CN115018206B

CN115018206B - New energy vehicle battery pack charging decision method and device

Info

Publication number: CN115018206B
Application number: CN202210855158.1A
Authority: CN
Inventors: 吴嘉俐
Original assignee: Shenzhen University
Current assignee: Shenzhen University
Priority date: 2022-07-20
Filing date: 2022-07-20
Publication date: 2022-10-28
Anticipated expiration: 2042-07-20
Also published as: CN115018206A

Abstract

The invention relates to a new energy vehicle battery pack charging decision method and a new energy vehicle battery pack charging decision device, wherein the method comprises the following steps: s1, acquiring the battery capacity, the current position and the target position of a vehicle to be charged; s2, current positions of all available battery swapping stations are obtained, and extra time corresponding to the available battery swapping stations is obtained based on the current positions of the vehicles to be swapped; s3, acquiring reserved time length of the vehicle to be switched based on the available switching station, and establishing a first objective function containing a combination relation of the available switching station and the vehicle to be switched so as to acquire a target combination relation according to the first objective function; s4, acquiring a target power changing station and a target vehicle according to the target combination relation; s5, monitoring the real-time working state of the target power changing station, 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 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.

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 charging 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 advanced technology, a new energy automobile power exchanging 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, obtaining all the electric vehicles to be replaced based on a received battery replacement request, and obtaining the battery capacity, the current position and the target position of the electric vehicles to be replaced;

s2, current positions of all available battery swapping stations are obtained, and extra time corresponding to the available battery swapping stations of the vehicle to be battery swapped is obtained on the basis of the current positions of the vehicle to be battery swapped, the target position of the vehicle to be battery swapped and the current positions of the available battery swapping 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 of 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:

，

and the first objective function is constrained according to the following constraints:

and in the first objective function,

for characterizing the combination of all the vehicles to be replaced and all the available replacing stations,

the representation corresponds the vehicle to be switched of i to the available switching station of j,

the number of all the electric vehicles to be replaced;

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;

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, the

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;

s33, acquiring the total waiting time of the vehicle to be swapped based on a preset combination relation, 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

；

S34, acquiring a first available battery changing station with the least corresponding vehicles to be charged and a second available battery changing station with the most available resources, randomly acquiring a relationship between one of the first available battery changing station and the second available battery changing station and the vehicle to be distributed to obtain a current candidate combination relationship, and executing a 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 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 or not, if so, executing a step S37, otherwise, taking the current candidate combination relation as the preset combination relation and executing a step S32;

s37, obtaining the current optimal candidate combination relationship in the candidate combination relationship set according to the first objective function, adding a cumulative count, and judging whether the current optimal candidate combination relationship is superior to the historical optimal candidate combination relationship, if so, executing a step S381, otherwise, executing a 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 the 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:

，

the above-mentioned

For the purpose of said first evaluation function,

for the purpose of said second evaluation function,

for the purpose of said third evaluation function,

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:

，

wherein, the first and the second end of the pipe are connected with each other,

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,

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:

wherein the content of the first and second substances,

t is the total charging time interval for the vehicle to be switched corresponding to the j available switching station,

the time interval is preset, and the time interval is preset,

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:

，

wherein the content of the first and second substances,

i is the number of charging time units corresponding to the battery to be charged corresponding to the vehicle to be charged,

and a, b and c are constants, namely the capacity of the corresponding battery to be charged of the vehicle to be charged.

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 of 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 charged are obtained based on the received charging request, and the battery electric quantity, the current position and the target position of the vehicles to be charged 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 on the basis of 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 reserved time of the vehicle to be switched based on the available switching station, establishing a first objective function containing a combination relation of the available switching station and the vehicle to be switched based on the reserved time and the extra time of the vehicle to be switched, 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 pass through the battery replacement station and reach the destination, 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 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 the 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; and S6, 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. 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:

，

and in the first objective function,

the characterization corresponds the i-standby power change vehicle to the j-available power change station,

the number of all the electric vehicles to be replaced;

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;

in the constraint condition, the number of the optical fiber,

The specific process is that for a new energy automobile user, the extra waiting time is mainly considered, wherein the extra waiting time comprises the time spent when the new energy automobile reaches a destination through a power conversion station and the waiting time (queuing time and waiting for a battery to be fully charged)Time of electricity, time of battery replacement operation). 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.

The calculation can be based on distance and speed, which can be obtained by the following formula:

the method is used for calculating the extra travel time of the new energy automobile to the destination through the battery replacement station.

