CN115018206A - New energy vehicle battery pack charging decision method and device - Google Patents

New energy vehicle battery pack charging decision method and device Download PDF

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CN115018206A
CN115018206A CN202210855158.1A CN202210855158A CN115018206A CN 115018206 A CN115018206 A CN 115018206A CN 202210855158 A CN202210855158 A CN 202210855158A CN 115018206 A CN115018206 A CN 115018206A
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吴嘉俐
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Abstract

The invention relates to a new energy vehicle battery pack charging decision method and a new energy vehicle battery pack charging decision device, which comprise the following steps: s1, acquiring the battery capacity, the current position and the target position of the vehicle to be replaced; s2, obtaining the current positions of all available battery replacement stations, and obtaining the extra time corresponding to the available battery replacement stations based on the current positions of the vehicles to be replaced; s3, acquiring reserved time length of the vehicle to be charged based on the available charging station, establishing a first objective function containing a combination relation of the available charging station and the vehicle to be charged, and acquiring a target combination relation according to the first objective function; s4, acquiring a target power exchanging station and a target vehicle according to the target combination relation; s5, monitoring the real-time working state of the target power swapping station, and establishing an evaluation function corresponding to a preset evaluation index of the target power swapping station based on the initial state of the target power swapping station and the real-time working state of the target power swapping station; and S6, establishing a second objective function based on the evaluation function, and triggering and updating the real-time working state of the target power swapping station according to the second objective function.

Description

New energy vehicle battery pack charging decision method and device
Technical Field
The invention relates to the technical field of new energy, in particular to a new energy vehicle battery pack decision method and device.
Background
The increase in the number of new energy vehicles not only reduces the use of fossil fuels, but also reduces greenhouse gas emissions by at least 1/3. However, new energy vehicles currently have some inherent disadvantages, such as short driving range, long charging time, and short battery life, compared to fuel-powered vehicles. During long distance driving, new energy vehicles often need to be charged at a Charging Station (CS) for a long time, which results in a decrease in comfort for the driver and an increase in range anxiety. Some research and investigation show that people are very interested in the practical problem of how to schedule the optimal charging station for the battery replacement station of the new energy automobile by considering the charging service waiting time of the new energy automobile in the driving process of the new energy automobile. In order to avoid the overload problem of the charging station caused by centralized battery replacement of the new energy automobile, the proposal is to provide a quick charging reservation service for the new energy automobile and prevent the charging delay condition which is experienced for a long time in the charging station. Although the intelligent decision model improves the service efficiency of the charging station, the battery needs at least 30 minutes to be fully charged even if the quick charge is used due to the limitation of the charging mode. In this case, a new energy vehicle charging station (BSS) model is a feasible option to overcome the disadvantages of the charging technology.
Under the promotion of the rapid increase of the number of new energy automobiles and the increasingly sophisticated technology, a new energy automobile power exchange station model is widely researched. However, current power station research only focuses on isolated decision models, such as scheduling models and charging scheduling models. An isolated decision model cannot effectively represent the actual situation, and cannot provide an optimal solution for new energy automobile drivers and power station changing operators.
Disclosure of Invention
The invention aims to provide a new energy vehicle battery pack charging decision method and device.
The technical scheme adopted by the invention for solving the technical problems is as follows: a new energy vehicle battery pack charging decision method is constructed, and the method comprises the following steps:
s1, acquiring all the vehicles to be charged based on the received charging request, and acquiring the battery capacity, the current position and the target position of the vehicles to be charged;
s2, obtaining the current positions of all available battery replacement stations, and obtaining the extra time corresponding to the available battery replacement stations of the battery replacement vehicles based on the current positions of the battery replacement vehicles, the target positions of the battery replacement vehicles and the current positions of the available battery replacement stations;
s3, obtaining a reserved time length of the vehicle to be charged based on the available charging station, and establishing a first objective function containing a combination relation between the available charging station and the vehicle to be charged based on the reserved time length and the extra time length of the vehicle to be charged so as to obtain a target combination relation according to the first objective function;
s4, acquiring a vehicle to be charged corresponding to a target charging station as a target vehicle according to the target combination relation, and acquiring a plurality of charging start times of the target vehicle corresponding to the target charging station and initial state information of the target charging station before the first charging start time;
s5, monitoring the real-time working state of the target power changing station by taking preset interval time as a time unit from the first charging starting time, and establishing an evaluation function corresponding to a preset evaluation index of the target power changing station based on the initial state of the target power changing station and the real-time working state of the target power changing station;
s6, establishing a second objective function based on the evaluation function, and triggering and updating the real-time working state of the target power changing station according to the second objective function.
Preferably, in the new energy vehicle battery pack charging decision method according to the present invention, the first objective function includes the following functions:
Figure 461987DEST_PATH_IMAGE001
，
and the first objective function is constrained according to the following constraints:
Figure 785652DEST_PATH_IMAGE002
Figure 96547DEST_PATH_IMAGE003
and in the first objective function,
Figure 898281DEST_PATH_IMAGE004
for characterizing the combination of all the vehicles to be replaced and all the available replacing stations,
Figure 509391DEST_PATH_IMAGE005
the representation corresponds the vehicle to be switched of i to the available switching station of j,
Figure 738378DEST_PATH_IMAGE006
the number of all the electric vehicles to be replaced;
Figure 739832DEST_PATH_IMAGE007
the method comprises the following steps that i, the extra time length of a vehicle to be charged is the difference between the time length of the vehicle to be charged when the vehicle reaches a target position of the vehicle to be charged through the current position of an available charging station and the time length of the vehicle to be charged when the vehicle directly reaches the target position of the vehicle to be charged;
Figure 469891DEST_PATH_IMAGE008
reserving time length for the i-standby battery replacing vehicle, wherein the reserved time length is the sum of the time length of queuing waiting time, the time length of waiting for full charge of the battery and the time length of battery replacing operation time;
in the constraint, the
Figure 40900DEST_PATH_IMAGE009
And the residual electric quantity is the residual electric quantity when the i-to-be-switched vehicle reaches the j-available switching station.
Preferably, in the method for deciding on charging of a battery pack of a new energy vehicle according to the present invention, in step S3, the obtaining a target combination relationship according to the first objective function includes:
s31, initializing parameters of an ATS self-adaptive tabu search algorithm, setting an initial accumulated count to be zero, acquiring a preset combination relation based on a strategy of a nearest range rule, and setting a candidate combination relation set as an empty set;
s32, judging whether the accumulated count is smaller than the total number of the vehicles of the electric vehicle to be replaced, if so, executing a step S33, otherwise, executing a step S35;
S33acquiring the total waiting time of the electric vehicle to be replaced based on a preset combination relationship, acquiring the accumulated probability of the electric vehicle to be replaced based on the total waiting time of the electric vehicle to be replaced, and acquiring the electric vehicle to be replaced as a vehicle to be distributed through roulette based on the accumulated probability, wherein the total waiting time of the electric vehicle to be replaced is
Figure 565422DEST_PATH_IMAGE010
；
S34, acquiring a first available power swapping station with the least corresponding vehicles to be swapped and a second available power swapping station with the most available resources, randomly acquiring a relationship between one of the first available power swapping station and the second available power swapping station and the vehicle to be allocated to obtain a current candidate combination relationship, and executing the step S36;
s35, randomly carrying out two-point exchange on the preset combination relation to obtain a current candidate combination relation;
s36, adding the current candidate combination relationship to the candidate combination relationship set, and judging whether the number of elements in the candidate combination relationship set is larger than a second preset value, if so, executing a step S37, otherwise, taking the current candidate combination relationship as the preset combination relationship and executing a step S32;
s37, obtaining the current optimal candidate combination relation in the candidate combination relation set according to the first objective function, adding a cumulative count, judging whether the current optimal candidate combination relation is superior to the historical optimal candidate combination relation, if so, executing the step S381, otherwise, executing the step S382;
s381, adding the current optimal candidate combination relationship to a taboo list, wherein when the length of the taboo list is larger than a preset threshold value, the taboo list is updated in a queue mode;
s382, judging whether the accumulated count is larger than a first preset value or not, if so, executing a step S392, otherwise, executing a step S391;
s391, taking the current optimal candidate combination relation as a preset combination relation and executing the step S32;
and S392, acquiring the optimal combination relation in the tabu list as a target combination relation.
