CN110490367A - Bullet train automatic Pilot energy conservation optimizing method based on maximal principle - Google Patents
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Abstract
Bullet train automatic Pilot energy conservation optimizing method disclosed by the invention based on maximal principle, first according to the mechanical characteristics and kinematics character in the operational process of bullet train, the moving model of bullet train is established, to obtain the majorized function of bullet train energy-saving run；Secondly according to Pang De Leah gold maximal principle, hamilton's function is constructed, and carries out the driving strategy of analysis and solution analysis bullet train energy-saving run；Then, according to the curve of traction characteristics of train, kinetics equation and driving strategy, the runing time of bullet train is calculated；Finally, by the obtained time compared with the standard time, if more than the standard time, then suitably increase the cruise time, it is on the contrary then increase inertia time, in the range of operation reaches the standard time at total time, export power consumption values, realization bullet train energy conservation automatic Pilot.Method disclosed by the invention can guarantee precisely parking, achieve the purpose that energy saving optimizing under the premise of punctual and comfortable.
Description
Technical field
The invention belongs to Train Control Technology fields, and in particular to a kind of bullet train based on maximal principle is driven automatically
Sail energy conservation optimizing method.
Background technique
In recent years, growing with China's economy, rail traffic has initially entered a new developing period, according to
The official website public data of Chinese Railway parent company is shown: 2018, china railway completed 563,000,000,000 yuan of capital expenditure, increased newly in operation
4600 kilometers of journey, wherein 3900 kilometers of high-speed rail.Ending for the end of the year 2018, china railway operation total kilometrage reaches 13.1 ten thousand kilometers, in
State's high-speed rail revenue kilometres reach 2.9 ten thousand kilometers, more than 2/3rds of world's high-speed rail total kilometrage, become in the world that high-speed rail mileage is most
The most complicated country of length, traffic density highest, networking operation scene.China railway completes 33.7 hundred million people of passenger's traffic volume, on year-on-year basis
Increase by 2.9 hundred million people, increase by 9.4%, wherein 20.05 hundred million people of EMU, increase by 16.8% on a year-on-year basis.Chinese high-speed rail EMU has added up
Transporting passengers break through 9,000,000,000 person-times, become the main channel of Chinese Railway Passenger Transport.
With the fast-developing and ever-increasing transportation demand of rail traffic, there is an urgent need to build high efficiency height for we
The Rail Transit System of density.Train automated driving system (ATO) is essential for the efficient requirement of rail traffic
, it be able to achieve train automatic running, accurate parking, platform automated job, nobody turn back, the function such as automatic train operation adjusts
Energy.Using advanced train automatic Pilot technology, efficiency, the safety of driving can be greatly improved, and is improving rail traffic
While system effectiveness, because the traction energy consumption of train accounts for 40% share in rail traffic total energy consumption, train
Traction energy consumption also becomes emphasis and to consider the problems of, therefore studies the energy-saving driving strategy of train to cutting operating costs, promote
It has important practical significance into low-carbon economy.
It is found in existing research, domestic and foreign scholars have done a large amount of research on the energy consumption problem of train, there is scholar
Genetic algorithm is combined with neural network fuzzy control applied to proposing a kind of Fast global optimization in train automatic Pilot
Algorithm solves the problems, such as neural network fuzzy control algorithm local minimum present in train operation optimization, but combines and calculate
Method haves the shortcomings that stability difference is not easy to the steady control of train.There is scholar by combining ant group algorithm and fuzzy theory,
It solves the problems, such as municipal rail train running optimizatin when becoming ramp and becoming speed limit, train energy-saving is solved by Integrated Algorithm and is driven
Optimal solution, wherein ant group algorithm reduces the calculation amount of layout designs in train operation optimization, and blurring process ensures operation plan
Practical property slightly can react train speed, acceleration and line slope and reduce the relationship between energy consumption, but ant colony is calculated
Method convergence is poor, the inefficient real-time that will affect train operation.There are also scholars using a kind of by particle group optimizing (PSO)
The hybrid mode that (ICS) is combined is searched for improvement cuckoo to realize train optimized operation curve, to the important ginseng of CS algorithm
Number is accordingly improved, and accelerates CS convergence, which is able to achieve the optimization to train operation curve, to generate
Optimal decision strategy is to control the operation of train.There are also some scholars on the basis of traditional ATO control strategy, analyzes one kind
Automatic Pilot (ATO) control method based on driving strategy, provides a kind of derivation algorithm of ATO energy-saving driving strategy, the algorithm
In guarantee train arrival time error under the premise of a certain range, by extending the coasting distance of train, reduces train and standing
Between the energy consumption that runs.
