CN109344996A - A kind of urban railway transit train optimization and energy saving method - Google Patents
A kind of urban railway transit train optimization and energy saving method Download PDFInfo
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Abstract
The invention discloses a kind of urban railway transit train optimization and energy saving methods, Train Schedule-energy consumption functional relation of each traffic coverage is obtained by analysis statisticaling data, and predict each section passenger capacity, optimize and revise distribution condition of the total run time in each traffic coverage of train, train is obtained in the total energy consumption function expression of whole route, and use particle swarm algorithm, using train whole route lowest energy consumption as optimization aim, runing time to train in each traffic coverage optimizes, each section runing time after obtaining optimization, for generating the traction curve of train, to reach energy-efficient purpose.The traction energy consumption of urban railway transit train can be effectively reduced in the present invention, reduce the operation cost of metro operation enterprise, and strong operability will not cause additional burden to operation enterprise.
Description
Technical field
The invention belongs to urban rail transit vehicles energy-saving control technology fields more particularly to a kind of urban track traffic to arrange
Vehicle optimization and energy saving method.
Background technique
Train is line length, the speed limit range according to the traffic coverage in the runing time arrangement of each traffic coverage at present
Etc. factors determine, mainly with convenient by bus for starting point, the rarer section in view of train that meets safe train operation and passenger
It can optimization problem.Foreign scholar is to Madrid-Guadalajara city (Madrid-Guadalajara), Guadalajara city-
In OK a karaoke club Ta Youde (Guadalajara-Calatayud) and OK a karaoke club Ta Youde-Saragossa (Calatayud-Zaragoza)
Three traffic coverages progress of same section of railway track is time-optimized, and total runtime is being maintained to be 1 hour 28 points of constant situation
Under, change the runing time in each section, can reach the target of highest energy conservation 33.63%.But inter-city rail transit and city rail
Traffic has many differences on line environment composition.Inter-city rail transit distance is remote, and main distance is distributed in countryside, with flat
It is less to encounter complicated line environment based on straight rail.Urban track traffic is usually walked between all kinds of buildings, underground cable
Road will also be by the restriction of geological environment and nearby buildings in process of construction, and line environment is complicated and changeable, generally comprises
More bend and ramp, so that the traction energy consumption of train changes greatly between different traffic coverages.
Summary of the invention
It is an object of the invention to: in view of the above problems, provide a kind of urban railway transit train energy saving optimizing
Operation method, efficiency of the present invention reduce the traction energy consumption of urban railway transit train, set compared to by increasing or being transformed hardware
Standby method, can more reduce the operation cost of metro operation enterprise, and strong operability will not cause additional bear to operation enterprise
Load.To achieve the goals above, the invention adopts the following technical scheme:
The present invention provides a kind of urban railway transit train optimization and energy saving methods, by statistical analysis train each
The energy consumption of traffic coverage and the rule of runing time optimize and revise distribution feelings of the total run time in each traffic coverage of train
Condition reaches energy-efficient purpose, mainly comprises the steps that
Step 1: by vehicle-mounted data logger read the runing time that train runs in each section, train gross mass,
Train traction energy consumption and train regenerative braking recover energy, and carry out classified finishing train operation number according to different traffic coverages
According to；
Step 2: being fitted to obtain the function of train energy consumption and runing time in each traffic coverage according to train operating data
Relationship, wherein train meets in operation energy consumption-runing time functional relation of i-th of section quality per ton:
qi=F (ti)；
Wherein, qiIndicate the operation energy consumption of i-th of section quality per ton, tiIndicate the runing time in i-th of section；
If train gross mass of the train in the section of the Operational Zone i-th is mi, then train is in the total of i-th traffic coverage
Energy consumption is；
Qi=Fi(ti)·mi, QiIndicate the total energy consumption of i-th of traffic coverage；
Step 3: being integrated by the functional relation to each traffic coverage of train, obtain train in each of whole route
The relationship of section runing time distribution and train total energy consumption, and the optimized operation time point is optimized by particle swarm algorithm
With scheme, each section runing time after optimizing is obtained, for generating the traction curve of train.
Above scheme is it is further preferred that carrying out integration to the functional relation of each traffic coverage of train is according to each fortune
Operation energy consumption-runing time functional relation of row section quality per ton is overlapped to obtain train in the total energy consumption of whole route
With the functional relation between runing time and train gross mass, then the optimized operation time optimized by particle swarm algorithm
Allocation plan, wherein runing time and train gross mass miIt is n-dimensional vector, n is the number of full line traffic coverage, then always
Functional relation between energy consumption and runing time and train gross mass meets the following conditions:
Train is in each section runing time tiMeet: t=[t1,t2,…,ti,…,tn]T, it is variable to be solved；
Train meets in the gross mass m of each traffic coverage, m=[m1,m2,…,mi,…,mn]T, for according to historical data
Predicted value, therefore, train full line total operation energy consumption Q meet:
Above scheme is it is further preferred that the train gross mass m is predicted each according to the Passenger's distribution progress counted on
The train gross mass of period each traffic coverage；By train in the operation energy consumption-runing time function in each section and each area of prediction
Between train gross mass bring particle swarm algorithm into, optimize, the constraint condition of solution are as follows:
Wherein, the value range of each section runing time t is ± the 10% of former runing time.