Preferably, in the new energy vehicle battery pack charging decision method, in step S5, the preset evaluation index of the target battery replacement station includes: the total load variance of the target power swapping station, the power cost of the target power swapping station, and the charging damage of the target power swapping station;
the merit function includes: a first evaluation function corresponding to the total load variance of the target power swapping station,
the charging management system comprises a second evaluation function corresponding to the power cost of the target power swapping station and a third evaluation function corresponding to the charging damage of the target power swapping station.
Preferably, in the new energy vehicle battery pack charging decision method according to the present invention, the second objective function includes the following functions:
Figure 991855DEST_PATH_IMAGE011
，
the above-mentioned
Figure 463288DEST_PATH_IMAGE012
For the purpose of said first evaluation function,
Figure 783411DEST_PATH_IMAGE013
for the purpose of said second evaluation function,
Figure 947676DEST_PATH_IMAGE014
for the purpose of said third evaluation function,
Figure 861405DEST_PATH_IMAGE015
and the available power station set is obtained.
Preferably, in the method for deciding charging of a battery pack of a new energy vehicle according to the present invention, the first evaluation function includes the following functions:
Figure 667687DEST_PATH_IMAGE016
Figure 45579DEST_PATH_IMAGE017
，
wherein,
Figure 52849DEST_PATH_IMAGE018
charging power of a battery to be charged corresponding to the vehicle to be charged is j available power change station within the n time period,
Figure 516192DEST_PATH_IMAGE019
and T is the total charging time period for the vehicle to be switched corresponding to the j available switching station.
Preferably, in the method for deciding on charging of a battery pack of a new energy vehicle according to the present invention, the second evaluation function includes the following functions:
Figure 391744DEST_PATH_IMAGE020
wherein,
Figure 624142DEST_PATH_IMAGE018
charging power of a battery to be charged corresponding to the vehicle to be charged is j available power change station within the n time period,
Figure 566428DEST_PATH_IMAGE021
t is the total charging time interval for the vehicle to be switched corresponding to the j available switching station,
Figure 251487DEST_PATH_IMAGE022
the time interval is preset, and the time interval is preset,
Figure 930730DEST_PATH_IMAGE023
is the time of use electricity price of the time period n.
Preferably, in the method for deciding on charging of a battery pack of a new energy vehicle according to the present invention, the third evaluation function includes the following functions:
Figure 17635DEST_PATH_IMAGE024
，
wherein,
Figure 632287DEST_PATH_IMAGE025
charging power of a battery to be charged corresponding to the vehicle to be charged is j available power conversion stations within the n time period,
Figure 601380DEST_PATH_IMAGE026
t is the total charging time interval for the vehicle to be switched corresponding to the j available switching station,
Figure 756418DEST_PATH_IMAGE027
i is the number of charging time units corresponding to the battery to be charged corresponding to the vehicle to be charged,
Figure 901092DEST_PATH_IMAGE028
and a, b and c are constants, wherein the capacity of the battery to be charged corresponding to the vehicle to be charged is i.
Preferably, in the new energy vehicle battery pack charging decision method, the second objective function is solved based on any one of NSGA-II, NSGA-III, PVEA and MOPSO.
The invention also constructs a new energy vehicle battery pack charging decision device, which comprises:
the first obtaining unit is used for obtaining all the vehicles to be subjected to battery replacement based on the received battery replacement request, and obtaining the battery electric quantity, the current position and the target position of the vehicles to be subjected to battery replacement;
a second obtaining unit, configured to obtain current positions of all available battery swapping stations, and obtain an extra duration of the vehicle to be battery swapped, which corresponds to the available battery swapping station, based on the current position of the vehicle to be battery swapped, a target position of the vehicle to be battery swapped, and the current position of the available battery swapping station;
a third obtaining unit, configured to obtain a reserved time length of the to-be-battery-replaced vehicle based on the available battery replacement station, and establish a first objective function including a combination relationship between the available battery replacement station and the to-be-battery-replaced vehicle based on the reserved time length and the extra time length of the to-be-battery-replaced vehicle, so as to obtain a target combination relationship according to the first objective function;
the first execution unit is used for acquiring a vehicle to be charged corresponding to a target charging station as a target vehicle according to the target combination relation, and acquiring a plurality of charging start times of the target vehicle corresponding to the target charging station and initial state information of the target charging station before a first charging start time;
a second execution unit, configured to monitor a real-time working state of the target power swapping station by using a preset interval time as a time unit from the first charging start time, and establish an evaluation function corresponding to a preset evaluation index of the target power swapping station based on an initial state of the target power swapping station and the real-time working state of the target power swapping station;
and the third execution unit is used for establishing a second target function based on the evaluation function and triggering and updating the real-time working state of the target power changing station according to the second target function.
The new energy vehicle battery pack charging decision method and the device have the following beneficial effects that: the optimal charging decision can be reasonably and quickly obtained, and the battery pack can be charged.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a flowchart illustrating a procedure of a new energy vehicle battery pack charging decision method according to an embodiment of the present invention;
FIG. 2 is a flowchart of the procedure of an embodiment of the ATS adaptive tabu search algorithm of the present invention;
FIG. 3 is a diagram illustrating an embodiment of an adaptive mutation operator according to the present invention;
FIG. 4 is a comparison box plot of the results of an embodiment of the adaptive mutation algorithm of the present invention;
FIG. 5 is a comparison box plot of results from another embodiment of the adaptive mutation algorithm of the present invention;
FIG. 6 is a diagram illustrating the charge loading calculation result according to an embodiment of the present invention based on a second objective function;
FIG. 7 is a diagram illustrating the corresponding power cost calculations of FIG. 6;
FIG. 8 is a diagram illustrating the results of a battery damage calculation corresponding to FIG. 6;
FIG. 9 is a diagram illustrating the charge loading calculation result according to another embodiment of the present invention based on the second objective function;
FIG. 10 is a graph illustrating the results of the power cost calculations corresponding to FIG. 9;
FIG. 11 is a diagram illustrating the results of a battery damage calculation corresponding to FIG. 9;
FIG. 12 is a diagram illustrating the charge loading calculation result according to another embodiment of the present invention based on the second objective function;
FIG. 13 is a graph illustrating the results of the power cost calculations corresponding to FIG. 12;
FIG. 14 is a graph illustrating the results of a battery damage calculation corresponding to FIG. 12;
fig. 15 is a logic block diagram of a new energy vehicle battery pack charging decision device according to an embodiment of the present invention.
Detailed Description
For a more clear understanding of the technical features, objects, and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
As shown in fig. 1, in a first embodiment of a new energy vehicle battery pack charging decision method of the present invention, the method includes the following steps: s1, all the vehicles to be replaced are obtained based on the received battery replacement request, and the battery electric quantity, the current position and the target position of the vehicles to be replaced are obtained; s2, obtaining the current positions of all available battery replacement stations, and obtaining the extra time corresponding to the available battery replacement stations of the vehicle to be replaced based on the current positions of the vehicle to be replaced, the target position of the vehicle to be replaced and the current positions of the available battery replacement stations; s3, acquiring a reserved time length of the vehicle to be subjected to power change based on the available power change station, establishing a first objective function containing a combination relation of the available power change station and the vehicle to be subjected to power change based on the reserved time length and the extra time length of the vehicle to be subjected to power change, and acquiring a target combination relation according to the first objective function; specifically, a plurality of new energy vehicles with low electric quantity are randomly distributed around a plurality of power conversion stations by taking a certain time as an initial time, and each new energy vehicle has different initial positions and destinations. Then, the driver of the new energy automobile sends the battery replacement request and vehicle information (such as the residual capacity, the current position and the destination) to the dispatching center. After receiving the battery replacement requirements and information of a plurality of vehicles, the dispatching center calculates the extra time of the new energy automobile by calculating the journey time spent when the vehicles reach the destination through the battery replacement station, the queuing time, the waiting time for full charge of the battery and the battery replacement operation time. And finally, the dispatching center sends the information to the new energy automobile drivers and suggests the new energy automobile drivers to go to a battery replacement station which can save extra time to perform battery replacement service. Based on the current power station swapping situation, the dispatching center obtains the most suitable power station for each vehicle to be swapped based on the first objective function, so that all the vehicles to be swapped can obtain the suitable power station.
