CN107067710B - It is a kind of to consider energy-efficient city bus running track optimization method - Google Patents
It is a kind of to consider energy-efficient city bus running track optimization method Download PDFInfo
- Publication number
- CN107067710B CN107067710B CN201710264660.4A CN201710264660A CN107067710B CN 107067710 B CN107067710 B CN 107067710B CN 201710264660 A CN201710264660 A CN 201710264660A CN 107067710 B CN107067710 B CN 107067710B
- Authority
- CN
- China
- Prior art keywords
- bus
- subinterval
- intersection
- vehicle
- speed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Abstract
Energy-efficient city bus running track optimization method is considered the present invention relates to a kind of, the following steps are included: 1) traffic coverage to stop between a little as defined in current bus running position to operational plan is successively divided into continuous multiple subintervals by roadway characteristic, roadway characteristic includes gradient section, speed-limit road section and intersection section, and roadway characteristic is corresponded to attribute value and assigns each subinterval；2) the city bus driving strategy dual-layer optimization computation model of energy factor is considered according to the subinterval building after division；3) city bus driving strategy dual-layer optimization computation model is solved, the final optimization track for obtaining the traffic coverage to stop between a little as defined in current bus running position to operational plan, travel speed, running time, position and tractive force and brake force including each subinterval bus.Compared with prior art, the present invention has many advantages, such as that dynamic is continuous, reduces energy consumption, improves punctuality rate and comfort.
Description
Technical field
The present invention relates to traffic programme technical fields, consider energy-efficient city bus running track more particularly, to a kind of
Optimization method.
Background technique
The process of Fast Urbanization and motorization, so that rapid development of China's Traffic Systems to energy demand.Have
In consideration of it, the energy-saving and emission-reduction of Traffic Systems have become the important component of planning, construction and operation " green city ".It is logical
It crosses and selects to implement the strategy of public transport appropriately exceeding development with public transport mode for leading traffic system, be that realization is green
The important means of the target of color urban transportation.
Public transport is acted in urban transportation and being gradually increased at the same time, energy consumption emission problem also ever more important.Its
In, city bus operation energy consumption is important a part of urban public transport energy consumption discharge, therefore its energy-saving and emission-reduction is pushed to anticipate
Justice is great.
It is directed to the method carried out therewith of automotive energy-saving emission-reducing at present, mainly includes policy instruments, such as Sun Bin " vehicle energy saving subtracts
The method of row is analyzed " in mention includes improving national legislation, give policy support, push the use and actively of new-energy automobile
Exploitation；And technological means: bus drives operational process optimization.
Acquisition for the location information, operational plan information and 3 category information of line information of bus, had sufficiently and
Mature research and application: GPS can be for example taken to obtain the location information of vehicle；Using wireless communication means such as 3G, 4G,
It realizes the continuous communiction of bus and control centre, obtains runing time, next between bus running plan information, including station
It stops station information (as shown in Figure 1)；Value of slope, the intersection position, line in bus running path are obtained using offline map
Road speed-limiting messages.
Public transport at present drives operational process and calculates auxiliary guidance there is no accurate, mostly based on the experience of driver, no
Same experience, technical level driver there are biggish difference, there is biggish random and energy conservations for bus driving process
Optimize space, there is statistical data to show, in actual operational process, even same line, the driving behavior between driver is poor
The different electric bus operation energy consumption that also results in generates bigger difference.Electronic public affairs from cities such as Tianjin, Jinan, Wuhan, Linyi
Hand over operating condition analysis, the design mileage of electric car with actually can distance travelled gap very greatly (its difference is even more than
40%).
And in urban public transport field, research and current art for energy-saving driving focus primarily upon city rail
Field of traffic, based on subway, such as the article " research of Beijing Metro Yi Zhuang alignment vehicle energy-saving driving " that Tang Tao etc. is delivered.Relatively
In urban track traffic, city bus operation has bigger randomness and increasingly complex service condition, and (such as signal is handed over
The interference of prong, other public vehicles), energy saving optimizing technology is difficult to be suitable for city bus in terms of existing urban track traffic
The application demand of vehicle
Summary of the invention
Continuous, reduction that it is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of dynamics
Energy consumption improves the considerations of punctuality rate and comfort energy-efficient city bus running track optimization method.
The purpose of the present invention can be achieved through the following technical solutions:
It is a kind of to consider energy-efficient city bus running track optimization method, comprising the following steps:
1) by the traffic coverage to stop between a little as defined in current bus running position to operational plan successively by road
Feature is divided into continuous multiple subintervals, and roadway characteristic includes gradient section, speed-limit road section and intersection section, and by road
Feature corresponds to attribute value and assigns each subinterval；
2) consider that the city bus driving strategy dual-layer optimization of energy factor calculates according to the subinterval building after division
Model；
3) city bus driving strategy dual-layer optimization computation model is solved, it is final to obtain current bus operation
The optimization track of the traffic coverage to be stopped between a little as defined in position to operational plan.
