CN116822912B - Intelligent dispatching method and device for electric vehicle trunk line long-distance transportation charging and changing - Google Patents

Abstract

The application provides an intelligent dispatching method and device for charging and changing electricity in long-distance transportation of electric vehicles, wherein the dispatching method and device can firstly determine fixed position data of an optional charging and changing electricity station, secondly construct a dispatching objective function, determine dispatching constraint conditions, finally obtain stay recommended data by utilizing a preset optimal solution algorithm, finish dispatching charging and changing electricity requirements of each electric vehicle, simultaneously provide specific charging and changing electricity schemes for execution, consider the cost problem of different charging and changing electricity schemes, provide a more economic scheme for users, and also support user-defined cost limit. Compared with the prior art, the application realizes the integral intelligent dispatching of the charging and replacing requirements of the electric vehicle when the electric vehicle executes the trunk line long-distance transportation task, and improves the integral efficiency of the electric vehicle for completing the trunk line long-distance transportation task.

Description

An intelligent dispatching method and device for charging and replacing electric vehicle trunk line long-distance transportation

Technical field

This application relates to the technical field of electric vehicles, and specifically relates to an intelligent dispatching method and device for charging and replacing batteries in long-distance transportation of electric vehicles on trunk lines.

Background technique

Electric vehicles have developed rapidly in recent years due to their energy-saving and environmentally friendly characteristics. As long-distance vehicles are major carbon emitters in the transportation field, it is urgent to fully promote electrification. In addition to environmental issues, users are more troubled by operational efficiency issues caused by slow charging, difficulty in charging, and anxiety about battery life. In order to solve the above problems, many electric vehicles can already support two energy replenishment modes: charging and battery swapping. At the same time, energy replenishment power stations that can provide charging and/or battery swapping services for electric vehicles have also emerged.

Due to the limited service capacity of each energy supplement power station, although some charging and/or battery swap scheduling plans have been developed for electric vehicles based on the operation conditions of the energy supplement power station, the existing technology is usually only based on the current surplus of electric vehicles. The battery capacity is recommended to be the most recent energy replenishment power station that can be charged and/or replaced. For electric vehicles that perform long-distance transportation tasks on trunk lines, they may need to replenish energy multiple times during one long-distance transportation task on trunk lines. However, charging and/or battery replacement scheduling plans developed using existing technologies may lead to the emergence of electric vehicles. The vehicle was charged and/or replaced too many times during the long-distance transport mission, resulting in the overall mission taking too long.

Contents of the invention

In view of this, one of the technical problems solved by the embodiments of the present invention is to provide an intelligent dispatching method and device for charging and replacing electric vehicles in long-distance long-distance transportation of electric vehicles, so as to overcome the problem in the prior art that electric vehicles have to perform long-distance transportation tasks on main lines. The problem is that excessive charging and/or battery replacement times lead to low overall task execution efficiency.

The first aspect of the embodiments of this application discloses an intelligent dispatching method for electric vehicle main line long-distance transportation charging and replacement. The method includes:

Determine the fixed location data of at least one optional charging and swapping station based on the current location data and destination location data of the electric vehicle during its long-distance transportation mission;

Construct a dispatch objective function; wherein the dispatch objective function is used to calculate the current location data of the electric vehicle, the destination location data, stay plan data, stay duration data, and all optional charging and swapping stations. The fixed location data is calculated to obtain the highest scheduling score; the stay plan data is used to characterize whether to stay at all the optional charging and swapping stations; the stay duration data is used to characterize the stay at all the optional charging and swapping stations. Length of stay; the scheduling score is at least determined based on the total time for the electric vehicle to complete the trunk long-distance transportation task;

Determine scheduling constraints based on the current location data, the destination location data, current driving status data of the electric vehicle, and the fixed location data of all optional charging and swapping stations; wherein, the scheduling The constraint conditions are used to restrict all the optional charging and swapping stations that the electric vehicle can reach when not charging and/or exchanging batteries. When the electric vehicle is fully charged, it starts from each of the optional charging and exchanging stations. Other optional charging and swapping stations that are reachable by the battery swapping station and have not been passed through, and all descriptions of the electric vehicle that can reach its destination without further charging and/or battery swapping when it is fully charged. Optional charging and swapping station; the current driving status data is used to represent the remaining available power of the power battery;

According to the scheduling objective function and the scheduling constraints, a preset optimal solution algorithm is used to obtain stay recommendation data; wherein the stay recommendation data is used to represent the recommended electric vehicles at all optional charging and swapping stations. Whether to stay.

The second aspect of the embodiment of the present application discloses an intelligent dispatching device for electric vehicle mainline long-distance transportation charging and replacement. The device includes:

An encoding module used to determine the fixed position data of at least one optional charging and swapping station based on the current position data and destination position data of the electric vehicle during the long-distance transportation mission on the trunk line;

An objective function building module is used to construct a scheduling objective function; wherein the scheduling objective function is used to calculate the current location data of the electric vehicle, the destination location data, stay plan data, stay duration data, and all The fixed location data of the optional charging and swapping stations are calculated to obtain the highest dispatch score; the stay plan data is used to represent whether to stay at all the optional charging and swapping stations; the stay duration data is used to represent whether to stay at all the optional charging and swapping stations. The length of stay at the optional charging and swapping station; the scheduling score is at least determined based on the total time for the electric vehicle to complete the trunk long-distance transportation task;

A constraint building module configured to determine scheduling constraints based on the current location data, the destination location data, the current driving status data of the electric vehicle, and the fixed location data of all optional charging and swapping stations. ; Wherein, the scheduling constraints are used to constrain all the optional charging and swapping stations that the electric vehicle can reach when not charging and/or swapping. The electric vehicle starts from every charging station when fully charged. Other optional charging and swapping stations that can be reached without passing through one of the optional charging and swapping stations, and the electric vehicle can reach them by starting out in a fully charged state and without further charging and/or swapping batteries. All the optional charging and swapping stations at the destination; the current driving status data is used to represent the remaining available power of the power battery;

An optimal solution algorithm calculation module, configured to obtain stay recommendation data using a preset optimal solution algorithm according to the dispatch objective function and the dispatch constraint conditions; wherein the stay recommendation data is used to characterize the recommended electric vehicle Whether to stop at all the optional charging and swapping stations mentioned above.

In the embodiment of the present invention, firstly, the fixed position data of the optional charging and swapping stations are determined, secondly, the dispatching objective function is constructed, the dispatching constraints are determined, and finally the preset optimal solution algorithm is used to obtain the stay recommendation data to complete the evaluation of each electric vehicle. Scheduling of charging and replacement needs. Compared with the existing technology, the present invention realizes the overall intelligent dispatching of the charging and replacing needs of electric vehicles when performing long-distance transportation tasks on trunk lines, so that electric vehicles have the most reasonable charging and replacing scheduling plan when performing transportation tasks, and improves the efficiency of electric vehicles. The overall efficiency of electric vehicles in completing long-distance transport tasks on trunk lines.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. Those of ordinary skill in the art can also obtain other drawings based on these drawings without exerting creative efforts.