Preferably, each section runing time t=[t that will be obtained after solution1,t2,…,ti,…,tn]TFor generating each area
Between train traction curve.
In conclusion there are the present invention following advantageous effects to be due to present invention employs above-mentioned technical proposal:
The present invention runs total time in point of each traffic coverage on a certain working line especially by adjustment train
With situation, to achieve the purpose that save traction energy consumption；The traction energy consumption of urban railway transit train can be effectively reduced in the present invention,
Reduce the operation cost of metro operation enterprise；While reducing traction energy consumption, experiencing by bus for passenger will not be significantly affected；Phase
Than in the method by increasing or hardware device being transformed, this method is at low cost, strong operability, will not cause volume to operation enterprise
Outer burden.
Detailed description of the invention
Fig. 1 is the traction energy consumption composition figure of urban railway transit train of the invention；
Fig. 2 is hauling speed curve graph particle swarm algorithm of the invention；
Fig. 3 is the calculation flow chart of population of the invention.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention more comprehensible, referring to the drawings and preferred reality is enumerated
Example is applied, the present invention is described in more detail.However, it is necessary to illustrate, many details listed in specification are only to be
Reader is set to have a thorough explanation to the one or more aspects of invention, it even without these specific details can also be real
Existing the aspects of the invention.
As shown in Figure 1, urban railway transit train in the process of running, has quite a few energy to be disappeared by route resistance
Consumption, but due to the line environment of each traffic coverage differ it is larger so that train different traffic coverages when driving consumed by energy
Amount has differences, the traction structure of energy consumption of train, as shown in Figure 1, the traction energy consumption of train is produced by train operation datum drag
It is raw, train operation datum drag refer to train when being run on level tangent track by resistance, with the speed of service of train have compared with
Big association.Due to the complexity of city rail traffic route environment, most operating mileage is not straight track circuit,
Therefore the operation additional drag of train alsies occupy sizable specific gravity, especially when train passes through the lesser bend of radius of curvature
When, wheel rail relation is deteriorated, and causes the energy consumption of train higher, the gravitional force of train is mainly train during starting, and is encountered
Uphill way climbing generates, for example descending section, and corresponding train gravitional force variation is negative, kinetic energy and the train speed of train
Degree is related, is the energy that will be consumed in train accelerator after outputing from station, and train is by traction electric machine driving, in unsteady flow, cold
But, there are the loss of energy during electric energy and mechanical energy are converted, the energy regenerating of regenerative braking is mainly derived from train
The efficiency of kinetic energy, gravitional force, energy regenerating is influenced by Electric equipment, and the electric energy of recycling makes for mobile unit etc.
With or feedback grid, offset train part traction energy consumption.
Under the line environment of different traffic coverages, the operation speed per hour of train is different from the performance of the energy consumption relationship of train.It is right
In the traffic coverage of part line environment complexity, the highest operation speed per hour of train is reduced, the operation energy consumption of train can be effectively reduced；
Operation speed per hour is improved in track circuit section straight for part, and shortening runing time can't be larger to the generation of the energy consumption of train
Influence；The present invention provides a kind of urban railway transit train optimization and energy saving method thus, is existed by statisticalling analyze train
The energy consumption of each traffic coverage and the rule of runing time optimize and revise distribution feelings of the total run time in each traffic coverage of train
Condition reaches energy-efficient purpose, mainly comprises the steps that
Step 1: by vehicle-mounted data logger read the runing time that train runs in each section, train gross mass,
Train traction energy consumption and train regenerative braking recover energy, and carry out classified finishing train operation number according to different traffic coverages
According to；
Step 2: being fitted to obtain the function of train energy consumption and runing time in each traffic coverage according to train operating data
Relationship, wherein train meets in operation energy consumption-runing time functional relation of i-th of section quality per ton:
qi=F (ti)；
Wherein, qiIndicate the operation energy consumption of i-th of section quality per ton, tiIndicate the runing time in i-th of section；
If train gross mass of the train in the section of the Operational Zone i-th is mi, then train is in the total of i-th traffic coverage
Energy consumption are as follows:
Qi=Fi(ti)mi, wherein QiIndicate the total energy consumption of i-th of traffic coverage；
Step 3: being integrated by the functional relation to each traffic coverage of train, obtain train in each of whole route
The relationship of section runing time distribution and train total energy consumption, and the optimized operation time point is optimized by particle swarm algorithm
With scheme, each section runing time after optimizing is obtained, for generating the traction curve of train；To each traffic coverage of train
Functional relation, which carries out integration, is folded according to operation energy consumption-runing time functional relation of each traffic coverage quality per ton
Add to obtain functional relation of the train between the total energy consumption and runing time and train gross mass of whole route, then passes through population
Algorithm optimizes optimized operation time allocation plan, wherein runing time and train gross mass miIt is n-dimensional vector, n
For the number of full line traffic coverage, then the functional relation between total energy consumption and runing time and train gross mass meets:
Train is in each section runing time tiMeet, t=[t1,t2,…,ti,…,tn]T, it is variable to be solved；