The step 1) specifically includes the following steps:
11) range of traffic coverage is determined according to the current location information of bus；
12) Operational Zone is obtained according to the range combining geographic information database of bus running plan information and traffic coverage
Interior roadway characteristic data, wherein the characteristic attribute value of gradient section includes gradient starting point coordinate, gradient terminal point coordinate and slope
Angle value, the characteristic attribute value of speed-limit road section include speed limit starting point coordinate, speed limit terminal point coordinate and speed limit value, the spy in intersection section
Levying attribute value includes intersection starting point coordinate, intersection terminal point coordinate and intersection green time；
13) by gradient starting point coordinate, gradient terminal point coordinate, speed limit starting point coordinate, the speed limit terminal in link characteristic information
Coordinate, intersection starting point coordinate and intersection terminal point coordinate successively sort, and are matched two-by-two, and point in each subinterval is obtained
Boundary's point coordinate；
14) traffic coverage is in turn divided by continuous multiple subintervals according to the separation coordinate in each subinterval, made
Each subinterval only includes unique roadway characteristic attribute value.
In the step 2), city bus driving strategy dual-layer optimization computation model includes upper layer model and lower layer's mould
Type, the output of upper layer model includes entrance velocity, muzzle velocity and the subinterval runing time three classes in each subinterval, as under
The input of layer model, underlying model output optimization track return to layer model.
The upper layer model is to minimize the total energy consumption E consumed between bus stop as objective function, expression formula are as follows:
Wherein, EiFor the bus total energy consumption in each subinterval, n subinterval number between the station of bus running.
The constraint condition of the upper layer model are as follows:
For the entrance velocity v in any i-th of subintervalin,iWith muzzle velocity vout,iGreater than 0 and it is less than route speed limit
VIim, iConstraint:
For the muzzle velocity v in any i-th of subintervalout,iMeeting in entrance velocity is vin,iWhen accessible maximum
Speed vu,iWith minimum speed vl,iConstraint:
vl,i≤vout,i≤vu,i, i=1,2 ..., n；
For the running time T in any i-th of subintervaliSignificant constraint: running time TiIn entrance velocity vin,i, go out
Mouth speed vout,iUnder the conditions of minimum allowable runing time tl,iWith maximum allowable runing time tu,iBetween:
tl,i≤Ti≤tu,i, i=1,2 ..., n；
The sum of subinterval runing time before intersection meets signal lamp constraint:
-tΔ+k*tc≤Tj≤-tΔ+k*tc+tg, j=1,2 ..., m；
Wherein, m is intersection number between station, and j is intersection serial number, and k is the feasible periodicity of intersection, tcTo intersect
The signal timing dial period of mouth, tgFor the long green light time of intersection, tΔStart for a cycle before the frequency of intersection
The difference at moment and frequency, TjAt the time of reaching j-th of intersection for vehicle.
In the underlying model,
For i-th of subinterval, the objective function of underlying model are as follows:
J2,i=pt*Ti
Wherein, J1,iVehicle movement energy consumption for bus in the subinterval, J2,iIt is operation of the bus in the subinterval
Fixed energy consumption, MbusFor bus vehicle mass, MloadFor bus load quality, ηtFor bus transmission system efficiency, xIn, i、
xOut, iEntrance and outlet port for i-th subinterval, ρ are the bus regenerative braking rate of recovery, ptFor electronic load power, x
For bus vehicle location, u (x) is control force output in the position, is that engine exports, as u (x) as u (x) > 0
When < 0, exported for braking system, as u (x)=0, bus is in unpowered sliding state；
In the underlying model, bus is run in the subinterval, meets following constraint condition:
1. control force output of the bus vehicle in the subinterval meets dynamics of vehicle constraint condition:
U-[v(x)]≤u(x)≤U+[v(x)]
Under the conditions of the formula indicates that the speed when bus vehicle is located at position x is v (x), the control force output of the position
In maximum power U+[v (x)] and maximum braking force U-Between [v (x)]；
2. operating status transfer of the bus vehicle in subinterval should meet the vehicle kinematics differential equation:
α (x)=u (x)-r [v (x)]+G (x)
Wherein: t (x) is that bus is located at the runing time at the x of position, and r [v (x)] is bus running drag braking degree,
G (x) is gradient coriolis acceleration, and a (x) is the actual acceleration of bus running, and g is acceleration of gravity, and f is rolling resistance
Coefficient, CDFor coefficient of air resistance, A is vehicle front face area, and γ is vehicle correction coefficient of rotating mass, and θ is vehicle in position x
The value of slope at place；
3. driving status of the bus vehicle in the subinterval meets the input of subinterval optimization submodel:
v(xin,i)=vin,i
v(xout,i)=vout,i
0≤v(x)≤Vlim,i,
The upper layer model is solved by sequential quadratic programming algorithm.
The underlying model is solved by the analytical algorithm based on Pang Te lia king maximal principle.