Figure 1 is a schematic flowchart of an intelligent dispatching method for battery charging and replacement in trunk line long-distance transportation of electric vehicles disclosed in Example 1 of this application;

Figure 2 is a schematic flowchart of obtaining the residence time of the second target site disclosed in Example 1 of the present application;

Figure 3 is a schematic flowchart of obtaining the residence time of the first target site disclosed in Example 1 of the present application;

Figure 4 is a schematic flowchart of obtaining the second time point and the second power consumption disclosed in Example 1 of the present application;

Figure 5 is a schematic flowchart of an intelligent dispatching method for battery charging and replacement in trunk line long-distance transportation of electric vehicles disclosed in Example 2 of this application;

Figure 6 is a schematic structural block diagram of an intelligent dispatching device for battery charging and replacement in trunk line long-distance transportation of electric vehicles disclosed in Example 3 of this application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

It should be noted that the terms “first”, “second”, “third” and “fourth” in the description and claims of this application are used to distinguish different objects, rather than to describe specific objects. order. The terms "comprising" and "having" and any variations thereof in the embodiments of this application are intended to cover non-exclusive inclusion, for example, a process, method, device, product or equipment that includes a series of steps or units and need not be limited to the clear Those steps or elements listed may instead include other steps or elements not expressly listed or inherent to the process, method, product or apparatus.

Example 1

As shown in Figure 1, Figure 1 is a schematic flow chart of an intelligent dispatching method for charging and replacing batteries in trunk line long-distance transportation of electric vehicles disclosed in Example 1 of the present application. The method includes:

Step S101: Determine the fixed position data of at least one optional charging and swapping station based on the current position data and destination position data of the electric vehicle during the long-distance transportation mission on the trunk line.

In this embodiment, a trunk line refers to a line that serves as the backbone of a transportation network that allows electric vehicles to perform long-distance transportation tasks. Since main lines are usually cross-regional connecting lines, electric vehicles usually need to be recharged midway when performing long-distance transportation tasks along the main lines to travel from the starting point to the destination. Therefore, there are charging stations at certain distances along the main lines for electric vehicles to charge. and/or multiple energy supplement power stations for power exchange. The locations of these energy supplement power stations are usually fixed and can meet the charging and/or power exchange needs of electric vehicles at least once during transportation tasks.

In this embodiment, the electric vehicle refers to an electric vehicle that performs long-distance transportation tasks along the trunk line. Its current latest geographical location can be characterized by current location data, that is, the current location data is used to characterize the electric vehicle when performing this long-distance transportation task on the trunk line. current geographical location. There are no restrictions on parameters such as the type, brand, type of power battery, number of passengers and maximum full load mass of electric vehicles.

In this embodiment, the geographical location of the destination where the electric vehicle performs this main line long-distance transportation task can be characterized by destination location data. The specific location of the destination where the electric vehicle performs this trunk line long-distance transportation task is not limited, and it may or may not be located along the trunk line. However, for all electric vehicles, during the long-distance transportation mission of this main line, they can directly reach the destination after the last charging and/or battery replacement at the power station along the main line.

In this embodiment, when an electric vehicle is performing a long-distance transportation mission on a trunk line and moves from its current location to its destination, it will pass along a number of energy-supplementing power stations that can be used for charging and/or exchanging electricity. These energy-supplementing power stations are: It is an optional charging and swapping station. The specific geographical location of optional charging and swapping stations is characterized by fixed location data.

Step S102: Construct a scheduling objective function.

In this embodiment, the dispatch objective function is used to calculate and obtain the highest dispatch score based on the electric vehicle's current location data, destination location data, stay plan data, stay duration data, and fixed location data of all optional charging and swapping stations.

In this embodiment, the stay plan data is used to represent whether to stay at all optional charging and swapping stations. The specific representation method is not limited and can be reasonably selected according to actual application requirements. For example, the stay plan data can use 5 coding bits, which correspond to a total of 5 optional charging and swapping stations A, B, C, D, and E distributed along the main line, where 1 means stay and 0 means no stay, then When the stay data is 01101, it means that the vehicle will not stay at the two optional charging and swapping stations A and D, but will stay at the three optional charging and swapping stations B, C and E.

In this embodiment, the stay time data is used to represent the stay time at all optional charging and swapping stations. When it is determined that the electric vehicle will stay at a specific optional charging and swapping station, a stay duration can be estimated or set in advance. The stay duration can be a fixed value or an indefinite value. The specific duration No limit to value.

For example, the fixed duration value can be to stay at an optional charging and swapping station for 30 minutes, 40 minutes, 50 minutes, etc.; the variable duration value can be to stay at an optional charging and swapping station for 35 minutes, 45 minutes, or 55 minutes, etc.

In this embodiment, the dispatching score is at least determined based on the total time it takes for the electric vehicle to complete the trunk long-distance transportation task. The specific calculation method of the dispatching score and the upper limit of the score are not limited, and can be reasonably selected according to actual application requirements.

For example, the dispatching score can be determined only based on the total time for electric vehicles to complete trunk long-distance transportation tasks, that is, the shorter the total time, the higher the dispatching score; the dispatching score can also be determined based on the total time for electric vehicles to complete trunk long-distance transportation tasks. , and the total cost or total mileage are comprehensively determined.

In this embodiment, based on the current location data, stay plan data, destination location data of the electric vehicle, and the fixed location data of all optional charging and swapping stations, the total travel distance of the electric vehicle from the current location to the destination location can be calculated. mileage, and further calculate the driving time of the electric vehicle based on the preset driving speed rules; based on the dwell time data, the energy replenishing time of the electric vehicle at all optional charging and swapping stations can be calculated; the driving time is related to The sum of the energy replenishment times is the total time for electric vehicles to complete long-distance transportation tasks on trunk lines.

Optionally, considering that the price of charging an electric vehicle is usually lower than the price of battery swapping, and the charging standards of optional charging and swapping stations located in different locations may also be different, in practical applications, electric vehicles perform long-distance trunk lines In transportation tasks, not only the time cost is usually considered, but also the transportation cost. Therefore, in order to make the final stay recommendation data better meet user needs, the cost can be further considered when formulating the scheduling objective function.

Specifically, the dispatch objective function is used to calculate the current location data, destination location data, stay plan data, stay duration data, charging and swapping arrangement data of electric vehicles, as well as the fixed location data and charging standards of all optional charging and swapping stations. Data is used to calculate the highest scheduling score.

Among them, the charging and swapping arrangement data is used to represent the starting time point and ending time point of charging of electric vehicles at all optional charging and swapping stations, as well as the starting time point and ending time of charging at all optional charging and swapping stations. Point, the specific representation method is not limited and can be selected according to the actual situation.

For example, when an electric vehicle is charged at optional charging and swapping station A only from 10:00-10:50, and is only charged at optional charging and swapping station B from 15:00-15:10, When C is not charging and swapping, the charging and swapping arrangement data can use 10:00 to represent the starting time point for charging at optional charging and swapping station A, and 10:50 to represent charging at optional charging and swapping station A. The end time point of , use a null value to represent the starting time point and end time point of battery swapping at optional charging and swapping station A; 15:00 can be used in the charging and swapping schedule data to represent battery swapping at optional charging and swapping station B. The starting time point of , 15:10 is used to represent the end time point of battery swapping at optional charging and swapping station B, and a null value is used to represent the starting and ending time points of charging at optional charging and swapping station B; charging Null values are used in the battery swap arrangement data to represent the starting time point and the end time point of charging and battery swapping at the optional charging and swapping station C.