S4, acquiring a to-be-charged vehicle corresponding to a target charging station as a target vehicle according to the target combination relation, and acquiring a plurality of charging start times of the target vehicle corresponding to the target charging station and initial state information of the target charging station before the first charging start time; s5, monitoring the real-time working state of the target power changing station by taking the preset interval time as a time unit from the first charging start time, and establishing an evaluation function corresponding to a preset evaluation index of the target power changing station based on the initial state of the target power changing station and the real-time working state of the target power changing station; and S6, establishing a second objective function based on the evaluation function, and triggering and updating the real-time working state of the target power swapping station according to the second objective function. After the optimal power swapping station matching relation obtained based on all current vehicles to be swapped is obtained, independent charging process evaluation is carried out on each power swapping station, a second objective function of the charging station working process is established based on real-time working state parameters and the evaluation function of the power swapping stations, and the working state of the target power swapping station is adjusted in real time based on the second objective function, so that the working state of the target power swapping station is the optimal working state.
Optionally, the first objective function includes the following functions:
Figure 748962DEST_PATH_IMAGE029
，
and the first objective function is constrained according to the following constraints:
Figure 470930DEST_PATH_IMAGE030
Figure 429659DEST_PATH_IMAGE031
and in the first objective function,
Figure 163260DEST_PATH_IMAGE032
for characterizing the combination of all the vehicles to be replaced and all the available replacing stations,
Figure 182031DEST_PATH_IMAGE033
the characterization corresponds the i-standby power change vehicle to the j-available power change station,
Figure 391296DEST_PATH_IMAGE034
the number of all the electric vehicles to be replaced;
Figure 153715DEST_PATH_IMAGE035
the additional time length is i of the vehicle to be switched, wherein the additional time length is the difference between the time length of the vehicle to be switched from the current position of the available switching station to the target position of the vehicle to be switched and the time length of the vehicle to be switched from the current position of the available switching station to the target position of the vehicle to be switched;
Figure 508867DEST_PATH_IMAGE036
reserving time length for the i-standby battery replacing vehicle, wherein the reserved time length is the sum of the queuing waiting time, the waiting battery full-charging time and the battery replacing operation time;
in the constraint condition, the number of the optical fiber,
Figure 432960DEST_PATH_IMAGE037
for i waiting to change vehiclesAnd the residual capacity when the available power station arrives at j.
The specific process is that for a new energy automobile user, extra waiting time is mainly considered, wherein the extra waiting time comprises time spent when the new energy automobile user arrives at a destination through a battery replacement station and waiting time (queuing time, time spent on waiting for full charge of a battery and battery replacement operation time). Wherein, the objective function formula (1) represents that the model needs to minimize the fitness function of the solution, namely, the average extra waiting time of the new energy automobile is minimized.
Figure 863942DEST_PATH_IMAGE038
The calculation can be made based on the distance and the speed, which can be obtained by using the following formula:
Figure 102156DEST_PATH_IMAGE039
the method is used for calculating the extra travel time of the new energy automobile to the destination through the battery replacement station.
Figure 138245DEST_PATH_IMAGE040
The average running speed of all new energy automobiles,
Figure 295557DEST_PATH_IMAGE041
for the initial position of the new energy automobile,
Figure 417097DEST_PATH_IMAGE042
the position of the dispatched power change station of the i new energy automobile,
Figure 459002DEST_PATH_IMAGE043
the destination position of the new energy automobile.
Figure 84019DEST_PATH_IMAGE044
The Euclidean distance from the initial position to the scheduled battery replacement position of the new energy automobile is represented by i,
Figure 677811DEST_PATH_IMAGE045
is new energy of iThe Euclidean distance from the position of the dispatched power exchange station to the position of the destination of the source automobile,
Figure 21068DEST_PATH_IMAGE046
euclidean distance from initial position to destination position for i new energy automobile
Wherein, the position of the i new energy automobile is calculated
Figure 866664DEST_PATH_IMAGE047
And j power station position
Figure 408504DEST_PATH_IMAGE048
The euclidean distance between them is as follows,
Figure 110880DEST_PATH_IMAGE049
，
wherein,
Figure 377652DEST_PATH_IMAGE050
is used for representing the position of an i point, the position of a j point (i corresponds to an automobile, and j corresponds to a power exchanging station),
Figure 823676DEST_PATH_IMAGE051
the abscissa used to characterize point i, the ordinate of point i,
Figure 485602DEST_PATH_IMAGE052
and is used for representing the abscissa of the j point and the ordinate of the j point.
Figure 358880DEST_PATH_IMAGE053
The method is used for indicating that the i new energy automobile must ensure that enough electric quantity reaches the scheduled j power change station, otherwise, the power change station scheduling scheme of the new energy automobile is regarded as an infeasible solution. Wherein, the following formula is used for calculation:
Figure 614412DEST_PATH_IMAGE054
in the formula, the first and second images are shown,
Figure 598548DEST_PATH_IMAGE055
representing the power consumption per kilometre of the vehicle,
Figure 380560DEST_PATH_IMAGE056
characterization i New energy automobile
Figure 424739DEST_PATH_IMAGE057
The amount of remaining power at the moment of time,
Figure 167567DEST_PATH_IMAGE058
and (5) representing the battery capacity of the new energy automobile.
Figure 955395DEST_PATH_IMAGE036
For reserving time, it can be obtained by the following formula:
Figure 591912DEST_PATH_IMAGE059
the method includes the steps that the reserved time required by the new energy automobile in the dispatching battery replacement station can be divided into three time sums according to corresponding actions, including queuing waiting time and battery full charging waiting time
Figure 806993DEST_PATH_IMAGE060
And battery replacement operation time
Figure 505959DEST_PATH_IMAGE061
. The time can be obtained according to historical experience values or power station working state parameters of the dispatching center. The additional reserved time can be reserved based on different application scenes or additional operation, and the waiting time of the new energy automobile in the battery replacement process must be larger than 0 according to the corresponding reservation condition.
Optionally, as shown in fig. 2, in step S3, the obtaining the target combination relationship according to the first objective function includes:
s31, initializing parameters of an ATS self-adaptive tabu search algorithm, setting an initial accumulated count to be zero, acquiring a preset combination relation based on a strategy of a nearest range rule, and setting a candidate combination relation set as an empty set;
s32, judging whether the accumulated count is smaller than the total number of the vehicles of the electric vehicle to be replaced, if so, executing a step S33, otherwise, executing a step S35;
s33, acquiring the total waiting time of the vehicle to be switched based on the preset combination relationship, acquiring the accumulated probability of the vehicle to be switched based on the total waiting time of the vehicle to be switched, and acquiring the vehicle to be switched as a vehicle to be distributed through roulette based on the accumulated probability, wherein the total waiting time of the vehicle to be switched is
Figure 159794DEST_PATH_IMAGE062
；
S34, acquiring a first available power exchanging station with the least corresponding vehicles to be switched and a second available power exchanging station with the most available resources, randomly acquiring a relationship between one of the first available power exchanging station and the second available power exchanging station and the vehicle to be allocated to obtain a current candidate combination relationship, and executing the step S36;
s35, randomly carrying out two-point exchange in the preset combination relation to obtain the current candidate combination relation;
s36, adding the current candidate combination relation to the candidate combination relation set, and judging whether the number of elements in the candidate combination relation set is larger than a second preset value, if so, executing a step S37, otherwise, taking the current candidate combination relation as a preset combination relation and executing a step S32;
s37, obtaining the current optimal candidate combination relation in the candidate combination relation set according to the first objective function, adding a cumulative count, judging whether the current optimal candidate combination relation is superior to the historical optimal candidate combination relation, if so, executing the step S381, otherwise, executing the step S382;
s381, adding the current optimal candidate combination relation to a taboo list, wherein when the length of the taboo list is larger than a preset threshold value, the taboo list is updated in a queue mode;
s382, determining whether the accumulated count is greater than a first predetermined value, if yes, executing step S392, otherwise, executing step S391,
s391, taking the current optimal candidate combination relation as a preset combination relation and executing the step S32;
and S392, acquiring the optimal combination relation in the tabu list as a target combination relation.
Specifically, the optimal allocation result of the corresponding battery replacement station is obtained based on the current vehicle to be charged, the allocation result which can be selected can be obtained through calculation by a self-adaptive tabu search algorithm, and the optimal allocation result is obtained based on the allocation result and the first objective function. The self-adaptive tabu algorithm combines a tabu algorithm and a self-adaptive selection operator and is used for solving the scheduling problem of the new energy automobile. The self-adaptive selection operator comprises selection of a self-adaptive new energy automobile and selection of a self-adaptive power exchanging station.
