CN116720714B

CN116720714B - Intelligent scheduling method and device for charging and power changing of electric vehicle

Info

Publication number: CN116720714B
Application number: CN202310983247.9A
Authority: CN
Inventors: 陈胜兵; 杨丽娅; 张东江; 贺荣霞; 张斌; 李萱
Original assignee: Shanghai Enneagon Energy Technology Co ltd; Beijing Jiuxing Zhiyan Transportation Technology Co ltd
Current assignee: Shanghai Enneagon Energy Technology Co ltd; Beijing Jiuxing Zhiyan Transportation Technology Co ltd
Priority date: 2023-08-07
Filing date: 2023-08-07
Publication date: 2023-10-20
Anticipated expiration: 2043-08-07
Also published as: CN116720714A

Abstract

The application provides an intelligent dispatching method and device for charging and power conversion of electric vehicles, wherein the method comprises the steps of determining the quantity of all electric vehicles in a range through a specified regional range, identifying by using first coding data, determining a planned receiving vehicle of an entity power station and 1 virtual power station, identifying by using second coding data, constructing an adaptability function, determining a first constraint condition, and then carrying out iteration by using a genetic algorithm to obtain recommended dispatching data. Compared with the prior art, the application performs overall dispatching for a plurality of electric vehicles which execute the transportation plan in the appointed region range at the same time, and can ensure that the time for completing charging and/or power changing of all the electric vehicles is as early as possible under the premise that all the electric vehicles can smoothly execute the transportation plan. And better user experience can be obtained.

Description

Intelligent scheduling method and device for charging and power changing of electric vehicle

Technical Field

The application relates to the technical field of electric vehicles, in particular to an intelligent scheduling method and device for charging and power changing of an electric vehicle.

Background

Electric vehicles have been rapidly developed in recent years because of their energy-saving and environment-friendly properties. However, the problem that the mileage is short and the consumer has mileage anxiety exists in both commercial vehicles and passenger vehicles. In order to solve the problems of anxiety of mileage and quick energy supplement of consumers, many electric commercial vehicles and electric passenger vehicles support a charging mode and a power conversion mode, and more energy supplementing power stations capable of providing charging and/or power conversion services are also available.

Because of the limited service capacity of each energy supplementing station, although some intelligent scheduling schemes for making charging and/or changing electricity for electric vehicles based on implementation and operation conditions of the energy supplementing stations have been presented at present, almost all of the schemes are used for intelligent scheduling for single trolley. However, commercial vehicles such as electric heavy trucks, electric stirrers, electric tractors and the like generally distribute and manage transportation tasks in a fleet manner, and an optimal comprehensive charging and/or power conversion scheme cannot be provided for a plurality of electric vehicles simultaneously executing the transportation tasks by utilizing the existing scheduling strategies.

Disclosure of Invention

In view of the above, the embodiment of the application provides an intelligent scheduling method and device for charging and power changing of an electric vehicle, which are used for solving the problem of unreasonable scheduling strategy for commercial electric vehicles in the prior art.

The first aspect of the embodiment of the application discloses an intelligent scheduling method for charging and power changing of an electric vehicle, which comprises the following steps:

determining first coded data and second coded data according to the number of electric vehicles and the number of entity power stations in a specified regional range; the first coded data are used for respectively identifying all the electric vehicles, and the second coded data are used for representing the electric vehicles originally scheduled to be received by each entity power station and 1 virtual power station; the number of the battery replacement cells and/or the charging guns of the entity power station is greater than 1; the number of the battery replacement cells and the charging guns of the virtual power station is 0, and the distances between the virtual power station and the current positions of all the electric vehicles are 0;

Constructing an intelligent scheduling fitness function; the intelligent scheduling fitness function is at least used for calculating and obtaining earliest time for all electric vehicles to complete charging and/or power changing according to current position data and current running state data of the electric vehicles and fixed position data and current service state data corresponding to all the entity power stations; the current running state data is at least used for representing available electric quantity of a battery; the current service state data is at least used for representing waiting time of charging and/or changing electricity;

determining a first constraint condition according to the current position data, the current running state data and the first target position data corresponding to all the electric vehicles and the fixed position data corresponding to all the entity power stations; the first target position data is used for representing the position of a first destination of the electric vehicle executing the current transportation plan; the first constraint condition is used for representing the electric vehicles which can reach the corresponding first destination without charging or changing electricity, and all the entity power stations which can be reached by each electric vehicle without charging or changing electricity;

According to the intelligent scheduling fitness function, the first constraint condition, the first coded data and the second coded data, performing iterative computation by using a preset genetic algorithm to obtain scheduling recommendation data; the scheduling recommendation data is used for representing all the electric vehicles received by the entity power station and the virtual power station in recommendation.