Among them, the charging standard data of the optional charging and swapping station is used to represent the charging service charging rules and battery swapping service charging rules of the optional charging and swapping station. The specific representation method is not limited and can be selected according to the actual situation.

Among them, the dispatch score is used to comprehensively evaluate the total time and total cost of electric vehicles completing trunk long-distance transportation tasks. The specific calculation method of the dispatch score is not limited and can be reasonably selected according to the actual application situation.

For example, a duration scoring standard can be determined based on the total time for electric vehicles to complete trunk long-distance transportation tasks. In the duration scoring standard, the shorter the total time, the higher the score; a cost score can be determined based on the total cost of electric vehicles to complete trunk long-distance transportation tasks. Standard, the less the total cost in the cost scoring standard, the higher the score; then set corresponding weight coefficients for each score in the duration scoring standard and cost scoring standard; after determining the duration score and cost score of a scheduling plan Finally, the two can be multiplied by the corresponding weight coefficients and then summed to calculate the scheduling score of the scheduling plan.

Among them, segmented calculations can be used to calculate the cost of electric vehicles at each optional charging and battery swapping station for charging and/or battery swapping, and then the total cost for electric vehicles to complete trunk long-distance transportation tasks can be obtained cumulatively.

Specifically, firstly, the first optional charging and battery swapping station for charging and/or battery swapping of the electric vehicle can be determined based on the stay plan data; and then based on the current location data of the electric vehicle and the third charging and/or battery swapping station. The fixed location data of an optional charging and swapping station uses a preset power consumption calculation model to calculate the first time an electric vehicle travels from its current location to the location of the first optional charging and swapping station for charging and/or battery swapping. Power consumption; based on the charging standard data of the first optional charging and swapping station for charging and/or power swapping, the charging and swapping arrangement data of the electric vehicle and the first power consumption, the charging time of the electric vehicle can be calculated and/or the cost of the first optional charging and swapping station for battery swapping.

When calculating the cost of an electric vehicle at the second and subsequent optional charging and swapping stations for charging and/or exchanging power, it is different from calculating the cost of the first optional charging and swapping station for charging and/or exchanging power for the electric vehicle. The biggest difference is that the calculation of power consumption is determined based on the location of the last optional charging and swapping station for charging and/or battery swapping and this optional charging and swapping station for charging and/or battery swapping, rather than The current location of the electric vehicle.

Step S103: Determine scheduling constraints based on the electric vehicle's current location data, destination location data, current driving status data, and fixed location data of all optional charging and swapping stations.

In this embodiment, the scheduling constraints are used to constrain the electric vehicle to all optional charging and swapping stations that can be reached from the current location when the electric vehicle is not charging and/or exchanging batteries; when the electric vehicle is fully charged, it can reach from each available charging and exchanging station. Other optional charging and swapping stations that can be reached by starting from the optional charging and swapping station; and all optional charging and swapping stations where the electric vehicle can reach the destination without charging and/or swapping batteries when it is fully charged. power station. The reason why these scheduling constraints are set is to ensure that the finalized stay recommendation data can ensure that electric vehicles will not break down due to insufficient power when performing trunk long-distance transportation tasks, and to ensure that electric vehicles can drive to their destinations.

In this embodiment, the current driving status data can at least be used to represent the remaining available power of the power battery, thereby estimating the distance that the electric vehicle can travel, and determining the specific optional charging and swapping stations that it can reach.

In this embodiment, the execution order of step S102 and step S103 is not limited, and can be reasonably selected according to actual application requirements.

Step S104: According to the scheduling objective function and scheduling constraints, use the preset optimal solution algorithm to obtain stay recommendation data.

In this embodiment, the specific type of the preset optimal solution algorithm is not limited, and the user can select according to actual needs. For example, simulated annealing algorithm, genetic algorithm, particle swarm optimization algorithm, etc. can be selected.

In this embodiment, the stay recommendation data is used to represent whether the recommended electric vehicle will stay at all optional charging and swapping stations. The specific representation method is not limited and can be reasonably selected according to actual application requirements. For example, the stay recommendation data can use 5 coding bits, which correspond to a total of 5 optional charging and swapping stations A, B, C, D, and E distributed along the main line, where 1 means stay and 0 means no stay, then When the stay recommendation data is 01101, it means not staying at the two optional charging and swapping stations A and D, but staying at the three optional charging and swapping stations B, C and E. Electric vehicles can complete the charging and replacement of batteries during the entire trunk transportation task based on the stay recommendation data.

Optionally, in order to calculate the optional stay time of the electric vehicle during charging and/or battery swapping, and to further calculate and obtain the stay duration data corresponding to the specific stay plan data, see Figure 2, step S104 may also include the following sub-steps: Steps S104d~S104g:

Sub-step S104d: Determine the first target site and the second target site based on the stay plan data of the electric vehicle.

Sub-step S104e, determine the second time when the electric vehicle arrives at the second target site based on the first time point when the electric vehicle leaves the first target site, as well as the fixed position data of the first target site and the fixed position data of the second target site. point and second power consumption.

Sub-step S104f, according to the second power consumption and the second service arrangement data, determine the second shortest stay time for the electric vehicle to charge and/or battery swap to a fully charged state after arriving at the second target site at the second time point.

Sub-step S104g, determine the second shortest stay time as the stay time of the electric vehicle at the second target site.

Among them, the first target site and the second target site are optional charging and swapping stations where the electric vehicles determined based on the stay plan data will stay in sequence during the execution of the trunk long-distance transportation mission. That is, the first target site and the second target site are Optional charging and swapping stations located between the current location of the electric vehicle and the destination location where the electric vehicle needs to stop and charge and/or swap batteries. For example, a total of five optional charging and swapping stations A, B, C, D, and E are distributed on the main line between the current location of the electric vehicle and the destination location. The stay plan data represents the three possible charging and swapping stations of the electric vehicle at B, D, and E. If you choose to stay for charging and/or battery exchange, then when the first target site is B, then the second target site is D; when the first target site is D, then the second target site is E.

The first time point is used to represent the time point when the electric vehicle leaves the first target site, and the second time point is used to represent the time point when the electric vehicle arrives at the second target site. The second power consumption is used to represent the power battery power consumption of the electric vehicle during the journey from the first target station to the second target station.

The second service arrangement data is used to represent the time period during which the electric vehicle can be charged and the battery can be exchanged after arriving at the second target site at the second time point. Since optional charging and swapping stations may only provide charging services or only battery swapping services, and there may be multiple charging guns and battery swapping equipment, the time period and available charging time of each optional charging and swapping station There is at least one total time period for power exchange, and there may be multiple data for both the time period for charging and the time period for power exchange. In addition, the starting and ending time points of the charging time period and the battery swapping time period may or may not overlap.

