CN110188401A - A kind of tramcar operation energy consumption optimization method based on improvement PSO - Google Patents
A kind of tramcar operation energy consumption optimization method based on improvement PSO Download PDFInfo
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- CN110188401A CN110188401A CN201910376254.6A CN201910376254A CN110188401A CN 110188401 A CN110188401 A CN 110188401A CN 201910376254 A CN201910376254 A CN 201910376254A CN 110188401 A CN110188401 A CN 110188401A
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Abstract
The invention discloses a kind of based on the tramcar operation energy consumption optimization method for improving PSO.This method are as follows: establish tramcar operation energy consumption model: in a traffic coverage, tramcar operational process is divided into: all one's effort traction working condition, tramcar are started since speed 0 with maximum drawbar pull, reach section maximum speed；At the uniform velocity cruising condition, tramcar carry out uniform motion with section maximum speed, and tractive force is equal to overall drag；Coasting operating condition, tramcar carry out coasting, tractive force 0；All one's effort damped condition, tramcar are braked with maximum braking force, and braking energy is carried out feedback storage；Tramcar multi-constraint condition is analyzed, tramcar operation energy consumption model optimization problem is simplified；Selection study mechanism is added on classical PSO algorithm, the operating condition turning point in solving model calculates the energy consumption of each traffic coverage of tramcar.The present invention has the advantages that calculating is accurate, fast convergence rate, treatment effeciency are high, practical.
Description
Technical field
It is especially a kind of based on the tramcar fortune for improving PSO the invention belongs to the energy-optimised technical field of tramcar
Row energy consumption optimization method.
Background technique
Tramcar is big with its freight volume, invests the advantages such as small, environmentally protective, long service life, becomes small and medium-sized cities development
The first choice of city rail traffic cause.How the status in short supply in face of global energy, effectively reduce tramcar operation energy consumption, become
The energy-optimised most important thing of tramcar.
The operating condition of tramcar includes traction, cruise, inertia, four kinds of braking, in braking link, will be generated largely
Braking energy, to braking energy carry out recycling will be helpful to reduce operation energy consumption.Tramcar operation reserve is divided into maximum energy
Power operation reserve with two kinds of timing operation strategy, the former does not have inertia link, and runing time is few, and it is larger to consume energy；The latter's energy consumption
It is smaller, it is the operation reserve of relative energy-saving.Currently, the tramcar operation energy consumption model established using energy-saving run strategy, only
Consider safety, the precisely indexs such as parking, punctual, comfort level, and does not consider the totality of the tramcar under different passenger's load conditions
Influence of the quality to operation energy consumption causes operation energy consumption model inaccurate.In addition, being directed to tramcar energy-saving run energy optimization
Energy optimization problem under problem, that is, multi-constraint condition mostly uses classical PSO algorithm to solve, and the algorithm search ability is strong, restrains
Speed is fast, but there are problems that being easily trapped into local optimum, and operation energy consumption is caused to calculate inaccuracy.In summary, existing rail
The energy-optimised technology of electric car, energy consumption model inaccuracy cannot accurately calculate operation energy consumption, be that tramcar operation energy consumption optimizes
Problem.
Summary of the invention
The purpose of the present invention is to provide it is a kind of calculate accurate, fast convergence rate, effectively avoid local optimum based on changing
Into the tramcar operation energy consumption optimization method of PSO.
Realizing the technical solution of the object of the invention is: a kind of based on the tramcar operation energy consumption optimization for improving PSO
Method, comprising the following steps:
Step 1, it is based on tramcar operating condition, establishes tramcar operation energy consumption model；
Step 2, tramcar multi-constraint condition is analyzed, tramcar operation energy consumption model optimization problem is simplified；
Step 3, using PSO algorithm i.e. addition selection study mechanism on the basis of classical PSO algorithm is improved, rail electricity is solved
Operating condition turning point in vehicle operation energy consumption model；
Step 4, using the calculated operating condition turning point of step 3, the energy consumption of each traffic coverage of tramcar is calculated.
Further, tramcar operation energy consumption model is established based on tramcar operating condition described in step 1, had
Body is as follows:
In a traffic coverage, tramcar operational process is divided into four operating conditions:
Operating condition I: all one's effort traction working condition, tramcar are started since speed 0 with maximum drawbar pull, reach section maximum
Speed Vmax；
Operating condition II: at the uniform velocity cruising condition, tramcar is with section maximum speed VmaxUniform motion is carried out, tractive force is equal to
Overall drag；
Operating condition III: coasting operating condition, tramcar carry out coasting, tractive force 0；
Operating condition IV: all one's effort damped condition, tramcar are braked with maximum braking force, and braking energy is fed back
Storage；
According to tramcar operation characteristic, punctual, safe, comfortable, accurate parking selection, passenger's load indicators used is added, builds
Vertical tramcar operation energy consumption model, it may be assumed that
In formula: f (x) is power dissipation obj ectives function；Ft(v)、Ftmax(v)、Fm(v)、FmmaxIt (v) is respectively that tramcar is being transported
Tractive force, tramcar tractive force maximum value when scanning frequency degree v, tramcar brake force, tramcar brake force maximum value；x
Currently to run kilometer post；v,VmaxRespectively tramcar operation instantaneous velocity and entire section maximum operational speed；ρ is system
Energy feedback factor；M is the tramcar overall quality under different passenger's load conditions；g1(x)、g2(x)、g3(x)、g4
(x)、g5It (x) is respectively punctual, accurate parking, safety, comfort level, passenger's load indicators used constraint condition；S0、Sp、D1、D2、D3Point
It Wei not section starting point kilometer post, terminal kilometer post, operating condition I~operating condition II turning point kilometer post, operating condition II~operating condition III turns
Break kilometer post, operating condition III~operating condition IV turning point kilometer post；Δ t, Δ s are respectively time error, range error.