The second aspect of the embodiment of the application discloses an intelligent scheduling device for charging and power changing of an electric vehicle, which comprises the following components:

the coding module is used for determining first coding data and second coding data according to the number of electric vehicles and the number of entity power stations in the appointed region range; the first coded data are used for respectively identifying all the electric vehicles, and the second coded data are used for representing the electric vehicles originally scheduled to be received by each entity power station and 1 virtual power station; the number of the battery replacement cells and/or the charging guns of the entity power station is greater than 1; the number of the battery replacement cells and the charging guns of the virtual power station is 0, and the distances between the virtual power station and the current positions of all the electric vehicles are 0;

the fitness function construction module is used for constructing an intelligent scheduling fitness function; the intelligent scheduling fitness function is at least used for calculating and obtaining earliest time for all electric vehicles to complete charging and/or power changing according to current position data and current running state data of the electric vehicles and fixed position data and current service state data corresponding to all the entity power stations; the current running state data is at least used for representing available electric quantity of a battery; the current service state data is at least used for representing waiting time of charging and/or changing electricity;

The constraint construction module is used for determining a first constraint condition according to the current position data, the current running state data and the first target position data corresponding to all the electric vehicles and the fixed position data corresponding to all the entity power stations; the first target position data is used for representing the position of a first destination of the electric vehicle executing the current transportation plan; the first constraint condition is used for representing the electric vehicles which can reach the corresponding first destination without charging or changing electricity, and all the entity power stations which can be reached by each electric vehicle without charging or changing electricity;

the optimal solution algorithm calculation module is used for carrying out iterative calculation by utilizing a preset genetic algorithm according to the intelligent scheduling fitness function, the first constraint condition, the first coded data and the second coded data to obtain scheduling recommendation data; the scheduling recommendation data is used for representing all the electric vehicles received by the entity power station and the virtual power station in recommendation.

The method comprises the steps of firstly determining the number of all electric vehicles in a range by specifying a region range, identifying by using first coding data, determining planned reception vehicles of an entity power station and 1 virtual power station, identifying by using second coding data, constructing a fitness function, determining a first constraint condition, and then carrying out iteration by using a genetic algorithm to obtain recommended dispatching data. Compared with the prior art, the invention performs overall dispatching for a plurality of electric vehicles which execute the transportation plan in the appointed region range at the same time, and can ensure that the time for completing charging and/or power changing of all the electric vehicles is as early as possible under the premise that all the electric vehicles can smoothly execute the transportation plan.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic flow chart of an intelligent dispatching method for charging and changing electric power of an electric vehicle according to an embodiment of the application;

fig. 2 is a schematic flow chart of an intelligent dispatching method for charging and changing electric power of an electric vehicle according to a second embodiment of the application;

fig. 3 is a schematic flow chart of an intelligent dispatching method for charging and changing electric power of an electric vehicle according to a third embodiment of the application;

fig. 4 is a schematic flow chart of an intelligent dispatching method for charging and changing electric power of an electric vehicle according to a fourth embodiment of the application;

fig. 5 is a schematic block diagram of a charging and power-changing intelligent dispatching device for an electric vehicle according to a fifth embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

It should be noted that the terms "first," "second," "third," and "fourth," etc. in the description and claims of the present application are used for distinguishing between different objects and not for describing a particular sequential order. The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, apparatus, article, or device that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or device.

Example one

As shown in fig. 1, fig. 1 is a schematic flowchart of an intelligent dispatching method for charging and changing electricity of an electric vehicle according to an embodiment of the present application, where the intelligent dispatching method for charging and changing electricity of an electric vehicle includes:

step S101, determining the first encoded data and the second encoded data according to the number of electric vehicles and the number of physical power stations located in the specified geographical area.