For each new energy vehicle driver, the longer extra waiting time means that more extra time will be spent in the scheduled power change station. Meanwhile, for all new energy automobiles, the longer extra waiting time represents unbalanced dispatching distribution, so that the waiting time of the vehicles is increased, and the number of currently available batteries in each battery replacement station is reduced. Therefore, the self-adaptive mutation operator is proposed in the application to reduce the extra waiting time of the new energy automobile and relieve the service pressure of the power conversion station.
As shown in fig. 3, the electric vehicle to be replaced is selected adaptively, and the basic part of the selection process is to select genes for crossover and mutation from the original solution. However, this method suffers from local search without explicit direction, and has a slow convergence rate, and cannot efficiently generate high-quality candidate solutions. Therefore, in the present application, a new energy automobile method is selected based on the adaptation of roulette. The method comprises the following specific steps:
a1, inputting the current solution
Figure 588501DEST_PATH_IMAGE063
And corresponding additional latency
Figure 407772DEST_PATH_IMAGE064
Wherein
Figure 187509DEST_PATH_IMAGE065
The additional waiting time spent when the new energy automobile reaches the destination through the dispatching battery replacement station is shown.
Figure 113877DEST_PATH_IMAGE066
Wherein,
Figure 334773DEST_PATH_IMAGE067
，
Figure 157236DEST_PATH_IMAGE068
is the sum of these three parts of time.
A2, normalization of i additional waiting time of new energy automobile
Figure 221007DEST_PATH_IMAGE069
。
Figure 154328DEST_PATH_IMAGE070
A3, calculating the cumulative probability C of the i new energy automobile i 。
Figure 229731DEST_PATH_IMAGE071
A4, rotating the wheel disc, and selecting the i new energy automobile pointed by the pointer as the variable position in the current decision scheme. Wherein the selection requirement is that new energy vehicles with less additional waiting time will have a greater probability of survival.
And then adaptively selecting the power changing stations, wherein the selection of the power changing stations needs to consider the service pressure among all the power changing stations, including the number of the upcoming new energy vehicles and the available time of batteries in the stations. A battery replacement station with the least number of vehicles and most of the time available for the battery will be selected. The main steps are as follows: based on the above-mentioned selection of the electric vehicle to be replaced,
b1, selecting the power change station which is to reach the least in the power change station set
Figure 223095DEST_PATH_IMAGE072
And power station that trades of battery usable time longest
Figure 774162DEST_PATH_IMAGE073
。
B2, random selection
Figure 245595DEST_PATH_IMAGE074
Or
Figure 441084DEST_PATH_IMAGE073
As a variant gene in current decision-making solutions.
After the self-adaptive selection, the current solution is subjected to mutation operation
Figure 605349DEST_PATH_IMAGE075
。
In one embodiment, given 10 new energy automobiles, two battery swapping stations are provided, and each battery swapping station is provided with three batteries. The strategy (NIR) solution based on the nearest range rule is
Figure 378133DEST_PATH_IMAGE076
And the corresponding additional waiting time of each new energy automobile is
Figure 653256DEST_PATH_IMAGE077
. Then, the extra latency is normalized
Figure 201787DEST_PATH_IMAGE078
And calculates the selection probability. It is worth noting that since
Figure 333691DEST_PATH_IMAGE079
Is 0, and thus will not select
Figure 797034DEST_PATH_IMAGE079
. Then, assume that the selection is made when the wheel is stopped
Figure 547952DEST_PATH_IMAGE080
While selecting the least number of oncoming vehicles or the longest battery life
Figure 780350DEST_PATH_IMAGE081
. Therefore, the new energy automobile is distributed to the battery replacement station
Figure 83156DEST_PATH_IMAGE081
And generating a child scheduling plan
Figure 33794DEST_PATH_IMAGE082
。
An adaptive full adaptive tabu search Algorithm (ATS) may be described with reference to the following steps.
C1. Initializing parameters of the ATS algorithm, e.g. initializing cumulative counts, setting maximum number of iterations
Figure 588403DEST_PATH_IMAGE083
Size of candidate set
Figure 675308DEST_PATH_IMAGE084
And length L of the tabu table.
C2. Computing current candidate combinatorial relationships according to NIR methods (nearest-range rule based strategies)
Figure 414594DEST_PATH_IMAGE085
。
C3. And setting the candidate combination relation set as an empty set.
C4. Judging the cumulative count
Figure 586949DEST_PATH_IMAGE086
If the sum of the current combination relation is less than the total number of the vehicles | E |, generating a new candidate combination relation by adopting a self-adaptive mutation operator if the sum of the current combination relation is less than the total number of the vehicles | E |, and otherwise, randomly generating the new candidate combination relation at the current combination relationTwo points are randomly selected to exchange to generate a new candidate combination relation.
C5. And adding the new candidate combination relation into the candidate combination relation set.
C6. Judging whether the number of the elements in the candidate combination relation set is greater than a second preset value
Figure 679670DEST_PATH_IMAGE084
If so, then perform C7, otherwise, perform C4.
C7. Calculating the current optimal candidate combination relation in the candidate combination relation set according to the solved objective function
Figure 886661DEST_PATH_IMAGE087
。
C8. Judging the current optimal candidate combination relation
Figure 531268DEST_PATH_IMAGE088
And history optimal candidate combination relation
Figure 190920DEST_PATH_IMAGE089
If the current best candidate combination relation
Figure 588797DEST_PATH_IMAGE088
Optimal candidate combinatorial relation superior to history
Figure 650294DEST_PATH_IMAGE090
Then the historical optimal candidate combination relation is obtained
Figure 731382DEST_PATH_IMAGE090
In relation to current candidate combinations
Figure 878330DEST_PATH_IMAGE091
Updating to the current optimal candidate combination relation
Figure 312853DEST_PATH_IMAGE088
. Otherwise, the set of candidate combinatorial relationships is obtained, but notThe best combination relation of the tabu list is the current candidate combination relation.
C9. Combining the current candidates
Figure 228857DEST_PATH_IMAGE092
Adding into a tabu list if the length of the tabu list is larger than a preset threshold
Figure 480846DEST_PATH_IMAGE093
The tabu list is updated in a queue fashion.
C10. Cumulative count
Figure 115090DEST_PATH_IMAGE092
And adding 1.
C11. Judging whether the cumulative count gen is greater than a first preset value
Figure 353304DEST_PATH_IMAGE083
If so, outputting the historical optimal solution as the decision solution, otherwise, executing C2.
Referring to fig. 4, in a specific embodiment, 30 new energy vehicles and 5 battery replacement stations are provided, and each battery replacement station is provided with 3 full-charge batteries. The power exchanging stations are distributed at different geographical positions, the electric quantity of each new energy automobile at the initial moment is low, the electric quantity is randomly distributed within a period of time after the new energy automobile is started, and the destinations are different. An adaptive tabu search Algorithm (ATS) is presented versus other algorithms such as: genetic Algorithm (GA), simulated annealing algorithm (SA) modified simulated annealing algorithm (ISA), and tabu search algorithm (TS). And (3) a box plot of the target value (average extra time) solved in the decision 1 model (the power station switching scheduling model of the new energy automobile). It can be seen from the figure that the use of the adaptive tabu search algorithm is superior to other evolutionary algorithms. Fig. 5 shows a case in which: 50 new energy automobiles and 5 battery replacement stations, wherein each battery replacement station is provided with 5 full-electricity batteries, and the self-adaptive tabu search algorithm is also superior to other evolutionary algorithms based on the table 1.
TABLE 1 comparison of Properties
Figure 123814DEST_PATH_IMAGE094
The a/b/c shows that the case comprises a new energy automobile and b battery replacement stations, and each battery replacement station is provided with c full batteries. Run time (sec), based on table 1, it can be concluded that in case 10/2/3, the adaptive tabu search algorithm ATS can get the best decision solution, and the algorithm run time is the shortest. In case 20/5/3, the ATS can get the best solution, and the resulting extra equal time has the smallest mean, the smallest variance, and the shortest algorithm run time. In case 50/5/5, the ATS results in the best decision solution performance, i.e., the extra waiting time of the new energy vehicle is only 13.55 minutes, less than NIR, GA, SA, ISA and TS. Although SA, ISA and TS have certain comparability from the numerical result, the ATS operation time is shortest. In conclusion, compared with other evolutionary algorithms, the performance of the adaptive tabu search algorithm ATS in the power station changing scheduling model of the new energy automobile is optimal.
