CN113135208A - Train operation optimization method with limited energy and free time - Google Patents
Train operation optimization method with limited energy and free time Download PDFInfo
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- CN113135208A CN113135208A CN202110582464.8A CN202110582464A CN113135208A CN 113135208 A CN113135208 A CN 113135208A CN 202110582464 A CN202110582464 A CN 202110582464A CN 113135208 A CN113135208 A CN 113135208A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/10—Operations, e.g. scheduling or time tables
- B61L27/16—Trackside optimisation of vehicle or vehicle train operation
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/40—Handling position reports or trackside vehicle data
Abstract
The invention discloses an energy-limited and time-free train operation optimization method, which comprises the following steps of: s1: acquiring basic data of a train, state information of the train, basic data of a line, basic data of energy consumption of an auxiliary system and basic data of a state of a vehicle-mounted energy storage system; s2: acquiring a train running speed curve with lowest accumulated energy consumption; s3: judging whether the lowest accumulated energy consumption is less than the residual electric quantity of the train battery, if so, entering a step S4, otherwise, entering a step S5; s4: taking the train running speed curve with the lowest accumulated energy consumption as a train running speed optimization curve with the lowest accumulated energy consumption; s5: and obtaining a train running speed optimization curve of the farthest running distance by using a constant-speed coasting strategy. The invention is suitable for a train with vehicle-mounted energy storage and power supply capacity, optimizes the train running speed curve, reduces the accumulated running energy consumption and enlarges the train running distance under limited energy storage capacity in the power-off self-rescue scene of a contact network.
Description
Technical Field
The invention belongs to the technical field of train operation optimization control with limited energy, and particularly relates to an optimization method suitable for emergency self-running of a train.
Background
The rail transit in China develops rapidly, the train operation quantity is large, the required power equipment is large, the power supply grid has power failure due to various reasons such as severe weather, falling of high-voltage cables, contact network faults, power supply system faults and the like because the members in China are wide, the line conditions are complex and the weather change in the east and west is large, and the train can only rely on the vehicle-mounted energy storage equipment to supply power at the moment. Because of the limited train space and the fact that the weight and size of the on-board energy storage device cannot be excessive to reduce unnecessary energy consumption, the capacity of the on-board energy storage device is limited due to the limitations of current battery technology, and how to safely and efficiently operate the train to a stopping point under the power supply of the on-board energy storage device and without time constraints is of great significance for safe operation of the railway system. Therefore, the invention provides a train operation optimization method with energy limited and free time.
Under the condition that only the train battery supplies power, the energy consumption in the subsequent running process of the train consists of the train traction energy consumption and the energy consumption of a train auxiliary system, the energy consumption of the train auxiliary system is mainly used for train air conditioning, oxygen generation, illumination and the like, and the auxiliary energy consumption required by the train accounts for a large proportion of the total energy consumption. The lower the train running speed is, the less the traction energy consumption is, but the train running time is lengthened, and the energy consumption of an auxiliary system of the train is in direct proportion to the running time under the condition of certain power, so that the sum of the traction energy consumption and the auxiliary energy consumption of the train is taken as a target function to optimize the subsequent running of the train, the energy consumption in the running process is reduced as much as possible, and the aim of optimizing and saving energy in the running of the train is fulfilled; and if the residual battery capacity of the train cannot reach the stopping target point, optimizing by adopting a new strategy so that the train has the maximum running distance.
At present, most of train operation optimization schemes do not consider the conditions of energy limitation and time freedom, and the existing optimization schemes only have train operation processes aiming at energy limitation and do not optimize train operation without time limitation.
Disclosure of Invention
The invention aims to solve the problem of optimization of train operation and provides an optimization method suitable for emergency self-running of a train.
The technical scheme of the invention is as follows: an optimization method suitable for emergency self-running of a train comprises the following steps:
s1: acquiring basic data of a train, state information of the train, basic data of a line, basic data of energy consumption of an auxiliary system and basic data of a state of a vehicle-mounted energy storage system;
s2: based on the basic data of the train, the state information of the train, the basic data of the line and the basic data of the energy consumption of the auxiliary system, taking a front station or a rescue station where the train runs as a parking target station, and acquiring a train running speed curve with the lowest accumulated energy consumption by using an inert energy-saving strategy;
s3: judging whether the lowest accumulated energy consumption is less than the residual electric quantity of the train battery according to the train running speed curve of the lowest accumulated energy consumption, if so, entering a step S4, otherwise, entering a step S5;
s4: taking the train running speed curve with the lowest accumulated energy consumption as a train running speed optimization curve with the lowest accumulated energy consumption to finish the optimized running of the train;
s5: and taking the residual electric quantity of the train battery as a constraint condition, and acquiring a train running speed optimization curve of the farthest running distance by using a constant-speed coasting strategy to finish the optimized running of the train.