In this embodiment, the designated area generally has a plurality of roads on which the electric vehicles can travel and a plurality of physical power stations on which the electric vehicles can be charged and/or replaced. Specific areas, terrains, climates and other conditions of the designated region range are not limited, and can be reasonably selected according to actual application requirements. For example, the specified geographic area may be a fixed area determined based on the geographic locations of the plurality of physical power stations or a variable area determined based on the real-time geographic locations of the plurality of electric vehicles.

In the present embodiment, parameters such as the type of electric vehicle, the brand, the type of power battery, the number of people on a nuclear load, and the maximum full load mass are not limited. For commercial vehicles such as an electric heavy truck, an electric mixer truck, an electric tractor and the like, the electric vehicle in the embodiment refers to an electric vehicle executing the current transportation plan in a specified region range, and the geographic position of a first destination of the current transportation plan executed by the electric vehicle can be represented by first target position data, that is, the first target position data is used for representing the position of the first destination of the electric vehicle executing the current transportation plan.

In this embodiment, the specific location of the first destination where the electric vehicle executes the present transportation plan is not limited, and may be located within the specified geographical area or not. However, for all electric vehicles, the first destination can be reached by only charging and/or replacing power in 0 or 1 entity power stations in the process of executing the transportation plan.

In this embodiment, the physical power station is built at a fixed position within a specified geographical area, and is provided with one or more charging and/or power exchanging devices, so that charging and/or power exchanging services can be provided for the electric vehicle, i.e. the number of power exchanging batteries and/or charging guns of the physical power station is greater than 1.

In this embodiment, the virtual power station is a power station that does not exist in reality, so that the number of battery cells and guns for charging that it has when executing the present method can be set to 0, and the distances from the current positions of all electric vehicles are also set to 0.

In this embodiment, the first encoded data is used to identify all electric vehicles respectively, and the specific identification mode is not limited, and may be reasonably selected according to actual application requirements. For example, 5 electric vehicles may be identified by numerals 1, 2, 3, 4, 5, or 5 electric vehicles may be identified by letters A, B, C, D, E.

In this embodiment, the second encoded data is used to represent the electric vehicle originally planned to be received by each entity power station and 1 virtual power station, and the specific representation mode is not limited, and may be reasonably selected according to the actual application requirements. In this embodiment, the electric vehicle that the virtual power station plans to receive is the electric vehicle that can reach the first destination where the present transportation plan is executed without charging and power exchange.

And S102, constructing an intelligent scheduling fitness function.

In this embodiment, the intelligent scheduling fitness function is at least used for calculating and obtaining the earliest time for all electric vehicles to complete charging and/or power exchange according to the current position data and the current running state data of the electric vehicles, and the fixed position data and the current service state data corresponding to all the entity power stations.

The current position data of the electric vehicle is used for representing the geographic position of the electric vehicle at the current moment, and the value of the data changes along with the real-time movement track of the electric vehicle.

The current driving state data is at least used to characterize the battery available power, i.e. the current driving state data of the specific electric vehicle is at least used to characterize the remaining available power of the battery powering the specific electric vehicle at the current moment.

The fixed location data is used to characterize a fixed geographical location, i.e. the fixed location data of a particular physical power station is used to characterize a specific geographical location of that physical power station within a specified geographical range.

The current service state data is at least used for representing waiting time of charging and/or changing electricity, namely the current service state data of the specific entity power station is at least used for representing waiting time length for starting charging and/or waiting time length for starting changing electricity after the electric vehicle runs to the specific entity power station at the current moment.

Optionally, the intelligent scheduling fitness function is used for calculating and obtaining earliest time for all electric vehicle vehicles to complete charging and/or power change according to current position data, current running state data, fixed position data corresponding to all entity power stations, current service state data and running terrain data of the electric vehicle.

The driving terrain data is used for representing the terrain distribution of the driving route between the current geographic position and the fixed geographic position of the entity power station, namely the terrain distribution of the driving route between the current geographic position of the specific electric vehicle and the fixed geographic position of all the entity power station.

For electric vehicles, power consumption is higher for uphill driving than for flat ground driving, and energy feedback is usually also available in downhill form, so that the topography of the route travelled can influence the furthest distance the electric vehicle can travel. Therefore, the running terrain data is further added to the intelligent scheduling fitness function to calculate the earliest time that all electric vehicles complete charging and/or power conversion, so that the calculation result is more accurate and reliable.

