CN107067710A - A kind of city bus running orbit optimization method for considering energy-conservation - Google Patents

A kind of city bus running orbit optimization method for considering energy-conservation Download PDF

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CN107067710A
CN107067710A CN201710264660.4A CN201710264660A CN107067710A CN 107067710 A CN107067710 A CN 107067710A CN 201710264660 A CN201710264660 A CN 201710264660A CN 107067710 A CN107067710 A CN 107067710A
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intersection
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speed
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CN107067710B (en
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暨育雄
王维旸
张智明
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Tongji University
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Abstract

The present invention relates to a kind of city bus running orbit optimization method for considering energy-conservation, comprise the following steps：1) traffic coverage between being stopped a little as defined in current bus run location to operational plan is divided into continuous multiple subintervals by roadway characteristic successively, roadway characteristic includes gradient section, speed-limit road section and intersection section, and assigns each subinterval by roadway characteristic correspondence property value；2) the city bus driving strategy dual-layer optimization computation model for considering energy factor is built according to the subinterval after division；3) city bus driving strategy dual-layer optimization computation model is solved, the final optimization track for obtaining the traffic coverage between being stopped a little as defined in current bus run location to operational plan, includes travel speed, running time, position and the tractive force and brake force of each subinterval bus.Compared with prior art, the present invention has the advantages that dynamic is continuous, reduces energy consumption, improves punctuality rate and comfortableness.

Description

A kind of city bus running orbit optimization method for considering energy-conservation
Technical field
The present invention relates to traffic programme technical field, more particularly, to a kind of city bus running orbit for considering energy-conservation Optimization method.
Background technology
Fast Urbanization and the process of 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 turned into planning, have built and run the important component of " green city ".It is logical Selection is crossed using public transport pattern for leading traffic system, the strategy of implementation public transport appropriately exceeding development, be realize it is green The important means of the target of color urban transportation.
At the same time public transport is acted in urban transportation and gradually increased, its energy consumption emission problem also ever more important.Its In, city bus operation energy consumption is an important part for urban public transport energy consumption discharge, therefore promotes its energy-saving and emission-reduction to anticipate Justice is great.
Currently for the method carried out therewith of automotive energy-saving emission-reducing, mainly including policy instruments, such as Sun Bin exists《Vehicle energy saving subtracts The method analysis of row》In mention include improve national legislation, give policy support, promote the use of new-energy automobile with actively Exploitation；And technological means：Bus drives running optimization.
The acquisition of positional information, operational plan information and the category information of line information 3 for bus, had fully and Ripe research and application：GPS can be for example taken to obtain the location information of vehicle；It is real using wireless communication means such as 3G, 4G Existing bus and the continuous communiction of control centre, obtain bus running plan information, including run time between station, next stop By station information (as shown in Figure 1)；Value of slope, intersection position, the circuit in bus running path are obtained using offline map Speed-limiting messages.
Current public transport drives running and does not have accurate calculating auxiliary guiding, more based on the experience of driver, no There is larger difference in same experience, the driver of technical merit, bus traveling process has larger randomness and energy-conservation Optimize space, there is statistics to show, in actual running, even same line, the driving behavior between driver is poor The different electric bus operation energy consumption that also results in produces bigger difference.Electronic public affairs from cities such as Tianjin, Jinan, Wuhan, Linyi Hand over running situation analysis, the design mileage of electric automobile 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, the article delivered such as Tang Tao《The alignment car energy-saving driving research of Beijing Metro Yi Zhuang》.Relatively In urban track traffic, city bus operation is with bigger randomness and increasingly complex service condition (such as signal friendship The interference of prong, other public vehicles), energy saving optimizing technology is difficult to be applied to city bus in terms of existing urban track traffic The application demand of car
The content of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of dynamic is continuous, reduce The city bus running orbit optimization method of the consideration energy-conservation of energy consumption, raising punctuality rate and comfortableness.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of city bus running orbit optimization method for considering energy-conservation, comprises the following steps：
1) by the traffic coverage between being stopped a little as defined in current bus run location 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 correspondence property value assigns each subinterval；
2) built according to the subinterval after division and consider that the city bus driving strategy dual-layer optimization of energy factor is calculated 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 traffic coverage between being stopped a little as defined in position to operational plan.