In the step 3), the optimization track of traffic coverage includes the travel speed of each subinterval bus, traveling
Time, position and tractive force and brake force.
Compared with prior art, the invention has the following advantages that
The present invention by according to bus running plan, car status information and intersection signal timing situation of change,
Dynamically, the driving strategy for considering energy factor is continuously calculated, optimizes the running track of bus, significantly reduces vehicle
Operation energy consumption reduces the work difficulty of bus drivers, reduces different driving behaviors difference caused by public transport operation, improves public
Capable punctuality rate and comfort are shipped, guarantees Public Transport Service quality.
Detailed description of the invention
Fig. 1 is the schematic diagram for obtaining bus positional information, bus running plan information in the prior art.
Fig. 2 is a kind of energy-efficient city bus running track optimization method flow chart of consideration of the embodiment of the present invention.
Fig. 3 is the flow chart of the reading bus status information of the embodiment of the present invention.
Fig. 4 is the division subinterval schematic diagram of the embodiment of the present invention.
Fig. 5 is the calculation flow chart of the building bi-level optimal model of the embodiment of the present invention.
Specific embodiment
With reference to the accompanying drawings and examples, specific embodiments of the present invention will be described in further detail.Implement below
Example is not intended to limit the scope of the invention for illustrating the present invention.
Embodiment:
As shown in Fig. 2, figure is a kind of energy-efficient city bus running track optimization method of consideration of the embodiment of the present invention
Flow chart, the present embodiment the following steps are included:
Step A1, bus running route, operational plan and current vehicle condition information are read, as shown in Fig. 3, tool
Body the following steps are included:
Step A11, bus running plan information is obtained；The mobile unit installed in bus can pass through 3G, 4G etc.
Wireless communication technique is communicated with bus dispatching center, obtains the operational plan information of this vehicle, including vehicle runs road
Information at the time of diameter, next runing time between point, vehicle station that stops, arrival next station.
Step A12, bus positional information is obtained, determines the traffic coverage range that optimization calculates；Bus passes through GPS
Vehicle location is carried out, by carrying out coordinate matching with offline map data pre-stored in vehicle, obtains bus present bit
Confidence breath；And be compared with step A11 operational plan obtained, obtain bus from it is next stop with a distance from a little, operation
Path domain, and being capable of a section runing time that stopped a little under reaching on the time.
Step A13, using method described in above-mentioned steps A11, A12, by with offline geographical information database queries
Method needs to optimize the interval circuit characteristic information of calculating before obtaining to next stop a little, comprising: route speed-limiting messages,
Line slope information, intersection location information, intersection signal timing information；
Step A2, it is divided according to the subinterval that route speed limit, gradient condition, intersection position optimize algorithm, such as Fig. 4
It is shown, specifically includes the following steps:
Step A21: line slope value, speed limit value, intersection position are listed according to form: line slope value table packet
Include 3 column: gradient starting point coordinate, gradient terminal point coordinate, value of slope.Wherein value of slope is expressed as uphill gradient greater than 0, downward grades
Less than 0, horizontal slope is equal to 0.Speed limit value table includes 3 column: speed limit starting point coordinate, speed limit terminal point coordinate, speed limit value.Crossing elimination
Mouth position table includes 3 column: intersection starting point coordinate, intersection terminal point coordinate, intersection green time.Wherein use closed interval form
Indicate that green light starts and end range, for example, [20,50] indicate from 20 seconds to 50 second within the scope of be green time；
Step A22: by line slope value table, route speed limit value table, the starting point coordinate of crossing elimination oral thermometer, terminal point coordinate according to
Minor sort, and repetition values are removed, separation coordinate sequence is obtained, table 1, table 2, table 3 respectively indicate line slope value table, route limit
Speed value table, crossing elimination mouth position table, then the separation coordinate sequence formed are as follows: 0,200,350,380,500.
1 line slope value table of table
| Gradient starting point coordinate | Gradient terminal point coordinate | Value of slope |
| 0 | 200 | 3 |
| 200 | 500 | -5 |
2 route speed limit value table of table
| Speed limit starting point coordinate | Speed limit terminal point coordinate | Speed limit value |
| 0 | 350 | 60 |
| 350 | 500 | 55 |
3 crossing elimination mouth position table of table
| Intersection starting point coordinate | Intersection terminal point coordinate | Intersection green time |
| 350 | 380 | [50,100],[170,220] |
Step A23: building subinterval tables of data, the table include 6 column: subinterval starting point, subinterval terminal, value of slope, limit
Speed value, whether intersection, intersection green time；Wherein subinterval starting point, subinterval terminal by step A22 separation coordinate
Successively continuous pairing two-by-two obtains, and inserts；Value of slope, speed limit value, whether intersection, intersection green time this 4 attributes
Line slope value table, route speed limit value table, the crossing elimination mouth position table in step A21 are looked by subinterval separation
It is written after inquiry；It only includes unique speed limit value, unique value of slope, unique section or intersection that subinterval tables of data, which requires each subinterval,
Mouth attribute.Such as hereafter: table 1, table 2, table 3 respectively indicate line slope value table, route speed limit value table, crossing elimination mouth position
Table, then subinterval tables of data are as follows:
Table 4: subinterval tables of data
Step A3, building considers the city bus driving strategy dual-layer optimization computation model of energy factor, the model
It is mainly characterized by the numerical solution algorithm that layer model uses sequential quadratic programming, underlying model, which uses, is based on Pang Te lia king pole
The analytical algorithm of big value principle (Pontryagin ' s Maximum Principle), as shown in figure 5, specifically including following step
It is rapid:
Step A31, determine model decision variable: the decision variable of Optimized model by each subinterval entrance velocity vin、
Muzzle velocity voutWith subinterval runing time tiThree classes composition, the sub-district that the number of all kinds of decision variables is divided by step A23
Between quantity determine.
