed PreSbi, the target speed curve change model is identified.

[0058] In the ATO system for three consecutive operation cycles, with the continuous update of the real position in train operation and the continuous setting of the time offset PreT of the ATO system, the target speed prediction PreSbi, the CurSbi and PreSbi two target speed curves can be obtained, as shown in Figure 1 By comparing the change law of the current target speed curve and the predicted target speed curve, the change model of the target curve tracking can be identified.

[0059] The target speed curve change model includes:

[0060] C-C model: the current is uniform speed, the predicted front is still uniform speed, PreSbi is higher than CurSbi, but PreSbi is uniform speed, as shown in Figure 2 (1) in the middle;

[0061] C-D model: the current is uniform speed, the predicted front will enter deceleration braking, as shown in Figure 2 (2) in the middle;

[0062] D-D model: the current is braking, the predicted front is still deceleration braking, as shown in Figure 2 (3) in the middle;

[0063] D-C model: the current is braking, the predicted front will enter uniform speed, PreSbi is higher than CurSbi, but PreSbi is uniform speed, as shown in Figure 2 (4) in the middle;

[0064] D-S model: the current is braking, the predicted front will stop;

[0065] Wherein, C represents the uniform speed stage, D represents the deceleration stage, and S represents the stop stage; in order to ensure the stability of the judgment of the target speed Sbi in the control stage, the target speed Sbi needs to be in uniform speed, deceleration or zero speed for three consecutive operation cycles to be considered to enter the corresponding stage.

[0066] According to the dynamic P coefficient overshoot control protection of the PID controller according to the current target speed CurSbi and the current speed Vc, if the overshoot protection is met, the P coefficient of the PID controller is adjusted;

[0067] The dynamic P coefficient overshoot control protection is specifically:

[0068] When the ATO system is in the cruise control stage and in the non-deceleration section, and the difference between the current vehicle speed Vc and the current target speed CurSbi is less than the "P coefficient adjustment speed and target speed difference threshold", the P coefficient of the PID controller is adjusted according to the "P coefficient adjustment coefficient", and the error response sensitivity is reduced, aiming to reduce the re-braking energy consumption caused by traction overshoot. In the embodiment, the parameter "P coefficient adjustment speed and target speed difference threshold" is 5 km / h, and the "P coefficient adjustment coefficient" is 0.7.

[0069] The on-board ATO system calculates the expected output control instruction Rst0 of the current period according to the PID controller, and optimizes the control instruction Rst0 based on the identified target speed curve change model to obtain the output control instruction Rst1.

[0070] When the ATO system is in the cruise control stage and in the non-deceleration section, and the difference between the current vehicle speed Vc and the current target speed CurSbi is less than the "P coefficient adjustment speed and target speed difference threshold", the P coefficient of the PID controller is adjusted according to the "P coefficient adjustment coefficient", and the error response sensitivity is reduced, aiming to reduce the re-braking energy consumption caused by traction overshoot. In the embodiment, the parameter "P coefficient adjustment speed and target speed difference threshold" is 5 km / h, and the "P coefficient adjustment coefficient" is 0.7.

[0071] The optimization of the control instruction Rst0 based on the target speed curve change model includes:

[0072] C-C model: When the current vehicle speed is expected to run at a constant speed between the current target speed and the predicted target speed, that is, PreSbi >= CurSbi, as much as possible, the coasting control should be used, that is, the target speed curve is tracked between the allowable traction speed threshold and the coasting speed threshold; PreSbi < CurSbi and the current vehicle speed Vc > PreSbi, at this time, if Rst0 > 0, the optimized output Rst1 = 0, filtering the traction energy consumption, at this time, if Rst0 < 0 but the current vehicle speed Vc is lower than the maximum target speed MaxSbi, the optimized output Rst1 = 0 is also used to filter the small section of the braking deceleration to coasting deceleration; in other scenarios, the output instruction Rst0 is kept, and the control instruction optimization curve is shown in Figure 3 ;

[0073] Under the C-D model: it is predicted that the deceleration section will be entered in a period of time, at this time, it is necessary to calculate whether small braking or coasting should be applied according to the current vehicle speed Vc and PreSbi, and the calculation formula of the small braking rate is:

[0074] DestV = CurSbi*a + PreSbi*b (1)

[0075] ErrorV = DestV - Vc (2)

[0076] BrakeMin = p*ErrorV + i*Xi + d*ErrorDiff + GradAcc (3)

[0077] Wherein, the coefficients a, b of formula (1) are the control coefficients of CurSbi and PreSbi, so as to obtain the speed difference ErrorV of the current vehicle speed Vc and PreSbi, and then the control result BrakeMin is obtained based on the PID controller calculation; the setting of a, b coefficients is positively related to the vehicle braking response characteristic, and in the present application, a = 0.7, b = 0.3.

[0078] Under this model, if Rst0 >= 0, when BrakeMin > 0, it is optimized as Rst1 = 0; when BrakeMin < GradAcc, it is optimized as Rst1 = GradAcc; in other scenarios of Rst0 > 0, it is optimized as Rst1 = BrakeMin, and when Rst0 < 0, no optimization processing is needed; the curve of the control instruction optimization is as shown in Figure 4 .

[0079] Under D-D model: it indicates continuous deceleration, and this model needs to prevent the deviation of the current vehicle speed Vc and CurSbi from being too large after subsequent secondary traction braking when the braking rate is large in the early stage, and the purpose is to filter the energy consumption of traction + braking; when Vc > PreSbi and Rst0 > 0, it is optimized as Rst1 = 0; the curve of the control instruction optimization is as shown in Figure 5 .

[0080] Under D-C model: it indicates that the end of the deceleration section is entered, and the PID controller has a serious problem of control overshoot in the end of the deceleration section of a large downhill, in order to filter the secondary traction energy consumption caused by brake overshoot, the braking rate Rst1 after optimization is output through the following formula;

[0081] BrkMin = (PreSbi - Vc) / TimeRateA (4)

[0082] Rst1 = BrkMin + GradAcc (5)

[0083] Wherein, TimeRateA is the time range of the calculation of the braking end acceleration; in the present application, the experimental experience parameter 3.5 seconds is taken, and the curve of the control instruction optimization is as shown in Figure 6 .

[0084] In addition to the optimization scenarios involved in the above models, the output result Rst0 of the control instruction is kept in the remaining scenarios.

[0085] If the PID control calculation instruction corresponding to the maximum target speed MaxSbi is a required output brake, the optimization revision of Rst0 in the above step is cleared;

[0086] The maximum target speed MaxSbi is the maximum target speed MaxSbi allowed to be reached at the current vehicle speed without considering the running level limit speed, and the PID control result RstMax calculated according to the maximum target speed MaxSbi is that the ATO is allowed to coast to reduce braking and achieve energy saving when no brake is required to be output; when RstMax requires brake to be output, the optimized control result is coasting or a braking rate smaller than RstMax, which is not allowed to prevent the ATO system from running at a speed risk.

[0087] The vehicle-mounted ATO system according to the present application calculates the recommended speed of the train running according to the received mobile authorization MA and the current position information, the slope, the static speed limit, whether the platform parking and the energy-saving running are required and other factors, and automatically completes the reasonable control of the train starting, accelerating, cruising, coasting, decelerating and stopping by controlling the current speed of the train (controlling the train to run according to the recommended speed). The present application has the function of online calculating the ATO target speed curve after fixed time offset, and can identify in advance that the front running is close to the deceleration area, exits the deceleration area or stops, and calculate the reasonable control operation to be applied in combination with the current running state to achieve the purpose of energy saving.

[0088] In order to make the advantages of the examples of the present application more clear, the present application uses field debugging test (Hohhot No. 1 line) to verify the energy-saving effect of the ATO energy-saving control strategy based on curve prediction. In addition to the above control method, the target parking brake level in the precise parking stage is adjusted simultaneously in the field experiment, the small interline is selected for testing, and the interval running curves before and after using the strategy are compared, and the test results are shown in Table 1,

[0089] Table 1, train test results

[0090]

[0091] Note: Energy saving ratio, under the commonly used level in daily operation, the single-kilometer energy consumption and single-train energy consumption after optimization of the single-car energy-saving strategy are compared with the single-kilometer energy consumption and single-train energy consumption of the single car without using the energy-saving strategy.