Step S103, determining a first constraint condition according to the current position data, the current running state data and the first target position data corresponding to all electric vehicles and the fixed position data corresponding to all entity power stations.

In this embodiment, the first constraint is used to characterize the electric vehicles that can reach the corresponding first destination without charging or changing electricity, and all the physical power stations that each electric vehicle can reach without charging or changing electricity.

Only electric vehicles which can reach the corresponding first destination without charging or changing electricity can be recommended as electric vehicles which are received by the virtual power station.

The electric vehicle can only be received by all corresponding physical power stations which can be reached without charging or power changing, otherwise, the electric vehicle is likely to be anchored in a half way due to insufficient electric quantity, and the transportation plan is difficult to finish.

Optionally, step S103 may further include: and determining a first constraint condition according to the current position data, the current running state data and the first target position data corresponding to all electric vehicles, the fixed position data corresponding to all entity power stations and the running topography data. By further adding the driving terrain data to calculate and determine the electric vehicles which can reach the corresponding first destination without charging or changing electricity and all the entity power stations which can be reached by each electric vehicle without charging or changing electricity, the calculation result can be accurate and reliable.

In this embodiment, the execution sequence of step S101, step S102 and step S103 is not limited, and may be reasonably selected according to the actual application requirement.

Step S104, performing iterative computation by using a preset genetic algorithm according to the intelligent scheduling fitness function, the first constraint condition, the first coded data and the second coded data to obtain scheduling recommendation data.

In this embodiment, the preset genetic algorithm is an optimal solution algorithm, and is configured to iteratively calculate the scheduling recommendation data according to the first encoded data and the second encoded data by using the intelligent scheduling fitness function and the first constraint condition. The specific types of the preset genetic algorithm are not limited, and can be reasonably selected according to actual application requirements.

In this embodiment, the scheduling recommendation data is used to characterize all electric vehicles received by the physical power station and the virtual power station, that is, the scheduling result characterized by the scheduling recommendation data can ensure that the time for completing charging and/or power changing of all electric vehicles is as early as possible on the premise that all electric vehicles can smoothly execute and complete the transportation plan.

As can be seen from the above embodiments of the present invention, in the embodiments of the present invention, by specifying a geographical range, the number of all electric vehicles within the range is determined and identified by using first encoded data, the planned reception vehicles of the entity power station and 1 virtual power station are determined and identified by using second encoded data, and an fitness function is constructed, and a first constraint condition is determined, and iteration is performed by using a genetic algorithm to obtain recommended scheduling data. Compared with the prior art, the method and the device for carrying out the whole dispatching of the electric vehicles can carry out the whole dispatching of the electric vehicles carrying out the transportation plan in the appointed region range, and the time for completing charging and/or power changing of all the electric vehicles is as early as possible on the premise that all the electric vehicles can smoothly carry out the transportation plan. In addition, when the calculation is carried out, a virtual power station is introduced and coding is carried out, and in the calculation process, the electric vehicle which does not need to be charged or replaced actually can be directly placed in the position to be received by the virtual power station, so that the calculation result is more efficient compared with other algorithms.

Example two

As shown in fig. 2, fig. 2 is a schematic flowchart of an intelligent scheduling method for charging and changing electricity of an electric vehicle according to a second embodiment of the present application, where the intelligent scheduling method for charging and changing electricity of an electric vehicle includes:

step S201, determining the first encoded data and the second encoded data according to the number of electric vehicles and the number of physical power stations located in the specified geographical area.

In this embodiment, step S201 is substantially the same as or similar to step S101 in the first embodiment, and will not be described herein.

Step S202, an intelligent scheduling fitness function is constructed.

In this embodiment, the step S202 is substantially the same as or similar to the step S102 in the first embodiment, and will not be described herein.

Step S203, determining a first constraint condition according to the current position data, the current driving state data, the first target position data, and the fixed position data corresponding to all the electric vehicles.

In this embodiment, step S203 is substantially the same as or similar to step S103 in the first embodiment, and will not be described herein.

Step S204, determining a second constraint condition of a preset genetic algorithm according to the first target position data corresponding to all electric vehicles and the fixed position data corresponding to all entity power stations.

