An Optimization Method for Emergency Self-Rescue Operation of EMUs Equipped with Onboard Energy Storage Devices

Abstract

This invention discloses an optimized method for emergency self-rescue operation of high-speed trains equipped with onboard energy storage devices. Specifically, when a train experiences a short-term power outage or prolonged power failure due to power supply system failure, natural disasters, overhead contact line failure, or other unforeseen factors, the train enters an emergency operation state after driver confirmation. Based on the current train status, track conditions, bidirectional station distances, train parameters and traction characteristics, onboard energy storage device status, and auxiliary system energy consumption, an improved genetic algorithm is used to make emergency operation decisions for the current state, obtaining forward and reverse emergency operation schemes for the fault point. Based on the feasibility analysis of the optimized schemes obtained from the two target stations, the optimal emergency operation solution is selected. This invention enables emergency self-rescue operation of trains, effectively solving the problem of short-term or prolonged power outages affecting normal daily train operations and potentially triggering secondary disasters.

Description

Technical Field

[0001] This invention belongs to the field of train emergency operation technology, specifically relating to an optimized method for emergency self-rescue operation of EMU trains equipped with onboard energy storage devices. Background Technology

[0002] With the expansion of railway networks, power outages can sometimes occur, either temporarily or for extended periods, due to power supply system failures, natural disasters, overhead contact line malfunctions, or other unforeseen circumstances. This disrupts normal train operations, especially on lines operating in tunnels, high-altitude areas, or at low temperatures. Power outages can easily lead to extreme temperatures, oxygen deprivation, and other problems inside the carriages, causing passenger panic and potentially triggering secondary disasters. However, if a high-speed train is equipped with an onboard energy storage system, it can have a certain degree of emergency self-propelled capability even without an overhead contact line. When an overhead contact line power outage puts the train in an emergency, the onboard energy storage device provides the necessary energy for emergency operation. Therefore, effectively utilizing the train's energy storage system to achieve emergency self-propelled operation and guide the train to a safe area is of great significance in minimizing losses from power outages.

[0003] When the overhead contact line experiences an abnormal power outage, the train enters an emergency state upon confirmation. At this time, only the onboard energy storage system provides power for the train's emergency operation, including traction energy consumption and the energy consumption of auxiliary systems such as emergency ventilation and lighting. However, the capacity of the onboard energy storage equipment is limited by installation space and energy density. To ensure sufficient energy for train operation, we consider utilizing the energy generated by regenerative braking. Therefore, we use the net emergency operation energy consumption calculated from the train's traction energy consumption, auxiliary energy consumption, and regenerative braking energy consumption as the objective function to optimize emergency operation after a power outage, minimizing energy consumption during operation to achieve energy-saving emergency operation. If the remaining power in the onboard energy storage device is insufficient to reach the target station, a new operation strategy is developed to maximize the train's reach and minimize the impact on other trains.

[0004] Currently, most train operation optimization schemes rarely include operational strategies for trains operating under limited energy conditions during emergency self-rescue. Therefore, optimized operation schemes for train emergency self-rescue are urgently needed. Summary of the Invention

[0005] In response to sudden power outages during train operation, in order to

[0006] This invention addresses the issue of train self-rescue during emergency operations, ensuring operational and passenger safety. It provides an optimized method for emergency self-rescue operation of high-speed trains equipped with onboard energy storage devices.

[0007] An optimization method for emergency self-rescue operation of a high-speed train equipped with an on-board energy storage device, according to the present invention, includes the following steps:

[0008] S1: After the train loses power and enters emergency mode, it acquires basic data of the emergency train, train status information, track condition data, emergency auxiliary system data, and on-board energy storage device system status data.

[0009] S2: Based on the train vehicle information and section line information, determine whether the existing kinetic energy at the current power outage point of the train can be used to coast to the target station; if so, proceed to step S3, otherwise proceed to step S4.

[0010] S3: As described in step S2, the train can coast to the target parking station, that is, the distance to the stop based on the current kinetic energy is greater than the distance to the target parking station. At this time, the braking curve calculated in reverse from the target emergency stopping point intersects with the train's emergency stopping point coasting curve to obtain the train's emergency operation curve.