Described step 1) specifically include following steps：
11) scope of traffic coverage is determined according to the current positional information of bus；
12) Operational Zone is obtained according to the scope 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 includes speed limit starting point coordinate, speed limit terminal point coordinate and speed limit, the spy in intersection section Levying property value includes intersection starting point coordinate, intersection terminal point coordinate and intersection green time；
13) by the gradient starting point coordinate in link characteristic information, gradient terminal point coordinate, speed limit starting point coordinate, speed limit terminal Coordinate, intersection starting point coordinate and intersection terminal point coordinate sort successively, and are matched two-by-two, obtain point in each subinterval Boundary's point coordinates；
14) traffic coverage is in turn divided into by continuous multiple subintervals according to the boundary point coordinates in each subinterval, made Each subinterval only includes unique roadway characteristic property value.
Described step 2) in, city bus driving strategy dual-layer optimization computation model includes upper layer model and lower floor's mould Type, the output of upper layer model includes the entrance velocity in each subinterval, muzzle velocity and the class of subinterval run time three, under The input of layer model, underlying model output optimization track returns to layer model.
Described upper layer model is to minimize the total energy consumption E consumed between bus stop as object function, and its expression formula is：
Wherein, EiFor the bus total energy consumption in each subinterval, n is subinterval number between the station of bus running.
The constraints of described upper layer model is：
1) for the entrance velocity v in any i-th of subintervalin,iWith muzzle velocity vout,iMore than 0 and less than circuit speed limit Vlim,iConstraint：
2) for the muzzle velocity v in any i-th of subintervalout,iIt is v to meet in entrance velocityin,iWhen it is accessible most Big speed vu,iWith minimum speed vl,iConstraint：
vl,i≤vout,i≤vu,i, i=1,2 ..., n；
3) for the running time T in any i-th of subintervaliMeaningful constraint：Running time TiIn entrance velocity vin,i、 Muzzle velocity vout,iUnder the conditions of minimum allowable run time tl,iWith maximum allowable run time tu,iBetween：
tl,i≤Ti≤tu,i, i=1,2 ..., n；
4) the preceding subinterval running time T in intersectioniSum 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 sequence number, and k is the feasible periodicity of intersection, tcTo intersect The signal timing dial cycle of mouth, tgFor the long green light time of intersection, tΔStart for a cycle before the frequency of intersection Moment and the difference of frequency, TjAt the time of j-th of intersection being reached for vehicle.
In described underlying model,
For i-th of subinterval, the object function of underlying model is：
minEi=J1,i+J2,i
J2,i=pt*Ti
Wherein, J1,iIt is bus in the vehicle movement energy consumption 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 that the controling power in the position is exported, and is engine output, as u (x) ＜ as u (x) ＞ 0 It is brakes output when 0, as u (x)=0, bus is in unpowered sliding state；
In described underlying model, bus is run in the subinterval, meets following constraints：
1. controling power output of the bus vehicle in the subinterval meets dynamics of vehicle constraints：
U-[v(x)]≤u(x)≤U+[v(x)]
Under the conditions of the speed that the formula represents when bus vehicle is located at position x is v (x), the output of the controling power of the position In maximum power U+[v (x)] and maximum braking force U-Between [v (x)]；
2. running 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 the run time that bus is located at the x of position, and r [v (x)] is bus running drag braking degree, G (x) is gradient coriolis acceleration, and g is acceleration of gravity, and f is coefficient of rolling resistance, CDFor coefficient of air resistance, A meets for vehicle Wind area, γ is vehicle correction coefficient of rotating mass, and θ is value of slope of the vehicle at the x of position；
3. transport condition of the bus vehicle in the subinterval meets the input that the subinterval optimizes submodel：
v(xin,i)=vin,i
v(xout,i)=vout,i
Described upper layer model is solved by sequential quadratic programming algorithm.
Described underlying model is solved by the analytical algorithm based on Pang Te lia king maximal principles.
Described step 3) in, 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 present invention has advantages below：
The present invention by according to bus running plan, car status information, and intersection signal timing situation of change, Dynamic, the driving strategy for continuously calculating consideration energy factor, optimize the running orbit of bus, significantly reduce vehicle Operation energy consumption, reduces the work difficulty of bus drivers, reduces the difference that different driving behaviors are caused to public transport operation, improves public Ship capable punctuality rate and comfortableness, it is ensured that Public Transport Service quality.
Brief description of the drawings
Fig. 1 is acquisition bus positional information, the schematic diagram of bus running plan information in the prior art.
The city bus running orbit optimization method flow chart that Fig. 2 saves for a kind 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 structure bi-level optimal model of the embodiment of the present invention.