Train meets in the gross mass m of each traffic coverage, m=[m1,m2,…,mi,…,mn]T, for according to historical data
Predicted value, T is the matrix transposition symbol of gross mass m, and for the matrix that easily expression n row 1 arranges, n is integer greater than 1, will
Its each element is write as the form of 1 row n column, and therefore, train meets in total operation energy consumption Q of full line:
Wherein, the train gross mass m is carried out according to the Passenger's distribution counted on, predicts each traffic coverage of day part
Train gross mass；Train is brought into the operation energy consumption-runing time function in each section and each shuttle train gross mass of prediction
Particle swarm algorithm optimizes, the constraint condition of solution are as follows:
Wherein, the value range of each section runing time t is that ± 10%, the s.t. of former runing time indicates constraint condition,
t0iIndicate the design and operation time in preceding i-th of the section of optimization.
Runing time t of the above-mentioned constraint mainly for train in i-th of sectioni.Solve Constrained equations target be
The smallest total operation energy consumption Q value is found, Q value value is the function of Train Schedule vector, train load vector；And train
Total run time is the sum of the runing time in each section, and runing time cannot be beyond setting after limiting the optimization of each traffic coverage
Boundary value, i.e., the value range of each section runing time t is ± the 10% of former runing time, and in whole route, excellent
Therefore the sum of each section runing time after change, the total run time that need to be less than or equal to design in former train operation schedule will be asked
Each section runing time t=[t obtained after solution1,t2,…,ti,…,tn]TFor generating the traction curve of each shuttle train.
Under classical traction policy, train each traffic coverage when driving, altogether experience starting, at the uniform velocity, coasting, brake this 4
A working condition, wherein regenerative braking and air damping can be also subdivided by braking.In some fortune for existing simultaneously ascents and descents
Row section, there may be regenerative brakings in constant velocity stage for train, to maintain the speed substantially constant of train.As shown in Fig. 2, should
In hauling speed curve, the runing time t between parameters and the station of train such as length of the highest of train operation speed per hour and constant velocity stagei
It is related.According to i-th of the section runing time t obtained after optimizationiProduce train the section hauling speed curve, to
Instruct the energy-saving run of train.
In the present invention, in conjunction with Fig. 3, the step of particle swarm algorithm, is as follows: by the runing time t in i-th of sectioniMake
For a particle, train uses tiTime passes through consumed energy q when i-th of sectioniCurrent location as i-th of particle；
In i-th of section, train energy consumption is equivalent to the fitness of i-th of particle with the situation of change of runing time time；By each particle
Current location integration after be global position.After successive ignition, individual extreme value and the overall situation pole of each particle can be obtained
Then value updates particle rapidity and particle position, be iterated the individual extreme value and global extremum that each particle is updated after calculating,
After the value tends towards stability with the increase of the number of iterations, if reach iteration precision requirement, if reaching required precision, finally
Individual extreme value and global extremum be optimal solution, otherwise continue to update particle rapidity and particle position carrying out iteration meter again
It calculates, global extremum therein is the most energy-saving scheme that train passes through whole route.The present invention is mainly suitable for urban track traffics
Train, due in city rail traffic route, the line environment of each traffic coverage is different, and leading to the energy consumption of train, there is larger
Difference.Train Schedule-energy consumption functional relation of each traffic coverage is obtained by analysis statisticaling data, and predicts each section
Passenger capacity obtains train in the total energy consumption function expression of whole route, and uses particle swarm algorithm, with train in whole route
Lowest energy consumption as optimization aim, the runing time to train in each traffic coverage optimizes, obtain optimization after
Each section runing time, for generating the hauling speed curve of train, to reach energy-efficient purpose.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the principle of the present invention, it can also make several improvements and retouch, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (4)
1. a kind of urban railway transit train optimization and energy saving method, it is characterised in that: by statistical analysis train in each fortune
The energy consumption in row section and the rule of runing time optimize and revise distribution condition of the total run time in each traffic coverage of train,
Reach energy-efficient purpose, mainly comprises the steps that
Step 1: runing time, the train gross mass, train that train is run in each section are read by vehicle-mounted data logger
Traction energy consumption and train regenerative braking recover energy, and carry out classified finishing train operating data according to different traffic coverages；
Step 2: it is fitted to obtain the functional relation of train energy consumption and runing time in each traffic coverage according to train operating data,
Wherein, train meets in operation energy consumption-runing time functional relation of i-th of section quality per ton:
qi=F (ti)；
Wherein, qiIndicate the operation energy consumption of i-th of section quality per ton, tiIndicate the runing time in i-th of section；
If train gross mass of the train in the section of the Operational Zone i-th is mi, then total energy consumption of the train in i-th of traffic coverage
For；
Qi=Fi(ti)·mi, QiIndicate the total energy consumption of i-th of traffic coverage；
Step 3: being integrated by the functional relation to each traffic coverage of train, obtain train in each section of whole route
The relationship of runing time distribution and train total energy consumption, and optimized operation time distribution side is optimized by particle swarm algorithm
Case, each section runing time after obtaining optimization, for generating the traction curve of train.