Summary of the invention
The object of the present invention is to provide a kind of the bullet train automatic Pilot energy conservation optimizing method based on maximal principle, solution
The problem for the Algorithm Convergence that existing research method uses and the stability difference of having determined.
The technical scheme adopted by the invention is that the bullet train automatic Pilot energy saving optimizing side based on maximal principle
Method, specific operation process include the following steps:
Step 1, according to the mechanical characteristics and kinematics character in the operational process of bullet train, the fortune of bullet train is established
Row model, to obtain the majorized function of bullet train energy-saving run；
Step 2, according to Pang De Leah gold maximal principle, hamilton's function, the optimization letter obtained in conjunction with step 1 are constructed
Several pairs of hamilton's functions are analyzed, and solution obtains the driving strategy of bullet train energy-saving run；
Step 3, according to the driving strategy of the curve of traction characteristics of train, kinetics equation and step 2, bullet train is calculated
Runing time；
Step 4, time step 3 obtained is if more than the standard time, then appropriate when increasing cruise compared with the standard time
Between, it is on the contrary then increase inertia time, in the range of operation reaches the standard time at total time, power consumption values are exported, realize high speed
Train energy-saving automatic Pilot.
Other features of the invention also reside in,
Detailed process is as follows for step 1:
The purpose of bullet train energy-saving driving is the traction energy consumption minimized in train travelling process, objective function such as formula (1)
It is shown:
In formula, E is the total energy consumption in train operation, and F is tractive force, and x is the position of train, and v is the speed of train, and S is
The total range ability of train；
Shown in the kinetics equation of train operation such as formula (2):
In formula, B is brake force, and w is the resistance being subject in operation, and g is gradient resistance, and v is the speed of train, and v (x) is column
Speed of the vehicle at the x of position；
Constraint condition:
0≤F≤Fmax (4)
0≤B≤Bmax (5)
0≤v≤vmax (6)
In formula, Fmax, BmaxMaximum drawbar pull and maximum braking force are respectively indicated, t is the time of bullet train operation, vmax
Indicate the speed limit of train.
Preferably, detailed process is as follows for step 2:
According to Pang De Leah gold maximal principle, construct shown in hamilton's function such as formula (7):
λ1And λ2It is Lagrange multiplier, it is contemplated that have inequality constraints, so introducing relaxation complementary factor M (x), then draw
Ge Lang multiplier λ1And λ2Meet formula (8) and formula (9) respectively:
Wherein, relaxation complementary factor M (x) meets formula (10) and formula (11):
It is obtained according to formula (8):
By formula (12) it is found that λ1It is during train operation all constant；
β=λ is enabled for simplifying the analysis2/ v (x) arranges formula (7) and obtains formula (13):
H=(β -1) F (v)+β (B (v)+w (v)+g (x)) (13)
Know that E to be made is minimized by maximal principle, then H will be maximized, so the value range of β determines that high speed arranges
The driving strategy of vehicle:
As β < 0, F=0, B=Bmax, train are in maximum braking maneuver operating condition；
As β=0, F=0, B are variable, applying portion brake force, at this point, train is in partial brake manipulation operating condition；
As β > 0, train can be at mobility operation operating condition.
Preferably, as 0 < β < 1, F=0, B=0, train are in coasting manipulation operating condition；
As β=1, F is variable, and applying portion tractive force, B=0, train is in part traction control operating condition at this time；
As β >=1, F=Fmax, B=0, train are in maximum and accelerate to manipulate operating condition.
Preferably, the process of step 3 comprises the following processes:
Step 3.1, column are calculated according to the driving strategy of the curve of traction characteristics of train, kinetics equation and step 2
The velocity series of vehicle operation；
Step 3.2, the time of train operation is calculated according to the kinetics equation of the velocity series of train and train.
Preferably, detailed process is as follows for step 3.1:
By the driving strategy obtained in the step 2: the optimal driving strategy of bullet train is maximum accelerates -- it patrols
-- inertia -- maximum deceleration of navigating passes through analysis high speed and arranges according to the current velocity series of this driving strategy calculating bullet train
The tractive force and speed data of the operational process of vehicle, using the tractive force of the cube fitting train of the quadratic power or speed of speed
Characteristic curve, respectively by comparing the wrong quadratic sum of quadratic term fitting and cubic term fitting, multiple coefficient of determination, freedom degree tune
Whole r quadratic sum root-mean-square error, determines the curve of traction characteristics of train；
The unit resistance w of train operation is determined according to the speed data of train operation0, then total in train travelling process
Resistance is shown in formula (25):
W=mgw0 (25)
Acceleration a is being obtained according to the curve of traction characteristics and (26) of train, wherein the sum of train stress FAlwaysSuch as formula (27)
It is shown:
FAlways=F ± w (27)
The velocity series in train operation are calculated further according to formula (28):
Wherein vx1It is train in position x1The speed at place, vx2It is train in position x2The speed at place, △ s are x1And x2Between
Distance.
Preferably, detailed process is as follows for step 3.2:
The total time of train operation is calculated by formula (29):
Preferably, pass through the difference △ T of calculating object time and real time in step 4:
Δ T=| T-t |
Wherein, T is the object run time, and t is actual run time；
The coasting time is suitably increased or decreased according to the value of △ T, in the range of operation reaches the standard time at total time,
Power consumption values are exported, realize bullet train energy conservation automatic Pilot.
The invention has the advantages that the bullet train automatic Pilot energy conservation optimizing method based on maximal principle, in Pang
On the basis of moral Leah gold maximal principle, energy consumption model is constructed using hamilton's function, solves energy consumption method for optimally controlling,
It can guarantee precisely parking, achieve the purpose that energy saving optimizing under the premise of punctual and comfortable；The present invention is based on maximum originals
The bullet train automatic Pilot energy conservation optimizing method of reason is realized by Pang De Leah gold maximal principle to bullet train energy conservation
The analysis and solution procedure for optimizing driving strategy, also provide fundamental basis for energy saving optimizing.
Detailed description of the invention
Fig. 1 is the flow chart of the bullet train automatic Pilot energy conservation optimizing method of the invention based on maximum principle；
Fig. 2 is involved in the bullet train automatic Pilot energy conservation optimizing method of the invention based on maximum principle to column
The quadratic term matched curve figure of vehicle curve of traction characteristics；
Fig. 3 is involved in the bullet train automatic Pilot energy conservation optimizing method of the invention based on maximum principle to column
The cubic term matched curve figure of vehicle curve of traction characteristics；
Fig. 4 is involved in the bullet train automatic Pilot energy conservation optimizing method of the invention based on maximum principle to column
The final matched curve figure of vehicle curve of traction characteristics；
Fig. 5 is the experiment operation speed of the bullet train automatic Pilot energy conservation optimizing method of the invention based on maximum principle
Degree figure；
Fig. 6 is that the experiment operation of the bullet train automatic Pilot energy conservation optimizing method of the invention based on maximum principle adds
Hodograph；
Fig. 7 is in the experiment operation of the bullet train automatic Pilot energy conservation optimizing method of the invention based on maximum principle
The curve graph of tractive force.
Specific embodiment
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments.
Bullet train automatic Pilot energy conservation optimizing method based on maximal principle of the invention, as shown in Figure 1, specific behaviour
Include the following steps: as process
Step 1, according to the mechanical characteristics and kinematics character in the operational process of bullet train, the fortune of bullet train is established
Row model, to obtain the majorized function of bullet train energy-saving run；
Detailed process is as follows for step 1:
The purpose of bullet train energy-saving driving is the traction energy consumption minimized in train travelling process, objective function such as formula (1)
It is shown:
In formula, E is the total energy consumption in train operation, and F is tractive force, and x is the position of train, and v is speed, and S is that train is total
Range ability；
Shown in the kinetics equation of train operation such as formula (2):
In formula, B is brake force, and w is the resistance being subject in operation, and g is gradient resistance, and v is speed, and v (x) is that train is in place
Set the speed at x；
Constraint condition:
0≤F≤Fmax (4)
0≤B≤Bmax (5)
0≤v≤vmax (6)
In formula, Fmax, BmaxMaximum drawbar pull and maximum braking force are respectively indicated, t is the time of bullet train operation, vmax
Indicate the speed limit of train.