Step A32, define upper and lower level model interface: 1. the input of the upper downward layer model of layer model is determining for Optimized model
Plan variable, by the entrance velocity v in each subintervalin, muzzle velocity voutWith subinterval runing time tiThree classes composition, it is all kinds of to determine
The subinterval quantity that the number of plan variable is divided by step A23 determines；2. the upward layer model of underlying model returns in the subinterval
Entrance velocity vin, muzzle velocity voutWith subinterval specified operation time tiBus section under conditions of determination, in the subinterval
It can running track.
Step A33, determine upper layer model objective function: upper layer model is to minimize the total energy consumption consumed between bus stop
Objective function, expression formula are as follows:
Wherein, EiFor the bus total energy consumption in each subinterval, n subinterval number between the station of bus running.This hair
In bright, the calculating of operation energy consumption is considered the following factors: 1. energy consumption when bus engine offer power output, 2. bus system
The energy consumption of dynamic regenerative braking recycling when slowing down, 3. energy consumption caused by the constant power during bus running (including control
System energy consumption processed, illumination, air conditioning energy consumption).Total energy consumption is equal to the sum of energy consumption that 1., 3. process consumes and subtracts regenerative braking 2. process
The energy consumption of recycling.
Step A34, the constraint condition of upper layer model, constraint condition are determined are as follows:
1. for the entrance velocity v in any i-th of subintervalin,iWith muzzle velocity vout,iGreater than 0 and it is less than route speed limit
Vlim,iConstraint:
2. for the muzzle velocity v in any i-th of subintervalout,iMeeting in entrance velocity is vin,iWhen it is accessible most
Big speed vu,iWith minimum speed vl,iConstraint:
vl,i≤vout,i≤vu,i, i=1,2 ..., n
Wherein vL, iCalculation method it is as follows: note i-th of subinterval length be liWith vin,iSpeed drive into, according to maximum
Distance required for brake force is decelerated to 0 is dl,i.If dl,i≤li, then vl,i=0；If dl,i> li, then search speed: vL, iMeet
Vehicle is from vIn, iV is decelerated to according to maximum braking forceL, iDistance dL, i=li。
Wherein vU, iCalculation method it is as follows: note i-th of subinterval length be liWith vIn, iSpeed drive into, according to maximum
Power accelerates to road speed limit VLim, iRequired distance is dM, i.If dM, i≤li, then vU, i=VLim, i；If dU, i> li, then search
Suo Sudu: vU, iMeet vehicle from vIn, iV is accelerated to according to maximum powerU, iDistance dU, i=li。
3. for the running time T in any i-th of subintervaliSignificant constraint: running time TiIn entrance velocity vIn, i、
Muzzle velocity vOut, iUnder the conditions of minimum allowable runing time tL, iWith maximum allowable runing time tU, iBetween:
tL, i≤Ti≤tU, i, i=1,2 ..., n
Wherein TL, iCalculation method it is as follows: note i-th of subinterval length be li, vehicle is in subinterval tL, iComposition
Successively accelerate, comprising maximum power in VLim, iUnder speed at the uniform velocity, the vehicle hour of maximum braking deceleration three phases, and root
According to liThe case where size and vehicle acceleration, braking distance, there are 1~2 stage is not present in three phases, (maximum power added
Speed, maximum the two stages of braking deceleration there must be one).
Wherein TU, iCalculation method it is as follows: note i-th of subinterval length be li, vehicle is in subinterval TU, iComposition
Successively comprising maximum braking deceleration, under the speed of v → 0 at the uniform velocity, maximum power accelerate the vehicle hours of three phases, and root
According to liThe case where size and vehicle acceleration, braking distance, there are 1~2 stage is not present in three phases, (maximum power added
Speed, maximum the two stages of braking deceleration there must be one).If constant velocity stage exists under the speed of v → 0, illustrate tU, iIt can
To level off to+∞.In order to which computer capacity considers, therefore tU, iFor the timetable moment.
