Subway train energy-saving optimization method based on improved genetic algorithm

Abstract

The invention relates to the technical field of train energy-saving optimization methods, and particularly discloses a subway train energy-saving optimization method based on an improved genetic algorithm, which comprises the following steps of: 1, performing stress analysis on a train, and establishing a train power model according to a traction calculation formula, including calculation of traction force, braking force and resistance of the train; 2, in the operation interval, generating a speed curve based on the process of maximum acceleration, cruising, coasting and maximum braking of the operation condition of the train; 3, establishing a genetic algorithm energy-saving optimization model, and solving an optimal speed curve based on the genetic algorithm energy-saving optimization model; 4, a comfort model is established, and the riding comfort of the passengers is analyzed, energy-saving optimization can be achieved, and meanwhile the experience feeling of the passengers is taken into consideration.

Description

Technical Field

[0001] The invention relates to the technical field of train energy-saving optimization methods, in particular to a subway train energy-saving optimization method based on an improved genetic algorithm. Background Art

[0002] Rail transit has attracted the attention of countries around the world for its convenience, safety, comfort and high efficiency. It has become an important trunk line transportation in these modern metropolises, which not only relieves traffic pressure but also realizes green travel. The operation of subway trains consumes a lot of energy. Therefore, it is of great significance to study the energy-saving optimization of subway trains.

[0003] In the prior art, the design of energy-saving optimization methods for subway trains only pursues how to reduce the energy consumption of trains without paying attention to the passengers' experience. How to achieve energy-saving optimization while taking into account the passengers' experience is an urgent problem to be solved. Summary of the invention

[0004] In view of the technical problem that in the prior art, the design of the energy-saving optimization method for subway trains only pursues how to reduce the energy consumption of the trains without paying attention to the passengers' experience, the present invention provides a subway train energy-saving optimization method based on an improved genetic algorithm.

[0005] The technical solution adopted by the present invention is: a subway train energy-saving optimization method based on an improved genetic algorithm, which specifically includes the following steps:

[0006] Conduct force analysis on the train and establish a train dynamic model based on the traction calculation formula, including calculating the train's traction, braking force and resistance;

[0007] In the operating range, a speed curve is generated based on the train's operating conditions of maximum acceleration-cruise-coast-maximum braking;

[0008] Establish a genetic algorithm energy-saving optimization model, and solve the optimal speed curve based on the genetic algorithm energy-saving optimization model;

[0009] Establish a comfort model to analyze passengers' riding comfort.

[0010] The present invention is further configured such that the traction force calculation formula of the train is as follows:

[0011]

[0012] Where F(v) is the train traction force (N), T F is the traction force in the constant traction force zone (N); v is the running speed of the train (m / s); v a is the turning speed in the constant traction zone (m / s); v bis the turning speed in the constant power zone (m / s); is the terminal speed in the natural characteristic zone (m / s).

[0013] The formula for calculating the braking force of the train is as follows:

[0014]

[0015] Where, B(v) is the electric braking force of the train (N), B F is the braking force in the constant traction zone (N); v d is the turning speed in the constant traction zone (m / s); v f is the terminal velocity of the natural characteristic zone (m / s);

[0016] The train resistance calculation formula is as follows:

[0017] w(v)=(a0+b0·v+c0·v 2 )·(K+G)·g

[0018] Where w(v) is the basic resistance of the train (N); K is the total weight of the train car and trailer (kg); G is the passenger weight (kg); a0, b0, c0 are empirical constants that depend on the type of vehicle and actual line conditions; v is the train speed (m / s).

[0019] The present invention is further configured that the train power model construction method is as follows:

[0020] It includes establishing train dynamic equations based on traction calculation formulas;

[0021] Reduce the work done by the train's traction force and establish the objective function;

[0022] Calculate the train's departure speed, station stopping speed, inter-station running time, line speed limit and maximum acceleration during operation.

[0023] The present invention is further configured to establish a train dynamics equation according to the traction calculation formula as follows:

[0024]

[0025] Where: m is the mass of the train; F(v) is the traction force; B(v) is the braking force; v is the running speed; t is the running time; w(v) is the basic running resistance; a, b, c are the basic resistance coefficients of the train; f(v) is the additional resistance of the train, which is calculated by the slope resistance f i , curve resistance f r and tunnel resistance f s The three together make up.