Among them, in order to shorten the time it takes for the electric vehicle to perform trunk long-distance transportation tasks as much as possible, when the electric vehicle leaves the first target site, the battery power of the electric vehicle is usually in a fully charged state. According to the fixed position data of the first target site and the fixed position data of the second target site, the driving distance of the electric vehicle from the first target site to the second target site can be determined, and further the preset power consumption model can be used to calculate The second time point is determined by the second power consumption and driving time of the electric vehicle traveling from the first target site to the second target site. According to the service arrangement of the second target site at the second time point, the second service arrangement data can be obtained. According to the second power consumption and the second service arrangement data, the earliest time point at which the electric vehicle is charged and/or battery swapped to a fully charged state at the second target station can be determined, that is, the second shortest stay duration is the second time point and The length of time between this earliest time point.

Compared with setting the optional length of stay of the electric vehicle during charging and/or battery swapping to a fixed value or a random indefinite value, it is calculated from the second power consumption and the second service arrangement data that the electric vehicle stays in the second The shortest stay time at the target site is used as the second shortest stay time for the electric vehicle at the second target site. Taking into account the specific service conditions of each optional charging and swapping station where the electric vehicle stops, the final calculation can be The total time it takes for electric vehicles to complete trunk long-distance transportation tasks is more accurate.

Further, sub-step S104f may also include: determining the second minimum stay duration and corresponding energy replenishment plan data based on the second power consumption and the second service arrangement data.

Among them, the energy replenishment plan data is used to characterize the starting time point and ending time point of the electric vehicle charging at the second target site, as well as the starting time point and ending time point of battery swapping. The specific representation method of energy replenishment plan data is not limited and can be reasonably selected according to actual application requirements. For example, when the electric vehicle is only charged at the second target station from 10:00 to 10:50, the energy replenishment plan data can use 10:00 to represent the starting time point of charging, and 10:50 to represent the termination of charging. Time point, use null value to represent the starting time point and ending time point of power exchange. For another example, when the electric vehicle is charged at the second target site from 10:00 to 10:50 and the battery is exchanged from 11:00 to 11:10, the energy replenishment plan data can be used to represent the start of charging at 10:00. As for time points, 10:50 is used to represent the termination time point of charging, 11:00 is used to represent the starting time point of battery replacement, and 11:10 is used to represent the termination time point of battery replacement.

After it is determined that the electric vehicle will stay at the optional charging and swapping station, the starting time point and the ending time point for charging and swapping at the optional charging and swapping station are further given, that is, the corresponding energy replenishment plan data is given, After arriving at the optional charging and swapping station, the electric vehicle can be charged and/or swapped according to the energy replenishment plan data, which is beneficial to improving user experience.

In addition, it should be noted that in this embodiment, only the second minimum stay time and energy replenishment plan data corresponding to the second target site are explained. In fact, corresponding energy replenishment plan data can be set for all optional charging and swapping stations. . For example, when the electric vehicle does not stop at an optional charging and swapping station, the starting time point and the ending time point for the electric vehicle to charge and swap at the optional charging and swapping station can be set to empty.

Furthermore, for electric vehicles that need to swap batteries at optional charging and swapping stations, after arriving at the optional charging and swapping stations, they may need to queue up for battery swapping. There are idle charging guns available for charging at the battery swap station. Electric vehicles do not need to wait in line if they are to be charged. Since the unit price of battery swapping for electric vehicles is usually higher than the unit price of charging, on the one hand, in order to save the energy replenishment cost of electric vehicles, on the other hand, in order to reduce the charging of batteries to be replaced by electric vehicles at optional charging and swapping stations. time, which can instruct electric vehicles to charge first while waiting in line for battery replacement.

Specifically, sub-step S104f may also include: when the first earliest time point at which the electric vehicle is swapped to a fully charged state at the second target site is earlier than the second earliest time point at which the electric vehicle is charged to a fully charged state, and the electric vehicle is at the second target site. When the duration between the third earliest time point at which charging can be performed at the second target site and the fourth earliest time point at which battery swapping can be performed is greater than or equal to the first preset duration threshold, the start of battery swapping for the electric vehicle at the second target site is determined. The time point and the end time point are the fourth earliest time point and the first earliest time point respectively. The starting time point for charging the electric vehicle at the second target site is the third earliest time point, and the electric vehicle is charging at the second target site. The duration between the end time point and the fourth earliest time point is greater than or equal to the second preset duration threshold.

Among them, the first earliest time point is used to represent the earliest time point at which the electric vehicle can leave the second target site after replacing the battery at the second target site to a fully charged state. The second earliest time point is used to represent the earliest time point at which the electric vehicle can leave the second target site after being charged to a fully charged state at the second target site. The third earliest time point is used to characterize the earliest time point at which the electric vehicle starts charging at the second target site. The fourth earliest time point is used to characterize the earliest time point when the electric vehicle starts to exchange electricity at the second target site.

Among them, the reason why it is necessary to set the first preset time threshold is to prevent the electric vehicle from charging first and then charging if the time interval between charging and battery swapping is too short after arriving at the second target power station. The solution of battery swapping will not have the beneficial effect of saving costs. On the contrary, it may increase the power consumption and costs of driving.

Among them, the reason why the second preset duration threshold needs to be determined is to ensure that if the electric vehicle is charged first and then swaps battery, it can arrive at the location where battery swap can be performed at the second target site no later than the fourth earliest time point. It will not cause any delay to the overall scheduling plan.

Furthermore, since the electric vehicle may not be in a fully charged state at the current location, in order to calculate the first optional stay time for charging and/or battery swapping after the electric vehicle departs from the current location, further calculations are performed to obtain each For the stay duration data corresponding to the stay plan data, see Figure 3. Step S104 may also include the following sub-steps S104a~S104c:

Sub-step S104a, determine the third time point when the electric vehicle arrives at the first target site and the first power consumption based on the current location data of the electric vehicle and the fixed location data of the first target site.

Sub-step S104b: According to the current driving status data of the electric vehicle, the first power consumption and the first service arrangement data, it is determined that after the electric vehicle arrives at the first target site at the third time point, the electric vehicle will be charged and/or replaced until it is fully charged. The first minimum stay duration of the state.

Sub-step S104c, determine the first shortest stay duration as the stay duration of the electric vehicle at the first target site.

Among them, in sub-steps S104a~S104c, the first target station is the first optional charging and swapping station for electric vehicles to charge and/or swap batteries after starting from the current location during the execution of trunk long-distance transportation tasks, that is, the first optional charging and swapping station. A target site is the first of at least one optional charging and swapping station located between the current location and the destination location of the electric vehicle that needs to stay in sequence.

The third time point is used to represent the time point when the electric vehicle reaches the first target site. The first power consumption is used to represent the power consumption of the power battery during the distance when the electric vehicle travels from the current location to the first target site.

Among them, similar to the aforementioned second service arrangement data, the first service arrangement data is used to represent the time period during which the electric vehicle can be charged and the battery can be exchanged after arriving at the first target site at the third time point.

Among them, according to the current position data of the electric vehicle and the fixed position data of the first target site, the driving distance of the electric vehicle from the current position to the first target site can be determined, and further the preset power consumption model can be used to calculate the driving distance of the electric vehicle. The first power consumption and driving time from the current location to the first target station are determined to determine the third time point. According to the service arrangement of the first target site at the first point in time, the first service arrangement data can be obtained. According to the first power consumption and the first service arrangement data, the earliest time point at which the electric vehicle is charged and/or battery swapped to a fully charged state at the first target station can be determined, that is, the first minimum stay duration is the third time point and The length of time between this earliest time point.