Optionally, in step S5, the preset evaluation index of the target power swapping station includes: the method comprises the following steps of (1) total load variance of a target power swapping station, electric power cost of the target power swapping station and charging damage of the target power swapping station; the evaluation function includes: the charging management system comprises a first evaluation function corresponding to the total load variance of the target power swapping station, a second evaluation function corresponding to the power cost of the target power swapping station, and a third evaluation function corresponding to the charging damage of the target power swapping station. In particular, for e-commerce operators, they are primarily concerned with charge load fluctuations over time. A chaotic charging schedule can damage the power system and cause a blockage of power resources. For example, during battery charging, ultra-high loads can cause harmonic pollution, reducing power quality. Therefore, it is necessary to reduce the total charge variance. Coordinating the battery charging schedule will reduce the deviation between the instantaneous load and the average load, thereby maintaining the stability of the grid. Therefore, the total load variance is used as a preset evaluation index of the target power station. For the substation operator, power costs are one of the most significant concerns in revenue costs. In power systems, TOU time of use electricity prices are used to motivate the charging station operator to try to charge at power load valley and avoid power load peak hours. First, the control center can help the power change station minimize the cost of purchasing power from the power grid. Therefore, in the work of the power station, the power cost is also used as a preset evaluation index of the work of the target power station. Furthermore, a number of studies have shown that the charging impairment of lithium ion batteries is related to the charging power. The damage to the battery using the high charging power is higher than that using the low charging power. Therefore, the working state of the target power conversion station needs to be reasonably set so that the charging damage index of the target power conversion station is optimal. Therefore, the charging damage is simultaneously used as an evaluation index of the target power replacement station.
And respectively establishing evaluation functions corresponding to the target power conversion station based on the total load variance, the power cost and the charging damage. Namely, a first evaluation function corresponding to the total load variance of the target power swapping station, a second evaluation function corresponding to the power cost of the target power swapping station, and a third evaluation function corresponding to the charging damage of the target power swapping station.
Optionally, the second objective function includes the following functions:
Figure 546705DEST_PATH_IMAGE095
，
Figure 340349DEST_PATH_IMAGE096
in order to be a first evaluation function,
Figure 178992DEST_PATH_IMAGE097
in order to be a function of the second evaluation function,
Figure 131905DEST_PATH_IMAGE098
in order to be a third evaluation function,
Figure 663380DEST_PATH_IMAGE099
is an available power station changing set. Specifically, based on multi-objective optimization, the optimization formula satisfies the following functions:
Figure 442855DEST_PATH_IMAGE100
wherein,
Figure 350768DEST_PATH_IMAGE101
and d is the length of the decision vector,
Figure 158187DEST_PATH_IMAGE102
is the decision space.
Figure 860564DEST_PATH_IMAGE103
Representative decision solutions
Figure 628800DEST_PATH_IMAGE104
An adaptation value at each target.
Figure 809245DEST_PATH_IMAGE105
Representing the number of optimization objectives. It is worth noting that there is a conflict before the objective function of the decision scheme.
The pareto rule of the following formula is therefore defined to compare the merits of two solutions:
Figure 736750DEST_PATH_IMAGE106
in the above formula, if according to
Figure 610028DEST_PATH_IMAGE107
All target adaptive values obtained by decision solution are not different from
Figure 865560DEST_PATH_IMAGE108
The decision solution, and at least on some target,
Figure 849697DEST_PATH_IMAGE107
the result of the decision solution is superior to
Figure 631708DEST_PATH_IMAGE108
The decision solution is called
Figure 675887DEST_PATH_IMAGE107
Decision solution governance
Figure 418715DEST_PATH_IMAGE108
And (6) decision making. Further, all non-dominant solutions are stored herein using a pareto solution set (PS), and fitness values corresponding to the non-dominant solutions in the pareto solution set are stored using a Pareto Frontier (PF). Thus, a second objective function can be derived based on the above formula,
Figure 206543DEST_PATH_IMAGE109
in other words, based on the corresponding multi-objective optimization function, a charging scheduling model of the power conversion station is set to be optimized based on three optimization objectives of total load variance, electric power cost and battery damage.
The charging power represents the energy charged into the depleted batteries based on the charging process of the charging station, the decision scheme of which is to distribute the charging power to each depleted battery during all available time slots.
Obtaining a power change station scheduling result of the new energy automobile based on the first objective function as follows:
Figure 843060DEST_PATH_IMAGE110
wherein when
Figure 58141DEST_PATH_IMAGE111
When the minimum value is taken, the minimum value is obtained,
Figure 518292DEST_PATH_IMAGE112
is a value of (a), wherein
Figure 109810DEST_PATH_IMAGE112
And (4) carrying out decision solution vector scheduling on the new energy automobile battery replacement station (a decision solution of the first objective function, corresponding to the objective combination relation). The exhausted battery is charged according to the average charging power of each battery replacement station, so that i rechargeable battery can be determined in the application
Figure 335255DEST_PATH_IMAGE113
Starting charging time of
Figure 658920DEST_PATH_IMAGE114
And ending the charging time
Figure 438657DEST_PATH_IMAGE115
. For a message from
Figure 630604DEST_PATH_IMAGE116
Rechargeable battery of new energy automobile
Figure 913818DEST_PATH_IMAGE113
In the battery replacement station
Figure 673963DEST_PATH_IMAGE117
The charging schedule of (c) is defined as follows:
Figure 675417DEST_PATH_IMAGE118
wherein,
Figure 405476DEST_PATH_IMAGE119
in the 1 st decision, namely the scheduling decision of the new energy automobile power changing station, the average charging power of the i battery in the j power changing station at the time t.
Figure 808776DEST_PATH_IMAGE120
In the 1 st decision, namely the scheduling decision of the new energy automobile power changing station, at the time t, the average charging power of the i battery in the j power changing station.
Figure 739823DEST_PATH_IMAGE121
And in the 1 st decision, namely the scheduling decision of the new energy automobile power changing station, charging schedule of the i battery in the j power changing station.
Figure 962993DEST_PATH_IMAGE122
: in the first decision, the initial moment
Figure 762322DEST_PATH_IMAGE123
The battery is correspondingly replaced in the ith decision solution
Figure 20128DEST_PATH_IMAGE124
Average charging power of (c).
Figure 355032DEST_PATH_IMAGE125
: in the first decision, the end time
Figure 331079DEST_PATH_IMAGE126
The battery is correspondingly replaced in the ith decision solution
Figure 668519DEST_PATH_IMAGE127
Average charging power of.
Figure 780832DEST_PATH_IMAGE128
: in decision 1, the ith decision is resolved.
Figure 53681DEST_PATH_IMAGE129
: total number of new energy vehicles.
At the first decision solution
Figure 251444DEST_PATH_IMAGE130
In, schedule to
Figure 126996DEST_PATH_IMAGE131
The new energy automobile group of the power change station is defined as follows:
Figure 297078DEST_PATH_IMAGE132
notably, the i rechargeable battery is from the j new energy automobile. And setting the response time of the new energy automobile as a preset interval time based on the second objective function. Therefore, the time gap for starting charging of the rechargeable battery
Figure 537566DEST_PATH_IMAGE133
And ending the charging time gap
Figure 550521DEST_PATH_IMAGE134
Is defined as follows:
Figure 167448DEST_PATH_IMAGE135
Figure 192035DEST_PATH_IMAGE136
Figure 603425DEST_PATH_IMAGE137
Figure 838097DEST_PATH_IMAGE138
in the above equation, the charge starting time of the rechargeable battery is set to i
Figure 258714DEST_PATH_IMAGE139
And ending the charging time
Figure 639274DEST_PATH_IMAGE115
Transition to a Start of Charge time gap
Figure 487144DEST_PATH_IMAGE140
And ending the charging time gap
Figure 209112DEST_PATH_IMAGE141
. Defining the earliest charging time of all the exhausted batteries as the initial time of the second decision (charging schedule decision of the charging station, corresponding to the second objective function), and converting the earliest charging time into the initial time gap
Figure 167841DEST_PATH_IMAGE142
。
Figure 167021DEST_PATH_IMAGE143
Is a preset interval time.
It is noted that in the slot conversion calculation, slight variations in the energy required for each depleted battery will be ignored in this application. Therefore, the entire time range of all the swapping stations is defined as follows:
Figure 185793DEST_PATH_IMAGE144
Figure 129478DEST_PATH_IMAGE145
wherein the maximum time range in the above formula
Figure 891897DEST_PATH_IMAGE146
Depending on the last end-of-charge time of all the depleted batteries. This expression represents the value set of T, i.e. the value range of T.
