CN105509760B - A kind of electric automobile - Google Patents
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- CN105509760B CN105509760B CN201510862904.XA CN201510862904A CN105509760B CN 105509760 B CN105509760 B CN 105509760B CN 201510862904 A CN201510862904 A CN 201510862904A CN 105509760 B CN105509760 B CN 105509760B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/36—Input/output arrangements for on-board computers
- G01C21/3605—Destination input or retrieval
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/36—Input/output arrangements for on-board computers
- G01C21/3691—Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions
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Abstract
A kind of electric automobile, including car body and the automated navigation system on car body, automated navigation system include signaling module, processing module and generation module.The present invention is using the routing algorithm optimized, it is contemplated that the operating various cost factors of vehicle, optimizing effect is good, solution efficiency is high, stable performance, enhances ability of searching optimum, can save the operating cost of vehicle to greatest extent, can play good energy-saving effect.
Description
Technical field
The present invention relates to automotive field, and in particular to a kind of electric automobile.
Background technology
On present electric automobile, automated navigation system is fitted with greatly.The purposes of automated navigation system is by vehicle GPS
(GPS) receiver monitors current vehicle position and compared data with user-defined destination, reference
Electronic map calculates travel route, and provides information to motorist in real time.It is widely used from leading in the world at present
Boat, it is to carry electronic map using in-vehicle navigation apparatus, positioning and navigation feature are all completed by mobile unit.
In actual use, the destination of driver often more than one, and require to reach the time of each destination
It is not quite similar.Therefore, how according to different destinations come select one can cost-effective vehicle working line to greatest extent,
It is a urgent problem to be solved.
The content of the invention
In view of the above-mentioned problems, the present invention provides a kind of electric automobile.
The purpose of the present invention is realized using following technical scheme：
A kind of electric automobile, including car body and the automated navigation system on car body, it is characterized in that, including signal mode
Block, processing module and generation module；
Signaling module, for receiving one or more destinations of user's input, and reach the estimated of each destination
Seeking time is wanted, and inquires whether car owner returns to one's starting point after leaving last destination；
Processing module, for selecting optimal path with geographical environment information is previously entered according to destination, specifically include：
Establish module：
Wherein, minS is the least cost in vehicle operation；M is the sum of departure place vehicle, is received by GPS system
Near signals of vehicles determine, if not receiving the signal of other vehicles, then m=1；U is destination quantity；b0For unit
Apart from carbon emission cost；ω0For carbon emission coefficient；φ*-φ0For zero load when unit distance Fuel Consumption；fijFor destination i
The distance between to destination j, wherein i=1,2 ..., U；J=1,2 ..., U；C is the loading capacity of vehicle；H is the maximum of vehicle
Loading capacity；φ * are full load unit distance Fuel Consumption；
T1Advanceed to for vehicle up to loss coefficient,
For the cost allowance when moment, G arrived at i in advance, T2It is late loss coefficient for vehicle,To be delayed to
Moment O arrives at cost allowance during i, OiAdvanceed at the time of during to arrive at i up to loss coefficient and late damage
Coefficient is lost to be used to consider the situation on schedule that vehicle reaches each destination, T1And T2For the coefficient being manually set；
Opportunity module：Assuming that share R node, γij(t) the tracking element intensity between t node i and node j is represented,
γij(0)=K, K are the less constant of numerical value, and vehicle selects shift direction in motion process according to the plain intensity of tracking, then vehicle
The probability that k is transferred to node j from node i is：
Wherein, g ∈ Ak；Ak=0,1 ..., and R-1 }-BkRepresent that vehicle k allows the set of point of selection in next step, with the time
In dynamic change, BkFor the taboo list of kth vehicle, the point being had been subjected to for registration of vehicle k, and k=1,2 ..., m；For
Heuristic greedy method, expected degree of the t by node i to node j is represented, is takenψ is information heuristic greedy method, μ
It is expected heuristic factor；αij(t) it is the time degree of next destination；It is relatively important for the time degree of next destination
Property, γig(t) t node i is represented to the tracking element intensity between node g,For heuristic greedy method, represent t by
Node i is to node g expected degree, αig(t) the time degree for being next destination g；
Optimization module：Introduce optimized variable Xij(t), it meets Xij(t+1)=σ X (t) [1-Xij(t)], wherein σ is control
Variable, X (t) are the optimized variable of t, draw the tracking element renewal rule of optimization：
γij(t+1)=(1- ζ) γij(t)+Δγij(t)+чXij(t)
Wherein,When user selects to return after last destination is left by signaling module
When going back to departure place, a circulation is formed, then
FkIt is kth vehicle in this circulation
Walked path length, I are the constant for tracking plain intensity,Represent that kth vehicle is on path (i, j) in this circulation
The tracking element intensity left；ζ is that the global volatilization factor of tracking element, ζ ∈ [0,1], and ζ are the ginseng according to equation below dynamic adjustment
Number：Wherein ζminIt is the minimum value being manually set；Δγij(t) this circulation is represented
In the summation of the plain intensity of tracking that is left on path (i, j) of all vehicles；ч is adjustable coefficient；
Initial module：Iterations DD=0 is made, carries out parameter initialization, adjusts each path trace element；Produce a scope
For the random number p of [0,1], if p ＜ give constant p0, next node j is selected according to the following formula： Wherein l ∈ Ak, the tracking element intensity between γ (i, l) expression initial time node is and node l,For heuristic greedy method, expected degree of the initial time by node i to node l is represented；α (i, l) be next destination l when
Between spend, μ for it is expected heuristic factor；Otherwise the new probability formula selection next node j in opportunity module, j is added and avoided
Table BkIn, it is repeated up to all node tasks and completes, obtains the initial set S of simulation algorithmi；
Solve module：Feasible solution S is obtained from one group current of initial set generation is newj, desired value variation delta S=Sj-Si,
If Δ S ＜ 0, receive new feasible solution SjFor optimal solution；Otherwise the influence of deviation is considered：R=exp (Δ S/N (t)), r ＞ 1,
Then receive SjFor optimal solution, new feasible solution is not otherwise received, optimal solution is still Si；
Judge module：When current optimal solution is less than some particular value, plain renewal is tracked；If epicycle taboo list Bk
Middle no data renewal, then the random number u of [0, a 1] scope is produced, if e1+e2,…,ei-1＜ u ＜ e1+e2,…,ei, then select
Probability is eiCandidate's inspection car as next destination node；
Generation module：For exporting the optimal path calculated, iterations DD=DD+1 is made, if DD ＜ DDmax, according to
Tracking element renewal rule, according to formula N (t+1)=N (t)；V carries out emptying taboo list Bk, wherein v ∈ [0,1], return to introductory die
Block, regenerate random number p；If DD=DDmax, then optimal solution is exported as optimal path, wherein DDmaxFor greatest iteration time
Number.
Beneficial effects of the present invention are：The present invention is using the routing algorithm optimized, it is contemplated that each in vehicle operation
Kind cost factor, optimizing effect is good, solution efficiency is high, stable performance, enhances ability of searching optimum, can be according to multiple destinations
And the time for requiring to reach saves the operating cost of vehicle to greatest extent, can play good energy-saving effect.
Brief description of the drawings
Using accompanying drawing, the invention will be further described, but the embodiment in accompanying drawing does not form any limit to the present invention
System, for one of ordinary skill in the art, on the premise of not paying creative work, can also be obtained according to the following drawings
Other accompanying drawings.
Fig. 1 is the structured flowchart of the present invention.
Reference：Signaling module -2；Establish module -4；Opportunity module -6；Optimization module -8；Initial module -10；Solve
Module -12；Judge module -14；Generation module -16.
Embodiment
The invention will be further described with the following Examples.
A kind of electric automobile, including car body and the automated navigation system on car body, it is characterized in that, including signal mode
Block 2, processing module and generation module 16；
Signaling module 2, for receiving one or more destinations of user's input, and reach the estimated of each destination
Seeking time is wanted, and inquires whether car owner returns to one's starting point after leaving last destination；
Processing module, for selecting optimal path with geographical environment information is previously entered according to destination, specifically include：
Establish module 4：
Wherein, minS is the least cost in vehicle operation；M is the sum of departure place vehicle, is received by GPS system
Near signals of vehicles determine, if not receiving the signal of other vehicles, then m=1；U is destination quantity；b0For unit
Apart from carbon emission cost；ω0For carbon emission coefficient；φ*-φ0For zero load when unit distance Fuel Consumption；fijFor destination i
The distance between to destination j, wherein i=1,2 ..., U；J=1,2 ..., U；C is the loading capacity of vehicle；H is the maximum of vehicle
Loading capacity；φ * are full load unit distance Fuel Consumption；
T1Advanceed to for vehicle up to loss coefficient,
For the cost allowance when moment, G arrived at i in advance, T2It is late loss coefficient for vehicle,To be delayed to
Moment O arrives at cost allowance during i, OiAt the time of during to arrive at i, advance to up to loss coefficient and late damage
Coefficient is lost to be used to consider the situation on schedule that vehicle reaches each destination, T1And T2For the coefficient being manually set；
Opportunity module 6：Assuming that share R node, γij(t) represent that the tracking element between t node i and node j is strong
Degree, γij(0)=K, K are the less constant of numerical value, and vehicle selects shift direction in motion process according to the plain intensity of tracking, then
The probability that vehicle k is transferred to node j from node i is：
Wherein, g ∈ Ak；Ak=0,1 ..., and R-1 }-BkRepresent that vehicle k allows the set of point of selection in next step, with the time
In dynamic change, BkFor the taboo list of kth vehicle, the point being had been subjected to for registration of vehicle k, and k=1,2 ..., m；For
Heuristic greedy method, expected degree of the t by node i to node j is represented, is takenψ is information heuristic greedy method, μ
It is expected heuristic factor；αij(t) it is the time degree of next destination；It is relatively important for the time degree of next destination
Property, γig(t) t node i is represented to the tracking element intensity between node g,For heuristic greedy method, represent t by
Node i is to node g expected degree, αig(t) the time degree for being next destination g；
Optimization module 8：Introduce optimized variable Xij(t), it meets Xij(t+1)=σ X (t) [1-Xij(t)], wherein σ is control
Variable processed, X (t) are the optimized variable of t, draw the tracking element renewal rule of optimization：
γij(t+1)=(1- ζ) γij(t)+Δγij(t)+чXij(t)
Wherein,When user selects to return after last destination is left by signaling module
When going back to departure place, a circulation is formed, then
FkIt is kth vehicle in this circulation
Walked path length, I are the constant for tracking plain intensity,Represent that kth vehicle is on path (i, j) in this circulation
The tracking element intensity left；ζ is that the global volatilization factor of tracking element, ζ ∈ [0,1], and ζ are the ginseng according to equation below dynamic adjustment
Number：Wherein ζminIt is the minimum value being manually set；Δγij(t) this circulation is represented
In the summation of the plain intensity of tracking that is left on path (i, j) of all vehicles；ч is adjustable coefficient；
Initial module 10：Iterations DD=0 is made, carries out parameter initialization, adjusts each path trace element；Produce a model
The random number p for [0,1] is enclosed, if p ＜ give constant p0, next node j is selected according to the following formula： Wherein l ∈ Ak, the tracking element intensity between γ (i, l) expression initial time node is and node l,For heuristic greedy method, expected degree of the initial time by node i to node l is represented；α (i, l) be next destination l when
Between spend, μ for it is expected heuristic factor；Otherwise the new probability formula selection next node j in opportunity module, j is added and avoided
Table BkIn, it is repeated up to all node tasks and completes, obtains the initial set S of simulation algorithmi；
Solve module 12：Feasible solution S is obtained from one group current of initial set generation is newj, desired value variation delta S=Sj-
SiIf Δ S ＜ 0, receive new feasible solution SjFor optimal solution；Otherwise the influence of deviation is considered：R=exp (Δ S/N (t)), r ＞
1, then receive SjFor optimal solution, new feasible solution is not otherwise received, optimal solution is still Si；
Judge module 14：When current optimal solution is less than some particular value, plain renewal is tracked；If epicycle taboo list
BkMiddle no data renewal, then the random number u of [0, a 1] scope is produced, if e1+e2,…,ei-1＜ u ＜ e1+e2,…,ei, then select
It is e to select probabilityiCandidate's inspection car as next destination node；
Generation module 16：For exporting the optimal path calculated, iterations DD=DD+1 is made, if DD ＜ DDmax, root
According to tracking element renewal rule, according to formula N (t+1)=N (t)；V carries out emptying taboo list Bk, wherein v ∈ [0,1], return to initial
Module, regenerate random number p；If DD=DDmax, then optimal solution is exported as optimal path, wherein DDmaxFor greatest iteration time
Number.
Beneficial effects of the present invention are：The present invention is using the routing algorithm optimized, it is contemplated that each in vehicle operation
Kind cost factor, optimizing effect is good, solution efficiency is high, stable performance, enhances ability of searching optimum, can be according to multiple destinations
And the time for requiring to reach saves the operating cost of vehicle to greatest extent, can play good energy-saving effect.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of scope is protected, although being explained with reference to preferred embodiment to the present invention, one of ordinary skill in the art should
Work as understanding, technical scheme can be modified or equivalent substitution, without departing from the reality of technical solution of the present invention
Matter and scope.
Claims (1)
1. a kind of electric automobile, including car body and the automated navigation system on car body, it is characterized in that, including signal mode
Block, processing module and generation module；
Signaling module, for receiving one or more destinations of user's input, and reach the estimated requirement of each destination
Time, and inquire whether car owner returns to one's starting point after leaving last destination；
Processing module, for selecting optimal path with geographical environment information is previously entered according to destination, specifically include：
Establish module：
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Wherein, minS is the least cost in vehicle operation；M is the sum of departure place vehicle, is received by GPS system attached
Nearly signals of vehicles determines, if not receiving the signal of other vehicles, then m=1；U is destination quantity；b0For unit distance
Carbon emission cost；ω0For carbon emission coefficient；φ*-φ0For zero load when unit distance Fuel Consumption；fijFor destination i to purpose
The distance between ground j, wherein i=1,2 ..., U；J=1,2 ..., U；C is the loading capacity of vehicle；H is the maximum load of vehicle
Amount；φ*For full load unit distance Fuel Consumption；
T1Advanceed to for vehicle up to loss coefficient,
For the cost allowance when moment, G arrived at i in advance, T2It is late loss coefficient for vehicle,To be delayed to
Moment O arrives at cost allowance during i, OiAt the time of during to arrive at i, advance to up to loss coefficient and late damage
Coefficient is lost to be used to consider the situation on schedule that vehicle reaches each destination, T1And T2For the coefficient being manually set；
Opportunity module：Assuming that share R node, γij(t) the tracking element intensity between t node i and node j, γ are representedij
(0)=K, K are the less constant of numerical value, and vehicle selects shift direction according to tracking plain intensity in motion process, then vehicle k from
The probability that node i is transferred to node j is：
Wherein, g ∈ Ak；Ak=0,1 ..., and R-1 }-BkRepresent that vehicle k allows the set of point of selection in next step, with the time in dynamic
State changes, BkFor the taboo list of kth vehicle, the point being had been subjected to for registration of vehicle k, and k=1,2 ..., m；To inspire
The formula factor, expected degree of the t by node i to node j is represented, is takenψ is information heuristic greedy method, and μ is scheduled to last
Hope heuristic factor；αij(t) it is the time degree of next destination；For the time degree relative importance of next destination, γig
(t) t node i is represented to the tracking element intensity between node g,For heuristic greedy method, represent t by node i to
Node g expected degree, αig(t) the time degree for being next destination g；
Optimization module：Introduce optimized variable Xij(t), it meets Xij(t+1)=σ X (t) [1-Xij(t)], wherein σ becomes for control
Amount, X (t) are the optimized variable of t, draw the tracking element renewal rule of optimization：
γij(t+1)=(1- ζ) γij(t)+Δγij(t)+чXij(t)
Wherein,When user selects to return out after last destination is left by signaling module
During hair ground, a circulation is formed, then
FkWalked by kth vehicle in this circulation
Electrical path length, I are the constant for tracking plain intensity,Expression kth vehicle in this circulation leaves on path (i, j)
Track plain intensity；ζ is that the global volatilization factor of tracking element, ζ ∈ [0,1], and ζ are the parameter according to equation below dynamic adjustment：Wherein ζminIt is the minimum value being manually set；Δγij(t) institute in this circulation is represented
There is the summation for tracking plain intensity that vehicle leaves on path (i, j)；ч is adjustable coefficient；
Initial module：Iterations DD=0 is made, carries out parameter initialization, adjusts each path trace element；Producing a scope is
The random number p of [0,1], if p ＜ give constant p0, next node j is selected according to the following formula： Wherein l ∈ Ak, the tracking element intensity between γ (i, l) expression initial time node is and node l,For heuristic greedy method, expected degree of the initial time by node i to node l is represented；α (i, l) be next destination l when
Between spend, μ for it is expected heuristic factor；Otherwise the new probability formula selection next node j in opportunity module, j is added and avoided
Table BkIn, it is repeated up to all node tasks and completes, obtains the initial set S of simulation algorithmi；
Solve module：Feasible solution S is obtained from one group current of initial set generation is newj, desired value variation delta S=Sj-SiIf Δ S
＜ 0, then receive new feasible solution SjFor optimal solution；Otherwise the influence of deviation is considered：R=exp (Δ S/N (t)), r ＞ 1, then connects
By SjFor optimal solution, new feasible solution is not otherwise received, optimal solution is still Si；
Judge module：When current optimal solution is less than some particular value, plain renewal is tracked；If epicycle taboo list BkMiddle nothing
Data update, then the random number u of [0, a 1] scope are produced, if e1+e2,…,ei-1＜ u ＜ e1+e2,…,ei, then select probability
For eiCandidate's inspection car as next destination node；
Generation module：For exporting the optimal path calculated, iterations DD=DD+1 is made, if DD ＜ DDmax, according to tracking
Element renewal rule, according to formula N (t+1)=N (t)；V carries out emptying taboo list Bk, wherein v ∈ [0,1], initial module is returned to,
Regenerate random number p；If DD=DDmax, then optimal solution is exported as optimal path, wherein DDmaxFor maximum iteration.
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