Step 2, according to Pang De Leah gold maximal principle, hamilton's function, the optimization letter obtained in conjunction with step 1 are constructed
Several pairs of hamilton's functions are analyzed, and solution obtains the driving strategy of bullet train energy-saving run；
Detailed process is as follows for step 2:
According to Pang De Leah gold maximal principle, construct shown in hamilton's function such as formula (7):
λ1And λ2It is Lagrange multiplier, it is contemplated that have inequality constraints, so introducing relaxation complementary factor M (x), then draw
Ge Lang multiplier λ1And λ2Meet formula (8) and formula (9) respectively:
Wherein, relaxation complementary factor M (x) meets formula (10) and formula (11):
It is obtained according to formula (8):
By formula (12) it is found that λ1It is during train operation all constant；
β=λ is enabled for simplifying the analysis2/ v (x) arranges formula (7) and obtains formula (13):
H=(β -1) F (v)+β (B (v)+w (v)+g (x)) (13)
Know that E to be made is minimized by maximal principle, then H will be maximized, so the value range of β determines that high speed arranges
The driving strategy of vehicle:
As β < 0, F=0, B=Bmax, train are in maximum braking maneuver operating condition；
As β=0, F=0, B are variable, applying portion brake force, at this point, train is in partial brake manipulation operating condition；
As 0 < β < 1, F=0, B=0, train are in coasting manipulation operating condition；
As β=1, F is variable, and applying portion tractive force, B=0, train is in part traction control operating condition at this time；
As β >=1, F=Fmax, B=0, train are in maximum and accelerate to manipulate operating condition；
Since train maximum accelerates, the application environment of coasting, these three manipulation operating conditions of maximum braking is obvious, exists respectively
Acceleration of starting, when energy conservation is adjusted and stopped, so specifically dividing the switching point of part traction and partial brake operating condition
Analysis, i.e., be analyzed as follows β=1 and β=0:
Because of β=λ2/ v (x), so both sides differential obtains:
dλ2=vd β+β dv (14)
It can be obtained according to formula (9):
Formula (13) both sides can obtain v derivation:
It is obtained by formula (14)-formula (16):
A) as β=1, train is in part traction control operating condition B=0, it was found from formula (17):
Assuming that V < Vmax, dM (x)=0 known to formula (10), bringing formula (18) into can obtain:
Because W (v) is the quadratic term function about v, and λ1It is constant, so only one solution of formula (19) vx；
Assuming that v=vmaxWhen, have:
V can be obtained by formula (20)max<vx, when train is in part traction control operating condition in summary, when train is in speed limit
Traveling, train is using part tractive force to keep vxSpeed operation；But when speed reaches speed limit, then with limiting operation and
Speed limit vmaxLess than vx, that is to say, that in the case where there is speed limit, speed is unable to reach vxWhen, then speed limit is kept with part tractive force
Operation；
B) as β=0, train is in partial brake manipulation operating condition F=0, it was found from formula (17):
That is:
The λ known to formula (17)1< 0, so having solution to above formula, then: v=vmax, train is in partial brake in summary
Operating condition is manipulated, is occurred when speed touches speed limit and maintains constant-speed operation.
So by being analyzed above it is found that the driving strategy of train energy-saving optimization is traveling-of give it the gun-being cruised by maximum
Inertia traveling-maximum braking traveling is formed.