Furthermore, considering that electric vehicles usually support single charging gun charging and dual charging gun charging, the charging time of dual charging gun charging is shorter than that of single charging gun charging. Therefore, in order to save the charging time of electric vehicles and improve user experience, in When scheduling electric vehicles, the time schedule for single charging gun charging and dual charging gun charging can also be given.

Specifically, the energy replenishment plan data can also be used to characterize the starting time point and ending time point of single charging gun charging of electric vehicles at the second target site, the starting time point and ending time point of dual charging gun charging, and The starting time point and the ending time point for power exchange.

Furthermore, considering that for electric vehicles, driving uphill consumes more power than driving on flat ground, and energy feedback is usually obtained when driving downhill, so the topography of the driving route will affect the power consumption of electric vehicles. . In order to more accurately calculate the arrival time of the electric vehicle at the optional charging and swapping station for charging and/or battery swapping and the power consumption during driving, referring to Figure 4, sub-step S104e may include:

Step A: Obtain driving route data based on the fixed location data of the first target site and the fixed location data of the second target site.

Step B: Determine the second time point and second power consumption of the electric vehicle when it reaches the second target site based on the driving route data.

Among them, the driving route data is used to represent the driving distance and terrain distribution of the electric vehicle from the first target site to the second target site. That is, the distance of the driving route of the electric vehicle from the first target site to the second target site, and the sequential distribution of terrain along the driving route.

It can be seen from the above embodiments of the present invention that in the embodiments of the present invention, the fixed location data of the optional charging and swapping stations are first determined, then the dispatching objective function is constructed, the dispatching constraints are determined, and finally the stay recommendation data is obtained using the preset optimal solution algorithm. Complete the scheduling of the charging and swapping needs of each electric vehicle. Compared with the existing technology, this embodiment realizes the overall intelligent dispatching of the charging and replacing needs of electric vehicles when performing trunk long-distance transportation tasks, so that electric vehicles have the most reasonable charging and replacing scheduling plan when performing transportation tasks, improving It improves the overall efficiency of electric vehicles in completing long-distance transportation tasks on trunk lines.

Example 2

As shown in Figure 5, Figure 5 is a schematic flow chart of an intelligent dispatching method for electric vehicle mainline long-distance transportation charging and replacement disclosed in Example 2 of the present application. The method includes:

Step S201: Determine the fixed position data of at least one optional charging and swapping station based on the current position data and destination position data of the electric vehicle during the long-distance transportation mission on the trunk line.

In this embodiment, step S201 is basically the same or similar to step S101 in the aforementioned example one, and will not be described again.

Step S202: Construct a scheduling objective function.

In this embodiment, step S202 is basically the same or similar to step S102 in the aforementioned example one, and will not be described again.

Step S203: Determine scheduling constraints based on the current location data, destination location data, current driving status data of the electric vehicle, and fixed location data of all optional charging and swapping stations.

In this embodiment, step S203 is basically the same or similar to step S103 in the aforementioned example one, and will not be described again.

Step S204: Determine cost constraints based on the charging and swapping arrangement data of the electric vehicle and the charging standard data of all optional charging and swapping stations.

In this embodiment, the charging and swapping schedule data is used to represent the starting time point and the ending time point for electric vehicles to charge at all optional charging and swapping stations, as well as the starting time point for charging at all optional charging and swapping stations. and termination time point.

In this embodiment, the charging standard data is used to represent charging service charging rules and battery replacement service charging rules. The specific representation method is not limited and can be set according to the actual situation.

In this embodiment, it is considered that the price of charging an electric vehicle is usually lower than the price of battery swapping, and the charging standards of optional charging and swapping stations located in different locations may also be different. In practical applications, when electric vehicles perform trunk long-distance transportation tasks, not only the time cost is usually considered, but also the cost of transportation. Therefore, in order to avoid excessive cost, cost constraints can be set to control electric vehicles to perform trunk long-distance transportation tasks. The cost, that is, the cost constraint is used to constrain the total cost of electric vehicles to complete trunk long-distance transportation tasks to be less than or equal to the preset cost threshold. There is no limit to the specific spending threshold, and users can make reasonable settings based on their actual acceptance range.

In this embodiment, the execution order of step S202, step S203 and step S204 is not limited, and can be reasonably selected according to actual application requirements.

Step S205: According to the scheduling objective function, the cost constraint condition and the scheduling constraint condition, use a preset optimal solution algorithm to obtain stay recommendation data.

In this embodiment, the main difference between step S205 and step S104 in the aforementioned example one is that the cost constraint is further added. Other contents are basically the same or similar, and will not be described again here.

It can be seen from the above embodiments of the present invention that the embodiments of the present invention first determine the fixed location data of the optional charging and swapping stations, secondly construct the dispatching objective function, determine the dispatching constraints and cost constraints, and finally use the preset optimal solution algorithm to obtain the stay Recommend data to complete the scheduling of the entire charging and replacement needs of electric vehicles. Compared with Example 1, this embodiment not only realizes the overall intelligent scheduling of the charging and replacement needs of electric vehicles when performing trunk long-distance transportation tasks, but also adds cost constraints, allowing users to freely control the cost value of the entire task. It improves the overall efficiency of electric vehicles in completing tasks and gives users more choices, which is conducive to further improving user experience.

Example three

As shown in Figure 6, Figure 6 is a schematic structural block diagram of an intelligent dispatching device for electric vehicle trunk line long-distance transportation charging and swapping disclosed in Example 3 of the present application. The device includes:

The encoding module is used to determine the fixed position data of at least one optional charging and swapping station based on the current position data and destination position data of the electric vehicle during the long-distance transportation mission on the trunk line.

In this embodiment, the main line refers to the line that serves as the backbone of the transportation network for long-distance transportation tasks that can be performed by electric vehicles. Since trunk lines are usually cross-regional connecting lines, electric vehicles usually need to be replenished midway when performing transportation tasks along trunk lines to travel from the starting point to the destination. Therefore, there are charging and charging stations at a certain distance along the trunk lines for electric vehicles. /or multiple energy-supply power stations for power exchange. The locations of these energy-supplement power stations are usually fixed and can meet the charging and/or power-exchange needs of electric vehicles at least once while performing transportation tasks.

In this embodiment, the electric vehicle refers to an electric vehicle that performs long-distance transportation tasks along the main line. Its current latest geographical location can be characterized by current location data, that is, the current location data is used to characterize the electric vehicle's position when performing this long-distance transportation task on the main line. Current location. There are no restrictions on parameters such as the type, brand, type of power battery, number of passengers and maximum full load mass of electric vehicles. The encoding module is used to obtain the current geographical location data.

In this embodiment, the geographical location of the destination of this trunk long-distance transportation task performed by the electric vehicle can be characterized by destination location data. The specific location of the destination where the electric vehicle performs this trunk line long-distance transportation task is not limited, and it may or may not be located along the trunk line. However, for all electric vehicles, during the long-distance transportation mission of this main line, they can directly reach the destination after the last charging and/or battery replacement at the power station along the main line. The encoding module is used to obtain destination location data.