Further, it is excellent to simplify tramcar operation energy consumption model for analysis tramcar multi-constraint condition described in step 2
Change problem, specific as follows:
Tramcar operates inIn section, runing time Δ T, have:
In formula, F is tramcar resultant force, Ft、Fm、FzRespectively tractive force, brake force, resistance, wherein resistance is rubbed by machinery
Resistance, additional resistance due to grade, additional resistance due to curve, air drag composition are wiped, a is operation acceleration, and M is different passenger's load feelings
Tramcar overall quality under condition, x1、x2Respectively run starting highway mark, the terminal kilometer post in subinterval, v1、v2Respectively
Speed, speed at terminal kilometer post, S at starting highway mark to run subinterval0、SpFor the starting kilometer in whole service section
Mark, terminal kilometer post；
In the section operating condition I, tramcar is with maximum drawbar pull Acceleration of starting to maximum speed limit value, tractive force characteristic curve
Are as follows:
In formula, Ft(v) be instantaneous velocity v when tramcar tractive force, FmaxFor permanent torque area tramcar tractive force, vt1、
VmaxRespectively invariable power area starting point train running speed, whole service section maximum operational speed；
By the operation energy consumption optimization problem of tramcar convert four-stage optimization problem, i.e. three phases turning point
Optimization problem: operating condition I~operating condition II turning point D1, operating condition II~operating condition III turning point D2, operating condition III~operating condition IV turn
Break D3；
F is determined by tractive force performance diagrammax、vt1And Vmax, corresponding F is determined by brake force characteristic curvem, and then really
Resultant force F, acceleration a, range ability, runing time are determined, so that it is determined that operating condition I~operating condition II turning point D1；Similarly, for work
Condition II~operating condition III turning point D2, can determine Vmax, having in the section operating condition III and the section operating condition IV is obtained by calculation
Rail electric car acceleration obtains operating condition turning point D3The speed V at place3With section maximum operational speed VmaxRelationship, pass through determine work
Condition III~operating condition IV turning point D3Operating condition II~operating condition III turning point D is pushed away to counter2, tramcar is determined in section
Operation energy consumption optimization problem is converted into operating condition turning point D3Locate speed V3Optimization problem.
Further, using PSO algorithm is improved, i.e. addition selects study on the basis of classical PSO algorithm described in step 3
Mechanism solves the operating condition turning point in tramcar operation energy consumption model, specific as follows:
Step 3.1, emulation data input: track data, train data and algorithm relevant parameter；
Step 3.2, initialization of population: being arranged the size N of population, and gives the value of single particle at random, single particle
Given value is no more than the speed limit value in emulation section；
The population that a particle number is m is initialized, each particle has the position attribution x of n dimensioni=(xi,1(t),xi,2
(t),...xi,n(t)) and n dimension Speed attribute vi=(vi,1(t),vi,2(t),...vi,n(t))；
Step 3.3, energy consumption index adapt to value function and solve, and update particle rapidity and position: by particle pair each in population
The value answered carries out tramcar operation energy consumption objective function and calculates, obtains the corresponding energy consumption index adaptive value of each particle；It takes
Study mechanism is selected, in selection region, pairs of is compared the particle in population, and particle of winning is directly entered the next generation
Population, failure particle study wins the speed of particle, position come after carrying out self-renewing, and enters next-generation population；
Setting for selection region, to prevent particle to be gathered in single-wide too early, first by particle by fitness by
Successively put into selection region to small greatly, calculate particle to be added and be added between particle alternate position spike and, and threshold value is set, when
Alternate position spike and be more than threshold value when, then the particle is not placed in selection region；When alternate position spike and be less than threshold value when, then by the particle
It is placed in selection region；
Given threshold is R, xiFor particle to be added, xjFor particle has been added, wherein j ∈ 1 ..., b, b are that particle has been added
Number；
If the two alternate position spike and satisfaction:
Then by xiIt is put into selection region, otherwise gives up the particle；
Selection for threshold value R meets with the increase of the number of iterations:
In formula, Rmin、RmaxRespectively minimum, max-thresholds, G, GmaxRespectively current iteration number, maximum number of iterations；
Position and speed after the failure chosen study of particle updates are as follows:
In formula,For the position and speed for the particle that fails after the G times iteration, kth time selection study；
For the position and speed for the particle that fails after the G+1 times iteration, kth time selection study；For the G times iteration, kth time
It wins after selection study the position and speed of particle；G is current iteration number；For random number in (0,1) section,
For particles all in group in the G times iteration, kth time selection study position mean；ω is inertial factor；
Step 3.4, termination condition judgement: current iteration number reaches maximum number of iterations, then meets termination condition, exits
Iterative cycles；Otherwise, 3.3 are entered step, solves energy consumption index adaptive value, and selection is continued to particle rapidity, position and is learned
It practises and updating, until meeting termination condition；
Termination condition is i.e.:
G≥Gmax
In formula, G, GmaxRespectively current iteration number, maximum number of iterations.