Further, in step S1, the basic data of the train includes the train weight and the traction braking characteristic;
the state information of the train comprises the running speed of the train and the position of the train;
the basic data of the line comprises station kilometer posts, speed limit, gradient and curve;
the basic data of the auxiliary system energy consumption comprises auxiliary system power;
the basic data of the vehicle-mounted energy storage system comprise the capacity of the vehicle-mounted energy storage system, the residual capacity of a train battery and the discharge characteristic.
Further, step S2 includes the following sub-steps:
s21: taking a front station or a rescue station where the train runs as a parking target station, and constructing a minimum accumulated energy consumption model according to the state information of the train and the basic data of the energy consumption of the auxiliary system;
s22: setting a constant speed step length, and determining a corresponding constant speed grade and a corresponding constant speed;
only one speed step needs to be set, and every time the speed grade is increased by one, the speed is increased by a set step on the original basis;
s23: calculating a traction constant-speed braking curve according to basic data of the train and constant-speed working conditions corresponding to different constant-speed speeds, taking the traction working condition of the traction constant-speed braking curve as a starting point and the braking working condition as an end point, and dividing the traction constant-speed braking curve into a plurality of subintervals;
s24: setting a lazy line starting point in each subinterval, and adding a lazy line working condition according to the lazy line starting point;
s25: traversing each coasting starting point based on the minimum accumulated energy consumption model, and calculating a train running speed curve and corresponding accumulated energy consumption after adding the coasting working condition according to the state information of the train and the basic data of the line;
s26: judging whether all the starting points of the lazy lines are traversed, if so, entering a step S27, and if not, returning to the step S25;
s27: taking the curve with the minimum accumulated energy consumption in the train running speed curves obtained by traversing all the coasting starting points as the most energy-saving speed curve;
s28: traversing each speed grade, judging whether all the speed grades are traversed or not, if so, entering the step S29, otherwise, returning to the step S22;
s29: and taking the curve with the minimum accumulated energy consumption in the train running speed curves obtained by traversing all the speed grades as the train running speed curve with the minimum accumulated energy consumption.
Further, in step S21, the expression of the minimum cumulative energy consumption model minE is:
minE＝Et+Eaux
Et＝∫F(t)ds
wherein E represents the total energy consumption of the train running process, EtRepresenting energy consumption of the train, EauxRepresenting auxiliary energy consumption of the train, F (t) representing tractive effort exerted by the train, Paux(t) represents auxiliary system power, v (t) represents train operating speed.
Further, step S5 includes the following sub-steps:
s51: constructing a train electric quantity constraint model according to the residual electric quantity of the train battery;
s52: setting a constant speed step length, and determining a corresponding constant speed grade and a corresponding constant speed;
s53: according to basic data of a vehicle-mounted energy storage system and constant speed working conditions corresponding to different constant speed speeds, calculating train operation speed curves under different constant speed grades, taking the traction working condition of the train operation speed curve as a starting point, taking the train constant speed operation until the residual battery electric quantity of the train is exhausted as an end point, and dividing the train operation speed curve into a plurality of subintervals;
s54: adding an inertia working condition in each subinterval, traversing each constant speed grade, updating the train running speed curve after the inertia working condition is added, and determining the running distance of the updated train running speed curve;
s55: judging whether all the constant speed grades are traversed or not, if so, entering a step S56, and otherwise, returning to the step S54;
s56: and taking the train running speed curve with the farthest running distance in all the constant speed grades as the train running speed optimization curve with the farthest running distance.
Further, in step S51, the expression of the train electric quantity constraint model E' is:
E'≤Ebattery
wherein E isbatteryRepresenting the remaining energy of the train battery.