[0011] S4: Based on the data collected in step S1, the forward and backward parking stations are taken as target stations respectively, and the remaining power of the train's on-board energy storage device is taken as a constraint. The genetic algorithm is used to obtain the operating condition with the lowest energy consumption during train operation and its operating condition transition position, thus obtaining the optimized speed curve for emergency train operation.

[0012] S5: Determine whether the energy required meets the remaining power constraint of the on-board energy storage device based on the train operation curve with the lowest energy consumption. If yes, proceed to step S6; otherwise, proceed to step S7.

[0013] S6: Based on the optimal speed curves obtained from the forward and reverse emergency operation optimization, a comprehensive feasibility analysis is conducted to obtain the most reasonable emergency operation plan and complete the optimization of train emergency self-rescue.

[0014] S7: Using an improved genetic algorithm, the position of the coasting transition point is calculated again to obtain the speed optimization curve that allows the train to travel the farthest distance with the current remaining power, thus completing the train emergency self-rescue optimization.

[0015] Furthermore, in step S1, the basic data of the train includes the train mass and the train traction and braking characteristics; the train status information includes the location information of the power outage and the current speed of the train; the track condition data includes the kilometer markers of the section, the kilometer markers of the preceding and following stations, the speed limit, the gradient, the curve and the tunnel; the emergency auxiliary system data includes the auxiliary system power under emergency operation; and the on-board energy storage device system status data includes the on-board energy storage system capacity, the remaining battery power, and the charging and discharging characteristics.

[0016] Furthermore, step S2 specifically involves:

[0017] S21: Based on the above train vehicle information and section track information, calculate the farthest distance the train can coast to a stop using the kinetic energy at the break point.

[0018] S22: Determine whether the distance calculated in step S21 is greater than or equal to the distance between the emergency running start point and the emergency target stopping point. If so, proceed to step S23.

[0019] S23: Calculate the braking curve in reverse from the target emergency stopping point and intersect it with the above-mentioned train emergency point coasting curve. The intersection point is the starting position of the train braking condition, and the train emergency self-rescue speed curve is obtained.

[0020] Furthermore, step S4 specifically involves:

[0021] S41: Designate the station ahead or behind the train as the target station for emergency stopping, and establish a minimum net energy consumption model for emergency operation based on the current vehicle status information and emergency auxiliary system data.

[0022] S42: Based on the four-stage operation design working condition switching rules and taking the remaining power of the train's on-board energy storage device as one of the constraints.

[0023] S43: An improved genetic algorithm is used to calculate the optimal train operation conditions and their switching positions for different constant speed targets.

[0024] S44: Compare the net energy consumption at different speed levels, and take into account the feasibility of the operation sequence with the minimum net energy consumption as the final optimized emergency self-rescue scheme.

[0025] Furthermore, in step S41, the net energy consumption minimization model minE net The expression is:

[0026] minE net =E tr -E re +E aux

[0027]

[0028]

[0029]

[0030] Among them, E net E represents the total energy consumption during train operation. tr E represents the traction energy consumption of the train. re E represents the energy generated by regenerative braking of the train. aux F represents the auxiliary energy consumption of the train. t (v) represents the traction force applied by the train, Fb (v) represents the electric braking force of the train, P aux P represents the auxiliary system power. brake P represents regenerative braking power. batmax η represents the maximum charging power of the battery, v(x) represents the train speed; mot η represents the efficiency of the train traction system. tra η represents the efficiency of the traction inverter. daa η represents the efficiency of the auxiliary converter. ad Indicates the efficiency of the bidirectional charger, η bat This indicates the efficiency of the energy storage device.

[0031] Furthermore, in step S42, the expression for the train power constraint model SOE is:

[0032] 10% ≤ SOE ≤ 90%

[0033]

[0034] E b =E in -E tr -E aux +E re

[0035] Wherein, SOE represents the train's battery state of energy. Considering the actual operating conditions, the value of SOE ranges from 0.1 to 0.9. b E represents the remaining energy of the vehicle's battery. cap E represents the total rated energy of the vehicle's battery. in Initial energy of the vehicle battery.