After the conversion, the charging scheduling of the i-type power conversion station and the final decision obtained based on the second objective function are defined as
Figure 745584DEST_PATH_IMAGE147
Figure 935257DEST_PATH_IMAGE148
Figure 631817DEST_PATH_IMAGE149
Figure 604453DEST_PATH_IMAGE150
Figure 374963DEST_PATH_IMAGE151
Wherein,
Figure 797854DEST_PATH_IMAGE152
for the charging schedule in the j switching station,
Figure 653814DEST_PATH_IMAGE153
in the second decision (new energy vehicle charging station scheduling), the j charging station charges the i battery in the time period n,
Figure 194255DEST_PATH_IMAGE154
at the second decision, j swapping stations are in time period
Figure 84850DEST_PATH_IMAGE140
Of the power to charge the i battery,
Figure 678643DEST_PATH_IMAGE155
in order to make the second decision, the j power conversion station is in the time period
Figure 21899DEST_PATH_IMAGE156
I battery charging power.
Figure 601916DEST_PATH_IMAGE157
The method is characterized in that the new energy vehicles are assigned to the j battery replacement stations.
Figure 347018DEST_PATH_IMAGE158
For power station set (subscript j)
Figure 111712DEST_PATH_IMAGE159
: a set of time periods (subscript n).
In order to track the charging power of each battery, a new variable is defined in the present application
Figure 942265DEST_PATH_IMAGE160
To predict whether i rechargeable batteries are charging during n time slots. If the i rechargeable battery is charged in the n time interval of the j charging station
Figure 325973DEST_PATH_IMAGE161
And if not, the step (B),
Figure 191161DEST_PATH_IMAGE162
. Meanwhile, charging power of the j power conversion station
Figure 126756DEST_PATH_IMAGE025
The maximum charging power of the charging station must be less than or equal to j in the n time interval
Figure 179025DEST_PATH_IMAGE163
. Which is defined based on the above equation, the energy requirement of the rechargeable battery is in the first decision approximately equal to the energy in the second decision. It is worth noting that slight inequality is due to the transition of the time range from the first decision to the second decision. And i, the electric quantity of the rechargeable battery is higher than an electric quantity threshold value after the charging process is finished, and the electric quantity threshold value is set to ensure the normal operation of the new energy automobile.
Figure 100845DEST_PATH_IMAGE164
The threshold of the fully charged battery.
Figure 882856DEST_PATH_IMAGE037
And the residual electric quantity of the i new energy automobile when the i new energy automobile reaches the j power change station.
In an embodiment, the first merit function includes the following functions:
Figure 927035DEST_PATH_IMAGE165
Figure 923327DEST_PATH_IMAGE166
，
wherein,
Figure 711155DEST_PATH_IMAGE153
charging power of a battery to be charged corresponding to the vehicle to be charged is j available power change station within the n time period,
Figure 347672DEST_PATH_IMAGE167
and T is the total charging time period for the vehicle to be switched corresponding to the j available switching station. It will be appreciated that spending the total period in the preset time interval may result in several periods.
Optionally, the second evaluation function includes the following functions:
Figure 562753DEST_PATH_IMAGE168
wherein,
Figure 792877DEST_PATH_IMAGE153
the charging power of the battery to be charged corresponding to the vehicle to be charged in the n time period i by the charging station is available for j,
Figure 118816DEST_PATH_IMAGE169
t is the total charging time interval for the vehicle to be switched corresponding to the j available switching station,
Figure 875420DEST_PATH_IMAGE170
the time interval is preset, and the time interval is preset,
Figure 261402DEST_PATH_IMAGE171
the time-of-use electricity price of the period n is understood to be a set of time-of-use electricity prices in the total charging period. In one embodiment of the present invention, the substrate is,
Figure 713243DEST_PATH_IMAGE172
is defined as 5 minutes.
Optionally, the third evaluation function includes the following functions:
Figure 842873DEST_PATH_IMAGE173
，
wherein,
Figure 453983DEST_PATH_IMAGE153
the available power change station for j corresponds to the vehicle to be changed in the time period n, iThe charging power of the battery to be charged,
Figure 682970DEST_PATH_IMAGE167
t is the total charging time interval for the vehicle to be switched corresponding to the j available switching station,
Figure 684424DEST_PATH_IMAGE027
i is the number of charging time units corresponding to the battery to be charged corresponding to the vehicle to be charged,
Figure 680062DEST_PATH_IMAGE174
and a, b and c are constants, wherein the capacity of the battery to be charged corresponding to the vehicle to be charged is i.
Wherein, considering different charging powers and battery capacity degradation degrees, the battery capacity degradation rate model is defined as follows:
Figure 83361DEST_PATH_IMAGE175
wherein DS is a battery capacity fading speed,
Figure 247364DEST_PATH_IMAGE176
a state of degradation of the battery capacity.
Figure 736114DEST_PATH_IMAGE177
Is the charge rate of the battery during time period n.
The relationship between charge rate and charge power is derived as follows:
Figure 535443DEST_PATH_IMAGE178
and finally obtaining a third evaluation function corresponding to the total charging damage of the battery. Wherein,
Figure 793249DEST_PATH_IMAGE027
is the number of time slots occupied by the rechargeable battery. Which satisfies the following conditions:
Figure 629618DEST_PATH_IMAGE179
in one embodiment, the first and second electrodes are, in one embodiment,
Figure 605664DEST_PATH_IMAGE180
。
optionally, the method further comprises solving the second objective function based on any one of non-dominated sorting genetic algorithm 2 (NSGA-II), non-dominated sorting genetic algorithm 3 (NSGA-III), multi-objective optimized evolution algorithm (PVEA) based on preference vector guidance, and multi-objective particle swarm algorithm (MOPSO). Specifically, the multi-objective function may be solved based on a plurality of different multi-objective algorithms. The charging scheduling problem of the power change station is defined as a multi-objective optimization problem (MOP), and a plurality of multi-objective evolutionary algorithms (MOEA) are applied to solve the multi-objective optimization problem. For example, NSGA-II, NSGA-III, RVEA and MOPSO. Since the charging schedule is a high dimensional solution in the second decision, the result is that three charging schedules are obtained using the multi-objective example subgroup algorithm in case 20/5/3 as shown in fig. 6-14, wherein fig. 6-8 are a scheduled charge load, power cost and battery damage, fig. 9-11 are a scheduled charge load, power cost and battery damage, and fig. 12-14 are a scheduled charge load, power cost and battery damage. Based on table 2, it is shown by the results that the use of the multi-objective particle swarm optimization (MOPSO) method is the best algorithm to solve the problem.
TABLE 2 Multi-objective particle swarm optimization (MOPSO) and other competitors' performance in C-Metric index
Figure 943105DEST_PATH_IMAGE181
The comparison of the MOPSO multi-target particle swarm optimization algorithm and the other three multi-target evolutionary algorithms on the C-metric index is shown in the table 2. As can be seen from the table, the value of C (MOPSO, -) in all the swapping station examples is greater than that of C (-, MOPSO), so that the multi-target particle swarm optimization (MOPSO) algorithm is superior to the other three algorithms in the C-metric index. Wherein, C is a C-Metric index, and C (S1, S2) calculates the proportion of the solutions in the solution set S2 that are at least weakly dominated by one solution in the solution set S1, and measures the coincidence degree between the two solution sets.
The system structure of the hybrid decision model with the power station changing dispatching and charging scheduling of the new energy automobile is finally realized through the scheme. First, a plurality of new energy vehicles and a plurality of power conversion stations are dispersed in a certain geographical area. For example, suppose all new Energy Vehicles (EVs) select the nearest charging station for energy replenishment. In the first decision, at 8: 23 to 8: 34, the EV5 goes to the nearest battery replacement station BSS0 from the initial position. Thereafter, EV5 performs a battery replacement operation at BSS0, which takes only five minutes, and finally at 8: 39 to 8: 52, the EV5 leaves the battery swap station BSS0 to the destination. The EV5 takes 29 minutes in the process described above, 21.8 minutes more often than going directly to the destination from the origin, so the extra wait time for EV5 is 21.8 minutes. Similarly, at 8: 26 to 8: 55, the EV9 goes to the nearest battery replacement station BSS1, however, other new energy vehicles arrive at the battery replacement station BSS1 relatively early, so the EV9 must wait for 23 minutes in the BSS1, the battery replacement cannot be performed by the EV9 until the previous new energy vehicle leaves the battery replacement cabin or the battery in the battery replacement station is fully charged, and after the battery replacement operation takes 5 minutes, the EV9 is at 9: 23 leaves the swapping station, and at 9: 38 to the destination. The EV9 takes 58.8 minutes more for the entire trip than directly from the origin to the destination, i.e., the EV9 has an additional wait time of 58.8 minutes. In the second decision (corresponding to the second objective function), after obtaining the scheduling result of the first decision (corresponding to the first objective function), each charging station obtains the time-of-use electricity price (TOU) and information of each depleted battery in the station (such as the current battery capacity (SOC), the rated capacity, the initial charging time and the end charging time) from the power grid system. The optimization objectives of a multi-objective optimization problem (MOP) optimizer are total load variance variation, power cost and battery charging damage, and by defining an optimal time window, a multi-objective optimization scheme is executed to obtain a decision scheme for battery charging schedule. Finally, the power plant operators can select a charging schedule scheme from the multi-objective optimizer and transmit corresponding charging power to the charging pile during the charging process of the exhausted battery.