4. the preceding subinterval running time T in intersectioniThe sum of meet signal lamp constraint:
-tΔ+k*tc≤Tj≤-tΔ+k*tc+tg, j=1,2 ..., m
Wherein m is intersection number between station, and j is intersection serial number.For j-th of intersection, kjFor the intersection can
Capable periodicity, tc,jFor the signal timing dial period of the intersection, tg,jFor the intersection long green light time, tΔ,jFor intersection hair
The difference of a cycle start time and frequency before the vehicle moment.TjAt the time of reaching jth intersection for vehicle,
Calculation method are as follows:
The subinterval quantity n that wherein the constraint condition number for 1., 2., 3. planting situation is divided by step A23 is determined；4.
The constraint condition number of kind situation (contains this letter by the crossing elimination mouth quantity m in step A21 in the table of crossing elimination mouth position
Breath) it determines.
Step A35, lower layer's computation model is defined, comprising:
For i-th of subinterval, the objective function of underlying model are as follows:
min Ei=J1,i+J2,i
Wherein J1,iIt is vehicle movement energy consumption of the bus in the subinterval, J2,iIt is that operation of the bus in the subinterval is solid
Surely consume.
J2,i=pt*Ti
In formula, MbusFor bus vehicle mass；MloadIt is related with the passenger delivered for bus load quality；ηtFor
Bus transmission system efficiency；xin,i、xout,iEntrance and outlet port for i-th subinterval；ρ returns for bus regenerative braking
Yield；ptFor electronic load power.X is bus vehicle location, and u (x) is unit mass control force (engine in the position
Power or brake system power) output.
Meanwhile bus is run in the subinterval, should also meet constraint condition:
1. control force output (engine power, brake system power) of the bus vehicle in the subinterval should meet vehicle
Dynamics constraint condition:
U-[v(x)]≤u(x)≤U+[v(x)]
Above-mentioned formula indicates that the speed when bus vehicle is located at position x is v (x), and control force in the position exports u
(x) (engine power or brake system power) should meet dynamics of vehicle constraint condition, i.e., maximum dynamic under present speed
Power U+[v (x)] and maximum braking force U-Between [v (x)].
2. operating status transfer of the bus vehicle in the subinterval should meet the vehicle kinematics differential equation:
Wherein:
A (x)=u (x)-r [v (x)]+G (x)
T (x), v (x) are the runing time and speed that bus is located at the x of position, and u (x) is bus engine or braking
The unit mass control force (i.e. acceleration) of system reality output: it as u > 0, is exported for engine；As u < 0, for braking
System output；As u=0, bus is in unpowered sliding state；For bus running
Drag braking degree；For gradient coriolis acceleration.In r [v (x)], G (x) expression formula, g is gravity acceleration
Degree；F is coefficient of rolling resistance；CDFor coefficient of air resistance；A is vehicle front face area；γ is vehicle correction coefficient of rotating mass；
θ is value of slope of the vehicle at the x of position, it is specified that value of slope is positive value when going up a slope, and when descending is negative value.
3. driving status of the bus vehicle in the subinterval (includes entrance velocity, muzzle velocity, runing time, operation
Distance) meet the input that the subinterval optimizes submodel:
v(xIn, i)=vin,i
v(xout,i)=vout,i
0≤v(x)≤Vlim,i,
The model of lower layer exports are as follows: bus is (including motor-driven to start when u (x) > 0 with the control force u (x) of change in location
Power is brake system power when u (x) < 0) and bus input according to u (x) as control and run in the subinterval
Bus running energy consumption Ei, by control force u (x), and given entrance velocity vin, by constraint condition 2. in vehicle transport
The dynamic differential equation of learning can obtain bus in the running track in the section, including speed is with distance change track v (x), runing time
With distance change track t (x).
Underlying model is based on Pang Te lia king maximal principle (Pontryagin ' s Maximum Principle)
Analytical algorithm, comprising:
1. constructing the Hamiltonian of bus running by each subinterval, the operating status in each subinterval is determined
Sequence includes: maximum power acceleration mode, partial power at the uniform velocity state, sliding state, partial brake power at the uniform velocity state and most
Big 5 kinds of state of braking deceleration；
2. according to given subinterval entrance velocity vin, muzzle velocity voutWith subinterval specified operation time tiAnd sub-district
Between length, determine the working range of above-mentioned several states:
Include (a) setting at the uniform velocity cruising speed Vi；
(b) it is calculated from inlet forward direction, so that speed reaches Vi；Such as subinterval entrance velocity vinLess than at the uniform velocity cruising
Speed ViWhen, take maximum power strategy to be accelerated at the uniform velocity cruising speed Vi；As subinterval entrance velocity vinGreater than at the uniform velocity
Cruising speed ViWhen, then take sliding state to be decelerated at the uniform velocity cruising speed Vi。
From exit retrospectively calculate, so that speed is from voutInverse reaches Vi；Such as subinterval muzzle velocity voutIt is less than
At the uniform velocity cruising speed ViWhen, according to muzzle velocity voutDifferent range, can take coastdown strategy and coastdown with most
The federation policies of big brake force braking deceleration are slowed down, until subinterval muzzle velocity meets vout。
According to subinterval length, V is determinediAt the uniform velocity length；(c) runing time t ' is calculated according to above-mentioned (b) stepi, than
Compared with itself and subinterval runing time tiBetween relationship, adjust the at the uniform velocity cruising speed V that sets in (a) step repeatedlyi, until t 'i
With subinterval specified operation time tiDeviation meet error requirements.It is suitable to determine that binary search algorithm can be used for example
Vi, the principle of lookup is as t 'iLess than specified operation time tiWhen then reduce at the uniform velocity cruising speed Vi, on the contrary then improve and at the uniform velocity patrol
Speed of a ship or plane degree Vi。
Subinterval entrance velocity v is being determined 3. returningin, muzzle velocity voutWith subinterval specified operation time tiUnder the conditions of,
Bus energy-conserving running track in the subinterval.