[0036] Furthermore, step S7 specifically includes:

[0037] S71: Obtain train-related information, route information, and optimized speed curves.

[0038] S72: Extract the constant speed running section from the optimized speed curve.

[0039] S73: The improved genetic algorithm is used to obtain the traction-coasting condition switching position, and the speed curve of the farthest distance traveled by the train is obtained for different coasting optimization times.

[0040] S74: Traverse the train's running distance for different number of coasting optimizations in step S73 until the iteration stopping condition is met to obtain the speed optimization curve with the longest train running distance. Take it into consideration as the optimized emergency operation plan.

[0041] The beneficial technical effects of this invention are as follows:

[0042] This invention is applicable to a type of train with onboard energy storage devices that have power supply capabilities. In scenarios where the train's power supply is interrupted for a short time or paralyzed for a long time due to power supply system failure, natural disasters, overhead contact line failure, or other unforeseen factors, the invention optimizes the train's emergency self-rescue speed curve and reduces operating energy consumption to extend the operating distance under limited energy conditions.

[0043] When a train is in emergency operation mode, the basic data of the emergency train, train status information, line condition data, emergency auxiliary system data, and on-board energy storage device system status data are comprehensively considered. The station ahead or behind the train is taken as the target station. The remaining power of the on-board energy storage device and the utilization of energy generated by regenerative braking are considered. An improved genetic algorithm is used to obtain the emergency operation conditions and their switching positions. Through comprehensive feasibility and safety analysis, an emergency self-rescue operation plan for the train is obtained, which effectively solves the problems that affect the normal daily operation of the train and may induce secondary disasters. Attached Figure Description

[0044] Figure 1 This is a flowchart of the emergency self-rescue operation optimization method of the present invention.

[0045] Figure 2 A flowchart for an improved genetic algorithm.

[0046] Figure 3 This is a flowchart of an improved genetic algorithm based on lazy optimization. Detailed Implementation

[0047] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0048] The process of the optimized emergency self-rescue operation method for EMUs with onboard energy storage devices according to the present invention is as follows: Figure 1 As shown, the specific steps include:

[0049] S1: After the train loses power and enters emergency mode, it acquires basic data of the emergency train, train status information, track condition data, emergency auxiliary system data, and on-board energy storage device system status data.

[0050] The train's basic data includes train mass and traction and braking characteristics; train status information includes the location of the power outage and the train's current speed; track condition data includes kilometer markers for the section, kilometer markers for the preceding and following stations, speed limits, gradients, curves, and tunnels; emergency auxiliary system data includes the power of the auxiliary system during emergency operation; and onboard energy storage device system status data includes the onboard energy storage system capacity, remaining battery power, and charging and discharging characteristics.

[0051] S2: Based on the train vehicle information and section line information, determine whether the existing kinetic energy at the current power outage point of the train can be used to coast to the target station; if so, proceed to step S3, otherwise proceed to step S4.

[0052] Step S2 is as follows:

[0053] S21: Based on the above train vehicle information and section track information, calculate the farthest distance the train can coast to a stop using the kinetic energy at the break point.

[0054] S22: Determine whether the distance calculated in step S21 is greater than or equal to the distance between the emergency running start point and the emergency target stopping point. If so, proceed to step S23.

[0055] S23: Calculate the braking curve in reverse from the target emergency stopping point and intersect it with the above-mentioned train emergency point coasting curve. The intersection point is the starting position of the train braking condition, and the train emergency self-rescue speed curve is obtained.

[0056] S3: As described in step S2, the train can coast to the target parking station, that is, the distance to the stop based on the current kinetic energy is greater than the distance to the target parking station. At this time, the braking curve calculated in reverse from the target emergency stopping point intersects with the train's emergency stopping point coasting curve to obtain the train's emergency operation curve.

[0057] S4: Based on the data collected in step S1, the forward and backward parking stations are taken as target stations respectively, and the remaining power of the train's on-board energy storage device is taken as a constraint. The genetic algorithm is used to obtain the operating condition with the lowest energy consumption during train operation and its operating condition transition position, thus obtaining the optimized speed curve for emergency train operation.