Further, the calculated operating condition turning point of utilization step 3 described in step 4 calculates each Operational Zone of tramcar
Between energy consumption, it is specific as follows:
Optimal solution and related operation curve are acquired by step 3, optimal solution is extracted and substitutes into tramcar energy saving optimizing model
In calculated, complete calculating to section operation energy consumption.
Compared with prior art, the present invention its remarkable advantage is: (1) passenger being added in tramcar operation energy consumption model and carry
Lotus index improves the accuracy of energy consumption model；(2) based on PSO algorithm is improved, it ensure that algorithm the convergence speed, effectively avoid
The problem of falling into local optimum；(3) single-objective problem is converted by the multi-objective problem of operation energy consumption, reduces answering for problem
Polygamy.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow diagrams for the tramcar operation energy consumption optimization method for improving PSO.
Fig. 2 is tramcar operating condition schematic diagram in the present invention.
Fig. 3 is tramcar working line schematic diagram in the embodiment of the present invention.
Fig. 4 is that tramcar draws force characteristic schematic diagram in the embodiment of the present invention.
Fig. 5 is that tramcar brakes force characteristic schematic diagram in the embodiment of the present invention.
Fig. 6 is that certain section adaptive optimal control value and traction energy consumption change schematic diagram in the embodiment of the present invention, wherein (a) is optimal
Adaptive value changes schematic diagram, (b) changes schematic diagram for traction energy consumption.
Fig. 7 is energy consumption-distance Curve and velocity-distance graph schematic diagram in certain section in the embodiment of the present invention, wherein (a)
It (b) is velocity-distance graph schematic diagram for energy consumption-distance Curve schematic diagram.
Fig. 8 is emulation energy consumption and actual motion energy consumption schematic diagram in the embodiment of the present invention.
Specific embodiment
A kind of tramcar operation energy consumption optimization method based on improvement PSO, comprising the following steps:
Step 1, it is based on tramcar operating condition, establishes tramcar operation energy consumption model；
Step 2, tramcar multi-constraint condition is analyzed, tramcar operation energy consumption model optimization problem is simplified；
Step 3, using PSO algorithm i.e. addition selection study mechanism on the basis of classical PSO algorithm is improved, rail electricity is solved
Operating condition turning point in vehicle operation energy consumption model；
Step 4, using the calculated operating condition turning point of step 3, the energy consumption of each traffic coverage of tramcar is calculated.
Further, tramcar operation energy consumption model is established based on tramcar operating condition described in step 1, had
Body is as follows:
In a traffic coverage, tramcar operational process is divided into four operating conditions:
Operating condition I: all one's effort traction working condition, tramcar are started since speed 0 with maximum drawbar pull, reach section maximum
Speed Vmax；
Operating condition II: at the uniform velocity cruising condition, tramcar is with section maximum speed VmaxUniform motion is carried out, tractive force is equal to
Overall drag；
Operating condition III: coasting operating condition, tramcar carry out coasting, tractive force 0；
Operating condition IV: all one's effort damped condition, tramcar are braked with maximum braking force, and braking energy is fed back
Storage；
According to tramcar operation characteristic, punctual, safe, comfortable, accurate parking selection, passenger's load indicators used is added, builds
Vertical tramcar operation energy consumption model, it may be assumed that
In formula: f (x) is power dissipation obj ectives function；Ft(v)、Ftmax(v)、Fm(v)、FmmaxIt (v) is respectively that tramcar is being transported
Tractive force, tramcar tractive force maximum value when scanning frequency degree v, tramcar brake force, tramcar brake force maximum value；x
Currently to run kilometer post；v,VmaxRespectively tramcar operation instantaneous velocity and entire section maximum operational speed；ρ is system
Energy feedback factor；M is the tramcar overall quality under different passenger's load conditions；g1(x)、g2(x)、g3(x)、g4
(x)、g5It (x) is respectively punctual, accurate parking, safety, comfort level, passenger's load indicators used constraint condition；S0、Sp、D1、D2、D3Point
It Wei not section starting point kilometer post, terminal kilometer post, operating condition I~operating condition II turning point kilometer post, operating condition II~operating condition III turns
Break kilometer post, operating condition III~operating condition IV turning point kilometer post；Δ t, Δ s are respectively time error, range error.