In this embodiment, the second constraint is used to characterize all the physical power stations that each electric vehicle starts in the full state and can reach the first destination geographic location.

In this embodiment, the execution sequence of step S201, step S202, step S203 and step S204 is not limited, and may be reasonably selected according to the actual application requirement.

Step S205, performing iterative computation by using a preset genetic algorithm according to the intelligent scheduling fitness function, the first constraint condition, the second constraint condition, the first coded data and the second coded data to obtain scheduling recommendation data.

In this embodiment, the difference between the step S205 and the step S104 in the first embodiment is that the second constraint condition is further added, and other contents are substantially the same or similar, and are not described herein.

Optionally, step S204 may further include: and determining a third constraint condition of the genetic algorithm according to the first target position data and the second target position data corresponding to all electric vehicles and the fixed position data corresponding to all entity power stations.

Wherein the second target location data is used to characterize the geographic location of a second destination at which the next transportation plan continues to be executed after the electric vehicle arrives at the first destination. Similar to the first destination, the second destination may or may not be located within the specified geographic area, which is not limited herein.

The third constraint condition is used for representing all entity power stations which start under the full power state and sequentially reach the first destination geographic position and the second destination geographic position, and the midway charging and/or power changing times are not more than 1.

Correspondingly, step S205 may be adjusted to: and carrying out iterative computation by utilizing a preset genetic algorithm according to the intelligent scheduling fitness function, the first constraint condition, the second constraint condition, the third constraint condition, the first coded data and the second coded data to obtain scheduling recommended data.

By setting the first constraint condition, the second constraint condition and the third constraint condition, the electric vehicle can be guaranteed to reach the entity power station to charge and/or change electricity, and finally obtained scheduling recommended data can be determined according to the two-time transportation plan recently executed by the electric vehicle, so that a scheduling result is more reasonable, and the time consumption of executing the transportation plan by a single electric vehicle is reduced. For example, if intelligent scheduling is performed by considering only one transportation plan, an electric vehicle may be recommended to go to 1 physical power station for charging and/or power exchange each time the transportation plan is executed, that is, to go to 2 physical power stations; by taking into account both transport plans for intelligent scheduling, it is possible that only 1 physical power station may be eventually needed for charging and/or replacement.

Further, in order to ensure the rationality and reliability of the scheduling result, it may be preferable that the first destination and the second destination are both located within a specified geographical range, that is, the specified geographical range may be determined according to the geographical positions of the first destination and the second destination corresponding to all electric vehicles.

As can be seen from the above embodiments of the present application, the embodiments of the present application determine the first encoded data identifier and the second encoded data by specifying the geographical range, construct the fitness function, determine the first constraint condition and the second constraint condition, and iterate by using a preset genetic algorithm to obtain the recommended scheduling data. Through addding the second constraint condition, can make electric vehicle go to an entity power station and charge and/or trade the electricity after, alright reach first destination, considered electric vehicle need charge or trade the electricity and finally be in order to reach the essential demand of destination in the practical application scene, promoted user experience when guaranteeing whole dispatch efficiency.

Example three

As shown in fig. 3, fig. 3 is a schematic flowchart of an intelligent dispatching method for charging and changing electricity of an electric vehicle according to a third embodiment of the present application, where the intelligent dispatching method for charging and changing electricity of an electric vehicle includes:

Step S301, determining the first encoded data and the second encoded data according to the number of electric vehicles and the number of physical power stations located within the specified geographical range.

In this embodiment, the step S301 is substantially the same as or similar to the step S101 in the first embodiment, and will not be described herein.

Step S302, an intelligent scheduling fitness function is constructed.

In this embodiment, the step S302 is substantially the same as or similar to the step S102 in the first embodiment, and will not be described herein.

Step S303, determining a first constraint condition according to the current position data, the current driving state data, the first target position data, and the fixed position data corresponding to all the electric vehicles and all the entity power stations.

In this embodiment, step S303 is substantially the same as or similar to step S103 in the first embodiment, and will not be described herein.

In this embodiment, the execution sequence of step S301, step S302, and step S303 is not limited, and may be reasonably selected according to the actual application requirement.

And step S304, performing iterative computation by using a preset genetic algorithm according to the intelligent scheduling fitness function, the first constraint condition, the first coded data and the second coded data to obtain scheduling recommendation data.