The average running speed of all new energy automobiles,

for the initial position of the new energy automobile,

the position of the dispatched power change station of the i new energy automobile,

the destination position of the new energy automobile.

The Euclidean distance from the initial position to the scheduled battery replacement position of the i new energy automobile,

the Euclidean distance from the position of the dispatched power exchange station to the position of the destination of the new energy automobile is represented by i,

the Euclidean distance from the initial position to the destination position for the i new energy automobile

Wherein, the position of the i new energy automobile is calculated

And j power station position

The euclidean distance between them is as follows,

，

wherein the content of the first and second substances,

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),

the abscissa used to characterize point i, the ordinate of point i,

and is used for representing the abscissa of the j point and the ordinate of the j point.

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:

in the formula, the first and second images are shown,

representing the power consumption per kilometre of the vehicle,

characterization i New energy automobile

The amount of remaining power at the moment of time,

and (5) representing i new energy automobile battery capacity.

For reserving time, it can be obtained by using the following formula:

the method includes the steps that the time required to be reserved for a new energy automobile in a scheduling power changing station can be divided into three parts of time sum according to corresponding actions, including queuing waiting time and waiting battery full-charging time

And time of battery replacement operation

. 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, obtaining the target combination relationship according to the first objective function includes:

s33, acquiring the total waiting time of the vehicles to be switched based on the preset combination relationship, acquiring the accumulated probability of the vehicles to be switched based on the total waiting time of the vehicles to be switched, and acquiring the vehicles to be switched as the vehicles to be distributed through roulette based on the accumulated probability, wherein the total waiting time of the vehicles to be switched is

；

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 on a preset combination relation to obtain a 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, acquiring a current optimal candidate combination relationship in the candidate combination relationship set according to a first objective function, adding a cumulative count, and judging whether the current optimal candidate combination relationship is superior to a historical optimal candidate combination relationship, if so, executing a step S381, otherwise, executing a 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,

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. In the present application, therefore, a new energy vehicle method is selected based on the adaptation of roulette. The method comprises the following specific steps:

a1, inputting the current solution

And corresponding additional latency

Wherein

The additional waiting time spent when the new energy automobile reaches the destination through the dispatching battery replacement station is shown.

Wherein the content of the first and second substances,

，

is the sum of these three parts of time.

A2, normalizing the extra waiting time of the i new energy automobile

。

A3, calculating the cumulative probability C of the i new energy automobile _i 。

And A4, rotating the wheel disc, and selecting the i new energy automobile pointed by the pointer as a 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 minimum in the power change station set

And power station that trades of battery usable time longest

。

B2, random selection

Or

As a variant gene in current decision-making solutions.

After the self-adaptive selection, the current solution is subjected to mutation operation

。

In one embodiment, given 10 new energy automobiles, two power conversion stations are provided, and each power conversion station is provided with three batteries. A policy (NIR) solution based on a recent range rule is

And the corresponding additional waiting time of each new energy automobile is

. Then, the extra latency is normalized

And calculates a selection probability. It is worth noting that since

Is 0, and thus will not select

. Then, assume that the selection is made when the wheel is stopped

At the same time, the selection is carried out according to the minimum number of vehicles to arrive or the longest available time of the battery

. Therefore, the new energy automobile is distributed to the battery replacement station

And generating a child scheduling plan

。

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

Size of candidate set

And length L of the tabu table.

C2. Computing current candidate combinatorial relationships according to NIR methods (nearest-range rule based strategies)

。

C3. And setting the candidate combination relation set as an empty set.

C4. Judging the cumulative count

And if the sum is less than the total number of the vehicles | E |, generating a new candidate combination relationship by adopting a self-adaptive mutation operator, otherwise, randomly selecting two points in the current combination relationship to exchange to generate a new candidate combination relationship.

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 larger than a second preset value

If yes, executing C7, otherwise executing C4.

C7. Calculating the current optimal candidate combination relation in the candidate combination relation set according to the solved objective function

。

C8. Judging the current optimal candidate combination relation

Relation with historical optimal candidate combination

If the current optimal candidate combination relation

Optimal candidate combinatorial relation superior to history

Then the historical optimal candidate combination relation is obtained

In relation to current candidate combinations

Updating to the current optimal candidate combination relation

. Otherwise, the best combination relation belonging to the candidate combination relation set but not belonging to the tabu list is obtained as the current candidate combination relation.