Step A4, the output of Optimized model, including output form, output as a result, specifically includes the following steps:
Step A41, the output of Optimized model includes reaching operational plan from bus current location, current vehicle speed
In the defined point process that stops, the driving status track of bus, the track is made of optimum results output table, and table includes 4
Column, are respectively as follows: public transport vehicle speed, time, position of bus, bus tractive force/brake force output valve；
Step A42, the bus running energy consumption numerical value being calculated is run according to above-mentioned steps A41.
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 technical principles of the invention, several improvements and modifications, these improvements and modifications can also be made
Also it should be regarded as protection scope of the present invention.
The invention patent makes full use of existing bus positional information, operational plan information, offline map routing information
The driving strategy of bus running process is optimized, algorithm has a wide range of application, and it is (electric energy-stored to be equally applicable to new energy
Formula) the operational process optimization of bus improves vehicle utilization rate, cutting operating costs, it is positive to have for promoting course continuation mileage
Meaning.
Claims (5)
1. a kind of consider energy-efficient city bus running track optimization method, which comprises the following steps:
1) by the traffic coverage to stop between a little as defined in current bus running position to operational plan successively by roadway characteristic
It is divided into continuous multiple subintervals, roadway characteristic includes gradient section, speed-limit road section and intersection section, and by roadway characteristic
Corresponding attribute value assigns each subinterval；
2) the city bus driving strategy dual-layer optimization computation model of energy factor is considered according to the subinterval building after division,
In the step 2), city bus driving strategy dual-layer optimization computation model includes upper layer model and underlying model, upper layer
The output of model includes entrance velocity, muzzle velocity and the subinterval runing time three classes in each subinterval, as underlying model
Input, underlying model output optimization track return to layer model；
The upper layer model is to minimize the total energy consumption E consumed between bus stop as objective function, expression formula are as follows:
Wherein, EiFor the bus total energy consumption in each subinterval, n subinterval number between the station of bus running；
The constraint condition of the upper layer model are as follows:
For the entrance velocity v in any i-th of subintervalIn, iWith muzzle velocity vOut, iGreater than 0 and it is less than route speed limit VLim, iAbout
Beam:
For the muzzle velocity v in any i-th of subintervalOut, iMeeting in entrance velocity is vIn, iWhen accessible maximum speed
vU, iWith minimum speed vL, iConstraint:
vL, i≤vOut, i≤vU, i, i=1,2 ..., n；
For the running time T in any i-th of subintervaliSignificant constraint: running time TiIn entrance velocity vIn, i, outlet speed
Spend vOut, iUnder the conditions of minimum allowable runing time tL, iWith maximum allowable runing time tU, iBetween:
tL, i≤Ti≤tU, i, i=1,2 ..., n；
The sum of subinterval runing time before intersection meets signal lamp constraint:
-tΔ+k*tc≤Tj≤-tΔ+k*tc+tg, j=1,2 ..., m；
Wherein, m is intersection number between station, and j is intersection serial number, and k is the feasible periodicity of intersection, tcFor intersection
Signal timing dial period, tgFor the long green light time of intersection, tΔFor a cycle start time before the frequency of intersection
And the difference of frequency, TjAt the time of reaching j-th of intersection for vehicle；
In the underlying model,
For i-th of subinterval, the objective function of underlying model are as follows:
min Ei=J1, i+J2, i
J2, i=pt*Ti
Wherein, J1, iVehicle movement energy consumption for bus in the subinterval, J2, iIt is that operation of the bus in the subinterval is fixed
Energy consumption, MbusFor bus vehicle mass, MloadFor bus load quality, ηtFor bus transmission system efficiency, xIn, i、xOut, i
Entrance and outlet port for i-th subinterval, ρ are the bus regenerative braking rate of recovery, ptFor electronic load power, x is public affairs
Vehicle vehicle location is handed over, it, as u (x) > 0, is that engine exports that u (x), which is control force output in the position, as u (x) < 0,
For braking system output, as u (x)=0, bus is in unpowered sliding state；
In the underlying model, bus is run in the subinterval, meets following constraint condition:
1. unit mass control force output of the bus vehicle in the subinterval meets dynamics of vehicle constraint condition:
U-[v(x)]≤u(x)≤U+[v(x)]
Under the conditions of the formula indicates that the speed when bus vehicle is located at position x is v (x), the unit mass control force of the position
Output is in unit mass maximum power U+[v (x)] and unit mass maximum braking force U-Between [v (x)]；
2. operating status transfer of the bus vehicle in subinterval should meet the vehicle kinematics differential equation:
A (x)=u (x)-r [v (x)]+G (x)
Wherein: t (x) is that bus is located at the runing time at the x of position, and r [v (x)] is bus running drag braking degree, G (x)
For gradient coriolis acceleration, a (x) is the actual acceleration of bus running, and g is acceleration of gravity, and f is coefficient of rolling resistance,
CDFor coefficient of air resistance, A is vehicle front face area, and γ is vehicle correction coefficient of rotating mass, and θ is vehicle at the x of position
Value of slope；
3. driving status of the bus vehicle in the subinterval meets the input of subinterval optimization submodel:
v(xIn, i)=vIn, i
v(xOut, i)=vOut, i
3) city bus driving strategy dual-layer optimization computation model is solved, it is final to obtain current bus running position
To the optimization track of the traffic coverage to be stopped between a little as defined in operational plan.