Step 3, according to the driving strategy of the curve of traction characteristics of train, kinetics equation and step 2, bullet train is calculated
Runing time, comprise the following processes:
Step 3.1, column are calculated according to the driving strategy of the curve of traction characteristics of train, kinetics equation and step 2
The velocity series of vehicle operation；
Detailed process is as follows for step 3.1:
By the driving strategy obtained in the step 2: the optimal driving strategy of bullet train is maximum accelerates -- it patrols
Boat -- inertia -- maximum deceleration calculates the current velocity series of bullet train according to this driving strategy；It is arranged by analysis high speed
The tractive force and speed data of the operational process of vehicle, using the tractive force of the cube fitting train of the quadratic power or speed of speed
Characteristic curve, respectively by comparing the wrong quadratic sum of quadratic term fitting and cubic term fitting, multiple coefficient of determination, freedom degree tune
Whole r quadratic sum root-mean-square error, determines the curve of traction characteristics of train；Train is determined according to the speed data of train operation
The unit resistance w of operation0,
1 part bullet train of table draws force data
| Speed km/h | Tractive force/KN | Speed km/h | Tractive force/KN | Speed km/h | Tractive force/KN |
| 0 | 300 | 5 | 298.58 | 10 | 297.15 |
| 15 | 295.73 | 20 | 294.30 | 25 | 292.88 |
| 30 | 292.45 | 35 | 290.03 | 40 | 288.60 |
| 45 | 287.18 | 50 | 285.75 | 55 | 284.33 |
| 60 | 282.90 | 65 | 282.48 | 70 | 280.05 |
| 75 | 278.63 | 80 | 277.20 | 85 | 275.78 |
| 90 | 274.35 | 95 | 272.93 | 100 | 271.50 |
| 105 | 270.08 | 110 | 268.65 | 115 | 267.23 |
| 120 | 263.17 | 125 | 253.11 | 130 | 243.40 |
| 135 | 234.02 | 140 | 225.78 | 145 | 217.81 |
| 150 | 210.71 | 155 | 204.23 | 160 | 197.84 |
Such as: it is directed to according to the data being given in paper " high-speed railway traction calculates and analogue system research " to train
Pulling figure carries out tractive force characteristic curve fitting, divides the data into two parts, when speed is more than or equal to 119km/h and is less than
119km/h, curve of traction characteristics when we are mainly more than or equal to 119km/h to speed are fitted, totally 37 groups of data, fitting
As a result as follows, table 1 is that part bullet train draws force data, and Fig. 2 is quadratic polynomial fitting result, and Fig. 3 is more three times
Item formula fitting result:
Fig. 2 is the fitting formula of quadratic term are as follows:
F=0.0040v^2-2.5103v+499.2875v >=119km/h
Fig. 3 is the fitting formula of cubic term are as follows:
F=-0.000019525v^3+0.0163v^2-4.9934v+659.0988v >=119km/h
Digital simulation error result, as shown in table 2:
2 error of fitting result of table
| Quadratic polynomial | Cubic polynomial | |
| SSE | 218.8662 | 18.9603 |
| R-square | 0.9970 | 0.9997 |
| Adjusted R-square | 0.9969 | 0.9997 |
| RMSE | 2.5372 | 0.7580 |
SSE: the quadratic sum of mistake, the deviation of the match value of this statistics measurement response.Value close to 0 indicates better
Match.
R-square is indicated: multiple coefficient of determination.The size of numerical value, closer to 1, shows the variable of equation between 0 to 1
It is stronger to the interpretability of y.
S-Adjusted R-square: freedom degree adjusts r squares.Value close to 1 indicates preferably matching.
RMSE: root-mean-square error.Value close to 0 indicates preferably matching.
Coefficient R-the square of cubic polynomial is closer to 1 as can be seen from Table 1, and smaller with variance SEE, comprehensive
Above it can be concluded that the fitting result of cubic polynomial is better than the fitting result of quadratic polynomial,
As shown in figure 4, shown in the curve of traction characteristics function such as formula (23) of train:
Shown in the unit resistance of train operation such as formula (24), drag overall is shown in formula (25):
w0=0.66+0.00245v+0.000132v2 (24)
W=mgw0 (25)
Acceleration a is calculated according to formula (23) and formula (26), wherein the sum of train stress FAlwaysAs shown in formula (27):
FAlways=F ± w (27)
The velocity series in train operation are calculated further according to formula (28):
Wherein, vx1It is train in position x1The speed at place, vx2It is train in position x2The speed at place, △ s are x1And x2Between
Distance.
Step 3.2, the time of train operation is calculated according to the kinetics equation of the velocity series of train and train；
Detailed process is as follows for step 3.2:
The total time of train operation is calculated by formula (29):
Step 4, time step 3 obtained is if more than the standard time, then appropriate when increasing cruise compared with the standard time
Between, it is on the contrary then increase inertia time, in the range of operation reaches the standard time at total time, power consumption values are exported, realize high speed
Train energy-saving automatic Pilot.
Further, pass through the difference △ T of calculating object time and real time in step 4:
Δ T=| T-t |
Wherein, T is the object run time, and t is actual run time；
The coasting time is suitably increased or decreased according to the value of △ T, in the range of operation reaches the standard time at total time,
Power consumption values are exported, realize bullet train energy conservation automatic Pilot.