The invention has the beneficial effects that:
(1) the invention is suitable for a train with vehicle-mounted energy storage and power supply capacity, optimizes the train running speed curve, reduces the accumulated running energy consumption and enlarges the train running distance under limited energy storage capacity in the power-off self-rescue scene of a contact network.
(2) And under the condition of limited energy time, optimizing the train running speed curve, and optimizing by taking the minimum sum of train running energy consumption and auxiliary system energy consumption as a target to obtain the most energy-saving speed curve, so that the energy-saving speed curve has energy-saving property.
(3) If the train can not run to a set position under the condition of limited energy, the train stops on a running line, the consequences such as multiple trains of the railway system at a later point, even train number cancellation and the like can be caused, so that the normal operation of the railway system is seriously influenced, the train can run for the farthest distance when the electric quantity of the battery is insufficient by optimizing the operation of the train, the influence on the railway system is reduced, and the train has high efficiency.
Drawings
Fig. 1 is a flow chart of a train operation optimization method.
Detailed Description
The embodiments of the present invention will be further described with reference to the accompanying drawings.
Before describing specific embodiments of the present invention, in order to make the solution of the present invention more clear and complete, the definitions of the abbreviations and key terms appearing in the present invention will be explained first:
energy-limited and time-free train operation: the method refers to the running process of the train which is powered by the vehicle-mounted energy storage system and needs to run to a parking target without time constraint.
As shown in fig. 1, the present invention provides an optimization method for emergency self-running of a train, comprising the following steps:
s1: acquiring basic data of a train, state information of the train, basic data of a line, basic data of energy consumption of an auxiliary system and basic data of a state of a vehicle-mounted energy storage system;
s2: based on the basic data of the train, the state information of the train, the basic data of the line and the basic data of the energy consumption of the auxiliary system, taking a front station or a rescue station where the train runs as a parking target station, and acquiring a train running speed curve with the lowest accumulated energy consumption by using an inert energy-saving strategy;
s3: judging whether the lowest accumulated energy consumption is less than the residual electric quantity of the train battery according to the train running speed curve of the lowest accumulated energy consumption, if so, entering a step S4, otherwise, entering a step S5;
s4: taking the train running speed curve with the lowest accumulated energy consumption as a train running speed optimization curve with the lowest accumulated energy consumption to finish the optimized running of the train;
s5: and taking the residual electric quantity of the train battery as a constraint condition, and acquiring a train running speed optimization curve of the farthest running distance by using a constant-speed coasting strategy to finish the optimized running of the train.
In the embodiment of the present invention, in step S1, the basic data of the train includes the train weight and the traction braking characteristic;
the state information of the train comprises the running speed of the train and the position of the train;
the basic data of the line comprises station kilometer posts, speed limit, gradient and curve;
the basic data of the auxiliary system energy consumption comprises auxiliary system power;
the basic data of the vehicle-mounted energy storage system comprise the capacity of the vehicle-mounted energy storage system, the residual capacity of a train battery and the discharge characteristic.
In the embodiment of the present invention, as shown in fig. 1, step S2 includes the following sub-steps:
s21: taking a front station or a rescue station where the train runs as a parking target station, and constructing a minimum accumulated energy consumption model according to the state information of the train and the basic data of the energy consumption of the auxiliary system;
s22: setting a constant speed step length, and determining a corresponding constant speed grade and a corresponding constant speed;
s23: calculating a traction constant-speed braking curve according to basic data of the train and constant-speed working conditions corresponding to different constant-speed speeds, taking the traction working condition of the traction constant-speed braking curve as a starting point and the braking working condition as an end point, and dividing the traction constant-speed braking curve into a plurality of subintervals;
s24: setting a lazy line starting point in each subinterval, and adding a lazy line working condition according to the lazy line starting point;
s25: traversing each coasting starting point based on the minimum accumulated energy consumption model, and calculating a train running speed curve and corresponding accumulated energy consumption after adding the coasting working condition according to the state information of the train and the basic data of the line;
s26: judging whether all the starting points of the lazy lines are traversed, if so, entering a step S27, and if not, returning to the step S25;
s27: taking the curve with the minimum accumulated energy consumption in the train running speed curves obtained by traversing all the coasting starting points as the most energy-saving speed curve;
s28: traversing each speed grade, judging whether all the speed grades are traversed or not, if so, entering the step S29, otherwise, returning to the step S22;
s29: and taking the curve with the minimum accumulated energy consumption in the train running speed curves obtained by traversing all the speed grades as the train running speed curve with the minimum accumulated energy consumption.