Further, it is excellent to simplify tramcar operation energy consumption model for analysis tramcar multi-constraint condition described in step 2
Change problem, specific as follows:
Tramcar operates inIn section, runing time Δ T, have:
In formula, F is tramcar resultant force, Ft、Fm、FzRespectively tractive force, brake force, resistance, wherein resistance is rubbed by machinery
Resistance, additional resistance due to grade, additional resistance due to curve, air drag composition are wiped, a is operation acceleration, and M is different passenger's load feelings
Tramcar overall quality under condition, x1、x2Respectively run starting highway mark, the terminal kilometer post in subinterval, v1、v2Respectively
Speed, speed at terminal kilometer post, S at starting highway mark to run subinterval0、SpFor the starting kilometer in whole service section
Mark, terminal kilometer post；
In the section operating condition I, tramcar is with maximum drawbar pull Acceleration of starting to maximum speed limit value, tractive force characteristic curve
Are as follows:
In formula, Ft(v) be instantaneous velocity v when tramcar tractive force, FmaxFor permanent torque area tramcar tractive force, vt1、
VmaxRespectively invariable power area starting point train running speed, whole service section maximum operational speed；
By the operation energy consumption optimization problem of tramcar convert four-stage optimization problem, i.e. three phases turning point
Optimization problem: operating condition I~operating condition II turning point D1, operating condition II~operating condition III turning point D2, operating condition III~operating condition IV turn
Break D3；
F is determined by tractive force performance diagrammax、vt1And Vmax, corresponding F is determined by brake force characteristic curvem, and then really
Resultant force F, acceleration a, range ability, runing time are determined, so that it is determined that operating condition I~operating condition II turning point D1；Similarly, for work
Condition II~operating condition III turning point D2, can determine Vmax, having in the section operating condition III and the section operating condition IV is obtained by calculation
Rail electric car acceleration obtains operating condition turning point D3The speed V at place3With section maximum operational speed VmaxRelationship, pass through determine work
Condition III~operating condition IV turning point D3Operating condition II~operating condition III turning point D is pushed away to counter2, tramcar is determined in section
Operation energy consumption optimization problem is converted into operating condition turning point D3Locate speed V3Optimization problem.
Further, using PSO algorithm is improved, i.e. addition selects study on the basis of classical PSO algorithm described in step 3
Mechanism solves the operating condition turning point in tramcar operation energy consumption model, specific as follows:
Step 3.1, emulation data input: track data, train data and algorithm relevant parameter；
Step 3.2, initialization of population: being arranged the size N of population, and gives the value of single particle at random, single particle
Given value is no more than the speed limit value in emulation section；
The population that a particle number is m is initialized, each particle has the position attribution x of n dimensioni=(xi,1(t),xi,2
(t),...xi,n(t)) and n dimension Speed attribute vi=(vi,1(t),vi,2(t),...vi,n(t))；
Step 3.3, energy consumption index adapt to value function and solve, and update particle rapidity and position: by particle pair each in population
The value answered carries out tramcar operation energy consumption objective function and calculates, obtains the corresponding energy consumption index adaptive value of each particle；It takes
Study mechanism is selected, in selection region, pairs of is compared the particle in population, and particle of winning is directly entered the next generation
Population, failure particle study wins the speed of particle, position come after carrying out self-renewing, and enters next-generation population；
Setting for selection region, to prevent particle to be gathered in single-wide too early, first by particle by fitness by
Successively put into selection region to small greatly, calculate particle to be added and be added between particle alternate position spike and, and threshold value is set, when
Alternate position spike and be more than threshold value when, then the particle is not placed in selection region；When alternate position spike and be less than threshold value when, then by the particle
It is placed in selection region；
Given threshold is R, xiFor particle to be added, xjFor particle has been added, wherein j ∈ 1 ..., b, b are that particle has been added
Number；
If the two alternate position spike and satisfaction:
Then by xiIt is put into selection region, otherwise gives up the particle；
Selection for threshold value R meets with the increase of the number of iterations:
In formula, Rmin、RmaxRespectively minimum, max-thresholds, G, GmaxRespectively current iteration number, maximum number of iterations；
Position and speed after the failure chosen study of particle updates are as follows:
In formula,For the position and speed for the particle that fails after the G times iteration, kth time selection study；
For the position and speed for the particle that fails after the G+1 times iteration, kth time selection study；For the G times iteration, kth time choosing
Select the position and speed for particle of winning after learning；G is current iteration number；For random number in (0,1) section,For
All particles position mean in the G times iteration, kth time selection study in group；ω is inertial factor；
Step 3.4, termination condition judgement: current iteration number reaches maximum number of iterations, then meets termination condition, exits
Iterative cycles；Otherwise, 3.3 are entered step, solves energy consumption index adaptive value, and selection is continued to particle rapidity, position and is learned
It practises and updating, until meeting termination condition；
Termination condition is i.e.:
G≥Gmax
In formula, G, GmaxRespectively current iteration number, maximum number of iterations.
Further, the calculated operating condition turning point of utilization step 3 described in step 4 calculates each Operational Zone of tramcar
Between energy consumption, it is specific as follows:
Optimal solution and related operation curve are acquired by step 3, optimal solution is extracted and substitutes into tramcar energy saving optimizing model
In calculated, complete calculating to section operation energy consumption.
Below in conjunction with the accompanying drawings and specific embodiment, the present invention is described in further details.