[0058] Step S4 is as follows:

[0059] S41: Designate the station ahead or behind the train as the target station for emergency stopping, and establish a minimum net energy consumption model for emergency operation based on current vehicle status information and emergency auxiliary system data. Minimum Net Energy Consumption Model minE net The expression is:

[0060] minE net =E tr -E re +E aux

[0061]

[0062]

[0063]

[0064] Among them, E netE represents the total energy consumption during train operation. tr E represents the traction energy consumption of the train. re E represents the energy generated by regenerative braking of the train. aux F represents the auxiliary energy consumption of the train. t (v) represents the traction force applied by the train, F b (v) represents the electric braking force of the train, P aux P represents the auxiliary system power. brake P represents regenerative braking power. batmax η represents the maximum charging power of the battery, v(x) represents the train speed; mot η represents the efficiency of the train traction system. tra η represents the efficiency of the traction inverter. daa η represents the efficiency of the auxiliary converter. ad Indicates the efficiency of the bidirectional charger, η bat This indicates the efficiency of the energy storage device.

[0065] In emergency operation, considering practical significance, it is proposed that when the conditions for regenerative braking energy to be utilized are met, the energy generated by regenerative braking can be used to charge the on-board emergency battery, power auxiliary systems (lighting, air conditioning systems), and dissipate excess energy through dissipation resistors; when the conditions are not met, the energy generated by regenerative braking will not be utilized.

[0066] When the energy generated by regenerative braking can be utilized, the energy consumed by the auxiliary system is directly supplied by it, and the auxiliary energy consumption is not included in the calculation of net energy consumption.

[0067] S42: Based on the four-stage operation design condition switching rules, and taking the remaining power of the train's onboard energy storage device as one of the constraints, the expression for the train power constraint model SOE is:

[0068] 10% ≤ SOE ≤ 90%

[0069]

[0070] E b =E in -E tr -E aux +E re

[0071] Wherein, SOE represents the train's battery state of energy. Considering the actual operating conditions, the value of SOE ranges from 0.1 to 0.9. b E represents the remaining energy of the vehicle's battery. cap E represents the total rated energy of the vehicle's battery. in Initial energy of the vehicle battery.

[0072] S43: An improved genetic algorithm is used to calculate the optimal train operation conditions and their switching positions for different constant speed targets.

[0073] Among them, improved genetic algorithms include Figure 2 As shown, it includes:

[0074] The emergency operation section of the train is divided into three different sub-sections based on the gradient: steep uphill, moderate, and steep downhill. The rule for dividing each sub-section is to start from the steep uphill or moderate gradient and end at the steep downhill.

[0075] The improved genetic algorithm adds an evolutionary direction guidance mechanism and an adaptive strategy to the genetic algorithm, and the resulting optimal manipulation sequence is formed by a series of working condition transition points (u i ,x i Composed of, where u i It is a fixed sequence of four-stage operating conditions, x i This corresponds to the operating condition transition position; the operating condition transition position x is randomly generated according to the operating condition setting principle. i Encode and generate an initial population based on the population size;

[0076] Calculate the fitness function values ​​of chromosomes in the population, calculate the train speed curve based on the corrected train control sequence, and statistically analyze operating energy consumption, operating time, etc.

[0077] 1. Precision stopping index is used to evaluate whether a train stops accurately at the designated stopping point.

[0078]

[0079] In the formula, S target Let S be the target parking spot, S be the current parking spot, and d be the parking margin.

[0080] 2. Safety and comfort

[0081]

[0082] In the formula, a d 'a' represents the maximum acceleration, and 'a' represents the current acceleration.

[0083] 3. Delay time: Considering that train energy consumption is inversely proportional to train travel time (lower energy consumption results in longer travel time), and to reduce the impact of unforeseen accidents on the line, delay time is considered as a constraint as shown below:

[0084]

[0085] In the formula, T represents the actual train travel time. target For the planned train running time, t d To delay time.

[0086] The fitness function value is calculated according to the formula. The fitness function is designed using the penalty function method. The corresponding penalty coefficient is set for the penalty function corresponding to each index. The fitness function is formed by multiplying the penalty function value with the objective function, as shown in the following formula.