In this embodiment, when an electric vehicle is performing a long-distance transportation mission on a trunk line and moves from its current location to its destination, it will pass along a number of energy-supplementing power stations that can be used for charging and/or exchanging electricity. These energy-supplementing power stations are: It is an optional charging and swapping station. The specific geographical location of optional charging and swapping stations is characterized by fixed location data. The encoding module is used to obtain fixed location data of optional charging and swapping stations.

Objective function building module, used to build scheduling objective functions.

In this embodiment, the dispatch objective function is used to calculate and obtain the highest dispatch score based on the electric vehicle's current location data, destination location data, stay plan data, stay duration data, and fixed location data of all optional charging and swapping stations.

In this embodiment, the stay plan data is used to represent whether to stay at all optional charging and swapping stations. The specific representation method is not limited and can be reasonably selected according to actual application requirements. For example, the stay plan data can use 5 coding bits, which correspond to a total of 5 optional charging and swapping stations A, B, C, D, and E distributed along the main line, where 1 means stay and 0 means no stay, then When the stay data is 01101, it means that the vehicle will not stay at the two optional charging and swapping stations A and D, but will stay at the three optional charging and swapping stations B, C and E. The objective function building block is used to obtain stay plan data.

In this embodiment, the stay time data is used to represent the stay time at all optional charging and swapping stations. When it is determined to stay at a specific optional charging and swapping station, a length of stay can be estimated or set in advance. The value of the length of stay can be a fixed value or an indefinite value. The specific length of time does not vary. limit. The objective function building block is used to obtain dwell time data.

For example, the fixed duration value can be to stay at an optional charging and swapping station for 30 minutes, 40 minutes, 50 minutes, etc.; the variable duration value can be to stay at an optional charging and swapping station for 35 minutes, 45 minutes, or 55 minutes, etc.

In this embodiment, the dispatching score is at least determined based on the total time it takes for the electric vehicle to complete the trunk long-distance transportation task. The specific calculation method of the dispatching score and the upper limit of the score are not limited, and can be reasonably selected according to actual application requirements.

For example, the dispatching score can be determined only based on the total time for electric vehicles to complete trunk long-distance transportation tasks, that is, the shorter the total time, the higher the dispatching score; the dispatching score can also be determined based on the total time for electric vehicles to complete trunk long-distance transportation tasks. , and the total cost or total mileage are comprehensively determined.

In this embodiment, based on the current location data, stay plan data, destination location data of the electric vehicle, and the fixed location data of all optional charging and swapping stations, the total travel distance of the electric vehicle from the current location to the destination location can be calculated. mileage, and further calculate the driving time of the electric vehicle based on the preset driving speed rules; based on the dwell time data, the energy replenishing time of the electric vehicle at all optional charging and swapping stations can be calculated; the driving time is related to The sum of the energy replenishment times is the total time for electric vehicles to complete long-distance transportation tasks on trunk lines. The objective function building block is used to calculate the highest scheduling score.

The constraint building module is used to determine scheduling constraints based on the current location data, destination location data, current driving status data of electric vehicles, and the fixed location data of all optional charging and swapping stations.

In this embodiment, the scheduling constraints are used to constrain the electric vehicle to all optional charging and swapping stations that can be reached from the current location when the electric vehicle is not charging and/or exchanging batteries; when the electric vehicle is fully charged, it can reach from each available charging and exchanging station. Other optional charging and swapping stations that can be reached by starting from the optional charging and swapping station; and all optional charging and swapping stations where the electric vehicle can reach the destination without charging and/or swapping batteries when it is fully charged. power station. The reason why these scheduling constraints are set is to ensure that the finalized stay recommendation data can ensure that electric vehicles will not break down due to insufficient power when performing trunk long-distance transportation tasks, and to ensure that electric vehicles can drive to their destinations. Constraint building blocks are used to determine constraints.

In this embodiment, the current driving status data can at least be used to represent the remaining available power of the power battery, thereby estimating the distance that the electric vehicle can travel, and determining the specific optional charging and swapping stations that it can reach.

The optimal solution algorithm calculation module is used to obtain stay recommendation data using the preset optimal solution algorithm based on the scheduling objective function and scheduling constraints.

In this embodiment, the specific type of the preset optimal solution algorithm is not limited, and the user can select according to actual needs. For example, simulated annealing algorithm, genetic algorithm, particle swarm optimization algorithm, etc. can be selected.

In this embodiment, the stay recommendation data is used to represent whether the recommended electric vehicle will stay at all optional charging and swapping stations. The specific representation method is not limited and can be reasonably selected according to actual application requirements. For example, the stay recommendation data can use 5 coding bits, which correspond to a total of 5 optional charging and swapping stations A, B, C, D, and E distributed along the main line, where 1 means stay and 0 means no stay, then When the stay recommendation data is 01101, it means not staying at the two optional charging and swapping stations A and D, but staying at the three optional charging and swapping stations B, C and E. Electric vehicles can complete the charging and replacement of batteries during the entire trunk transportation task based on the stay recommendation data. The optimal solution algorithm calculation module is used to derive stay recommendation data.

Optionally, the optimal solution algorithm calculation module is also used to determine the first target site and the second target site based on the stay plan data of the electric vehicle; wherein the first target site and the second target site are electric vehicles performing long-distance travel on trunk lines. Optional charging and swapping stations that will stop in sequence during the transportation mission;

Determine the second time point when the electric vehicle arrives at the second target site and the second time point when the electric vehicle arrives at the second target site based on the first time point when the electric vehicle leaves the first target site, as well as the fixed position data of the first target site and the fixed position data of the second target site. power consumption;

According to the second power consumption and the second service arrangement data, determine the second shortest stay time for the electric vehicle to charge and/or replace electricity to a fully charged state after arriving at the second target site at the second point in time; wherein, the second The service arrangement data is used to characterize the time period during which the electric vehicle can be charged and the battery can be swapped after arriving at the second target site at the second point in time;

The second shortest stay time is determined as the stay time of the electric vehicle at the second target site.

Further, the optimal solution algorithm calculation module is also used to determine the third time point and the first power consumption of the electric vehicle when it arrives at the first target site based on the current location data of the electric vehicle and the fixed location data of the first target site;

According to the current driving status data of the electric vehicle, the first power consumption and the first service arrangement data, it is determined that after the electric vehicle arrives at the first target site at the third time point, the first time for charging and/or battery swapping to a fully charged state is determined. The minimum length of stay; wherein, the first service arrangement data is used to represent the time period during which the electric vehicle can be charged and the battery can be exchanged after arriving at the first target site at the third time point;

The first shortest stay time is determined as the stay time of the electric vehicle at the first target site.

Further, the optimal solution algorithm calculation module is also used to determine the second shortest stay duration and corresponding energy replenishment plan data based on the second power consumption and the second service arrangement data; wherein the energy replenishment plan data is used to characterize the electric vehicle The starting time point and the ending time point for charging at the second target station, and the starting time point and ending time point for battery swapping.

Furthermore, the optimal solution algorithm calculation module is also used when the first earliest time point for the electric vehicle to be charged to a fully charged state at the second target site is earlier than the second earliest time point for charging to a fully charged state, and the electric vehicle When the duration between the third earliest time point at which charging can be performed at the second target site and the fourth earliest time point at which battery swapping can be performed is greater than or equal to the first preset duration threshold, it is determined that the electric vehicle performs battery swapping at the second target site. The starting time point and the ending time point are the fourth earliest time point and the first earliest time point respectively. The starting time point for charging the electric vehicle at the second target site is the third earliest time point. The electric vehicle is charging at the second target site. The time between the end time point of charging and the fourth earliest time point is greater than or equal to the second preset time length threshold.