Embodiment 1
In conjunction with Fig. 1, the present invention is based on the tramcar operation energy consumption optimization methods of PSO, comprising the following steps:
Step 1, it is based on tramcar operating condition, establishes tramcar operation energy consumption model, specific as follows:
In S0~SpIn traffic coverage, tramcar energy-saving run process is divided into four operating conditions: drawing (I) with all strength, at the uniform velocity patrols
It navigates (II), coasting (III), with all strength braking (IV), operating condition schematic diagram is as shown in Fig. 2, in figure:
1) in the section operating condition I, tramcar is started since speed 0 with maximum drawbar pull, reaches section maximum speed
Vmax；
2) in the section operating condition II, tramcar is with section maximum speed VmaxUniform motion is carried out, tractive force is equal to synthesis
Resistance；
3) in the section operating condition III, tramcar carries out coasting, tractive force 0；
4) in the section operating condition IV, tramcar is braked with maximum braking force, and braking energy is carried out feedback and is deposited
Storage.
Tramcar power dissipation obj ectives function are as follows:
In formula: Ft、FmRespectively tramcar tractive force and tramcar brake force；S0、SpRespectively traffic coverage starting point
Kilometer post and terminal kilometer post；ρ is braking energy feedback factor.
Conventional index relevant to operation energy consumption includes tramcar operation characteristic, precisely stops, is punctual, is safe, is comfortable
Furthermore degree is set passenger's load indicators used, the variation of tramcar overall quality under non-stop passenger's load is reflected in this, in turn
Operation energy consumption is influenced, to make this operation energy consumption model better authenticity, practicability.
Punctual index relevant constraint are as follows:
g1(x)=T-T0≤Δt
In formula, T and T0Respectively tramcar actual run time and timetable stipulated time, Δ t are time error；
Accurate parking selection relevant constraint are as follows:
G2 (x)=| S-Sp|≤Δs
In formula, S and SpThe respectively practical parking position of tramcar and regulation parking position, range error Δ s takes
0.25m；
Safety index relevant constraint are as follows:
In formula, v and VmaxRespectively tramcar actual motion speed and maximum permission speed, work as g3(x) when taking 0, meet
Safety index；
Comfort level index relevant constraint are as follows:
g4(x)=0
In formula, comfort level index only considers whether the ancillary equipments such as air-conditioning, illumination work normally, and normal work then takes 0.One
As in the case of, to meet passenger comfort, acceleration should be no more than 1.8m/s in accelerator2, deceleration in moderating process
1.5m/s should be no more than2When, it is analyzed by the equation of motion of tramcar, the peak acceleration of tramcar is 1.28m/s2, most
Big retarding degree is m/s2, it is not above limit value, therefore for comfort index without the concern for acceleration factor.
Passenger's load indicators used relevant constraint are as follows:
Wherein AW0、AW1、AW2、AW3Respectively indicate unloaded (self weight), standard (be self-possessed+completely attending a banquet), fully loaded (self weight+full seat
Seat+n1People/m2), overload (be self-possessed+completely attend a banquet+n2People/m2), M is complete vehicle quality.Under different passenger's load conditions, will have not
Same complete vehicle quality, and complete vehicle quality will affect tractive force, brake force, the resistance of train, and then influence operation energy consumption.Addition multiplies
Objective load indicators used can be effectively reflected in the case of different loads, the variation of operation energy consumption pair.
Other constraint conditions are as follows:
In formula, in S0~SpIn traffic coverage, start instantaneous speed v (S0) and stopping instantaneous speed v (Sp) it is 0, operation
Speed v is not more than maximum operational speed Vmax, operating condition turning point D1、D2、D3In S0~SpIn traffic coverage.
To sum up, tramcar operation energy consumption model are as follows:
Step 2, tramcar multi-constraint condition is analyzed, simplifies tramcar operation energy consumption model optimization problem, specifically such as
Under:
Tramcar operates inSection in runing time Δ T, has:
In formula, F is tramcar resultant force, Ft、Fm、FzRespectively tractive force, brake force, resistance, wherein resistance is rubbed by machinery
Resistance, additional resistance due to grade, additional resistance due to curve, air drag composition are wiped, a is operation acceleration, and M is different passenger's load feelings
Tramcar overall quality under condition, x1、x2Respectively run starting highway mark, the terminal kilometer post in subinterval, v1、v2Respectively
Speed, speed at terminal kilometer post, S at starting highway mark to run subinterval0、SpFor the starting kilometer in whole service section
Mark, terminal kilometer post.
In the section operating condition I, tramcar is with maximum drawbar pull Acceleration of starting to maximum speed limit value, tractive force characteristic curve
Are as follows:
In formula, Ft(v) be instantaneous velocity v when tramcar tractive force, FmaxFor permanent torque area tramcar tractive force, vt1、
VmaxRespectively invariable power area starting point train running speed, whole service section maximum operational speed.