In addition, as shown in fig. 15, the new energy vehicle battery pack charging decision device of the present invention includes:
a first obtaining unit 110, configured to obtain all the vehicles to be replaced based on the received power replacement request, and obtain a battery capacity, a current position, and a target position of the vehicle to be replaced;
a second obtaining unit 120, configured to obtain current positions of all available battery swapping stations, and obtain an extra duration corresponding to an available battery swapping station of a vehicle to be swapped based on a current position of the vehicle to be swapped, a target position of the vehicle to be swapped, and the current position of the available battery swapping station;
a third obtaining unit 130, configured to obtain a reserved time length of the vehicle to be charged based on the available charging station, and establish a first objective function including a combination relationship between the available charging station and the vehicle to be charged based on the reserved time length and the extra time length of the vehicle to be charged, so as to obtain a target combination relationship according to the first objective function;
the first execution unit 140 is configured to obtain, according to the target combination relationship, a to-be-charged vehicle corresponding to a target charging station as a target vehicle, and obtain a plurality of charging start times of the target vehicle corresponding to the target charging station and initial state information of the target charging station before a first charging start time;
the second execution unit 150 is configured to monitor a real-time working state of the target power swapping station by using a preset interval time as a time unit from the first charging start time, and establish an evaluation function corresponding to a preset evaluation index of the target power swapping station based on the initial state of the target power swapping station and the real-time working state of the target power swapping station;
and the third execution unit 160 is configured to establish a second objective function based on the evaluation function, and trigger to update the real-time working state of the target power swapping station according to the second objective function.
Specifically, the specific coordination operation process among the units of the new energy vehicle battery pack charging decision device may specifically refer to the new energy vehicle battery pack charging decision method, and is not described herein again.
It is to be understood that the foregoing examples, while indicating the preferred embodiments of the invention, are given by way of illustration and description, and are not to be construed as limiting the scope of the invention; it should be noted that, for those skilled in the art, the above technical features can be freely combined, and several changes and modifications can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention; therefore, all equivalent changes and modifications made within the scope of the claims of the present invention should be covered by the claims of the present invention.

Claims (10)

1. A new energy vehicle battery pack charging decision method is characterized by comprising the following steps:
s1, acquiring all the vehicles to be charged based on the received charging request, and acquiring the battery capacity, the current position and the target position of the vehicles to be charged;
s2, obtaining the current positions of all available battery replacement stations, and obtaining the extra time corresponding to the available battery replacement stations of the battery replacement vehicles based on the current positions of the battery replacement vehicles, the target positions of the battery replacement vehicles and the current positions of the available battery replacement stations;
s3, obtaining a reserved time length of the vehicle to be charged based on the available charging station, and establishing a first objective function containing a combination relation between the available charging station and the vehicle to be charged based on the reserved time length and the extra time length of the vehicle to be charged so as to obtain a target combination relation according to the first objective function;
s4, acquiring a vehicle to be charged corresponding to a target charging station as a target vehicle according to the target combination relation, and acquiring a plurality of charging start times of the target vehicle corresponding to the target charging station and initial state information of the target charging station before the first charging start time;
s5, monitoring the real-time working state of the target power changing station by taking preset interval time as a time unit from the first charging starting time, and establishing an evaluation function corresponding to a preset evaluation index of the target power changing station based on the initial state of the target power changing station and the real-time working state of the target power changing station;
s6, establishing a second objective function based on the evaluation function, and triggering and updating the real-time working state of the target power changing station according to the second objective function.
2. The new energy vehicle battery pack charging decision method according to claim 1, wherein the first objective function comprises the following functions:
Figure 24856DEST_PATH_IMAGE001
，
and constraining the first objective function according to the following constraints:
Figure 684507DEST_PATH_IMAGE002
Figure 705553DEST_PATH_IMAGE003
and in the first objective function,
Figure 501471DEST_PATH_IMAGE004
for characterizing the combination of all the vehicles to be replaced and all the available replacing stations,
Figure 457925DEST_PATH_IMAGE005
the characterization corresponds the i-standby power change vehicle to the j-available power change station,
Figure 604873DEST_PATH_IMAGE006
the number of all the electric vehicles to be replaced;
Figure 429610DEST_PATH_IMAGE007
the method comprises the following steps that i, the extra time length of a vehicle to be charged is the difference between the time length of the vehicle to be charged when the vehicle reaches a target position of the vehicle to be charged through the current position of an available charging station and the time length of the vehicle to be charged when the vehicle directly reaches the target position of the vehicle to be charged;
Figure 345613DEST_PATH_IMAGE008
reserving time length for the i-standby battery replacing vehicle, wherein the reserved time length is the sum of the time length of queuing waiting time, the time length of waiting for full charge of the battery and the time length of battery replacing operation time;
in the constraint, the
Figure 207390DEST_PATH_IMAGE009
And the residual electric quantity is the residual electric quantity when the i-to-be-switched vehicle reaches the j-available switching station.
3. The new energy vehicle battery pack charging decision method according to claim 2, wherein in the step S3, the obtaining the target combination relation according to the first target function includes:
s31, initializing parameters of an ATS self-adaptive tabu search algorithm, setting an initial accumulated count to be zero, acquiring a preset combination relation based on a strategy of a nearest range rule, and setting a candidate combination relation set as an empty set;
s32, judging whether the accumulated count is smaller than the total number of the vehicles of the electric vehicle to be replaced, if so, executing a step S33, otherwise, executing a step S35;
s33, acquiring the total waiting time of the vehicle to be swapped based on a preset combination relationship, acquiring the accumulated probability of the vehicle to be swapped based on the total waiting time of the vehicle to be swapped, and acquiring the vehicle to be swapped as a vehicle to be distributed through roulette based on the accumulated probability, wherein the total waiting time of the vehicle to be swapped is
Figure 841633DEST_PATH_IMAGE010
；
S34, acquiring a first available power swapping station with the least corresponding vehicles to be swapped and a second available power swapping station with the most available resources, randomly acquiring a relationship between one of the first available power swapping station and the second available power swapping station and the vehicle to be allocated to obtain a current candidate combination relationship, and executing the step S36;
s35, randomly carrying out two-point exchange on the preset combination relation to obtain a current candidate combination relation;
s36, adding the current candidate combination relationship to the candidate combination relationship set, and judging whether the number of elements in the candidate combination relationship set is larger than a second preset value, if so, executing a step S37, otherwise, taking the current candidate combination relationship as the preset combination relationship and executing a step S32;
s37, obtaining the current optimal candidate combination relation in the candidate combination relation set according to the first objective function, adding a cumulative count, judging whether the current optimal candidate combination relation is superior to the historical optimal candidate combination relation, if so, executing the step S381, otherwise, executing the step S382;
s381, adding the current optimal candidate combination relationship to a taboo list, wherein when the length of the taboo list is larger than a preset threshold value, the taboo list is updated in a queue mode;
s382, judging whether the accumulated count is larger than a first preset value, if so, executing the step S392, otherwise, executing the step S391,
s391, taking the current optimal candidate combination relation as a preset combination relation and executing the step S32;
and S392, acquiring the optimal combination relation in the tabu list as a target combination relation.
4. The new energy vehicle battery pack charging decision method as claimed in claim 1, wherein in the step S5, the preset evaluation index of the target power swapping station comprises: the total load variance of the target power swapping station, the power cost of the target power swapping station and the charging damage of the target power swapping station;
the merit function includes: a first evaluation function corresponding to the total load variance of the target power swapping station,
the charging management system comprises a second evaluation function corresponding to the power cost of the target power swapping station and a third evaluation function corresponding to the charging damage of the target power swapping station.