Embodiment
With reference to the accompanying drawings and examples, the embodiment to the present invention is described in further detail.Implement below Example is used to illustrate the present invention, but is not limited to the scope of the present invention.
Embodiment：
As shown in Fig. 2 figure is a kind of city bus running orbit optimization method of consideration energy-conservation of the embodiment of the present invention Flow chart, the present embodiment comprises the following steps：
Step A1, reading bus running circuit, operational plan and current vehicle condition information, as shown in figure 3, tool Body comprises the following steps：
Step A11, acquisition bus running plan information；The mobile unit installed in bus can pass through 3G, 4G etc. Wireless communication technology, is communicated with bus dispatching center, obtains the operational plan information of this car, including vehicle operation road Information at the time of footpath, next run time between point, vehicle station that stops, arrival next station.
Step A12, acquisition bus positional information, it is determined that the traffic coverage scope that optimization is calculated；Bus is entered by GPS Row vehicle location, by carrying out coordinate matching with the offline map datum prestored in vehicle, obtains bus current location Information；And be compared with the step A11 operational plans obtained, obtain bus and stop with a distance from a little from next, run road Footpath scope, and being capable of the next interval run time stopped a little of reaching on the time.
Step A13, using the method described in above-mentioned steps A11, A12, by with offline geographical information database queries Method, obtaining to next stop a little needs to optimize the interval circuit characteristic information of calculating, including：Circuit speed-limiting messages, Line slope information, intersection positional information, intersection signal timing information；
Step A2, the subinterval for optimizing according to circuit speed limit, gradient condition, intersection position algorithm are divided, such as Fig. 4 It is shown, specifically include following steps：
Step A21：Line slope value, speed limit, intersection position are listed according to form：Line slope value table bag Include 3 row：Gradient starting point coordinate, gradient terminal point coordinate, value of slope.Wherein value of slope is expressed as uphill gradient more than 0, downward grades Less than 0, horizontal slope is equal to 0.Speed limit table includes 3 row：Speed limit starting point coordinate, speed limit terminal point coordinate, speed limit.Crossing elimination Mouth position table includes 3 row：Intersection starting point coordinate, intersection terminal point coordinate, intersection green time.Wherein use closed interval form Represent that green light starts and end range, such as [20,50] represent to be green time in the range of 20 seconds to 50 seconds；
Step A22：By line slope value table, circuit speed limit 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 represent line slope value table, circuit limit respectively Speed is worth table, crossing elimination mouthful position table, then the separation coordinate sequence constituted is：0,200,350,380,500.
The line slope value table of table 1
Gradient starting point coordinate Gradient terminal point coordinate Value of slope
0 200 3
200 500 ‐5
The circuit speed limit table of table 2
Speed limit starting point coordinate Speed limit terminal point coordinate Speed limit
0 350 60
350 500 55
The crossing elimination mouthful of table 3 position table
Intersection starting point coordinate Intersection terminal point coordinate Intersection green time
350 380 [50,100],[170,220]
Step A23：Subinterval tables of data is built, the table includes 6 row：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 boundary point coordinates Continuous pairing two-by-two is obtained successively, and is inserted；Value of slope, speed limit, whether intersection, intersection green time this 4 attributes The line slope value table in step A21, circuit speed limit table, crossing elimination mouthful position table are looked into by subinterval separation Write after inquiry；Subinterval tables of data requires that each subinterval only includes unique speed limit, unique value of slope, unique section or intersection Mouth attribute.For example hereafter：Table 1, table 2, table 3 represent line slope value table, circuit speed limit table, crossing elimination mouthful position respectively Table, then subinterval tables of data be：
Table 4：Subinterval tables of data
Step A3, the city bus driving strategy dual-layer optimization computation model for building consideration energy factor, the model It is mainly characterized by the numerical solution algorithm that layer model uses SQP, underlying model, which is used, is based on Pang Te lia kings pole The analytical algorithm of big value principle (Pontryagin ' s Maximum Principle), as shown in figure 5, specifically including following step Suddenly：
Step A31, determine model decision variable：The decision variable of Optimized model by each subinterval entrance velocity vin、 Muzzle velocity voutWith subinterval run time tiThree classes are constituted, the subinterval that the numbers of all kinds of decision variables is divided by step A23 Quantity is determined.
Step A32, definition levels model interface：1. the input of the downward layer model of upper layer model is determining for Optimized model Plan variable, the entrance velocity v by each subintervalin, muzzle velocity voutWith subinterval run time tiThree classes are constituted, all kinds of to determine The subinterval quantity that the number of plan variable is divided by step A23 is determined；2. the upward layer model of underlying model is returned in the subinterval Entrance velocity vin, muzzle velocity voutWith subinterval specified operation time tiIt is determined that under conditions of, the bus section in the subinterval Can running orbit.
Step A33, determine upper strata model objective function：Upper layer model using minimize the total energy consumption consumed between bus stop as Object function, its expression formula is：
Wherein, EiFor the bus total energy consumption in each subinterval, n is subinterval number between the station of bus running.This hair In bright, the calculating of operation energy consumption considers following factor：1. energy consumption, 2. bus system when bus engine provides power output Energy consumption that constant power during energy consumption that dynamic regenerative braking when slowing down is reclaimed, 3. bus running is caused (including control System energy consumption processed, illumination, air conditioning energy consumption).Total energy consumption is equal to the energy consumption sum that 1., 3. process is consumed and subtracts regenerative braking 2. process The energy consumption of recovery.
The constraints of layer model on step A34, determination, constraints is：
1. for the entrance velocity v in any i-th of subintervalin,iWith muzzle velocity vout,iMore than 0 and less than circuit speed limit Vlim,iConstraint：
2. for the muzzle velocity v in any i-th of subintervalout,iIt is v to meet in entrance velocityin,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,iComputational methods it is as follows：The length for remembering i-th of subinterval is liWith vin,iSpeed drive into, according to maximum The distance that brake force is decelerated to required for 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,iApart from dl,i=li。
Wherein vu,iComputational methods it is as follows：The length for remembering i-th of subinterval is 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,iVehicle is met from vin,iV is accelerated to according to maximum poweru,iApart from du,i=li。
3. for the running time T in any i-th of subintervaliMeaningful constraint：Running time TiIn entrance velocity vin,i、 Muzzle velocity vout,iUnder the conditions of minimum allowable run time tl,iWith maximum allowable run time tu,iBetween：
tl,i≤Ti≤tu,i, i=1,2 ..., n
Wherein Tl,iComputational methods it is as follows：The length for remembering i-th of subinterval is li, vehicle is in subinterval tl,iComposition Accelerate, in V comprising maximum power successivelylim,iUnder speed at the uniform velocity, the vehicle hour of maximum braking deceleration three phases, and root According to liSize and vehicle accelerate, braking distance, and there is 1~2 stage non-existent situation in three phases, (maximum power adds Speed, maximum the two stages of braking deceleration there must be one).
Wherein Tu,iComputational methods it is as follows：The length for remembering i-th of subinterval is 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 liSize and vehicle accelerate, braking distance, and there is 1~2 stage non-existent situation in three phases, (maximum power adds Speed, maximum the two stages of braking deceleration there must be one).If constant velocity stage is present under the speed of v → 0, illustrate tu,iCan 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 intersectioniSum 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 sequence number.For j-th of intersection, kjFor the intersection can Capable periodicity, tc,jFor the signal timing dial cycle of the intersection, tg,jFor the intersection long green light time, tΔ,jSent out for the intersection The difference of a cycle start time and frequency before the car moment.TjAt the time of j-th of intersection being reached for vehicle, meter Calculation method is：
The subinterval quantity n that the wherein the constraints number for 1., 2., 3. planting situation is divided by step A23 is determined；4. The constraints number of the situation of kind (is believed by the crossing elimination mouthful quantity m in step A21 in the table of crossing elimination mouthful position containing this Breath) determine.
Step A35, definition lower floor computation model, including：
For i-th of subinterval, the object function of underlying model is：
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 consolidated Surely consume.
J2,i=pt*Ti
In formula, MbusFor bus vehicle mass；MloadIt is relevant 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 is that bus regenerative braking is returned Yield；ptFor electronic load power.X is bus vehicle location, and u (x) is the unit mass controling power (engine in the position Power or brake system power) output.
Meanwhile, bus is run in the subinterval, should also meet constraints：
1. controling power output (engine power, brake system power) of the bus vehicle in the subinterval should meet car Dynamics constraint condition：
U-[v(x)]≤u(x)≤U+[v(x)]
Above-mentioned formula represents that the speed when bus vehicle is located at position x is v (x), and the controling power in the position exports u (x) (engine power or brake system power) should meet dynamics of vehicle constraints, i.e., maximum dynamic under present speed Power U+[v (x)] and maximum braking force U-Between [v (x)].
2. running 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 run time and speed that bus is located at the x of position, and u (x) is bus engine or braking The unit mass controling power (i.e. acceleration) of system reality output：It is engine output as u ＞ 0；As u ＜ 0, for braking System is exported；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 formulas, g accelerates for gravity 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 be value of slope of the vehicle the x of position at, regulation value of slope go up a slope when be on the occasion of, during descending be negative value.
3. transport condition of the bus vehicle in the subinterval (includes entrance velocity, muzzle velocity, run time, operation Distance) meet the input that the subinterval optimizes submodel：
v(xin,i)=vin,i
v(xout,i)=vout,i
The model of lower floor is output as：Bus is with the controling power u (x) of change in location (including to start motor-driven during u (x) ＞ 0 Power, is brake system power during u (x) ＜ 0), and bus runs according to u (x) as control input in the subinterval Bus running energy consumption Ei, by controling power u (x), and given entrance velocity vin, by constraints 2. in vehicle transport The dynamic differential equation of learning can obtain bus in the interval running orbit, including speed is with distance change track v (x), run time With distance change track t (x).
Underlying model is based on Pang Te lia kings maximal principle (Pontryagin ' s Maximum Principle) Analytical algorithm, including：
1. the Hamiltonian of bus running is built by each subinterval, the running 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 braking deceleration state；
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：
At the uniform velocity cruising speed V is set including (a)i；
(b) from porch, forward direction is calculated so that speed reaches Vi；For example 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 vinMore than at the uniform velocity Cruising speed ViWhen, then take sliding state to be decelerated at the uniform velocity cruising speed Vi。
From exit backwards calculation so that speed is from voutWork back to up to Vi；For example 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, and v is met to subinterval muzzle velocityout。
According to subinterval length, V is determinediAt the uniform velocity length；(c) calculated according to above-mentioned (b) step and obtain run time t 'i, than Compared with itself and subinterval run time tiBetween relation, the at the uniform velocity cruising speed V that sets in (a) step is adjusted repeatedlyi, until t 'i With subinterval specified operation time tiDeviation meet error requirements.It is suitable to determine that binary search algorithm can for example be used Vi, the principle of lookup is to work as t 'iLess than specified operation time tiWhen then reduce at the uniform velocity cruising speed Vi, it is on the contrary then improve and at the uniform velocity patrol Speed of a ship or plane degree Vi。
3. return it is determined that subinterval entrance velocity vin, muzzle velocity voutWith subinterval specified operation time tiUnder the conditions of, Bus energy-conserving running orbit in the subinterval.
Step A4, Optimized model output, including output form, output result, specifically include following steps：
Step A41, the output of Optimized model include, from bus current location, current vehicle speed, reaching operational plan In the defined point process that stops, the transport condition track of bus, the track is made up of optimum results output table, and form includes 4 Row, be respectively：Public transport vehicle speed, time, position of bus, bus tractive force/brake force output valve；
Step A42, the bus running energy consumption numerical value obtained according to above-mentioned steps A41 operation calculating.
Described above is only the 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, some improvement and modification can also be made, these improvement and modification Also it should be regarded as protection scope of the present invention.
Patent of the present invention 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, be equally applicable to new energy (electric energy-stored Formula) bus running optimization, for lifting course continuation mileage, improve vehicle utilization rate, cut operating costs with positive Meaning.

Claims (10)

1. a kind of city bus running orbit optimization method for considering energy-conservation, it is characterised in that comprise the following steps：
1) by the traffic coverage between being stopped a little as defined in current bus run location 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 Correspondence property value assigns each subinterval；
2) the city bus driving strategy dual-layer optimization computation model for considering energy factor is built according to the subinterval after division；
3) city bus driving strategy dual-layer optimization computation model is solved, it is final to obtain current bus run location The optimization track of traffic coverage between stopping a little as defined in operational plan.
2. a kind of city bus running orbit optimization method for considering energy-conservation according to claim 1, it is characterised in that Described step 1) specifically include following steps：
11) scope of traffic coverage is determined according to the current positional information of bus；
12) obtained according to the scope combining geographic information database of bus running plan information and traffic coverage in 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, the feature in intersection section Property value includes intersection starting point coordinate, intersection terminal point coordinate and intersection green time；
13) by the gradient starting point coordinate in link characteristic information, gradient terminal point coordinate, speed limit starting point coordinate, speed limit terminal point coordinate, Intersection starting point coordinate and intersection terminal point coordinate sort successively, and are matched two-by-two, obtain the separation in each subinterval Coordinate；
14) traffic coverage is in turn divided into by continuous multiple subintervals according to the boundary point coordinates in each subinterval, made each Subinterval only includes unique roadway characteristic property value.
3. a kind of city bus running orbit optimization method for considering energy-conservation according to claim 1, it is characterised in that Described step 2) in, city bus driving strategy dual-layer optimization computation model includes upper layer model and underlying model, upper strata The output of model includes the entrance velocity, muzzle velocity and the class of subinterval run time three in each subinterval, is used as underlying model Input, underlying model output optimization track return to layer model.
4. a kind of city bus running orbit optimization method for considering energy-conservation according to claim 3, it is characterised in that Described upper layer model is to minimize the total energy consumption E consumed between bus stop as object function, and its expression formula is：
Wherein, EiFor the bus total energy consumption in each subinterval, n is subinterval number between the station of bus running.
5. a kind of city bus running orbit optimization method for considering energy-conservation according to claim 4, it is characterised in that The constraints of described upper layer model is：
1) for the entrance velocity v in any i-th of subintervalin,iWith muzzle velocity vout,iMore than 0 and less than circuit speed limit Vlim,i Constraint：
2) for the muzzle velocity v in any i-th of subintervalout,iIt is v to meet in entrance velocityin,iWhen accessible maximum speed Spend vu,iWith minimum speed vl,iConstraint：
vl,i≤vout,i≤vu,i, i=1,2 ..., n；
3) for the running time T in any i-th of subintervaliMeaningful constraint：Running time TiIn entrance velocity vin,i, outlet Speed vout,iUnder the conditions of minimum allowable run time tl,iWith maximum allowable run time tu,iBetween：
tl,i≤Ti≤tu,i, i=1,2 ..., n；
4) the preceding subinterval running time T in intersectioniSum meets signal lamp constraint：
-tΔ+k*ty≤Tj≤-tΔ+k*tc+tg, j=1,2 ..., m；
Wherein, m is intersection number between station, and j is intersection sequence number, and k is the feasible periodicity of intersection, tcFor intersection Signal timing dial cycle, 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 j-th of intersection being reached for vehicle.
6. a kind of city bus running orbit optimization method for considering energy-conservation according to claim 5, it is characterised in that In described underlying model,
For i-th of subinterval, the object function of underlying model is：
J2,i=pt*Ti
Wherein, J1,iIt is bus in the vehicle movement energy consumption 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 Car vehicle location is handed over, u (x) is that the controling power in the position is exported, and is engine output as u (x) ＞ 0, as u (x) ＜ 0, Exported for brakes, as u (x)=0, bus is in unpowered sliding state.
7. a kind of city bus running orbit optimization method for considering energy-conservation according to claim 6, it is characterised in that In described underlying model, bus is run in the subinterval, meets following constraints：
1. controling power output of the bus vehicle in the subinterval meets dynamics of vehicle constraints：
U-[v(x)]≤u(x)≤U+[v(x)]
Under the conditions of the speed that the formula represents when bus vehicle is located at position x is v (x), the controling power of the position is exported most Big power U+[v (x)] and maximum braking force U_Between [v (x)]；
2. running 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 the run time that bus is located at the x of position, and r [v (x)] is bus running drag braking degree, G (x) For gradient coriolis acceleration, g is acceleration of gravity, and f is coefficient of rolling resistance, CDFor coefficient of air resistance, A is vehicle windward side Product, γ is vehicle correction coefficient of rotating mass, and θ is value of slope of the vehicle at the x of position；
3. transport condition of the bus vehicle in the subinterval meets the input that the subinterval optimizes submodel：
v(xin,i)=vin,i
v(xout,i)=vout,i
8. a kind of city bus running orbit optimization method for considering energy-conservation according to claim 4, it is characterised in that Described upper layer model is solved by sequential quadratic programming algorithm.
9. a kind of city bus running orbit optimization method for considering energy-conservation according to claim 6, it is characterised in that Described underlying model is solved by the analytical algorithm based on Pang Te lia king maximal principles.
10. a kind of city bus running orbit optimization method for considering energy-conservation according to claim 1, its feature exists In described step 3) in, the travel speed of the optimization track including each subinterval bus of traffic coverage, running time, Position and tractive force and brake force.
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