[0092] The statistical method is to use the traction energy consumption data displayed on the vehicle TCMS screen, to collect the train running kilometers and the corresponding energy consumption data analysis of traction energy consumption, the energy consumption of a single vehicle per kilometer is 10.87 Kwh, and the energy saving is 9.52%. After comparing the energy consumption of the train before and after the upgrade of the energy saving strategy in the winter of 2021, it is concluded that the energy consumption per ten thousand vehicle kilometers can be saved by 4.27%; the monthly energy consumption is saved by 3.2%.

[0093] The following explains the deviation of the above statistical data:

[0094] (1) The energy consumption statistics of the power supply professional is in the operation of carrying passengers, and the TCMS energy consumption statistics of the signal during debugging is empty load debugging.(2) The energy consumption of the air conditioner of the vehicle in winter is also improved, and the energy consumption base is large, which will also affect the percentage of energy consumption reduction.(3) The energy consumption statistics unit of the power supply professional is relatively large, and the energy consumption statistics of a single vehicle per circle can only be obtained by running more kilometers, so as to take the average value. But the working condition of the multi-run is slightly different, and it is found that the percentage of energy consumption reduction by vehicle statistics and daily statistics will be different.(4) The energy consumption statistics of the power supply professional is the overall consumption of the vehicle, not only the traction energy consumption; the TCMS screen statistics is only traction energy consumption statistics, which affects the percentage of energy consumption reduction.

[0095] The above is only a preferred embodiment of the present application, but the protection scope of the present application is not limited thereto, any skilled person in the art can think of changes or replacements within the technical range disclosed by the present application without creative labor, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be limited by the protection scope defined in the claims.

Claims

1. A method for reducing energy consumption of a train by ATO control, characterized in that, The method comprises the following steps: The vehicle-mounted ATO system calculates a current target speed CurSbi, and pushes the current position of the train in the running direction by a running distance Pre After Dist, the estimated new position of the train is obtained, and the corresponding predicted target speed PreSbi at this position is calculated, and the highest target speed MaxSbi allowed to be reached at the current position is calculated; According to the change rule of the ATO system of the current target speed CurSbi and the predicted target speed PreSbi in three continuous periods, a target speed curve change model is identified; According to the current target speed CurSbi and the current vehicle speed Vc, a dynamic P coefficient overshoot control protection check is performed on the PID controller, and if the overshoot protection is met, the P coefficient of the PID controller is adjusted; The vehicle-mounted ATO system calculates the expected output control instruction Rst0 of the current period according to the PID controller, optimizes the control instruction Rst0 based on the identified target speed curve change model, and obtains the output control instruction Rst1. A highest target speed braking protection check is performed, and if the PID control instruction corresponding to the highest target speed value needs to output braking, the optimization revision of Rst0 in the above steps is cleared.

2. The ATO train control method of claim 1, wherein, A time offset PreT is set, the distance of the current position of the train to the running direction is obtained based on the current speed Vc of the train, that is, PreDist = Vc * PreT, and the estimated new position of the train is obtained, the slope, static speed limit and target stopping point information corresponding to the estimated new position of the train are combined, and the predicted target speed PreSbi of the ATO system at the position is calculated.

3. The ATO train control method of reducing energy consumption of a train according to claim 2, characterized in that, In three continuous running periods of the ATO system, the predicted target speed PreSbi changes with the continuous update of the real position during train running and the continuous setting of the time offset PreT of the ATO system, that is, the CurSbi and PreSbi target speed curves are obtained, and the change rule of the current target speed curve and the predicted target speed curve is compared, that is, the target speed curve change model is identified.

4. The ATO train control method of reducing energy consumption of a train according to claim 3, characterized in that, The target speed curve change model comprises: C-C model: the current is uniform speed, the front is still uniform speed, PreSbi is higher than CurSbi, but PreSbi is uniform speed, which also belongs to this model; C-D model: the current is uniform speed, and the front will enter deceleration braking soon; D-D model: the current is braking, and the front is still deceleration braking; D-C model: the current is braking, and the front will enter uniform speed soon, PreSbi is higher than CurSbi, but PreSbi is uniform speed, which also belongs to this model; D-S model: the current is braking, and the front will stop soon; Wherein, C represents the uniform speed stage, D represents the deceleration stage, and S represents the stopping stage; in order to ensure the stability of the judgment of the control stage of the target speed Sbi, the target speed Sbi needs to be in the uniform speed, deceleration or zero speed for three continuous running periods to be considered to enter the corresponding stage.