C9. Combining the current candidates

Adding into a tabu list if the length of the tabu list is larger than a preset threshold

The tabu list is updated in a queue fashion.

C10. Cumulative count

And adding 1.

C11. Judging whether the cumulative count gen is greater than a first preset value

And if the historical optimal solution is larger than the decision solution, outputting the historical optimal solution as a 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 of each new energy automobile 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

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 a period of time. A chaotic charging schedule can damage the power system and cause a blockage in 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 charge damage is simultaneously used as an evaluation index of the target charging station.

And respectively establishing an evaluation function corresponding to the target power 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:

，

in order to be a first evaluation function,

in order to be a function of the second evaluation function,

in order to be a third evaluation function,

is an available power station changing set. Specifically, based on multi-objective optimization, the optimization formula satisfies the following functions:

wherein the content of the first and second substances,

and d is the length of the decision vector,

is the decision space.

Representative decision solutions

An adaptation value at each target.

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:

in the above formula, if according to

Each target adaptive value obtained by decision solution is not different from

The decision solution, and at least on some target,

the result of the decision solution is superior to

The decision solution is called

Decision solution governance

And (6) decision making. Furthermore, all non-dominant solutions are stored herein using a pareto solution set (PS) in which fitness values corresponding to the non-dominant solutions are stored using a Pareto Frontier (PF). Thus, a second objective function can be derived based on the above formula,

in other words, a charging schedule model of the power conversion station is set to be optimized based on three optimization targets of total load variance, electric power cost and battery damage in a corresponding multi-objective optimization function.

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 station changing scheduling result of the new energy automobile based on the first objective function as follows:

wherein when

When the minimum value is taken, the minimum value is obtained,

is a value of (a), wherein

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 depleted battery is charged according to the average charging power of each battery replacement station, so that the i-rechargeable battery can be determined in the application

Starting charging time of

And ending the charging time

. For a message from

Rechargeable battery of new energy automobile

In the battery replacement station

The charging schedule of (c) is defined as follows:

wherein the content of the first and second substances,

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.

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.

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.

: in the first decision, the initial moment

I cell at i decision solution pairPower station should be traded

Average charging power of.

: in the first decision, the end time

The battery is correspondingly replaced in the ith decision solution

Average charging power of (c).

: in decision 1, the ith decision is resolved.

: total number of new energy vehicles.

At the first decision solution

In, schedule to

The new energy automobile group of the power change station is defined as follows:

it is noted that 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

And ending the charging time gap

Is defined as follows:

in the above equation, charge start time of i rechargeable battery

And ending the charging time

Transition to a Start of Charge time gap

And ending the charging time gap

. 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

。

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:

wherein the maximum time range in the above formula

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

Wherein the content of the first and second substances,

for the charging schedule in the j charging station,

in the second decision (new energy vehicle charging station scheduling), the j charging station charges the i battery in the time period n,

at the second decision, j swap station is in time slot

Of the power to charge the i battery,

in order to make the second decision, the j power conversion station is in the time period

To charge the i battery.

The method is characterized in that the new energy vehicles are assigned to the j battery replacement stations.

For power station set (subscript j)

: 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

To predict whether i rechargeable batteries are charging during n time slots. If the i rechargeable battery is charged at the n time interval of the j charging station, the charging station is started

And if not, the step (B),

. Meanwhile, charging power of the j power conversion station

The maximum charging power of the charging station must be less than or equal to j in the n time interval

. 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 the slight inequality is due to the transition in 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 that the new energy automobile can normally run.

The threshold of the fully charged battery.

And the residual electric quantity of the i new energy automobile when the i new energy automobile reaches the j battery replacement station.

In one embodiment, the first merit function includes the following functions:

，

wherein the content of the first and second substances,

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:

wherein the content of the first and second substances,

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,

the time interval is preset, and the time interval is preset,

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,

is defined as 5 minutes.

Optionally, the third evaluation function includes the following functions:

，

wherein the content of the first and second substances,

for j, a battery to be charged corresponding to the vehicle to be charged in the time period n, i by the charging stationThe charging power of (2) is set,

t is the total charging time interval for the vehicle to be charged corresponding to the j available charging station,

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:

wherein DS is a battery capacity fading speed,

a state of degradation of the battery capacity.

Is the charge rate of the battery during time period n.