The operation accumulated energy consumption in the step S2.1 refers to the sum of train traction energy consumption and train auxiliary system energy consumption in the train operation process.
In the embodiment of the present invention, in step S21, the expression of the minimum cumulative energy consumption model minE is:
minE＝Et+Eaux
Et＝∫F(t)ds
wherein E represents the total energy consumption of the train running process, EtRepresenting energy consumption of the train, EauxRepresenting auxiliary energy consumption of the train, F (t) representing tractive effort exerted by the train, Paux(t) represents auxiliary system power, v (t) represents train operating speed.
In the embodiment of the present invention, step S5 includes the following sub-steps:
s51: constructing a train electric quantity constraint model according to the residual electric quantity of the train battery;
s52: setting a constant speed step length, and determining a corresponding constant speed grade and a corresponding constant speed;
s53: according to basic data of a vehicle-mounted energy storage system and constant speed working conditions corresponding to different constant speed speeds, calculating train operation speed curves under different constant speed grades, taking the traction working condition of the train operation speed curve as a starting point, taking the train constant speed operation until the residual battery electric quantity of the train is exhausted as an end point, and dividing the train operation speed curve into a plurality of subintervals;
s54: adding an inertia working condition in each subinterval, traversing each constant speed grade, updating the train running speed curve after the inertia working condition is added, and determining the running distance of the updated train running speed curve;
s55: judging whether all the constant speed grades are traversed or not, if so, entering a step S56, and otherwise, returning to the step S54;
s56: and taking the train running speed curve with the farthest running distance in all the constant speed grades as the train running speed optimization curve with the farthest running distance.
In the embodiment of the present invention, in step S51, the expression of the train electric quantity constraint model E' is:
E'≤Ebattery
wherein E isbatteryRepresenting the remaining energy of the train battery.
The working principle and the process of the invention are as follows: firstly, acquiring basic data of a train, state information of the train, basic data of a line, basic data of energy consumption of an auxiliary system and basic data of a state of a vehicle-mounted energy storage system; then, based on the basic data of the train, the state information of the train, the basic data of the line and the basic data of the energy consumption of the auxiliary system, taking a front station or a rescue station where the train runs as a parking target station, and acquiring a train running speed curve with the lowest accumulated energy consumption by using an inert energy-saving strategy; and judging whether the lowest accumulated energy consumption is less than the residual electric quantity of the train battery according to the train running speed curve of the lowest accumulated energy consumption, and optimizing different curves.
The invention has the beneficial effects that:
(1) the invention is suitable for a train with vehicle-mounted energy storage and power supply capacity, optimizes the train running speed curve, reduces the accumulated running energy consumption and enlarges the train running distance under limited energy storage capacity in the power-off self-rescue scene of a contact network.
(2) And under the condition of limited energy time, optimizing the train running speed curve, and optimizing by taking the minimum sum of train running energy consumption and auxiliary system energy consumption as a target to obtain the most energy-saving speed curve, so that the energy-saving speed curve has energy-saving property.
(3) If the train can not run to a set position under the condition of limited energy, the train stops on a running line, the consequences such as multiple trains of the railway system at a later point, even train number cancellation and the like can be caused, so that the normal operation of the railway system is seriously influenced, the train can run for the farthest distance when the electric quantity of the battery is insufficient by optimizing the operation of the train, the influence on the railway system is reduced, and the train has high efficiency.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (6)
1. An energy-limited and time-free train operation optimization method is characterized by comprising the following steps:
s1: acquiring basic data of a train, state information of the train, basic data of a line, basic data of energy consumption of an auxiliary system and basic data of a state of a vehicle-mounted energy storage system;
s2: based on the basic data of the train, the state information of the train, the basic data of the line and the basic data of the energy consumption of the auxiliary system, taking a front station or a rescue station where the train runs as a parking target station, and acquiring a train running speed curve with the lowest accumulated energy consumption by using an inert energy-saving strategy;
s3: judging whether the lowest accumulated energy consumption is less than the residual electric quantity of the train battery according to the train running speed curve of the lowest accumulated energy consumption, if so, entering a step S4, otherwise, entering a step S5;
s4: taking the train running speed curve with the lowest accumulated energy consumption as a train running speed optimization curve with the lowest accumulated energy consumption to finish the optimized running of the train;
s5: and taking the residual electric quantity of the train battery as a constraint condition, and acquiring a train running speed optimization curve of the farthest running distance by using a constant-speed coasting strategy to finish the optimized running of the train.