2. a kind of energy-efficient city bus running track optimization method of consideration according to claim 1, which is characterized in that
The step 1) specifically includes the following steps:
11) range of traffic coverage is determined according to the current location information of bus；
12) it is obtained in traffic coverage according to the range combining geographic information database of bus running plan information and traffic coverage
Roadway characteristic data, wherein the characteristic attribute value of gradient section includes gradient starting point coordinate, gradient terminal point coordinate and the gradient
Value, the characteristic attribute value of speed-limit road section includes speed limit starting point coordinate, speed limit terminal point coordinate and speed limit value, the feature in intersection section
Attribute value includes intersection starting point coordinate, intersection terminal point coordinate and intersection green time；
13) by link characteristic information gradient starting point coordinate, gradient terminal point coordinate, speed limit starting point coordinate, speed limit terminal point coordinate,
Intersection starting point coordinate and intersection terminal point coordinate successively sort, and are matched two-by-two, and the separation in each subinterval is obtained
Coordinate；
14) traffic coverage is in turn divided by continuous multiple subintervals according to the separation coordinate in each subinterval, made each
Subinterval only includes unique roadway characteristic attribute value.
3. a kind of energy-efficient city bus running track optimization method of consideration according to claim 1, which is characterized in that
The upper layer model is solved by sequential quadratic programming algorithm.
4. a kind of energy-efficient city bus running track optimization method of consideration according to claim 1, which is characterized in that
The underlying model is solved by the analytical algorithm based on Pang Te lia king maximal principle.
5. a kind of energy-efficient city bus running track optimization method of consideration according to claim 1, which is characterized in that
In the step 3), the optimization track of traffic coverage includes the travel speed of each subinterval bus, running time, position
And tractive force and brake force.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710264660.4A CN107067710B (en) | 2017-04-21 | 2017-04-21 | It is a kind of to consider energy-efficient city bus running track optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710264660.4A CN107067710B (en) | 2017-04-21 | 2017-04-21 | It is a kind of to consider energy-efficient city bus running track optimization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107067710A CN107067710A (en) | 2017-08-18 |
| CN107067710B true CN107067710B (en) | 2019-11-29 |
Family
ID=59600726
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710264660.4A Active CN107067710B (en) | 2017-04-21 | 2017-04-21 | It is a kind of to consider energy-efficient city bus running track optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107067710B (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109190153B (en) * | 2018-07-24 | 2023-01-31 | 中国第一汽车股份有限公司 | Energy consumption calculation method and system |
| CN109448364B (en) * | 2018-10-15 | 2020-08-14 | 同济大学 | Bus dynamic trajectory optimization method considering comfort level, energy conservation and emission reduction |
| CN109409008B (en) * | 2018-11-23 | 2022-06-17 | 哈尔滨理工大学 | Construction method for typical speed-time running condition of urban rail vehicle line |
| CN110136470B (en) * | 2019-06-20 | 2021-12-21 | 四川长虹电器股份有限公司 | Bus arrival time calculation method and query system |
| CN111540225B (en) * | 2020-04-22 | 2021-03-26 | 山东大学 | Multi-objective optimization-based bus running interval speed optimization control method and system |
| RU2764479C2 (en) | 2020-04-23 | 2022-01-17 | Общество с ограниченной ответственностью «Яндекс Беспилотные Технологии» | Method and system for controlling the operation of a self-propelled car |
| CN111968377B (en) * | 2020-08-31 | 2022-07-15 | 姜忠太 | Vehicle network-based vehicle track optimization method for fuel saving and driving comfort |
| CN112233451B (en) * | 2020-10-13 | 2022-05-27 | 安徽交欣科技股份有限公司 | Intelligent traveling plan compiling system considering endurance mileage of pure electric bus |
| CN113327444A (en) * | 2021-06-09 | 2021-08-31 | 上海智能新能源汽车科创功能平台有限公司 | Control method for cooperatively optimizing vehicle speed based on vehicle road cloud |
| CN113734175B (en) * | 2021-07-30 | 2023-06-23 | 中汽研（天津）汽车工程研究院有限公司 | Real-time optimizing method for energy-saving driving strategy between two intersections under time-varying condition |