Furthermore, the time period during which charging is possible includes the time period during which single charging gun charging is possible and the time period during which dual charging gun charging is possible;

Correspondingly, the energy replenishment plan data is used to characterize the starting time point and the ending time point of the electric vehicle charging with a single charging gun at the second target site, the starting time point and the ending time point of charging the double charging gun, and the replacement time point. The start time point and the end time point of electricity.

Further, the optimal solution calculation module is also used to obtain driving route data based on the fixed position data of the first target site and the fixed position data of the second target site; the driving route data is used to represent the driving from the first target site to the second target. The driving distance and terrain distribution of the site;

According to the driving route data, a second time point and a second power consumption of the electric vehicle arriving at the second target site are determined.

Optionally, the objective function building module is also used to base on the electric vehicle's current location data, destination location data, stay plan data, stay duration data, charging and swapping arrangement data, as well as fixed location data and all optional charging and swapping stations. Charging standard data is used to calculate the highest dispatch score;

Among them, the charging and swapping arrangement data is used to represent the starting time point and ending time point of charging at all optional charging and swapping stations, as well as the starting time point and ending time point of charging at all optional charging and swapping stations; Charging standard data is used to characterize charging service charging rules and battery replacement service charging rules;

The dispatch score is used to comprehensively evaluate the total time and total cost of electric vehicles completing trunk long-distance transportation tasks.

Optionally, the constraint building module is also used to determine the cost constraints based on the charging and swapping arrangement data of the electric vehicle and the charging standard data of all optional charging and swapping stations; wherein the charging and swapping arrangement data is used to characterize the charging and swapping arrangement data in all available charging and swapping stations. The starting time point and the ending time point for charging at the optional charging and swapping stations, as well as the starting time point and ending time point for charging at all optional charging and swapping stations; charging standard data is used to characterize charging service charging rules and battery swapping Service charging rules; cost constraints are used to constrain the total cost of electric vehicles to complete trunk long-distance transportation tasks to be less than or equal to the preset cost threshold;

Correspondingly, based on the scheduling objective function and scheduling constraints, using the preset optimal solution algorithm to obtain stay recommendation data includes:

According to the scheduling objective function, cost constraints and scheduling constraints, the preset optimal solution algorithm is used to obtain stay recommendation data.

It can be seen from the above embodiments of the present invention that through an intelligent dispatching device for electric vehicle main line long-distance transportation charging and swapping in this embodiment, the corresponding intelligent dispatching method for electric vehicle main line long-distance transportation charging and replacing in the multiple method embodiments mentioned above can be realized. And it has the beneficial effects of the corresponding method embodiments, and the specific beneficial effects will not be described again here.

Up to this point, specific embodiments of the present application have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the specific order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous.

The present application is described with reference to flowcharts and/or block diagrams of methods according to embodiments of the present application. It will be understood that each process and/or block in the flowchart illustrations and/or block diagrams, and combinations of processes and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a use A device for realizing the functions specified in a process or processes in a flowchart and/or a block or blocks in a block diagram.

It should also be noted that the terms "comprises," "comprises," or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements not only includes those elements, but also includes Other elements are not expressly listed or are inherent to the process, method, article or equipment. Without further limitation, an element qualified by the statement "comprises a..." does not exclude the presence of additional identical elements in the process, method, good, or device that includes the element.

Those skilled in the art will understand that embodiments of the present application may be provided as methods or devices. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer usable program code embodied therein.

Each embodiment in this specification is described in a progressive manner. The same and similar parts between the various embodiments can be referred to each other. Each embodiment focuses on its differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple. For relevant details, please refer to the partial description of the method embodiment.

The above are only examples of the present application and are not used to limit the present application. To those skilled in the art, various modifications and variations may be made to this application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this application shall be included in the scope of the claims of this application.

Claims (7)