The relationship between charge rate and charge power is derived as follows:

and finally obtaining a third evaluation function corresponding to the total charging damage of the battery. Wherein, the first and the second end of the pipe are connected with each other,

is the number of time slots occupied by the rechargeable battery. Which satisfies the following conditions:

in one embodiment, the first and second electrodes are, in one embodiment,

。

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), preference vector guided based multi-objective optimized evolution algorithm (PVEA) 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 three charging schedules obtained using the multi-objective example subgroup algorithm in cases 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

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 examples of the power change stations is greater than that of C (-, MOPSO), so that it can be concluded 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, C (S1, S2) calculates a proportion that a solution in the solution set S2 is at least weakly dominated by one solution in the solution set S1, and measures a 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, EV5 goes to the nearest battery replacement station BSS0 from the initial position. After that, EV5 performs a battery replacement operation at BSS0, which only takes five minutes, and finally at 8:39 to 8:52, EV5 leaves the battery replacement station BSS0 to go to the destination. EV5 takes 29 minutes in the above process, which is 21.8 minutes more useful than going directly from the home location to the destination, so the extra wait time for EV5 is 21.8 minutes. Similarly, in 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 from the charging station, and at 9:38 to the destination. The EV9 spends 58.8 minutes more on 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 a person 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

1. A new energy vehicle battery pack charging decision method is characterized by comprising the following steps:

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;

s6, establishing a second target function based on the evaluation function, and triggering and updating the real-time working state of the target power exchanging station according to the second target function;

wherein the first objective function comprises the following function:

，

and constraining the first objective function according to the following constraints:

and in the first objective function,

the characterization maps the i-standby battery replacing vehicle to the j-available battery replacing station,

the number of all the electric vehicles to be replaced;

for i the additional duration of the standby battery car,the extra time length is the time length difference between the time length when the vehicle to be charged reaches the target position of the vehicle to be charged through the current position of the available charging station and the time length when the vehicle to be charged directly reaches the target position of the vehicle to be charged;

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

The residual electric quantity when the i-to-be-switched vehicle reaches the j-available switching station is obtained;

in step S3, the obtaining a target combination relationship according to the first objective function includes:

s33, obtaining the total waiting time of the electric vehicle to be replaced based on a preset combination relationship, obtaining the accumulated probability of the electric vehicle to be replaced based on the total waiting time of the electric vehicle to be replaced, obtaining 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

；

S34, acquiring a first available battery swapping station with the fewest corresponding vehicles to be charged and a second available battery swapping station with the most available resources, randomly acquiring a relationship between one of the first available battery swapping station and the second available battery swapping station and the vehicle to be allocated to obtain a current candidate combination relationship, and executing a step S36;

s382, judging whether the accumulated count is larger than a first preset value, if so, executing the step S392, otherwise, executing the step S391,

2. The new energy vehicle battery pack charging decision method according to claim 1, wherein in the 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;

3. The new energy vehicle battery pack charging decision method according to claim 2, wherein the second objective function comprises the following functions:

，

the above-mentioned

For the purpose of said first evaluation function,

for the purpose of said second evaluation function,

for the purpose of said third evaluation function,

and the available power station set is obtained.

4. The new energy vehicle battery pack charging decision method according to claim 3,

the first merit function includes the following functions:

，

wherein the content of the first and second substances,

5. The new energy vehicle battery pack charging decision method according to claim 3,

the second merit function includes the following functions:

wherein the content of the first and second substances,

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,

the time interval is preset, and the time interval is preset,

is the time of use electricity price of the time period n.

6. The new energy vehicle battery pack charging decision method according to claim 3,

the third merit function includes the following functions:

，

wherein the content of the first and second substances,

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,

7. The new energy vehicle battery pack charging decision method as claimed in claim 3, further comprising solving the second objective function based on any one of NSGA-II, NSGA-III, RVEA and MOPSO.

8. The utility model provides a new energy automobile battery package decision-making device that charges which characterized in that includes:

wherein the first objective function comprises the following function:

，

and in the first objective function,

the number of all the electric vehicles to be replaced;

an extra time length of the vehicle waiting for power change, wherein the extra time length is the time length that the vehicle waiting for power change reaches the vehicle waiting for power change through the current position of the available power change stationThe time duration difference between the target position and the time duration when the target position of the electric vehicle directly reaches the target position of the electric vehicle to be replaced is obtained;

in the constraint, the

The residual electric quantity is the residual electric quantity when the i-to-be-switched vehicle reaches the j-available switching station;

the obtaining of the target combination relationship according to the first target function includes:

；

s392, acquiring the optimal combination relation in the tabu list as a target combination relation;

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;