[0087] f=J+λ1f s +λ2f a +λ3f t

[0088] Where λ1, λ2, and λ3 are the maximum coefficients, J = E net .

[0089] Selection: Chromosomes in the population are sorted using a sorting-based selection method, and individuals are selected from the parent population to participate in crossover using a binary tournament method.

[0090] Crossover: The encoding method used in this embodiment of the invention is real number encoding. The crossover operator is designed using the linear recombination (also called arithmetic crossover) method. After crossover, the new chromosomes are reordered according to the working condition transition point position.

[0091]

[0092] Where x1 and x2 are the crossover points of two chromosomes with crossover, x1' and x'2 are the crossover points of the new chromosome, and γ is a random number between 0 and 1.

[0093] Mutation: The mutation operator uses multi-point mutation, randomly selecting several working condition transition points as mutation points in the train operation sequence to be mutated, and the mutation operation only changes the position of the working condition transition points in the train operation sequence, without changing the working condition.

[0094] x' i =γx i-1 +(1-γ)x i+1

[0095] Where, x' i Indicates the location of the change in operating condition after mutation, x i Indicates the operating condition switching position of the mutation point on the chromosome to be mutated, x i-1 and x i+1 This indicates the operating condition switching position before and after the mutation point of the chromosome to be mutated, where γ is a random number between 0 and 1.

[0096] The chromosomes in the population are sorted according to their fitness function values ​​until the iteration stops. The optimal chromosome, i.e., the train control sequence with the smallest fitness function value, is then output.

[0097] S44: Compare the net energy consumption at different speed levels, and take into account the feasibility of the operation sequence with the minimum net energy consumption as the final optimized emergency self-rescue scheme.

[0098] S5: Determine whether the energy required meets the remaining power constraint of the on-board energy storage device based on the train operation curve with the lowest energy consumption. If yes, proceed to step S6; otherwise, proceed to step S7.

[0099] S6: Based on the optimal speed curves obtained from the forward and reverse emergency operation optimization, a comprehensive feasibility analysis is conducted to obtain the most reasonable emergency operation plan and complete the optimization of train emergency self-rescue.

[0100] S7: Using an improved genetic algorithm, the location of the coasting transition point is calculated again to obtain the speed optimization curve that allows the train to travel the furthest distance with the current remaining battery power, thus completing the train's emergency self-rescue optimization. For example... Figure 3 As shown, specifically:

[0101] S71: Obtain train-related information, route information, and optimized speed curves.

[0102] S72: Extract the constant speed running section from the optimized speed curve.

[0103] S73: The improved genetic algorithm is used to obtain the traction-coasting condition switching position, and the speed curve of the farthest distance traveled by the train is obtained for different coasting optimization times.

[0104] S74: Traverse the train's running distance for different number of coasting optimizations in step S73 until the iteration stopping condition is met to obtain the speed optimization curve with the longest train running distance. Take it into consideration as the optimized emergency operation plan.

[0105] This invention, after a train enters emergency mode due to power failure, acquires basic data of the emergency train, train status information, track condition data, emergency auxiliary system data, and onboard energy storage device system status data. Taking into account the above data, the invention identifies the station ahead or behind the train as the target station. Considering the remaining power of the onboard energy storage device and the utilization of energy generated by regenerative braking, an improved genetic algorithm is used to obtain the emergency operating conditions and their switching positions. Through comprehensive feasibility and safety analysis, the invention enables the train to perform emergency self-rescue operation, effectively solving the problem of short-term or long-term power outages or paralysis of trains caused by power supply system failures, natural disasters, overhead contact line failures, and other accidental factors, which affect normal daily train operations and may induce secondary disasters.