[0026] The present invention is further configured to reduce the work done by the train traction force, and establish the objective function as follows:

[0027] Set the objective function to:

[0028] Where: E—traction energy consumption; μ f —Truck force utilization coefficient; S—Train running distance; x—Train position;

[0029] The present invention is further configured to calculate the departure starting speed, station entry and stopping speed, station-to-station running time, line speed limit and maximum acceleration of the train during operation as follows:

[0030]

[0031] Where:

[0032] V line (x)—line speed limit at position x; a max —The maximum absolute value of acceleration allowed for train operation; μ b —Train braking force coefficient; T p —The train is given running time.

[0033] The present invention is further configured to establish a genetic algorithm energy-saving optimization model specifically including the following:

[0034] Obtain input data, including train performance parameters, line data, and operation data;

[0035] Line section division: the line is divided into n fixed sections according to the differences in slope curvature and speed limit at different positions of the line;

[0036] Establish an initial population, set the population size to N, the initial population to P, divide the initial population pairs according to the energy consumption and optimal cruising speed of each interval, and generate the corresponding energy-saving state matrix X = [E1, u1, E2, u2, ..., E n ,u n ],X i (i=1,…,n represents the energy consumption E of each train in the corresponding section i on the running line n and cruising speed.

[0037] The present invention is further configured such that, for a given X=[E1,u1,E2,u2,…,E n ,u n ], using E i and u i The method for inferring the optimal speed curve of interval i is as follows:

[0038] 1. Assume that the initial speed of the train in the section is v0, and the speed limit at the exit of the section is v t, The interval length is S. The interval is divided into N equal parts. In this way, the acceleration can be considered constant within each Δx after division, and the speed sequence of the train during the acceleration phase is calculated;

[0039] Second, let i = i + 1 and repeat Step 1 when E = 0 and v i <u Denote the stopping point as i = k, and then enter Step 4; conversely, when v i = u and E > 0, the train transfers to the cruising condition, and the speed sequence of the train under the cruising condition is calculated;

[0040]

[0041] F(v i ) = mΔ(w(v i ) + f(x i ))

[0042] v i+1 = u

[0043] E = E - F(v i )Δx

[0044] Third, let i = i + 1 and repeat Step 2 until E = 0. Denote the stopping point as i = k and enter Step 4; Step

[0045] Fourth, let i = k + 1, r(v i ) be the acceleration generated by the basic resistance, g(x i ) be the acceleration generated by the additional resistance, and calculate the speed sequence of the train during coasting;

[0046]

[0047] Δx = x i+1 -x i

[0048] v 2 = v 2 + 2a(Δx)

[0049] Fifth, repeat Step 4 until the train speed sequence intersects with the pre-calculated speed protection curve, and denote the stopping point as i = j;

[0050] Sixth, for the speeds after i = j in the sequence, select the values of the pre-calculated speed curve as the optimal speed sequence;

[0051] Seventh, based on the optimal speed sequence calculated according to the above steps, obtain the inter-station running time of the train on the line;

[0052]

[0053] Construct the genetic algorithm objective function and calculate the individual fitness;

[0054]

[0055] The present invention is further configured to establish a comfort model and analyze the riding comfort of passengers. The specific method is as follows:

[0056] minU=min{α·ΔT+β·ΔS+γ·E}

[0057]

[0058] Where α, β, and γ are weight coefficients, ranging from [0, 1], satisfying α+β+γ=1; ΔT is the running time error (s); t is the actual running time (s); T is the running time specified in the train operation schedule (s); ΔS is the running distance error (m); s is the actual running distance (m); S is the actual distance between the two stations (m); θ is the running time error threshold (s); δ is the running distance threshold (m), and a max is the maximum acceleration limit (m / s 2 ); α is the instantaneous acceleration (m / s 2 ); κ is the impact rate threshold (m / s 3 ). BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flowchart of the present invention.

[0060] The beneficial effects of the present invention are as follows: in the present invention, a train power model can be established to generate a speed curve, a genetic algorithm energy-saving optimization model can be established, the optimal speed curve can be solved based on the genetic algorithm energy-saving optimization model, a comfort model can be established, and the riding comfort of passengers can be analyzed. By determining the cruising speed and the inter-station running time, a unique energy-saving recommended speed curve can be calculated, and then the optimal curve can be generated by establishing the genetic algorithm energy-saving optimization model, thereby achieving the goal of optimizing the train's traction energy consumption. While optimizing the energy conservation of the train, the comfort experience of the passengers on the train is also taken into account, and the passengers will not feel a greater impact and cause discomfort. DETAILED DESCRIPTION

[0061] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] In order to solve the problems existing in the background technology, the present application proposes the following technical solution: a subway train energy-saving optimization method based on an improved genetic algorithm, which specifically includes the following steps:

[0063] Step 1: Perform force analysis on the train and establish a train dynamic model based on the traction calculation formula, which includes calculating the traction, braking force and resistance of the train;

[0064] Step 2: within the operating range, a speed curve is generated based on the train's operating conditions of maximum acceleration-cruise-coasting-maximum braking;

[0065] Step 3: Establish a genetic algorithm energy-saving optimization model, and solve the optimal speed curve based on the genetic algorithm energy-saving optimization model;

[0066] Step 4: Establish a comfort model to analyze passengers' riding comfort.

[0067] The above technical solution is explained as follows: by conducting a force analysis on the train, a train dynamic model is established according to the traction calculation formula to facilitate subsequent model optimization. By establishing a genetic algorithm energy-saving optimization model, the optimal speed curve is solved based on the genetic algorithm energy-saving optimization model, and the train is recommended to travel according to the optimal speed curve. In addition, by establishing a comfort model, the passengers' riding comfort is analyzed, and the train is driven according to the optimal curve to ensure that the passengers' comfort is not too low.

[0068] In further design, the traction force calculation formula of the train is as follows:

[0069]

[0070] Where F(v) is the train traction force (N), T F is the traction force in the constant traction force zone (N); v is the running speed of the train (m / s); v a is the turning speed in the constant traction zone (m / s); v b is the turning speed in the constant power zone (m / s); is the terminal speed in the natural characteristic zone (m / s).

[0071] The formula for calculating the braking force of the train is as follows:

[0072]

[0073] Where, B(v) is the electric braking force of the train (N), B F is the braking force in the constant traction zone (N); v d is the turning speed in the constant traction zone (m / s); v f is the terminal velocity of the natural characteristic zone (m / s);

[0074] The train resistance calculation formula is as follows:

[0075] w(v)=(a0+b0·v+c0·v 2 )·(K+G)·g

[0076] Where w(v) is the basic resistance of the train (N); K is the total weight of the train car and trailer (kg); G is the passenger weight (kg); a0, b0, c0 are empirical constants that depend on the type of vehicle and actual line conditions; v is the train speed (m / s).

[0077] The above technical solution is explained as follows: the subway train receives electricity from the contact network through the current collector, and the traction motor converts the electrical energy into mechanical energy through the power transmission device and transmits it to the wheels. The wheels obtain a rotational torque to generate traction, and then through the adhesion between the wheel and the rail, an external force in the same direction as the movement of the EMU is generated. This external force is the traction of the train; when the subway train is at a high speed, electric braking can maintain the braking force required for braking, but when the speed is low, it will be difficult to meet the required braking force by electric braking alone. Therefore, electric braking and air braking are required to provide braking force for the train, and the resistance of the train during operation is resistance. By calculating the traction, braking force and resistance of the train in detail, it is convenient to construct the specific train dynamic model in the future.

[0078] In the further design, the train power model is constructed as follows:

[0079] Step one includes establishing a train dynamics equation based on a traction calculation formula;

[0080] Step 2: Reduce the work done by the train traction force and establish the objective function;

[0081] Step three, calculate the train's departure speed, station entry and stopping speed, inter-station running time, line speed limit and maximum acceleration during operation.

[0082] Among them, the train dynamics equation is established according to the traction calculation formula as follows:

[0083]

[0084] Where: m is the mass of the train; F(v) is the traction force; B(v) is the braking force; v is the running speed; t is the running time; w(v) is the basic running resistance; a, b, c are the basic resistance coefficients of the train; f(v) is the additional resistance of the train, which is calculated by the slope resistance f i , curve resistance f r and tunnel resistance f s The three together make up.

[0085] Among them, the work done by reducing the train traction force is established as follows:

[0086] Set the objective function to:

[0087] Where: E—traction energy consumption; μ f —Truck force utilization coefficient; S—Train running distance; x—Train position;

[0088] Among them, the calculation of the train's departure speed, station entry and stop speed, station-to-station running time, line speed limit and maximum acceleration during operation are as follows:

[0089]

[0090] Where:

[0091] V line (x)—line speed limit at position x; a max —The maximum absolute value of acceleration allowed for train operation; μ b —Train braking force coefficient; T p —The train is given running time.

[0092] The above technical solution is explained as follows: Based on the above, a train dynamic model can be established to calculate the speed curve of the train, and the genetic algorithm used subsequently can generate the optimal speed curve when the train's operating conditions are "maximum acceleration-cruise-coasting-maximum braking";

[0093] In the further design, the establishment of the genetic algorithm energy-saving optimization model specifically includes the following:

[0094] Step 1: Obtain input data, including train performance parameters, line data, and operation data;

[0095] Step 2: dividing the route into sections, dividing the route into n fixed sections according to the differences in slope curvature and speed limits at different locations of the route;

[0096] Step 3: Establish an initial population, set the population size to N, the initial population to P, divide the initial population pairs according to the energy consumption and optimal cruising speed of each interval, and generate the corresponding energy-saving state matrix X = [E1,u1,E2,u2,…,E n ,u n ],X i (i=1,…,n) represents the energy consumption E of each train in the corresponding section i on the running line n and cruising speed.

[0097] Specifically, for a given X=[E1,u1,E2,u2,…,E n ,u n ], using E i and u iThe method for calculating the optimal speed curve in the reverse thrust interval i is as follows:

[0098] 1. Assume that the initial speed of the train in the interval is v0, the speed limit at the interval exit is v t , the interval length is S, divide the interval into N equal parts, so that the acceleration can be considered constant within each Δx after division, and calculate the speed sequence of the train during the acceleration phase;

[0099] 2. Let i = i + 1 and repeat step 1. When E = 0 and v i <u Denote the stopping point as i = k, and then enter step 4; conversely, when v i = u and E > 0, the train transfers to the cruise mode, and calculate the speed sequence of the train in the cruise mode;

[0100]

[0101] F(v i ) = mΔ(w(v i ) + f(x i ))

[0102] v i+1 = u

[0103] E = E - F(v i )Δx

[0104] 3. Let i = i + 1 and repeat step 2 until E = 0, denote the stopping point as i = k, and enter step 4; step

[0105] 4. Let i = k + 1, r(v i ) be the acceleration generated by the basic resistance, g(x i ) be the acceleration generated by the additional resistance, and calculate the speed sequence of the train during coasting;

[0106]

[0107] Δx = x i+1 - x i

[0108] v 2 = v 2 + 2a(Δx)

[0109] 5. Repeat step 4 until the train speed sequence intersects with the pre-calculated speed protection curve, and denote the stopping point as i = j;

[0110] 6. For the speeds in the sequence after i = j, select the values of the pre-calculated speed curve as the optimal speed sequence;

[0111] 7. Based on the optimal speed sequence calculated according to the above steps, obtain the running time between stations of the train on the line;

[0112]

[0113] Construct the genetic algorithm objective function and calculate the individual fitness;

[0114]

[0115] The above technical solution is explained as follows:

[0116] When the train is running between stations, traction force is output only in traction and cruising conditions. In coasting and braking conditions, the train does not exert traction and consumes no electrical energy. The train energy consumption curve can be adjusted by simply determining the conversion point between traction and cruising conditions, and the timing of the conversion point depends on whether the train has reached the cruising speed. As long as the cruising speed and the running time between stations are determined, the only energy-saving recommended speed curve can be calculated, and the optimal curve can be generated by establishing a genetic algorithm energy-saving optimization model, thereby achieving the goal of optimizing the train traction energy consumption.