5. The ATO train control method of reducing energy consumption of a train according to claim 1, wherein, The dynamic P coefficient overshoot control protection is specifically: When the ATO system is in the cruise control stage and in the non-deceleration section, the difference between the current vehicle speed Vc and the current target speed CurSbi is less than the "P coefficient adjustment vehicle speed and target speed difference threshold", and the P coefficient of the PID controller is adjusted according to the "P coefficient adjustment coefficient".

6. The ATO train control method of reducing energy consumption of a train according to claim 4, wherein, The optimization of the control instruction Rst0 based on the target speed curve change model is specifically: C-C model: When the current vehicle speed is expected to run at a constant speed between the current target speed and the predicted target speed, that is, PreSbi >= CurSbi, as much as possible, the coasting control should be adopted, that is, the target speed curve is tracked between the allowed traction speed threshold and the coasting speed threshold; PreSbi < CurSbi and the current vehicle speed Vc > PreSbi, at this time, if Rst0 > 0, the optimized output Rst1 = 0, filtering the traction energy consumption, at this time, if Rst0 < 0 but the current vehicle speed Vc is lower than the maximum target speed MaxSbi, also the optimized output Rst1 = 0, filtering the small brake deceleration to coasting deceleration; other scenarios except the above situations keep the output instruction Rst0; Under C-D model: It is predicted that the deceleration zone will be entered in a period of time, at this time, it is necessary to calculate whether small brake or coasting should be applied according to the current vehicle speed Vc and PreSbi, the calculation formula of small brake rate is: DestV = CurSbi*a + PreSbi*b(1) ErrorV = DestV - Vc(2) BrakeMin = p*ErrorV + i*Xi + d*ErrorDiff + GradAcc(3) Wherein, the coefficients a, b of formula (1) are the control coefficients of CurSbi and PreSbi, so as to obtain the speed difference ErrorV of current vehicle speed Vc and PreSbi, and then the control result BrakeMin is calculated based on the PID controller of ErrorV; the setting of a, b coefficients is positively related to the brake response characteristics of the vehicle, and GradAcc is the slope acceleration compensation; Under this model, if Rst0 >= 0, when BrakeMin > 0, the optimization is Rst1 = 0; when BrakeMin < GradAcc, the optimization is Rst1 = GradAcc; other scenarios of Rst0 > 0, the optimization is Rst1 = BrakeMin, when Rst0 < 0, there is no optimization processing; Under D-D model: It is predicted that the deceleration is continuous, this model needs to prevent the deviation of current vehicle speed Vc and CurSbi from being too large after secondary traction braking when the early brake rate is large; when Vc > PreSbi and Rst0 > 0, the optimization is Rst1 = 0; Under D-C model: It is predicted that the end of the deceleration zone is entered, and the PID controller has a serious control overshoot problem in the end of the large downhill deceleration. In order to filter the secondary traction energy consumption caused by brake overshoot, the brake rate Rst1 after optimization is output by the following formula: BrkMin = (PreSbi - Vc) / TimeRateA(4) Rst1 = BrkMin + GradAcc(5) Wherein, TimeRateA is the time range of the calculation of the brake end acceleration; In addition to the optimization scenarios involved in the above models, the remaining scenarios keep the output result Rst0 of the control instruction.

7. The ATO train control method of reducing energy consumption of a train according to claim 6, characterized in that, The highest target speed MaxSbi is the highest target speed MaxSbi allowed to be reached at the current vehicle speed without considering the operating level speed limit, and the PID control result RstMax calculated according to the highest target speed MaxSbi is that the ATO is allowed to coast without outputting braking to reduce braking and achieve energy saving; when RstMax needs to output braking, the optimized control result is the coasting or the braking rate less than RstMax, which is not allowed, and the ATO system is prevented from running at a speed risk.

Images