1. An intelligent dispatching method for electric vehicle trunk line long-distance transportation charging and swapping, characterized in that the method includes: Determine the fixed location data of at least one optional charging and swapping station based on the current location data and destination location data of the electric vehicle during its long-distance transportation mission; Construct a dispatch objective function; wherein the dispatch objective function is used to calculate the current location data of the electric vehicle, the destination location data, stay plan data, stay duration data, and all optional charging and swapping stations. The fixed location data is calculated to obtain the highest scheduling score; the stay plan data is used to characterize whether to stay at all the optional charging and swapping stations; the stay duration data is used to characterize the stay at all the optional charging and swapping stations. Length of stay; the scheduling score is at least determined based on the total time for the electric vehicle to complete the trunk long-distance transportation task; Determine scheduling constraints based on the current location data, the destination location data, current driving status data of the electric vehicle, and the fixed location data of all optional charging and swapping stations; wherein, the scheduling The constraint conditions are used to restrict all the optional charging and swapping stations that the electric vehicle can reach when not charging and/or exchanging batteries. When the electric vehicle is fully charged, it starts from each of the optional charging and exchanging stations. Other optional charging and swapping stations that are reachable by the battery swapping station and have not been passed through, and all descriptions of the electric vehicle that can reach its destination without further charging and/or battery swapping when it is fully charged. Optional charging and swapping station; the current driving status data is used to represent the remaining available power of the power battery; According to the scheduling objective function and the scheduling constraints, a preset optimal solution algorithm is used to obtain stay recommendation data; wherein the stay recommendation data is used to represent the recommended electric vehicles at all optional charging and swapping stations. whether to stay; The method of obtaining stay recommendation data using a preset optimal solution algorithm based on the scheduling objective function and the scheduling constraints includes: According to the stay plan data of the electric vehicle, a first target site and a second target site are determined; wherein the first target site and the second target site are for the electric vehicle when performing the trunk long-distance transportation. The optional charging and swapping stations that will be visited during the mission; According to the first time point when the electric vehicle leaves the first target site, and the fixed position data of the first target site and the fixed position data of the second target site, it is determined that the electric vehicle The second time point and second power consumption when the vehicle arrives at the second target site; According to the second power consumption and the second service arrangement data, it is determined that after the electric vehicle arrives at the second target site at the second time point, it is charged and/or replaced with a second battery to a fully charged state. The minimum length of stay and the corresponding recharge plan data; wherein the second service arrangement data is used to represent the time period and the period during which the electric vehicle can be charged after arriving at the second target site at the second time point. The time period during which power exchange can be carried out; the energy replenishment plan data is used to characterize the starting time point and the end time point of charging of the electric vehicle at the second target site, as well as the starting time point and the end time point of power swapping. Termination time point; when the first earliest time point at which the electric vehicle is charged to a fully charged state at the second target site is earlier than the second earliest time point at which the electric vehicle is charged to a fully charged state, and the electric vehicle is at the second target site When the duration between the third earliest time point at which charging is possible at the second target site and the fourth earliest time point at which battery swapping is possible is greater than or equal to the first preset duration threshold, it is determined that the electric vehicle is charging at the second target site The starting time point and the ending time point of power exchange are the fourth earliest time point and the first earliest time point respectively, and the starting time point of charging the electric vehicle at the second target site is The starting time point is the third earliest time point, and the time length from the end time point when the electric vehicle is charged at the second target site to the fourth earliest time point is greater than or equal to the second preset time length threshold. ; The second shortest stay duration is determined as the stay duration of the electric vehicle at the second target site. 2. The method according to claim 1, characterized in that when the first target site is the first optional charging and swapping station where the electric vehicle needs to stay after the current location, the method further include: Determine the third time point and first power consumption of the electric vehicle when it arrives at the first target site according to the current location data of the electric vehicle and the fixed location data of the first target site; According to the current driving status data of the electric vehicle, the first power consumption and the first service arrangement data, it is determined that the electric vehicle will be charged after arriving at the first target site at the third time point. and/or the first minimum stay time for battery swapping to a fully charged state; wherein the first service arrangement data is used to represent that the electric vehicle can perform operations after arriving at the first target site at the third time point. The charging time period and the battery replacement time period; The first shortest stay duration is determined as the stay duration of the electric vehicle at the first target site. 3. The method according to claim 1, wherein the charging time period includes a single charging gun charging time period and a dual charging gun charging time period; Correspondingly, the energy replenishment plan data is used to characterize the starting time point and the ending time point for single charging gun charging of the electric vehicle at the second target site, and the starting time point and ending time point for dual charging gun charging. time point, as well as the starting time point and ending time point of battery replacement. 4. The method of claim 1, wherein the first time point at which the electric vehicle leaves from the first target site, and the fixed location data of the first target site and The fixed location data of the second target site, determining the second time point and the second power consumption of the electric vehicle arriving at the second target site include: According to the fixed location data of the first target site and the fixed location data of the second target site, travel route data is obtained; the travel route data is used to represent the first target site traveling to the The driving distance and terrain distribution of the second target site; According to the driving route data, a second time point and a second power consumption of the electric vehicle arriving at the second target site are determined. 5. The method according to claim 1, characterized in that the scheduling objective function is used to calculate the electric vehicle according to the current location data, the destination location data, stay plan data, stay duration data, charging The battery swap arrangement data, as well as the fixed location data and charging standard data of all the optional charging and swap stations, are calculated to obtain the highest dispatch score; Wherein, the charging and swapping arrangement data is used to represent the starting time point and the ending time point of charging at all the optional charging and swapping stations, as well as the starting time of charging at all the optional charging and swapping stations. point and termination time point; the charging standard data is used to characterize charging service charging rules and battery replacement service charging rules; The scheduling score is used to comprehensively evaluate the total time and total cost of the electric vehicle completing the trunk long-distance transportation task. 6. The method according to claim 1, characterized in that, the method further comprises: According to the charging and swapping arrangement data of the electric vehicle and the charging standard data of all the optional charging and swapping stations, the cost constraints are determined; wherein the charging and swapping arrangement data is used to characterize the charging and swapping arrangements in all the optional charging and swapping stations. The starting time point and the ending time point for charging at the battery swapping station, as well as the starting time point and ending time point for charging at all the optional charging and swapping stations; the charging standard data is used to characterize charging service charging rules and Charging rules for power exchange services; the cost constraint conditions are used to constrain the total cost of the electric vehicle to complete the trunk long-distance transportation task to be less than or equal to the preset cost threshold; Correspondingly, using the preset optimal solution algorithm to obtain stay recommendation data according to the scheduling objective function and the scheduling constraints includes: According to the scheduling objective function, the cost constraint condition and the scheduling constraint condition, a preset optimal solution algorithm is used to obtain stay recommendation data. 7. An intelligent dispatching device for battery charging and replacement in trunk line long-distance transportation of electric vehicles, characterized in that the device includes: An encoding module used to determine the fixed position data of at least one optional charging and swapping station based on the current position data and destination position data of the electric vehicle during the long-distance transportation mission on the trunk line; An objective function building module is used to construct a scheduling objective function; wherein the scheduling objective function is used to calculate the current location data of the electric vehicle, the destination location data, stay plan data, stay duration data, and all The fixed location data of the optional charging and swapping stations are calculated to obtain the highest dispatch score; the stay plan data is used to represent whether to stay at all the optional charging and swapping stations; the stay duration data is used to represent whether to stay at all the optional charging and swapping stations. The length of stay at the optional charging and swapping station; the scheduling score is at least determined based on the total time for the electric vehicle to complete the trunk long-distance transportation task; A constraint building module configured to determine scheduling constraints based on the current location data, the destination location data, the current driving status data of the electric vehicle, and the fixed location data of all optional charging and swapping stations. ; Wherein, the scheduling constraints are used to constrain all the optional charging and swapping stations that the electric vehicle can reach when not charging and/or swapping. The electric vehicle starts from every charging station when fully charged. Other optional charging and swapping stations that can be reached without passing through one of the optional charging and swapping stations, and the electric vehicle can reach them by starting out in a fully charged state and without further charging and/or swapping batteries. All the optional charging and swapping stations at the destination; the current driving status data is used to represent the remaining available power of the power battery; An optimal solution algorithm calculation module, configured to obtain stay recommendation data using a preset optimal solution algorithm according to the dispatch objective function and the dispatch constraint conditions; wherein the stay recommendation data is used to characterize the recommended electric vehicle Whether to stop at all the optional charging and swapping stations; The optimal solution algorithm calculation module is also used to determine a first target site and a second target site according to the stay plan data of the electric vehicle; wherein the first target site and the second target site are The optional charging and swapping station where the electric vehicle will stop in sequence while performing the trunk long-distance transportation task; According to the first time point when the electric vehicle leaves the first target site, and the fixed position data of the first target site and the fixed position data of the second target site, it is determined that the electric vehicle The second time point and second power consumption when the vehicle arrives at the second target site; According to the second power consumption and the second service arrangement data, it is determined that after the electric vehicle arrives at the second target site at the second time point, it is charged and/or replaced with a second battery to a fully charged state. The minimum length of stay and the corresponding recharge plan data; wherein the second service arrangement data is used to represent the time period and the period during which the electric vehicle can be charged after arriving at the second target site at the second time point. The time period during which power exchange can be carried out; the energy replenishment plan data is used to characterize the starting time point and the end time point of charging of the electric vehicle at the second target site, as well as the starting time point and the end time point of power swapping. Termination time point; when the first earliest time point at which the electric vehicle is charged to a fully charged state at the second target site is earlier than the second earliest time point at which the electric vehicle is charged to a fully charged state, and the electric vehicle is at the second target site When the duration between the third earliest time point at which charging is possible at the second target site and the fourth earliest time point at which battery swapping is possible is greater than or equal to the first preset duration threshold, it is determined that the electric vehicle is charging at the second target site The starting time point and the ending time point of power exchange are the fourth earliest time point and the first earliest time point respectively, and the starting time point of charging the electric vehicle at the second target site is The starting time point is the third earliest time point, and the time length from the end time point when the electric vehicle is charged at the second target site to the fourth earliest time point is greater than or equal to the second preset time length threshold. ; The second shortest stay duration is determined as the stay duration of the electric vehicle at the second target site.