Claims

1. An emergency self-rescue operation optimization method for a motor train unit with an on-board energy storage device, characterized in that, Comprise the following steps: S1: after the train power failure enters the emergency state, obtain the basic data of the emergency train, train state information, line condition data, emergency auxiliary system data and vehicle energy storage device system state data; S2: based on the train vehicle information and the data of the section line information, judge whether the existing kinetic energy of the train at the current power failure place can be used to coast to the target parking station; if yes, go to step S3, otherwise go to step S4; S3: according to the train which can coast to the target parking station according to step S2, that is, the distance of coasting to the parking station according to the current kinetic energy is greater than the distance from the target parking station, at this time, the braking curve is calculated from the target emergency parking point, and the train emergency point coasting curve is intersected to obtain the train emergency running curve; S4: based on the data collected in step S1, the forward and rear parking stations are taken as the target stations respectively, and the residual capacity of the train on-board energy storage device is taken as the constraint condition, the genetic algorithm is used to obtain the operating scheme with the lowest energy consumption in the train running process and the working condition conversion position, and the train emergency running optimization speed curve is obtained; S41: take the station in front of or behind the train running as the emergency parking target station, and establish the minimum net energy consumption model of emergency running according to the current vehicle state information and emergency auxiliary system data; Net energy consumption minimization model The expression is: ； ； ； ； wherein, represents the total energy consumption of the train during the operation, represents the traction energy consumption of the train, represents the energy generated by the regenerative braking of the train, represents the auxiliary energy consumption of the train, represents the traction force applied by the train, represents the electric braking force of the train, represents the auxiliary system power, represents the regenerative braking power, represents the maximum charging power of the battery, represents the train operation speed; represents the train traction system efficiency, represents the traction inverter efficiency, represents the auxiliary converter efficiency, represents the bidirectional charger efficiency, represents the energy storage device efficiency; S42: based on the four-stage operating design working condition switching rule and taking the residual capacity of the train on-board energy storage device as one of the constraint conditions; S43: for different constant speed targets, the improved genetic algorithm is used to calculate the optimal train operating working condition and its switching position; S44: compare the size of the net energy consumption under different speed levels, and take the minimum net energy consumption operating sequence as the final optimization obtained emergency self-rescue optimization scheme considering its feasibility; S5: according to the train running curve with the lowest energy consumption obtained, judge whether the energy consumption meets the residual capacity constraint of the on-board energy storage device, if yes, go to step S6, otherwise go to step S7; S6: according to the optimal speed curve obtained by the forward and reverse emergency running optimization, comprehensive feasibility analysis is carried out to obtain the most reasonable emergency running scheme, and the train emergency self-rescue optimization is completed; S7: the improved genetic algorithm is used to calculate the position of the coasting conversion point again to obtain the speed optimization curve with the farthest running distance of the train under the current residual capacity, and the train emergency self-rescue optimization is completed; S71: obtain the train related information, line information and optimized speed curve; S72: extract the constant speed running section in the optimized speed curve; S73: use the improved genetic algorithm to obtain the traction-coasting working condition switching position, and obtain the train running distance speed curve under different coasting optimization times; S74: traverse the train running distance under different coasting optimization times in step S73 until the iteration stopping condition is met to obtain the train running distance speed optimization curve, and take it as the optimized emergency running scheme considering comprehensively.

2. The method of claim 1, wherein the method further comprises: The basic data of the train in the step S1 includes train mass and train traction and braking characteristics; the train state information includes train power-off position information and current train speed; the line condition data includes section kilometer marker, front and rear station kilometer markers, speed limit, slope, curve and tunnel; the emergency auxiliary system data includes auxiliary system power in emergency operation state; the on-board energy storage device system state data includes on-board energy storage system capacity, battery residual capacity, charging and discharging characteristics.

3. The method of claim 1, wherein the method further comprises: The step S2 is specifically: S21: calculating the farthest distance of the train from the power-off point to the stop point by using the kinetic energy of the train according to the train vehicle information and the section line information; S22: judging whether the distance calculated in the step S21 is greater than or equal to the distance between the emergency operation starting point and the emergency target stop point of the train, if yes, entering the step S23; S23: reversely calculating the intersection of the braking curve and the train emergency point coasting curve from the target emergency stop point, the intersection point being the starting position of the train braking working condition, and obtaining the train emergency self-rescue speed curve.

4. The method of claim 1, wherein the method further comprises: In the step S42, the train power constraint model The expression of the train power constraint model is: ； ； ； wherein, represents the state of charge of the train battery, taking into account the actual operating conditions has a value ranging from 0.1 to 0.9, represents the residual energy of the on-board battery, represents the total nominal energy of the on-board battery, represents the initial energy of the on-board battery.

Images