2. a kind of urban railway transit train optimization and energy saving method according to claim 1, it is characterised in that: to column
It is operation energy consumption-runing time according to each traffic coverage quality per ton that the functional relation of each traffic coverage of vehicle, which carries out integration,
Functional relation be overlapped to obtain function of the train between the total energy consumption and runing time and train gross mass of whole route
Relationship, then optimized operation time allocation plan is optimized by particle swarm algorithm, wherein runing time and the total matter of train
Measure miIt is n-dimensional vector, n is the number of full line traffic coverage, then between total energy consumption and runing time and train gross mass
Functional relation meets the following conditions:
Train is in each section runing time tiMeet: t=[t1,t2,…,ti,…,tn]T, it is variable to be solved；
Train meets in the gross mass m of each traffic coverage, m=[m1,m2,…,mi,…,mn]T, for according to the pre- of historical data
Measured value, T indicate the matrix transposition symbol of gross mass m, and therefore, train meets in total operation energy consumption Q of full line:
3. a kind of urban railway transit train optimization and energy saving method according to claim 2, it is characterised in that: described
Train gross mass m is carried out according to the Passenger's distribution counted on, predicts the train gross mass of each traffic coverage of day part；By train
Particle swarm algorithm is brought into the operation energy consumption-runing time function in each section and each shuttle train gross mass of prediction, is carried out excellent
Change and solves, the constraint condition of solution are as follows:
Wherein, the value range of each section runing time t is ± the 10% of former runing time.
4. a kind of urban railway transit train optimization and energy saving method according to claim 2, it is characterised in that: will ask
Each section runing time t=[t obtained after solution1,t2,…,ti,…,tn]TFor generating the traction curve of each shuttle train.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110544010A (en) * | 2019-07-30 | 2019-12-06 | 同济大学 | Identification method of key elements influencing global efficiency emergence of rail transit system |
| CN111311017A (en) * | 2020-03-04 | 2020-06-19 | 广西大学 | Urban rail transit train operation schedule and speed operation curve optimization method |
| 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 |
| CN112960016A (en) * | 2021-03-05 | 2021-06-15 | 国网北京市电力公司 | Non-invasive rail transit train operation situation sensing method and device |
| 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 |
| CN113591301A (en) * | 2021-07-28 | 2021-11-02 | 广西大学 | Urban rail transit train operation parameter optimization algorithm |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110544010A (en) * | 2019-07-30 | 2019-12-06 | 同济大学 | Identification method of key elements influencing global efficiency emergence of rail transit system |
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| 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 |
| CN111311017A (en) * | 2020-03-04 | 2020-06-19 | 广西大学 | Urban rail transit train operation schedule and speed operation curve optimization method |
| CN111311017B (en) * | 2020-03-04 | 2022-10-11 | 广西大学 | Urban rail transit train operation schedule and speed operation curve optimization method |
| 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 |
| CN112960016A (en) * | 2021-03-05 | 2021-06-15 | 国网北京市电力公司 | Non-invasive rail transit train operation situation sensing method and device |
| CN112960016B (en) * | 2021-03-05 | 2023-04-18 | 国网北京市电力公司 | Non-invasive rail transit train operation situation sensing method and device |
| CN113591301A (en) * | 2021-07-28 | 2021-11-02 | 广西大学 | Urban rail transit train operation parameter optimization algorithm |
| CN113591301B (en) * | 2021-07-28 | 2023-12-08 | 广西大学 | Urban rail transit train operation parameter optimization algorithm |
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Application publication date: 20190215 |