5. The new energy vehicle battery pack charging decision method according to claim 4, wherein the second objective function comprises the following functions:
Figure 204482DEST_PATH_IMAGE011
，
the above-mentioned
Figure 506150DEST_PATH_IMAGE012
For the purpose of said first evaluation function,
Figure 601145DEST_PATH_IMAGE013
for the purpose of said second evaluation function,
Figure 158903DEST_PATH_IMAGE014
for the purpose of said third evaluation function,
Figure 263125DEST_PATH_IMAGE015
and the available power station set is obtained.
6. The new energy vehicle battery pack charging decision method according to claim 5,
the first merit function includes the following functions:
Figure 950458DEST_PATH_IMAGE016
Figure 481934DEST_PATH_IMAGE017
，
wherein,
Figure 762874DEST_PATH_IMAGE018
charging power of a battery to be charged corresponding to the vehicle to be charged is j available power change station within the n time period,
Figure 670787DEST_PATH_IMAGE019
and T is the total charging time period for the vehicle to be switched corresponding to the j available switching station.
7. The new energy vehicle battery pack charging decision method according to claim 5,
the second merit function includes the following functions:
Figure 478206DEST_PATH_IMAGE020
wherein,
Figure 180583DEST_PATH_IMAGE018
charging power of a battery to be charged corresponding to the vehicle to be charged in the period of n for the available power conversion station,
Figure 948819DEST_PATH_IMAGE019
t is the total charging time interval for the vehicle to be switched corresponding to the j available switching station,
Figure 394843DEST_PATH_IMAGE021
the time interval is preset, and the time interval is preset,
Figure 56769DEST_PATH_IMAGE022
is the time of use electricity price of the time period n.
8. The new energy vehicle battery pack charging decision method according to claim 5,
the third merit function includes the following functions:
Figure 930047DEST_PATH_IMAGE023
，
wherein,
Figure 185579DEST_PATH_IMAGE024
charging power of a battery to be charged corresponding to the vehicle to be charged is j available power change station within the n time period,
Figure 169715DEST_PATH_IMAGE025
t is the total charging time interval for the vehicle to be switched corresponding to the j available switching station,
Figure 154989DEST_PATH_IMAGE026
i is the number of charging time units corresponding to the battery to be charged corresponding to the vehicle to be charged,
Figure 261485DEST_PATH_IMAGE027
and a, b and c are constants, wherein the capacity of the battery to be charged corresponding to the vehicle to be charged is i.
9. The new energy vehicle battery pack charging decision method according to claim 5, further comprising solving the second objective function based on any one of NSGA-II, NSGA-III, RVEA and MOPSO.
10. The utility model provides a new energy automobile battery package decision-making device that charges which characterized in that includes:
the first obtaining unit is used for obtaining all the electric vehicles to be replaced based on the received battery replacement request and obtaining the battery electric quantity, the current position and the target position of the electric vehicles to be replaced;
a second obtaining unit, configured to obtain current positions of all available battery swapping stations, and obtain an extra duration of the vehicle to be battery swapped, which corresponds to the available battery swapping station, based on the current position of the vehicle to be battery swapped, a target position of the vehicle to be battery swapped, and the current position of the available battery swapping station;
a third obtaining unit, configured to obtain a reserved time length of the to-be-battery-replaced vehicle based on the available battery replacement station, and establish a first objective function including a combination relationship between the available battery replacement station and the to-be-battery-replaced vehicle based on the reserved time length and the extra time length of the to-be-battery-replaced vehicle, so as to obtain a target combination relationship according to the first objective function;
the first execution unit is used for acquiring a to-be-charged vehicle corresponding to a target charging station as a target vehicle according to the target combination relation, and acquiring a plurality of charging start times of the target vehicle corresponding to the target charging station and initial state information of the target charging station before a first charging start time;
a second execution unit, configured to monitor a real-time working state of the target power swapping station by using a preset interval time as a time unit from the first charging start time, and establish an evaluation function corresponding to a preset evaluation index of the target power swapping station based on an initial state of the target power swapping station and the real-time working state of the target power swapping station;
and the third execution unit is used for establishing a second target function based on the evaluation function and triggering and updating the real-time working state of the target power changing station according to the second target function.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117172503A (en) * 2023-09-21 2023-12-05 深圳云天励飞技术股份有限公司 New energy vehicle battery replacement scheduling method and device, electronic equipment and storage medium

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105809278A (en) * 2016-03-03 2016-07-27 华北电力大学（保定） Queuing theory algorithm based electric vehicle power change station's location choosing and planning method
CN110580606A (en) * 2019-09-12 2019-12-17 东方钢铁电子商务有限公司 matching method of railway transportation data
US20200198494A1 (en) * 2016-03-18 2020-06-25 Nio Nextev Limited Cloud storage-based system and method for electric vehicle battery exchange
CN111413628A (en) * 2020-02-27 2020-07-14 蓝谷智慧(北京)能源科技有限公司 Charging and battery replacing station with battery evaluation function and evaluation method
CN112838598A (en) * 2019-11-25 2021-05-25 国家电网公司 Optimization control strategy based on self-adaptive continuous tabu search algorithm
US20220126725A1 (en) * 2020-10-22 2022-04-28 Harbin Engineering University Method for scheduling multi agent and unmanned electric vehicle battery swap based on internet of vehicles
CN114493040A (en) * 2022-02-17 2022-05-13 国网浙江省电力有限公司双创中心 Energy storage vehicle scheduling method, system and device
CN114611900A (en) * 2022-03-01 2022-06-10 北京海博思创科技股份有限公司 Battery replacement scheduling method, device and equipment
CN114648420A (en) * 2022-05-24 2022-06-21 苏州琞能能源科技有限公司 Battery replacement station management method and device, electronic equipment and storage medium
CN114693040A (en) * 2020-12-31 2022-07-01 奥动新能源汽车科技有限公司 Recommendation method and system for battery replacement station, electronic device and storage medium

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105809278A (en) * 2016-03-03 2016-07-27 华北电力大学（保定） Queuing theory algorithm based electric vehicle power change station's location choosing and planning method
US20200198494A1 (en) * 2016-03-18 2020-06-25 Nio Nextev Limited Cloud storage-based system and method for electric vehicle battery exchange
CN110580606A (en) * 2019-09-12 2019-12-17 东方钢铁电子商务有限公司 matching method of railway transportation data
CN112838598A (en) * 2019-11-25 2021-05-25 国家电网公司 Optimization control strategy based on self-adaptive continuous tabu search algorithm
CN111413628A (en) * 2020-02-27 2020-07-14 蓝谷智慧(北京)能源科技有限公司 Charging and battery replacing station with battery evaluation function and evaluation method
US20220126725A1 (en) * 2020-10-22 2022-04-28 Harbin Engineering University Method for scheduling multi agent and unmanned electric vehicle battery swap based on internet of vehicles
CN114693040A (en) * 2020-12-31 2022-07-01 奥动新能源汽车科技有限公司 Recommendation method and system for battery replacement station, electronic device and storage medium
WO2022143879A1 (en) * 2020-12-31 2022-07-07 奥动新能源汽车科技有限公司 Method for recommending battery replacement station, system, electronic device and storage medium
CN114493040A (en) * 2022-02-17 2022-05-13 国网浙江省电力有限公司双创中心 Energy storage vehicle scheduling method, system and device
CN114611900A (en) * 2022-03-01 2022-06-10 北京海博思创科技股份有限公司 Battery replacement scheduling method, device and equipment
CN114648420A (en) * 2022-05-24 2022-06-21 苏州琞能能源科技有限公司 Battery replacement station management method and device, electronic equipment and storage medium

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
HAO WU ETAL: "An Optimization Model for Electric Vehicle", 《IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》 *
HAO WU: ""A Survey of Battery Swapping Stations for Electric Vehicles: Operation Modes and Decision Scenarios"", 《HTTPS://IEEEXPLORE.IEEE.ORG/DOCUMENT/9613817》 *
张聪 等: "基于自适应遗传算法的规模化电动汽车", 《电力***保护与控制》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117172503A (en) * 2023-09-21 2023-12-05 深圳云天励飞技术股份有限公司 New energy vehicle battery replacement scheduling method and device, electronic equipment and storage medium

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