[0117] In addition, the specific methods of establishing a comfort model and analyzing passengers' riding comfort are as follows:

[0118] minU=min{α·ΔT+β·ΔS+γ·E}

[0119]

[0120] Where α, β, and γ are weight coefficients, ranging from [0, 1], satisfying α+β+γ=1; ΔT is the running time error (s); t is the actual running time (s); T is the running time specified in the train operation schedule (s); ΔS is the running distance error (m); s is the actual running distance (m); S is the actual distance between the two stations (m); θ is the running time error threshold (s); δ is the running distance threshold (m), and a max is the maximum acceleration limit (m / s 2 ); α is the instantaneous acceleration (m / s 2 ); κ is the impact rate threshold (m / s 3 ).

[0121] Comfort is a very broad concept that is difficult to measure accurately. In a broad sense, "comfort" refers to the comprehensive evaluation of the passengers on the quality of train rides, which not only includes the running status of the train (speed, acceleration, vibration, impact, etc.). The comfort referred to in this embodiment is the impact of the train on the passengers.

[0122] Generally when r shock ≤0.8m / s 3 The ride comfort is better.

[0123] In summary, in the present invention, a train power model can be established to generate a speed curve, a genetic algorithm energy-saving optimization model can be established, the optimal speed curve can be solved based on the genetic algorithm energy-saving optimization model, a comfort model can be established, and the passenger riding comfort can be analyzed. By determining the cruising speed and the inter-station running time, a unique energy-saving recommended speed curve can be calculated, and then the optimal curve can be generated by establishing a genetic algorithm energy-saving optimization model, thereby achieving the goal of optimizing the train traction energy consumption. While optimizing the energy saving of the train, the comfort experience of the passengers on the train is also taken into account, and the passengers will not feel a greater impact and cause discomfort.

[0124] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A subway train energy-saving optimization method based on an improved genetic algorithm, characterized in that: The specific steps include: Conduct force analysis on the train and establish a train dynamic model based on the traction calculation formula, including calculating the train's traction, braking force and resistance; In the operating range, a speed curve is generated based on the train's operating conditions of maximum acceleration-cruise-coast-maximum braking; Establish a genetic algorithm energy-saving optimization model, and solve the optimal speed curve based on the genetic algorithm energy-saving optimization model; Establish a comfort model to analyze passengers' riding comfort.

2. The subway train energy saving optimization method based on improved genetic algorithm according to claim 1 is characterized in that: The formula for calculating the train's traction is as follows: Where F(v) is the train traction force (N), T F is the traction force in the constant traction force zone (N); v is the running speed of the train (m / s); v a is the turning speed in the constant traction zone (m / s); v b is the turning speed in the constant power zone (m / s); is the terminal speed in the natural characteristic zone (m / s). The formula for calculating the braking force of the train is as follows: Where, B(v) is the electric braking force of the train (N), B F is the braking force in the constant traction zone (N); v d is the turning speed in the constant traction zone (m / s); v f is the terminal velocity of the natural characteristic zone (m / s); The train resistance calculation formula is as follows: w(v)=(a0+b0·v+c0·v 2 )·(K+G)·g Where w(v) is the basic resistance of the train (N); K is the total weight of the train car and trailer (kg); G is the passenger weight (kg); a0, b0, c0 are empirical constants that depend on the type of vehicle and actual line conditions; v is the train speed (m / s).

3. The subway train energy saving optimization method based on improved genetic algorithm according to claim 2 is characterized in that: The train dynamics model is constructed as follows: It includes establishing train dynamic equations based on traction calculation formulas; Reduce the work done by the train's traction force and establish the objective function; Calculate the train's departure speed, station stopping speed, inter-station running time, line speed limit and maximum acceleration during operation.