The data of train in paper " high-speed railway traction calculates and analogue system research " are divided as procedure described above
Analysis, finally obtained train running speed figure is as shown in figure 5, acceleration operation figure schemes such as Fig. 7 as shown in fig. 6, tractive force is run
It is shown.Dotted line represents the Speed limit curve in whole service section in Fig. 5, and '-' indicates the rate curve of real data, and solid line is imitative
The rate curve really obtained.It can be seen that emulation obtains rate curve and raw velocity curve tendency is roughly the same, but solid line wave
Dynamic number is significantly less than the fluctuation number of dot-dash curve, i.e., actual motion speed exists on frequent during train operation
Lower floating, speed switching times are less than the number of speed switching in initial data during simulation run.It is ok from Fig. 6,7
Than there is frequent fluctuation for simulation result in the acceleration and control force for finding out true train operation.The acceleration frequently changed
Degree will be greatly reduced the comfort level of passenger's seating, and frequent switching control force also will increase the loss of the energy；
The present invention is based on the bullet train automatic Pilot energy conservation optimizing methods of maximal principle, with Pang De Leah gold maximum
Based on principle, optimization object function is constructed by mathematical modeling first, then constructs hamilton's function using objective function,
Hamilton's function is analyzed and solved by the maximal principle of Pang De Leah gold, obtains five big fortune of train energy-saving optimization operation
Row strategy solves the velocity series of train operation further according to train operation strategy and dynamics formula, optimal by what is obtained
The actual run time of train is calculated in initial velocity sequence solution, finally by obtained actual run time and standard time into
Row compares, and is adjusted to it: suitably increasing the cruise time if the time is greater than the standard time, otherwise increase inertia
Between, in the range of operation reaches acceptable time at total time, then export corresponding power consumption values.The present invention is based on maximum originals
The bullet train automatic Pilot energy conservation optimizing method of reason utilizes Hamilton on the basis of Pang De Leah gold maximum principle
Function constructs energy consumption model, solves energy consumption optimal control algorithm, which can guarantee precisely parking, punctual and comfortable
Under the premise of achieve the purpose that energy saving optimizing.
Claims (8)
1. the bullet train automatic Pilot energy conservation optimizing method based on maximal principle, which is characterized in that specific operation process packet
Include following steps:
Step 1, according to the mechanical characteristics and kinematics character in the operational process of bullet train, the operation mould of bullet train is established
Type, to obtain the majorized function of bullet train energy-saving run；
Step 2, according to Pang De Leah gold maximal principle, hamilton's function, the majorized function pair obtained in conjunction with step 1 are constructed
Hamilton's function is analyzed, and solution obtains the driving strategy of bullet train energy-saving run；
Step 3, according to the driving strategy of the curve of traction characteristics of train, kinetics equation and step 2, the fortune of bullet train is calculated
The row time；
Step 4, time step 3 obtained compared with the standard time, if more than the standard time, then suitably increases the cruise time,
It is on the contrary then increase inertia time, in the range of operation reaches the standard time at total time, power consumption values are exported, realize bullet train
Energy saving automatic Pilot.
2. the bullet train automatic Pilot energy conservation optimizing method based on maximal principle, feature exist as described in claim 1
In detailed process is as follows for the step 1:
The purpose of bullet train energy-saving driving is the traction energy consumption minimized in train travelling process, shown in objective function such as formula (1):
In formula, E is the total energy consumption in train operation, and F is tractive force, and x is the position of train, and v is the speed of train, and S is train
Total range ability；
Shown in the kinetics equation of train operation such as formula (2):
In formula, B is brake force, and w is the resistance being subject in operation, and g is gradient resistance, and v is the speed of train, and v (x) is that train exists
Speed at the x of position；
Constraint condition:
0≤F≤Fmax (4)
0≤B≤Bmax (5)
0≤v≤vmax (6)
In formula, Fmax, BmaxMaximum drawbar pull and maximum braking force are respectively indicated, t is the time of bullet train operation, vmaxIt indicates
The speed limit of train.
3. the bullet train automatic Pilot energy conservation optimizing method based on maximal principle, feature exist as claimed in claim 2
In detailed process is as follows for the step 2:
According to Pang De Leah gold maximal principle, construct shown in hamilton's function such as formula (7):
λ1And λ2It is Lagrange multiplier, it is contemplated that have inequality constraints, so introducing relaxation complementary factor M (x), then glug is bright
Day multiplier λ1And λ2Meet formula (8) and formula (9) respectively:
Wherein, relaxation complementary factor M (x) meets formula (10) and formula (11):
It is obtained according to formula (8):
By formula (12) it is found that λ1It is during train operation all constant；
β=λ is enabled for simplifying the analysis2/ v (x) arranges formula (7) and obtains formula (13):
H=(β -1) F (v)+β (B (v)+w (v)+g (x)) (13)
Know that E to be made is minimized by maximal principle, then H will be maximized, so the value range of β determines bullet train
Driving strategy:
As β < 0, F=0, B=Bmax, train are in maximum braking maneuver operating condition；
As β=0, F=0, B are variable, applying portion brake force, at this point, train is in partial brake manipulation operating condition；
As β > 0, train can be at mobility operation operating condition.
4. the bullet train automatic Pilot energy conservation optimizing method based on maximal principle, feature exist as claimed in claim 3
In as 0 < β < 1, F=0, B=0, train are in coasting manipulation operating condition；
As β=1, F is variable, and applying portion tractive force, B=0, train is in part traction control operating condition at this time；
As β >=1, F=Fmax, B=0, train are in maximum and accelerate to manipulate operating condition.
5. the bullet train automatic Pilot energy conservation optimizing method based on maximal principle, feature exist as claimed in claim 4
In the process of the step 3 comprises the following processes:
Step 3.1, train fortune is calculated according to the driving strategy of the curve of traction characteristics of train, kinetics equation and step 2
Capable velocity series；
Step 3.2, the time of train operation is calculated according to the kinetics equation of the velocity series of train and train.
6. the bullet train automatic Pilot energy conservation optimizing method based on maximal principle, feature exist as claimed in claim 5
In detailed process is as follows for the step 3.1:
By the driving strategy obtained in the step 2: the optimal driving strategy of bullet train is maximum to accelerate -- cruise -- lazy
Property -- maximum deceleration calculates the current velocity series of bullet train according to this driving strategy, by the fortune for analyzing bullet train
The tractive force and speed data of row process, the traction force characteristic using the cube fitting train of the quadratic power or speed of speed are bent
Line is flat by comparing the wrong quadratic sum of quadratic term fitting and cubic term fitting, multiple coefficient of determination, freedom degree adjustment r respectively
Side and root-mean-square error determine the curve of traction characteristics of train；
The unit resistance w of train operation is determined according to the speed data of train operation0, then the drag overall in train travelling process
For shown in formula (25):
W=mgw0 (25)
Acceleration a is being obtained according to the curve of traction characteristics and (26) of train, wherein the sum of train stress FAlwaysSuch as formula (27) institute
Show:
FAlways=F ± w (27)
The velocity series in train operation are calculated further according to formula (28):
Wherein vx1It is train in position x1The speed at place, vx2It is train in position x2The speed at place, △ s are x1And x2Between away from
From.
7. the bullet train automatic Pilot energy conservation optimizing method based on maximal principle, feature exist as claimed in claim 6
In detailed process is as follows for the step 3.2:
The total time of train operation is calculated by formula (29):
8. the bullet train automatic Pilot energy conservation optimizing method based on maximal principle, feature exist as described in claim 1
In, in the step 4 by calculate object time and real time difference △ T:
Δ T=| T-t |
Wherein, T is the object run time, and t is actual run time；
The coasting time is suitably increased or decreased according to the value of △ T, in the range of operation reaches the standard time at total time, output
Power consumption values realize bullet train energy conservation automatic Pilot.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111325462A (en) * | 2020-02-18 | 2020-06-23 | 中国铁道科学研究院集团有限公司 | Motor train unit auxiliary driving method and system |
| CN111688760A (en) * | 2020-06-23 | 2020-09-22 | 北京全路通信信号研究设计院集团有限公司 | Rapid energy-saving optimization method and device for train passing through steep slope section |
| CN112084558A (en) * | 2020-08-31 | 2020-12-15 | 成都理工大学 | Track irregularity state optimization simulation fine adjustment method |
| CN112307564A (en) * | 2020-11-10 | 2021-02-02 | 交控科技股份有限公司 | Method and device for optimizing ATO target running speed curve of train |
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| CN113361061A (en) * | 2020-03-06 | 2021-09-07 | 中移智行网络科技有限公司 | Train operation strategy optimization method and device, storage medium and computer equipment |
| CN113449436A (en) * | 2021-07-22 | 2021-09-28 | 中铁二院工程集团有限责任公司 | Method for acquiring locomotive traction characteristic curve in complex operation environment |
| CN113635943A (en) * | 2021-10-18 | 2021-11-12 | 西南交通大学 | Train driving assisting method, system, equipment and computer readable storage medium |
| CN114044032A (en) * | 2021-11-05 | 2022-02-15 | 北京全路通信信号研究设计院集团有限公司 | Dynamic optimization method and system for energy-saving driving curve of train |
| CN114655277A (en) * | 2022-04-02 | 2022-06-24 | 株洲中车时代电气股份有限公司 | Method for calculating intelligent driving overspeed protection curve of heavy-duty train and related equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104134378A (en) * | 2014-06-23 | 2014-11-05 | 北京交通大学 | Urban rail train intelligent control method based on driving experience and online study |
| CN109815523A (en) * | 2018-12-05 | 2019-05-28 | 南京工程学院 | Train operation multiple target differential evolution algorithm based on decomposition |
| CN109978350A (en) * | 2019-03-13 | 2019-07-05 | 北京工业大学 | A kind of subway train energy conservation optimizing method based on regime decomposition dynamic programming algorithm |
-
2019
- 2019-07-15 CN CN201910636902.7A patent/CN110490367A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104134378A (en) * | 2014-06-23 | 2014-11-05 | 北京交通大学 | Urban rail train intelligent control method based on driving experience and online study |
| CN109815523A (en) * | 2018-12-05 | 2019-05-28 | 南京工程学院 | Train operation multiple target differential evolution algorithm based on decomposition |
| CN109978350A (en) * | 2019-03-13 | 2019-07-05 | 北京工业大学 | A kind of subway train energy conservation optimizing method based on regime decomposition dynamic programming algorithm |
Non-Patent Citations (3)
| Title |
|---|
| 吕希奎 等: "高速铁路牵引计算与仿真系统研究", 《铁道标准设计》 * |
| 宿帅，唐涛: "城市轨道交通ATO的节能优化研究", 《铁道学报》 * |
| 耿超: "高速列车节能优化操纵方法的研究", 《中国优秀硕博士学位论文全文数据库（硕士） 工程科技II辑》 * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111325462A (en) * | 2020-02-18 | 2020-06-23 | 中国铁道科学研究院集团有限公司 | Motor train unit auxiliary driving method and system |
| CN113361061A (en) * | 2020-03-06 | 2021-09-07 | 中移智行网络科技有限公司 | Train operation strategy optimization method and device, storage medium and computer equipment |
| CN111688760B (en) * | 2020-06-23 | 2022-04-08 | 北京全路通信信号研究设计院集团有限公司 | Rapid energy-saving optimization method and device for train passing through steep slope section |
| CN111688760A (en) * | 2020-06-23 | 2020-09-22 | 北京全路通信信号研究设计院集团有限公司 | Rapid energy-saving optimization method and device for train passing through steep slope section |
| CN112084558A (en) * | 2020-08-31 | 2020-12-15 | 成都理工大学 | Track irregularity state optimization simulation fine adjustment method |
| CN112307564A (en) * | 2020-11-10 | 2021-02-02 | 交控科技股份有限公司 | Method and device for optimizing ATO target running speed curve of train |
| CN112633596A (en) * | 2020-12-30 | 2021-04-09 | 南京理工大学 | Integrated optimization method for speed curve and interval running time of subway train |
| CN112633596B (en) * | 2020-12-30 | 2022-09-27 | 南京理工大学 | Integrated optimization method for speed curve and interval running time of subway train |
| CN113449436A (en) * | 2021-07-22 | 2021-09-28 | 中铁二院工程集团有限责任公司 | Method for acquiring locomotive traction characteristic curve in complex operation environment |
| CN113449436B (en) * | 2021-07-22 | 2022-03-25 | 中铁二院工程集团有限责任公司 | Method for acquiring locomotive traction characteristic curve in complex operation environment |
| CN113635943A (en) * | 2021-10-18 | 2021-11-12 | 西南交通大学 | Train driving assisting method, system, equipment and computer readable storage medium |
| CN114044032A (en) * | 2021-11-05 | 2022-02-15 | 北京全路通信信号研究设计院集团有限公司 | Dynamic optimization method and system for energy-saving driving curve of train |
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