| CN114597951B (en) * | 2022-04-06 | 2023-05-05 | 西南交通大学 | Optimization method for energy-saving operation of electric automobile participating in alternating-current urban rail power supply system |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8998617B2 (en) * | 2006-03-20 | 2015-04-07 | General Electric Company | System, method, and computer software code for instructing an operator to control a powered system having an autonomous controller |
| US8750319B2 (en) * | 2010-11-03 | 2014-06-10 | Broadcom Corporation | Data bridge |
| CN102332122A (en) * | 2011-10-18 | 2012-01-25 | 东南大学 | Layout optimization method for urban public bicycle rental stations |
| CN103176408B (en) * | 2013-02-01 | 2015-11-04 | 浙江大学 | Based on the polypropylene production process grade switching and controlling method of double-deck hierarchical structure |
| CN103472723A (en) * | 2013-08-19 | 2013-12-25 | 上海交通大学 | Predictive control method and system based on multi-model generalized predictive controller |
| CN104751536A (en) * | 2013-12-25 | 2015-07-01 | 中兴通讯股份有限公司 | Bus station optimization evaluation method and system |
| CN103847749B (en) * | 2014-03-27 | 2016-03-30 | 中车信息技术有限公司 | Sequence generating method handled by a kind of double-deck multiple-objection optimization railway locomotive |
| CN104361543B (en) * | 2014-10-22 | 2018-06-19 | 广州地理研究所 | A kind of public bus network evaluation method based on space-time chain model |
| CN104731096B (en) * | 2015-02-04 | 2017-03-15 | 北京交通大学 | Train interval running orbit planing method based on rolling optimization |
-
2017
- 2017-04-21 CN CN201710264660.4A patent/CN107067710B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN107067710A (en) | 2017-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107067710B (en) | It is a kind of to consider energy-efficient city bus running track optimization method | |
| CN105551337B (en) | A kind of train operator's auxiliary driving method and system | |
| Zeng et al. | Globally energy-optimal speed planning for road vehicles on a given route | |
| Nie et al. | Real-time dynamic predictive cruise control for enhancing eco-driving of electric vehicles, considering traffic constraints and signal phase and timing (SPaT) information, using artificial-neural-network-based energy consumption model | |
| CN109927709A (en) | A kind of route or travel by vehicle working condition determining method, energy management method and system | |
| CN110782079A (en) | Method for adjusting energy-saving high-speed railway train running chart based on stop scheme | |
| CN111460633A (en) | Train energy-saving operation method based on multi-target particle swarm algorithm | |
| CN112668848B (en) | Energy management method for modern tramcar hybrid energy storage system based on working condition analysis | |
| Zhai et al. | Cooperative look-ahead control of vehicle platoon for maximizing fuel efficiency under system constraints | |
| CN102052926A (en) | Methods for reducing the consumption and cost of fuel | |
| CN116187161A (en) | Intelligent energy management method and system for hybrid electric bus in intelligent networking environment | |
| Hamid et al. | Investigation into train positioning systems for saving energy with optimised train trajectories | |
| Bin et al. | Energy-efficient speed profile optimization for high-speed railway considering neutral sections | |
| CN115935672A (en) | Fuel cell automobile energy consumption calculation method fusing working condition prediction information | |
| CN113442791B (en) | Electric energy supplementing method for electric automobile | |
| CN109584546B (en) | Method for determining departure flow threshold of quick-conventional bus sharing lane | |
| Jin et al. | Energy-optimal speed control for connected electric buses considering passenger load | |
| CN105741585A (en) | Fuel-economizing-oriented vehicle track smoothing control method based on Internet-of-vehicles | |
| CN114537420B (en) | Urban bus rapid transit energy-saving driving control method based on dynamic planning | |
| CN111126878A (en) | Urban traffic operation evaluation method based on ecological index | |
| Rui et al. | Simulated annealing algorithm for solving a bi-level optimization model on high-speed railway station location | |
| Ren et al. | An intersection platoon speed control model considering traffic efficiency and energy consumption in cvis | |
| Keskin et al. | Energy efficient motion control for a light rail vehicle using the big bang big crunch algorithm | |
| Cao et al. | Optimisation of recommended speed profile for train operation based on ant colony algorithm | |
| Wu et al. | Velocity trajectory planning of the autonomous-rail rapid tram considering terrain and traffic lights |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |