CN115496277B - Mobile power supply device scheduling method and system based on improved cat swarm algorithm - Google Patents
Mobile power supply device scheduling method and system based on improved cat swarm algorithm Download PDFInfo
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Abstract
The invention discloses a mobile power supplementing device scheduling method based on an improved cat swarm algorithm, which comprises the following steps of: acquiring information of all electric vehicles to be charged in a target area and position related information of all operable mobile power supply devices; obtaining a plurality of path optimization scheme information according to a given limiting condition; calculating the power-supplementing response time of the mobile power-supplementing device according to a preset rule; and carrying out ascending order sorting on the power-on response time, and displaying the preset number of path optimization scheme information according to the sorting result. The scheduling system comprises: the system comprises a data acquisition module, an algorithm optimization module and a display output module. According to the invention, the optimal power compensation path with the shortest response time is calculated according to the improved cat swarm algorithm, the defect that the vehicle is difficult to charge can be effectively solved, the continuous mileage is prolonged, the problem that the existing fixed charging pile falls to the ground is solved, and meanwhile, the path response time is optimized to be short so as to meet the emergency endurance requirement of the vehicle.
Description
Technical Field
The invention relates to a scheduling method and a system, in particular to a mobile power supplementing device scheduling method and a system based on an improved cat swarm algorithm, and belongs to the technical field of new energy.
Background
The mobile power supplementing device is equivalent to a small-sized charging station, and is used as a distributed energy storage power supply or an emergency power supply to charge the electric automobile in an emergency way at any time and any place. And in the emergency such as a pile-free area or a charging facility fault, the electric automobile is charged for 10 minutes by using the quick charging mode, and can be driven for about 40 km. However, when the new energy automobile sends out a charging demand, how the mobile power supplementing device quickly reaches a point to be charged; or when the new energy automobile sends out a charging demand, the system responds to the path planning giving the shortest time to enable the new energy automobile to be connected with the mobile electricity supplementing device for electricity supplementing. Therefore, the research on the optimization of the power supply path of the mobile power supplementing device is a key link of the development of the new energy automobile industry.
Disclosure of Invention
In order to solve the defects of the technology, the invention provides a mobile power-supplementing device scheduling method and system based on an improved cat swarm algorithm, and solves the problem of difficult charging of the existing electric automobile by providing a scheduling optimization path of the mobile power-supplementing device.
In order to solve the technical problems, the invention adopts the following technical scheme: a mobile power-supplementing device scheduling method based on an improved cat swarm algorithm comprises the following steps:
acquiring position information and cruising information of all electric vehicles to be charged in a target area and position related information of all operable mobile power supply devices;
obtaining a plurality of path optimization scheme information according to the given limiting conditions, the position information, the cruising information and the road condition information of all electric vehicles to be charged and the position related information of all operable mobile power supply devices;
calculating the power supply response time of the mobile power supply device of the multiple path optimization scheme information according to a preset power supply continuous mileage rule;
and carrying out ascending order sequencing on the power supply response time of the mobile power supply device of the plurality of path optimization scheme information, and displaying the preset number of path optimization scheme information according to the sequencing result.
Preferably, the information of the power-compensating response time of the mobile power-compensating device comprises the position information of the mobile power-compensating device, the position information when the electric vehicle to be charged sends the power-compensating information, and the time for the mobile power-compensating device to reach the position of the electric vehicle to be charged.
Preferably, the given constraints include: the mileage number which can be continued when the electric vehicle to be charged sends out the electricity compensation information can not reach the position of the fixed charging pile, or the nearby charging pile is free for charging, and the road condition of the traffic position where the electric vehicle to be charged is located accords with the traffic regulation of the mobile electricity compensation device.
Preferably, the mobile power-up device response time of the plurality of path optimization scheme information is sorted in ascending order, including: sequencing and displaying according to the response time of the compensation circuits of all the operable mobile compensation devices;
if the response time of at least two path optimization schemes is the same when the sorting display is carried out according to the power-supplementing response time of all the mobile power-supplementing devices, the ascending sorting is carried out on the at least two path optimization schemes according to the total distance;
if the at least two path optimization schemes are sequenced in ascending order according to the total path, the total paths of the at least two path optimization schemes are basically the same, and sequencing is carried out according to the number of the movable power supply devices which can work near the position after each movable power supply device reaches the position of the electric vehicle to be charged.
A system for a mobile power-up device scheduling method based on an improved cat swarm algorithm, the system comprising:
and a data acquisition module: the method comprises the steps of acquiring position information and cruising information of all electric vehicles to be charged in a target area, position information of a movable power supply device capable of working and position related information of idle fixed charging piles adjacent to the positions of the movable power supply device;
algorithm optimization module: the method comprises the steps of obtaining a plurality of path optimization scheme information according to given limiting conditions, position information and cruising information of all electric vehicles to be charged and position information of all operable mobile power supply devices;
the algorithm optimization module is also used for calculating response time of the path optimization scheme information according to the preset position information of the plurality of electric vehicles to be charged, and sequencing the response time of the path optimization scheme information in ascending order;
and a display output module: and the scheme information is used for displaying a preset path optimization scheme according to the sequencing result.
Preferably, the given limiting condition includes that the electric vehicle to be charged cannot be cruised to the position of the fixed charging pile, or the fixed charging pile has no idle charging pile, and the operable mobile power supplementing device at least can cruise the electric vehicle to be charged to the next fixed charging point or complete cruising requirements, and the position of the electric vehicle to be charged is convenient for the mobile power supplementing device to work and accords with the limiting condition of traffic regulations.
Preferably, the algorithm optimization module is used for sorting the time of the mobile power compensating device reaching the electric vehicle to be charged according to the ascending order of the time of the mobile power compensating device reaching the electric vehicle to be charged in the plurality of path optimization scheme information;
if the time of at least two path optimization schemes is the same when the ascending order is performed according to the time when the mobile power supplementing device reaches the electric vehicle to be charged, the ascending order is performed on the at least two path optimization schemes according to the running distance of the mobile power supplementing device or the total time of power supplementing and returning;
if the total distance and the total time length of at least two path optimization schemes are the same when the at least two path optimization schemes are sequenced in an ascending order according to the total time length, sequencing according to the number of the movable power supply devices which can work near the position after each movable power supply device reaches the position of the electric vehicle to be charged.
Preferably, the algorithm optimization module calculates an optimal power compensation path of the shortest response time according to the improved cat swarm algorithm; firstly, a mobile power-supplementing device is adopted for supplementing positions aiming at the network address points, and the constraint conditions are as follows:
a. the position where the electric vehicle needs to be charged is far away from the fixed charging pile during traveling;
b. the response time preset of the mobile power supplementing device is smaller than the time when the electric vehicle completely consumes power to zero;
c. the movable power supplementing device is provided with network points which meet the requirements of the electric vehicle on a charging station in a traveling mode;
d. the travel condition meets the traffic regulation limit.
Preferably, the cat swarm algorithm comprises the steps of:
step 1, initializing a cat group;
step 2, randomly grouping cat clusters according to MR, namely dividing the cat clusters into a searching mode and a tracking mode;
step 3, executing corresponding operators to update the positions of the cats, calculating the fitness of all the cats, selecting and recording, and finally reserving the cats with optimal fitness in the population;
step 4, immediately stopping the algorithm if the ending condition is met, otherwise, returning to the step 2;
assuming that the position and the speed of the ith cat, namely the electric vehicle to be charged, in the D-dimensional space are as follows:
x i ＝(x i,1 ,x i,2 ,x i,3 ,…,x i,D ),i＝1,2,3…,D
v i ＝(v i,1 ,v i,2 ,v i,3 ,…,v i,D ),i＝1,2,3…,D
cats with locally optimal solutions during the operation are expressed as:
x g,best ＝(x g,best,1 ,x g,best,2 ,x g,best,3 ,…x g,best,D )
first determining the speed of cat renewal in this mode, i.e
v i (n+1)＝v i (n)+c·rand[x g,best (n)-x i (n)]
Wherein: v i (n+1) is the speed value of the ith cat after the position update, c is a constant value, rand is [0,1 ]]Random values of (a);
the position of the cat is changed by the speed change, and then the position of the ith cat is updated as follows:
x i (n+1)＝x i (n)+v i (n+1)＝x i (n)+v i (n)+c·rand[x g,best (n)-x i (n)]。
preferably, the fuzzy theory is adopted to debug the parameter c of the cat swarm algorithm, and the improved fuzzy cat swarm algorithm update formula is as follows:
x i (n+1)＝x i (n)+v i (n+1)＝x i (n)+v i (n)+c x(n) ·rand[x g,best (n)-x i (n)]
c x(n) ＝a+x(n)(b-a)
x(n)＝ux(n-1)[1-x(n-1)]
wherein, position variable c of cat group x(n) U is a fuzzy control parameter, and a fuzzy range value is selected according to an empirical method;
adopting an improved cat swarm algorithm to carry out optimal scheduling solving on the electric vehicle demand model, wherein the improved cat swarm algorithm comprises the following steps:
step 1, randomly initializing the position of a cat group which accords with constraint conditions, namely the initial position of an electric vehicle to be charged, setting the group size as N and the grouping rate as MR in the interval [ a, b ], and dynamically updating the MR value in order to accelerate the calculation speed by considering the fixed MR value, wherein the calculation formula is as follows:
wherein DT is the current iteration number;
step 2, calculating a cat population fitness value by referring to the initial position of the cat population, selecting a proper position and recording the maximum fitness value in the population;
step 3, randomly grouping cat groups according to the new dynamic grouping rate MR;
step 4, searching mode: copying the cat sample, and storing the copied sample into a memory SMP;
step 5, tracking mode: the best position experienced by the whole cat group is the searched best solution;
step 6, calculating and recording fitness values, and finally reserving cats with optimal fitness in the population;
step 7, judging whether constraint conditions are met: if yes, outputting an optimal solution path and ending the program; if not, updating the parameters of the cat swarm algorithm by using a fuzzy control strategy, and repeating the steps 2 to 7 to perform optimizing iteration processing.
The invention provides a mobile power supplementing device scheduling method and system based on an improved cat swarm algorithm, wherein a scheduling system calculates an optimal power supplementing path with the shortest response time according to the improved cat swarm algorithm, so that the defect that an effective vehicle is difficult to charge can be effectively solved, the electric vehicle is charged for 10 minutes, the electric vehicle can be driven for about 40 km, the continuous mileage is prolonged, the problem that the existing fixed charging pile lands on the ground is solved, and meanwhile, the response time of the path is optimized to be short, and the emergency endurance requirement of the vehicle is met.
According to the dispatching system disclosed by the invention, electric automobile electric quantity information and travel distance information can be input into the system when electric automobile users go out, the system can predict the next charging time and give out information of the on-road charging piles so as to facilitate the users to charge in time, meanwhile, a model is constructed to predict the charging demands of a plurality of electric automobiles when the electric automobiles go out, a cat swarm algorithm is introduced to solve the model, and the path in the dispatching process of the mobile power supplementing device is improved and optimized on the basis of the traditional cat swarm, so that the dispatching time of the mobile power supplementing device is shortened, and the power supplementing response is rapid.
Drawings
Fig. 1 is a flow chart of the improved cat swarm algorithm of the present invention.
Fig. 2 is a path optimization route diagram a of the mobile power compensating device according to an embodiment of the invention.
Fig. 3 is a path optimization roadmap b of the mobile power supply device according to an embodiment of the invention.
Fig. 4 is a diagram of a scheduling system of a mobile power supply device according to an embodiment of the present invention.
Detailed Description
The invention will be described in further detail with reference to the drawings and the detailed description.
A mobile power-supplementing device scheduling method based on an improved cat swarm algorithm comprises the following steps:
acquiring position information and cruising information of all electric vehicles to be charged in a target area and position related information of all operable mobile power supply devices;
after a user sends out a charging demand, the system can directly give out an optimal electricity supplementing path guiding scheme, and according to charging demand information sent out by users at different places in the same time period, the system gives out an optimal scheduling scheme by combining information (idle available) of fixed charging piles in a target area and information (available power supply quantity and geographic position) of nearby available mobile electricity supplementing devices, wherein the targets are shortest paths and shortest connection time.
The position information and the utilization rate information of the charging stations in the target areas along the way and near the parking and the position information of all electric vehicles waiting for charging can be directly provided for the user;
obtaining a plurality of path optimization scheme information according to the given limiting conditions, namely the position information, the cruising information and the road condition information of all electric vehicles to be charged and the position related information (including the time of reaching the electric vehicles to be charged) of all operable mobile power supplementing devices;
wherein the given constraints include: the mileage number which can be continued when the electric vehicle to be charged sends out the electricity compensation information can not reach the position of the fixed charging pile, or the nearby charging pile is free for charging, and the road condition of the traffic position where the electric vehicle to be charged is located accords with the traffic regulation of the mobile electricity compensation device.
The given constraints may also be: the distance between the electric vehicle to be charged and the fixed charging pile is not more than the distance between the electric vehicle to be charged and the fixed charging pile, the distance between the position of the movable charging device and the electric vehicle to be charged is not more than the preset distance, and the electric vehicle to be charged can be found to stop to wait for charging on a road capable of being charged and driven or within a range allowed by continuous voyage.
Calculating the power-supplementing response time of the mobile power-supplementing device of the multiple path optimization scheme information according to a preset power-supplementing continuous mileage rule (the waiting time preset by the information input by a user);
and carrying out ascending order sequencing on the power supply response time of the mobile power supply device of the plurality of path optimization scheme information, and displaying the preset number of path optimization scheme information according to the sequencing result.
The mobile power-supplementing response time information of the mobile power-supplementing device comprises position information of the mobile power-supplementing device, position information when the electric vehicle to be charged sends out the power-supplementing information, and time period for the mobile power-supplementing device to reach the position of the electric vehicle to be charged.
The method for ascending order of response time of the mobile power-up device of the plurality of path optimization scheme information comprises the following steps: sequencing and displaying according to the response time of the compensation circuits of all the operable mobile compensation devices;
if the response time of at least two path optimization schemes is the same when the sorting display is carried out according to the power-supplementing response time of all the mobile power-supplementing devices, the ascending sorting is carried out on the at least two path optimization schemes according to the total distance;
if the at least two path optimization schemes are sequenced in ascending order according to the total path, the total paths of the at least two path optimization schemes are basically the same, and sequencing is carried out according to the number of the movable power supply devices which can work near the position after each movable power supply device reaches the position of the electric vehicle to be charged.
The ascending order of the time of the mobile power supplementing device of the path optimization scheme information reaching the electric vehicle to be charged (the shorter the waiting time is, the better) further comprises: descending order sorting is carried out according to the chargeable journey in the information of the multiple path optimization schemes; here, the cruising paths are ordered according to the cruising paths, and the cruising paths are the longest and optimal, so the descending order is adopted for ordering.
If the time for the mobile power supplementing device according to the information of the multiple path optimization schemes to reach the electric vehicle to be charged is the same, sequencing at least two path optimization schemes in descending order according to the cruising distance after charging;
and if the at least two path optimization schemes are in descending order according to the cruising distance after charging and the cruising total distance of the at least two path optimization schemes is the same, ordering according to the index of the returning duration of the mobile power supplementing device after power supplementing. The movable electricity supplementing device is used for supplementing electricity by returning to the centralized point after the electric vehicle to be charged is fully charged or after a charging task is completed.
As shown in fig. 4, a system for a mobile power-up device scheduling method based on an improved cat swarm algorithm, the system comprising:
and a data acquisition module: the method comprises the steps of acquiring position information and cruising information of all electric vehicles to be charged in a target area, position information of a movable power supply device capable of working and position related information of idle fixed charging piles adjacent to the positions of the movable power supply device;
the acquired data includes: after the charging electric vehicle sends out a charging request, the geographic position information of the charging electric vehicle, the information that the residual electric quantity of the vehicle can be used for cruising, and the like.
Algorithm optimization module: the method comprises the steps of obtaining a plurality of path optimization scheme information according to given limiting conditions, position information and cruising information of all electric vehicles to be charged and position information of all operable mobile power supply devices;
the algorithm optimization module is also used for calculating response time of the path optimization scheme information according to the preset position information of the plurality of electric vehicles to be charged, and sequencing the response time of the path optimization scheme information in ascending order;
and a display output module: and the scheme information is used for displaying a preset path optimization scheme according to the sequencing result.
The given limiting conditions include that the electric vehicle to be charged cannot be cruised to the position of the fixed charging pile, or the fixed charging pile has no idle charging pile, and the operable mobile power supplementing device at least can cruise to the next fixed charging point or finish cruising requirements for the electric vehicle to be charged, and the position of the electric vehicle to be charged is convenient for the mobile power supplementing device to work and accords with the limiting conditions of traffic regulations.
The response time comprises the time period for the operable mobile power supplementing device to reach the position of the electric vehicle to be charged.
The algorithm optimization module is used for sorting the time of the mobile power supplementing device reaching the electric vehicle to be charged according to the time ascending sequence in the multiple path optimization scheme information;
if the time of at least two path optimization schemes is the same when the ascending order is performed according to the time when the mobile power supplementing device reaches the electric vehicle to be charged, the ascending order is performed on the at least two path optimization schemes according to the running distance of the mobile power supplementing device or the total time of power supplementing and returning;
if the total distance and the total time length of at least two path optimization schemes are the same when the at least two path optimization schemes are sequenced in an ascending order according to the total time length, sequencing according to the number of the movable power supply devices which can work near the position after each movable power supply device reaches the position of the electric vehicle to be charged.
That is, if the vehicle is in the neutral position, the mobile power supply device can be dispatched from two places to dispatch a larger number of mobile power supply devices that can be operated by the dispatch place, that is, if there is only one mobile power supply device in another place, the next request can be waited for to be dispatched.
According to the system, data information required to be charged when the electric automobile travels and position information of the existing fixed charging piles are analyzed through an algorithm optimization module, and network points required to be provided with the mobile power supplementing device are obtained.
Electric automobile charging demand model construction:
travel time: the electric automobile once goes out moment t obeys normal distribution, and the probability density function is:
where μ is the expected value of the stroke start time and σ is the standard deviation.
Travel distance: each section of travel distance d of the user for the electric vehicle obeys the lognormal distribution, and the probability density line number is as follows:
middle sigma D Is the standard variance of travel distance, mu D Is the expected value of travel distance.
Residence time: after reaching the destination, the time t to reach the beginning of the next trip obeys a normal distribution:
driver behavior decision: the charge initiation charge state x obeys a normal distribution:
the probability density function of the electrically charged lowest charge state x is:
the charging demand model is analyzed when a user goes out, and combined with the fact that the existing fixed charging pile websites are marked (marked by triangles) in the map, as shown in fig. 2 and 3, the system can set each fixed charging pile as a virtual website of the mobile charging device at first, consider the additional site selection construction model of the mobile charging device (namely the mobile charging car) in terms of investment construction cost and user convenience, allocate the optimal mobile charging device additional setting point, set each fixed charging pile as the virtual website of the mobile charging device at first, facilitate the dispatching system to calculate the optimal response time value (if the electric vehicle to be charged is closer to the fixed charging pile, and the cruising can be completed, the system does not need to give an optimal path, and the running cost is saved).
Analyzing the error of the existing fixed charging pile website and the website calculated by the model, and supplementing the website by adopting a mobile power supplementing device aiming at the deficient website, wherein the constraint conditions are as follows:
a. the position where the electric vehicle needs to be charged is far away from the fixed charging pile during traveling;
b. the response time preset of the mobile power supplementing device is smaller than the time when the electric vehicle completely consumes power to zero;
c. the movable power supplementing device is provided with network points which meet the requirements of the electric vehicle on a charging station in a traveling mode;
d. the travel condition meets the traffic regulation limit.
The cat swarm algorithm comprises the following steps:
step 1, initializing a cat group;
step 2, randomly grouping cat clusters according to MR (mixture ratio), namely, dividing the cat clusters into a search mode and a tracking mode (MR generally takes a smaller value);
step 3, executing corresponding operators to update the positions of the cats, calculating the fitness of all the cats, selecting and recording, and finally reserving the cats with optimal fitness in the population;
step 4, immediately stopping the algorithm if the ending condition is met, otherwise, returning to the step 2;
assuming that the position and the speed of the ith cat, namely the electric vehicle to be charged, in the D-dimensional space are as follows:
x i ＝(x i,1 ,x i,2 ,x i,3 ,…,x i,D ),i＝1,2,3…,D
v i ＝(v i,1 ,v i,2 ,v i,3 ,…,v i,D ),i＝1,2,3…,D
cats with locally optimal solutions during the operation are expressed as:
x g,best ＝(x g,best,1 ,x g,best,2 ,x g,best,3 ,…x g,best,D )
wherein x is g, b est Representing the optimal position of the vehicle after algorithm optimization;
first determining the speed of cat renewal in this mode, i.e
v i (n+1)＝v i (n)+c·rand[x g,best (n)-x i (n)]
Wherein: v i (n+1) is the speed value of the ith cat after the position update, c is a constant value, rand is [0,1 ]]Random values of (a);
the position of the cat is changed by the speed change, and then the position of the ith cat is updated as follows:
x i (n+1)＝x i (n)+v i (n+1)＝x i (n)+v i (n)+c·rand[x g,best (n)-x i (n)]。
the cat swarm algorithm has the advantages of simple principle and few setting parameters, but has a plurality of limitations. The different modes performed by the cats in the cat swarm algorithm are randomly partitioned according to a packet rate, which is a constant value, and the number of cats in the 2 modes throughout the algorithm is constant. Some cats also have a tracking mode in the early stage of the algorithm, the cats lack global searching property, and the algorithm diversity is poor; part of cats are still in a search mode at the later stage of the algorithm, the convergence rate is reduced in the process of no-target search, and the overall optimal value tracking precision is not high.
The parameter c plays an important role in the optimizing process of the cat swarm algorithm, so that the global optimizing capability of the algorithm can be improved by changing the value of c in a certain range. The fuzzy theory is adopted to debug the parameter c of the cat swarm algorithm, and the improved fuzzy cat swarm algorithm update formula is as follows:
x i (n+1)＝x i (n)+v i (n+1)＝x i (n)+v i (n)+c x(n) ·rand[x g,best (n)-x i (n)]
c x(n) ＝a+x(n)(b-a)
x(n)＝ux(n-1)[1-x(n-1)]
wherein, position variable c of cat group x(n) U is a fuzzy control parameter, and a fuzzy range value is selected according to an empirical method;
as shown in fig. 1, an improved cat swarm algorithm is used for carrying out optimal scheduling solving on an electric vehicle demand model, and comprises the following steps:
step 1, randomly initializing the position of a cat group which accords with constraint conditions, namely the initial position of an electric vehicle to be charged, setting the group size as N and the grouping rate as MR (mixture ratio) in the interval [ a, b ], and dynamically updating the MR value in order to accelerate the calculation speed by considering the fixed MR value, wherein the calculation formula is as follows:
wherein DT is the current iteration number;
step 2, calculating a cat population fitness value by referring to the initial position of the cat population, selecting a proper position and recording the maximum fitness value in the population;
step 3, randomly grouping cat groups according to the new dynamic grouping rate MR;
step 4, searching mode: copying the cat sample, and storing the copied sample into a memory SMP;
step 5, tracking mode: the best position experienced by the whole cat group is the searched best solution;
step 6, calculating and recording fitness values, and finally reserving cats with optimal fitness in the population;
step 7, judging whether constraint conditions are met: if yes, outputting an optimal solution path and ending the program; if not, updating the parameters of the cat swarm algorithm by using a fuzzy control strategy, and repeating the steps 2 to 7 to perform optimizing iteration processing.
And obtaining the optimal electricity compensation path of the electric vehicle to be charged through the improved cat swarm algorithm. Taking a Chong Chun region and a development region of Nantong as an example, an electric vehicle to be charged is set to appear at a certain point, and a scheduling path plan given by the system is shown in fig. 2 and 3.
The above embodiments are not intended to limit the present invention, and the present invention is not limited to the above examples, but is also intended to be limited to the following claims.
Claims (8)
1. A mobile power supplementing device scheduling system based on an improved cat swarm algorithm is characterized in that: the system comprises:
and a data acquisition module: the method comprises the steps of acquiring position information and cruising information of all electric vehicles to be charged in a target area, position information of a movable power supply device capable of working and position related information of idle fixed charging piles adjacent to the positions of the movable power supply device;
algorithm optimization module: the method comprises the steps of obtaining a plurality of path optimization scheme information according to given limiting conditions, position information and cruising information of all electric vehicles to be charged and position information of all operable mobile power supply devices;
the algorithm optimization module is also used for calculating response time of the path optimization scheme information according to the preset position information of the plurality of electric vehicles to be charged, and sequencing the response time of the path optimization scheme information in ascending order;
and a display output module: the scheme information is used for displaying a preset path optimization scheme according to the sequencing result;
the algorithm optimization module calculates an optimal compensation path of the shortest response time according to the improved cat swarm algorithm; firstly, a mobile power-supplementing device is adopted for supplementing positions aiming at the network address points, and the constraint conditions are as follows:
a. the position where the electric vehicle needs to be charged is far away from the fixed charging pile during traveling;
b. the response time preset of the mobile power supplementing device is smaller than the time when the electric vehicle completely consumes power to zero;
c. the movable power supplementing device is provided with network points which meet the requirements of the electric vehicle on a charging station in a traveling mode;
d. the travel condition accords with the traffic regulation limit;
the fuzzy theory is adopted to debug the parameter c of the cat swarm algorithm, and the improved fuzzy cat swarm algorithm update formula is as follows:
x i (n+1)＝x i (n)+v i (n+1)＝x i (n)+v i (n)+c x(n) ·rand[x g,best (n)-x i (n)]
c x(n) ＝a+x(n)(b-a)
x(n)＝ux(n-1)[1-x(n-1)]
wherein, position variable c of cat group x(n) U is a fuzzy control parameter, and a fuzzy range value is selected according to an empirical method;
adopting an improved cat swarm algorithm to carry out optimal scheduling solving on the electric vehicle demand model, wherein the improved cat swarm algorithm comprises the following steps:
step 1, randomly initializing the position of a cat group which accords with constraint conditions, namely the initial position of an electric vehicle to be charged, setting the group size as N and the grouping rate as MR in the interval [ a, b ], and dynamically updating the MR value in order to accelerate the calculation speed by considering the fixed MR value, wherein the calculation formula is as follows:
wherein DT is the current iteration number;
step 2, calculating a cat population fitness value by referring to the initial position of the cat population, selecting a proper position and recording the maximum fitness value in the population;
step 3, randomly grouping cat groups according to the new dynamic grouping rate MR;
step 4, searching mode: copying the cat sample, and storing the copied sample into a memory SMP;
step 5, tracking mode: the best position experienced by the whole cat group is the searched best solution;
step 6, calculating and recording fitness values, and finally reserving cats with optimal fitness in the population;
step 7, judging whether constraint conditions are met: if yes, outputting an optimal solution path and ending the program; if not, updating the parameters of the cat swarm algorithm by using a fuzzy control strategy, and repeating the steps 2 to 7 to perform optimizing iteration processing.
2. The mobile power replenishment device scheduling system based on the improved cat swarm algorithm of claim 1, wherein: the given limiting conditions include that the electric vehicle to be charged cannot be cruised to the position of the fixed charging pile, or the fixed charging pile has no idle charging pile, and the operable mobile power supplementing device at least can cruise to the next fixed charging point or finish cruising requirements for the electric vehicle to be charged, and the position of the electric vehicle to be charged is convenient for the mobile power supplementing device to work and accords with the limiting conditions of traffic regulations.
3. The mobile power replenishment device scheduling system based on the improved cat swarm algorithm of claim 1, wherein: the algorithm optimization module is used for sorting the time of the mobile power supplementing device reaching the electric vehicle to be charged according to the time ascending sequence in the multiple path optimization scheme information;
if the time of at least two path optimization schemes is the same when the ascending order is performed according to the time when the mobile power supplementing device reaches the electric vehicle to be charged, the ascending order is performed on the at least two path optimization schemes according to the running distance of the mobile power supplementing device or the total time of power supplementing and returning;
if the total distance and the total time length of at least two path optimization schemes are the same when the at least two path optimization schemes are sequenced in an ascending order according to the total time length, sequencing according to the number of the movable power supply devices which can work near the position after each movable power supply device reaches the position of the electric vehicle to be charged.
4. The mobile power replenishment device scheduling system based on the improved cat swarm algorithm of claim 1, wherein: the cat swarm algorithm comprises the following steps:
step 1, initializing a cat group;
step 2, randomly grouping cat clusters according to MR, namely dividing the cat clusters into a searching mode and a tracking mode;
step 3, executing corresponding operators to update the positions of the cats, calculating the fitness of all the cats, selecting and recording, and finally reserving the cats with optimal fitness in the population;
step 4, immediately stopping the algorithm if the ending condition is met, otherwise, returning to the step 2;
assuming that the position and the speed of the ith cat, namely the electric vehicle to be charged, in the D-dimensional space are as follows:
x i ＝(x i,1 ,x i,2 ,x i,3 ,…,x i,D ),i＝1,2,3…,D
v i ＝(v i,1 ,v i,2 ,v i,3 ,…,v i,D ),i＝1,2,3…,D
cats with locally optimal solutions during the operation are expressed as:
x g,best ＝(x g,best,1 ,x g,best,2 ,x g,best,3 ,…x g,best,D )
first determining the speed of cat renewal in this mode, i.e
v i (n+1)＝v i (n)+c·rand[x g,best (n)-x i (n)]
Wherein: v i (n+1) is the speed value of the ith cat after the position update, c is a constant value, rand is [0,1 ]]Random values of (a);
the position of the cat is changed by the speed change, and then the position of the ith cat is updated as follows:
x i (n+1)＝x i (n)+v i (n+1)＝x i (n)+v i (n)+c·rand[x g,best (n)-x i (n)]。
5. a method of improving a cat swarm algorithm-based mobile power replenishment device scheduling system according to any of claims 1-4, wherein: the scheduling method comprises the following steps:
acquiring position information and cruising information of all electric vehicles to be charged in a target area and position related information of all operable mobile power supply devices;
obtaining a plurality of path optimization scheme information according to the given limiting conditions, the position information, the cruising information and the road condition information of all electric vehicles to be charged and the position related information of all operable mobile power supply devices;
calculating the power supply response time of the mobile power supply device of the multiple path optimization scheme information according to a preset power supply continuous mileage rule;
and carrying out ascending order sequencing on the power supply response time of the mobile power supply device of the plurality of path optimization scheme information, and displaying the preset number of path optimization scheme information according to the sequencing result.
6. The method for improving a cat swarm algorithm-based mobile power replenishment device scheduling system according to claim 5, wherein: the mobile power-supplementing response time information of the mobile power-supplementing device comprises position information of the mobile power-supplementing device, position information when the electric vehicle to be charged sends out the power-supplementing information, and time period for the mobile power-supplementing device to reach the position of the electric vehicle to be charged.
7. The method for improving a cat swarm algorithm-based mobile power replenishment device scheduling system according to claim 6, wherein: the given constraints include: the mileage number which can be continued when the electric vehicle to be charged sends out the electricity compensation information can not reach the position of the fixed charging pile, or the nearby charging pile is free for charging, and the road condition of the traffic position where the electric vehicle to be charged is located accords with the traffic regulation of the mobile electricity compensation device.
8. The method for improving a cat swarm algorithm-based mobile power replenishment device scheduling system according to claim 7, wherein: the method for ascending order of response time of the mobile power-up device of the plurality of path optimization scheme information comprises the following steps: sequencing and displaying according to the response time of the compensation circuits of all the operable mobile compensation devices;
if the response time of at least two path optimization schemes is the same when the sorting display is carried out according to the power-supplementing response time of all the mobile power-supplementing devices, the ascending sorting is carried out on the at least two path optimization schemes according to the total distance;
if the at least two path optimization schemes are sequenced in ascending order according to the total path, the total paths of the at least two path optimization schemes are basically the same, and sequencing is carried out according to the number of the movable power supply devices which can work near the position after each movable power supply device reaches the position of the electric vehicle to be charged.
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