By the operation energy consumption optimization problem of tramcar convert four-stage optimization problem, i.e. three phases turning point
Optimization problem: operating condition I~operating condition II turning point D1, operating condition II~operating condition III turning point D2, operating condition III~operating condition IV turn
Break D3.F can be determined by tractive force performance diagrammax、vt1And Vmax, can be determined accordingly by brake force characteristic curve
Fm, and then determine resultant force F, acceleration a, range ability, runing time, so that it is determined that operating condition I~operating condition II turning point D1.Together
Reason, for operating condition II~operating condition III turning point D2, can determine Vmax, operating condition III operating condition section and work is obtained by calculation
Tramcar acceleration in condition IV operating condition section, obtains operating condition turning point D3The speed V at place3With section maximum operational speed Vmax
Relationship, pass through determine operating condition III~operating condition IV turning point D3Operating condition II~operating condition III turning point D is pushed away to counter2.Cause
This, tramcar, which determines the operation energy consumption optimization problem in section, can be converted into operating condition turning point D3Locate speed V3Optimization problem.
Step 3, using PSO algorithm is improved, the operating condition turning point in tramcar energy saving optimizing model is solved, specifically such as
Under:
PSO algorithm is improved i.e. on the basis of classical particle group algorithm (PSO), addition selection study mechanism, basic thought
That is: the population that a particle number is m is initialized, each particle has the position attribution x of n dimensioni=(xi,1(t),xi,2
(t),...xi,n(t)) and n dimension Speed attribute vi=(vi,1(t),vi,2(t),...vi,n(t)).Cancel classical particle group algorithm
(PSO) locally optimal solution, the globally optimal solution more new particle in take out particle and ratio in population in couples in selection region
Compared with its adaptive value, fitness is more preferably directly entered next-generation population as particle of winning, and fitness is worse as failure grain
The win speed of particle, position of son study carries out self-renewing, subsequently into next-generation population；For setting for selection region
It sets, to prevent particle to be gathered in single-wide too early, first successively puts particle in selection region by fitness is descending into,
Calculate particle to be added and be added between particle alternate position spike and, and certain threshold value is set, when alternate position spike and when being more than threshold value, then
The particle is not placed in selection region；When alternate position spike and be less than threshold value when, then the particle is placed in selection region.
Given threshold is R, xiFor particle to be added, xjFor particle has been added, wherein j ∈ 1 ..., k, k are that particle has been added
Number；
If the two alternate position spike and satisfaction:
Then by xiIt is put into selection region, otherwise gives up the particle；
Selection for threshold value R, first time iteration are set as Rmax, with the increase of the number of iterations, R meets:
In formula, Rmin、RmaxRespectively minimum, max-thresholds, G, GmaxRespectively current iteration number, maximum number of iterations.
Position and speed after the failure chosen study of particle updates are as follows:
In formula,For the position and speed for the particle that fails after the G times iteration, kth time selection study；
For the position and speed for the particle that fails after the G+1 times iteration, kth time selection study；For the G times iteration, kth time choosing
Select the position and speed for particle of winning after learning；G is current iteration number；For random number in (0,1) section,For
All particles position mean in the G times iteration, kth time selection study in group；ω is inertial factor；
Improve PSO algorithm following steps:
Step 3.1, emulation data input: track data, train data and algorithm relevant parameter；
Step 3.2, initialization of population: being arranged the size N of population, and gives the value of single particle at random, single particle
Given value is no more than the speed limit value in emulation section；
Step 3.3, energy consumption index adapt to value function and solve, and update particle rapidity and position: by particle pair each in population
The value answered carries out tramcar operation energy consumption objective function and calculates, obtains the corresponding energy consumption index adaptive value of each particle.It takes
Study mechanism is selected, in selection region, pairs of is compared the particle in population, and particle of winning is directly entered the next generation
Population, failure particle study wins the speed of particle, position come after carrying out self-renewing, and enters next-generation population；
Step 3.4, termination condition judgement: current iteration number reaches maximum number of iterations, then meets termination condition, exits
Iterative cycles；Otherwise, 3.3 are entered step, solves energy consumption index adaptive value, and selection is continued to particle rapidity, position and is learned
It practises and updating, until meeting termination condition, it may be assumed that
G≥Gmax
In formula, G, GmaxRespectively current iteration number, maximum number of iterations；
Step 4, using the calculated operating condition turning point of step 3, the energy consumption of each traffic coverage of tramcar is calculated, specifically such as
Under:
Optimal solution and related operation curve are acquired by step 3, optimal solution is extracted and substitutes into tramcar energy saving optimizing model
In calculated, complete calculating to section operation energy consumption.
Using the tramcar operation energy consumption optimization method of the invention based on improvement PSO, and energy is established using MATLAB
Consumption Optimized model is emulated: using Guangzhou Zhuhai tramcar THZ1 line as research object, route map such as Fig. 3 institute
Show；The traction force characteristic of tramcar THZ1 line locomotive is as shown in figure 4, tramcar is to be invariable power at 26.5km/h in speed
Point, maximum drawbar pull 96kN；The braking force characteristic of tramcar THZ1 line locomotive as shown in figure 5, tramcar in speed
It is invariable power point, maximum braking force 102kN at 56km/h.
By to tramcar operation energy consumption model analysis, final energy optimization variable, that is, operating condition turning point D3Locate speed,
Consider this speed by traffic coverage maximum operational speed VmaxLimitation, i.e., the speed of operating condition turning point is in [0,50] interval range
It is interior, at the same in order to more intuitively observe convergence during algorithm iteration as a result, be therefore set as preliminary examination particle group velocity (0,
10,20,30,40,50)；Meanwhile in order to accelerate to restrain, the mobile maximum speed of particle is set as 2；Finally, by the simulation run time
Function is solved as particle adaptive value with the difference of specified operation time, to judge mass particle.Each interval censored data is substituted into, into
After row competition mechanism particle swarm algorithm iteration, between the continent Pa bridge-exhibitions East, the adaptive value optimal solution in the section and
Shown in energy consumption iterative process such as Fig. 6 (a), (b), in the algorithm rigid incipient stage, due to when the number of iterations is less inertial factor w compared with
Greatly, particle movement speed, search range are larger, thus to move up and down amplitude larger for optimal solution adaptive value；With the number of iterations
Increase, inertial factor w reduces, and movement speed reduces, convergence rate is accelerated, and particle optimal solution gradually levels off to global optimum.
By input Guangzhou Zhuhai tramcar THZ1 line tramcar data and track data, energy is carried out to tramcar
Consumption modeling carries out particle swarm algorithm calculating to each station-station traffic coverage and seeks section optimum operating condition transfer point, then carries out again each
The energy consumption calculation in station-station section emulates.Energy consumption-distance Curve and speed between the continent Pa bridge-exhibitions East, in the section
Degree-distance Curve is respectively as shown in Fig. 7 (a), (b).
It will be compared by the calculated operation energy consumption of energy consumption moving model and practical each section operation energy consumption, such as Fig. 8
It is shown.Energy consumption moving model makes full use of coasting operating condition, is guaranteeing tramcar operational safety, precisely parking, punctual, comfort level
While index, traction energy consumption is reduced, the operating point of energy saving optimizing model, convergence speed are calculated by improving PSO optimization algorithm
Degree is fast, avoids falling into local optimum, and it is obvious to calculate effect.
Claims (5)
1. a kind of based on the tramcar operation energy consumption optimization method for improving PSO, which comprises the following steps:
Step 1, it is based on tramcar operating condition, establishes tramcar operation energy consumption model；
Step 2, tramcar multi-constraint condition is analyzed, tramcar operation energy consumption model optimization problem is simplified；
Step 3, using PSO algorithm i.e. addition selection study mechanism on the basis of classical PSO algorithm is improved, tramcar fortune is solved
Operating condition turning point in row energy consumption model；
Step 4, using the calculated operating condition turning point of step 3, the energy consumption of each traffic coverage of tramcar is calculated.
2. according to claim 1 based on the tramcar operation energy consumption optimization method for improving PSO, which is characterized in that step
Based on tramcar operating condition described in rapid 1, tramcar operation energy consumption model is established, specific as follows:
In a traffic coverage, tramcar operational process is divided into four operating conditions:
Operating condition I: all one's effort traction working condition, tramcar are started since speed 0 with maximum drawbar pull, reach section maximum speed
Vmax；
Operating condition II: at the uniform velocity cruising condition, tramcar is with section maximum speed VmaxUniform motion is carried out, tractive force is equal to synthesis
Resistance；
Operating condition III: coasting operating condition, tramcar carry out coasting, tractive force 0；
Operating condition IV: all one's effort damped condition, tramcar are braked with maximum braking force, and braking energy is carried out feedback and is deposited
Storage；
According to tramcar operation characteristic, punctual, safe, comfortable, accurate parking selection, passenger's load indicators used is added, foundation has
Rail electric car operation energy consumption model, it may be assumed that
In formula: f (x) is power dissipation obj ectives function；Ft(v)、Ftmax(v)、Fm(v)、FmmaxIt (v) is respectively tramcar in the speed of service
Tractive force, tramcar tractive force maximum value when v, tramcar brake force, tramcar brake force maximum value；X is current
Run kilometer post；v,VmaxRespectively tramcar operation instantaneous velocity and entire section maximum operational speed；ρ is braking energy
Feedback factor；M is the tramcar overall quality under different passenger's load conditions；g1(x)、g2(x)、g3(x)、g4(x)、g5(x)
Respectively punctual, accurate parking, safety, comfort level, passenger's load indicators used constraint condition；S0、Sp、D1、D2、D3Respectively section is risen
Point kilometer post, terminal kilometer post, operating condition I~operating condition II turning point kilometer post, operating condition II~operating condition III turning point kilometer
Mark, operating condition III~operating condition IV turning point kilometer post；Δ t, Δ s are respectively time error, range error.