4. The subway train energy saving optimization method based on improved genetic algorithm according to claim 3 is characterized in that: The train dynamics equation is established according to the traction calculation formula as follows: Where: m is the mass of the train; F(ν) is the traction force; B(ν) is the braking force; ν is the running speed; t is the running time; w(v) is the basic running resistance; a, b, c—basic resistance coefficients of the train; f(v)—additional resistance of the train, which is determined by the slope resistance f i , curve resistance f r and tunnel resistance f s The three together make up.

5. The subway train energy saving optimization method based on improved genetic algorithm according to claim 4 is characterized in that: To reduce the work done by the train's traction force, the objective function is established as follows: Set the objective function to: Where: E—traction energy consumption; μ f —Truck force utilization coefficient; S—Train running distance; x—Train position.

6. The subway train energy saving optimization method based on improved genetic algorithm according to claim 5 is characterized in that: The calculation of the train's departure speed, station entry and stop speed, station-to-station running time, line speed limit and maximum acceleration are as follows: Where: V line (x)—line speed limit at position x; a max —The maximum absolute value of acceleration allowed for train operation; μ b —Train braking force coefficient; T p —The train is given running time.

7. The subway train energy saving optimization method based on improved genetic algorithm according to claim 6 is characterized in that: The establishment of the genetic algorithm energy-saving optimization model specifically includes the following: Obtain input data, including train performance parameters, line data, and operation data; Line section division: the line is divided into n fixed sections according to the differences in slope curvature and speed limit at different positions of the line; Establish an initial population, set the population size to N, the initial population to P, divide the initial population pairs according to the energy consumption and optimal cruising speed of each interval, and generate the corresponding energy-saving state matrix X = [E1, u1, E2, u2, ..., E n ,u n ],X i (i=1,…,n) represents the energy consumption E of each train in the corresponding section i on the running line n and cruising speed.

8. The subway train energy saving optimization method based on improved genetic algorithm according to claim 7, characterized in that: For a given X=[E1,u1,E2,u2,…,E n ,u n ], using E i and u i The method for inferring the optimal speed curve of interval i is as follows:

1. Assume that the initial speed of the train in the section is ν0, and the speed limit at the exit of the section is ν t , the interval length is S, the interval is divided into N equal parts, so that the acceleration can be considered to remain unchanged in each Δx after the division, and the speed sequence of the train in the acceleration stage is calculated; Second, let i = i + 1 and repeat Step 1 when E = 0 and ν i <u Denote the stopping point as i = k, then enter Step 4; conversely, when ν i = u and E > 0, the train transfers to the cruise mode and calculates the speed sequence of the train in the cruise mode; F(n i 0=mΔ(w(ν i )+f(x i )) n i+1 =u E=E-F(ν i )Δx 3. Set i=i+1 and repeat step 2 until E=0, record the stop point as i=k, and go to step 4; 4. Let i = k + 1, r (ν i ) is the acceleration generated by the basic resistance, g(x i ) is the acceleration caused by the additional resistance, and the speed series of the train coasting is calculated; Δx=x i+1 -x i v 2 =v 2 +2a(Δx) 5. Repeat step 4 until the train speed sequence intersects the pre-calculated speed protection curve, and record the stop point as i=j; 6. From the speed after i=j in the sequence, the value of the pre-calculated speed curve is selected as the optimal speed sequence; 7. According to the optimal speed sequence calculated in the above steps, the running time between stations of the train on the line is obtained; Construct the genetic algorithm objective function and calculate the individual fitness; 9. The subway train energy saving optimization method based on improved genetic algorithm according to claim 8, characterized in that: The specific methods for establishing a comfort model and analyzing passengers' riding comfort are as follows: minU=min{α·ΔT+β·ΔS+γ·E} Where α, β, and γ are weight coefficients, ranging from [0, 1], satisfying α+β+γ=1; ΔT is the running time error (s); t is the actual running time (s); T is the running time specified in the train operation schedule (s); ΔS is the running distance error (m); s is the actual running distance (m); S is the actual distance between the two stations (m); θ is the running time error threshold (s); δ is the running distance threshold (m), and a max is the maximum acceleration limit (m / s 2 ); α is the instantaneous acceleration (m / s 2 ); κ is the impact rate threshold (m / s 3 ).

Images