2. The energy-limited and time-free train operation optimization method according to claim 1, wherein the basic data of the train includes a train weight and a traction braking characteristic in the step S1;
the state information of the train comprises the running speed of the train and the position of the train;
the basic data of the line comprises station kilometer posts, speed limit, gradient and curve;
the basic data of the auxiliary system energy consumption comprises auxiliary system power;
the basic data of the vehicle-mounted energy storage system comprise the capacity of the vehicle-mounted energy storage system, the residual capacity of a train battery and the discharge characteristic.
3. The energy-limited and time-free train operation optimization method according to claim 1, wherein the step S2 includes the sub-steps of:
s21: taking a front station or a rescue station where the train runs as a parking target station, and constructing a minimum accumulated energy consumption model according to the state information of the train and the basic data of the energy consumption of the auxiliary system;
s22: setting a constant speed step length, and determining a corresponding constant speed grade and a corresponding constant speed;
s23: calculating a traction constant-speed braking curve according to basic data of the train and constant-speed working conditions corresponding to different constant-speed speeds, taking the traction working condition of the traction constant-speed braking curve as a starting point and the braking working condition as an end point, and dividing the traction constant-speed braking curve into a plurality of subintervals;
s24: setting a lazy line starting point in each subinterval, and adding a lazy line working condition according to the lazy line starting point;
s25: traversing each coasting starting point based on the minimum accumulated energy consumption model, and calculating a train running speed curve and corresponding accumulated energy consumption after adding the coasting working condition according to the state information of the train and the basic data of the line;
s26: judging whether all the starting points of the lazy lines are traversed, if so, entering a step S27, and if not, returning to the step S25;
s27: taking the curve with the minimum accumulated energy consumption in the train running speed curves obtained by traversing all the coasting starting points as the most energy-saving speed curve;
s28: traversing each speed grade, judging whether all the speed grades are traversed or not, if so, entering the step S29, otherwise, returning to the step S22;
s29: and taking the curve with the minimum accumulated energy consumption in the train running speed curves obtained by traversing all the speed grades as the train running speed curve with the minimum accumulated energy consumption.
4. The energy-limited and time-free train operation optimization method according to claim 3, wherein in the step S21, the expression of the minimum cumulative energy consumption model minE is as follows:
min E＝Et+Eaux
Et＝∫F(t)ds
wherein E represents the total energy consumption of the train running process, EtRepresenting energy consumption of the train, EauxRepresenting auxiliary energy consumption of the train, F (t) representing tractive effort exerted by the train, Paux(t) represents a cofactorAuxiliary system power, v (t), represents train operating speed.
5. The energy-limited and time-free train operation optimization method according to claim 2, wherein the step S5 includes the sub-steps of:
s51: constructing a train electric quantity constraint model according to the residual electric quantity of the train battery;
s52: setting a constant speed step length, and determining a corresponding constant speed grade and a corresponding constant speed;
s53: according to basic data of a vehicle-mounted energy storage system and constant speed working conditions corresponding to different constant speed speeds, calculating train operation speed curves under different constant speed grades, taking the traction working condition of the train operation speed curve as a starting point, taking the train constant speed operation until the residual battery electric quantity of the train is exhausted as an end point, and dividing the train operation speed curve into a plurality of subintervals;
s54: adding an inertia working condition in each subinterval, traversing each constant speed grade, updating the train running speed curve after the inertia working condition is added, and determining the running distance of the updated train running speed curve;
s55: judging whether all the constant speed grades are traversed or not, if so, entering a step S56, and otherwise, returning to the step S54;
s56: and taking the train running speed curve with the farthest running distance in all the constant speed grades as the train running speed optimization curve with the farthest running distance.
6. The energy-limited and time-free train operation optimization method according to claim 5, wherein in the step S51, the expression of the train electric quantity constraint model E' is:
E'≤Ebattery
wherein E isbatteryRepresenting the remaining energy of the train battery.
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