3. according to claim 2 based on the tramcar operation energy consumption optimization method for improving PSO, which is characterized in that step
Analysis tramcar multi-constraint condition described in rapid 2 simplifies tramcar operation energy consumption model optimization problem, specific as follows:
Tramcar operates inIn section, runing time Δ T, have:
In formula, F is tramcar resultant force, Ft、Fm、FzRespectively tractive force, brake force, resistance, wherein resistance is hindered by mechanical friction
Power, additional resistance due to grade, additional resistance due to curve, air drag composition, a are operation acceleration, and M is under different passenger's load conditions
Tramcar overall quality, x1、x2Respectively run starting highway mark, the terminal kilometer post in subinterval, v1、v2Respectively transport
Speed at the starting highway mark in row subinterval, speed at terminal kilometer post, S0、SpFor the starting kilometer post in whole service section, end
Point kilometer post；
In the section operating condition I, tramcar is with maximum drawbar pull Acceleration of starting to maximum speed limit value, tractive force characteristic curve are as follows:
In formula, Ft(v) be instantaneous velocity v when tramcar tractive force, FmaxFor permanent torque area tramcar tractive force, vt1、Vmax
Respectively invariable power area starting point train running speed, whole service section maximum operational speed；
Four-stage optimization problem, the i.e. optimization of three phases turning point are converted by the operation energy consumption optimization problem of tramcar
Problem: operating condition I~operating condition II turning point D1, operating condition II~operating condition III turning point D2, operating condition III~operating condition IV turning point
D3；
F is determined by tractive force performance diagrammax、vt1And Vmax, corresponding F is determined by brake force characteristic curvem, and then determine and close
Power F, acceleration a, range ability, runing time, so that it is determined that operating condition I~operating condition II turning point D1；Similarly, for operating condition II
The turning point D of~operating condition III2, can determine Vmax, the rail electricity in the section operating condition III and the section operating condition IV is obtained by calculation
Vehicle acceleration obtains operating condition turning point D3The speed V at place3With section maximum operational speed VmaxRelationship, pass through determine operating condition III
The turning point D of~operating condition IV3Operating condition II~operating condition III turning point D is pushed away to counter2, tramcar is determined to the operation energy in section
Consumption optimization problem is converted into operating condition turning point D3Locate speed V3Optimization problem.
4. according to claim 2 or 3 based on the tramcar operation energy consumption optimization method for improving PSO, which is characterized in that
Using PSO algorithm i.e. addition selection study mechanism on the basis of classical PSO algorithm is improved described in step 3, tramcar is solved
Operating condition turning point in operation energy consumption model, specific as follows:
Step 3.1, emulation data input: track data, train data and algorithm relevant parameter；
Step 3.2, initialization of population: being arranged the size N of population, and gives the value of single particle at random, and single particle gives
Value is no more than the speed limit value in emulation section；
The population that a particle number is m is initialized, each particle has the position attribution x of n dimensioni=(xi,1(t),xi,2
(t),...xi,n(t)) and n dimension Speed attribute vi=(vi,1(t),vi,2(t),...vi,n(t))；
Step 3.3, energy consumption index adapt to value function and solve, and update particle rapidity and position: particle each in population is corresponding
Value carries out tramcar operation energy consumption objective function and calculates, obtains the corresponding energy consumption index adaptive value of each particle；Take selection
Study mechanism, in selection region, pairs of is compared the particle in population, and particle of winning is directly entered next-generation kind
Group, failure particle study wins the speed of particle, position come after carrying out self-renewing, and enters next-generation population；
Setting for selection region, to prevent particle to be gathered in single-wide too early, first by particle by fitness by greatly to
It is small successively to put into selection region, calculate particle to be added and be added between particle alternate position spike and, and threshold value is set, works as position
Difference and be more than threshold value when, then the particle is not placed in selection region；When alternate position spike and be less than threshold value when, then the particle is placed on
In selection region；
Given threshold is R, xiFor particle to be added, xjFor particle has been added, wherein j ∈ 1 ..., b, b are that particle has been added
Number；
If the two alternate position spike and satisfaction:
Then by xiIt is put into selection region, otherwise gives up the particle；
Selection for threshold value R meets with the increase of the number of iterations:
In formula, Rmin、RmaxRespectively minimum, max-thresholds, G, GmaxRespectively current iteration number, maximum number of iterations；
Position and speed after the failure chosen study of particle updates are as follows:
In formula,For the position and speed for the particle that fails after the G times iteration, kth time selection study； It is
Fail the position and speed of particle after G+1 iteration, kth time selection study；For the G times iteration, kth time selection
It wins after study the position and speed of particle；G is current iteration number；For random number in (0,1) section,For group
All particles position mean in the G times iteration, kth time selection study in body；ω is inertial factor；
Step 3.4, termination condition judgement: current iteration number reaches maximum number of iterations, then meets termination condition, exits iteration
Circulation；Otherwise, 3.3 are entered step, solves energy consumption index adaptive value, and selection study is continued more to particle rapidity, position
Newly, until meeting termination condition；
Termination condition is i.e.:
G≥Gmax
In formula, G, GmaxRespectively current iteration number, maximum number of iterations.
5. according to claim 4 based on the tramcar operation energy consumption optimization method for improving PSO, which is characterized in that step
The calculated operating condition turning point of utilization step 3 described in rapid 4 calculates the energy consumption of each traffic coverage of tramcar, specific as follows:
Acquire optimal solution and related operation curve by step 3, extract optimal solution substitute into tramcar energy saving optimizing model into
Row calculates, and completes the calculating to section operation energy consumption.
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