In this embodiment, the step S304 is substantially the same as or similar to the step S104 in the first embodiment, and will not be described herein.

Step S305, according to the scheduling recommendation data, the current position data and the current driving state data corresponding to the electric vehicle, and the fixed position data and the current service state data corresponding to all the entity power stations, the charging and switching plan data is obtained.

In this embodiment, the charging and battery-replacing plan data is at least used to instruct at least one of charging and battery-replacing modes after the electric vehicle reaches the recommended physical power station, that is, three possible charging recommendation schemes of only charging, only battery-replacing and battery-replacing after charging can be used in order to reach a full state after the electric vehicle reaches the physical power station.

Optionally, considering that in the practical application process, the scheduling recommendation data is to intelligently schedule charging and/or power changing of a plurality of electric vehicles, for the electric vehicles whose indication needs to change power at the entity power station, a phenomenon that the power changing needs to be queued may occur after the electric vehicles reach the entity power station, and the entity power station has a charging gun for charging before starting the power changing. Since the unit price of the electric vehicle for changing the electric power is generally higher than the unit price of the electric vehicle for charging, the electric vehicle may be instructed to charge first while waiting for the electric power change in line in order to save the energy charge of the electric vehicle on the one hand and to reduce the charging time of the electric vehicle to be changed by the physical power station on the other hand.

Specifically, when it is determined that the time interval value at which the charging can be started is equal to or greater than the first preset duration threshold after the target vehicle arrives at the target power station, the charging plan data is further used for indicating that the target vehicle is subjected to the power change after the charging duration of the target power station is the second preset duration threshold.

The target vehicle is an electric vehicle which is characterized by scheduling recommended data and needs to be subjected to power change. The target power station is an entity power station characterized by the dispatch recommendation data that can provide charging and/or battery replacement services for the target vehicle.

The first preset time threshold is required to be set, and if the time interval value for starting the charging is too short after the target vehicle arrives at the target power station, the charging is performed first and then the charging is performed again, so that the effect of saving the cost cannot be achieved, and the power consumption and the cost are possibly increased.

The second preset duration threshold is used for representing the net charging duration of the target vehicle in the target power station. The second preset duration threshold needs to be determined in order to ensure that the delay is not caused to the overall dispatch plan if the target vehicle is charged and then is powered off.

The magnitudes of the first preset duration threshold and the second preset duration threshold are not limited, namely, the magnitude relation of the first preset duration threshold has three types: the first preset duration threshold is greater than the second preset duration threshold, the first preset duration threshold is equal to the second preset duration threshold, and the first preset duration threshold is less than the second preset duration threshold.

Further, it may be preferable that the first preset duration threshold is greater than the second preset duration threshold.

When the first preset duration threshold is greater than the second preset duration threshold, the target vehicle can ensure that the power can be replaced at the earliest time point when the power can be replaced, so that the time consumption of the target vehicle for completing the transportation plan is reduced

Optionally, since the current position data and the current driving state data corresponding to all electric vehicles may change over time and the service state of the physical power station may also change, in order to enable the electric vehicle to get to or already get to the physical power station indicated by the schedule recommendation data, a more reasonable charging and/or power exchange scheme may be obtained, and the embodiment may further include step S306: and updating the charging and changing plan data according to the updated current position data and current running state data corresponding to all electric vehicles and the updated current service state data corresponding to all entity power stations.

As can be seen from the above embodiments of the present application, the present embodiment not only makes the time for completing charging and/or power exchanging of all electric vehicles as early as possible, but also further guides the specific charging and/or power exchanging arrangement of the electric vehicles after reaching the allocated physical power station through the charging and power exchanging plan data, which is beneficial to improving user experience.

Example four

As shown in fig. 4, fig. 4 is a schematic flowchart of an intelligent scheduling method for charging and changing electricity of an electric vehicle according to a fourth embodiment of the present application, where the intelligent scheduling method for charging and changing electricity of an electric vehicle includes:

step S401, determining the first encoded data and the second encoded data according to the number of electric vehicles and the number of physical power stations located in the specified geographical area.

In this embodiment, the step S401 is substantially the same as or similar to the step S101 in the first embodiment, and will not be described herein.

Step S402, an intelligent scheduling fitness function is constructed.

In this embodiment, the step S402 is substantially the same as or similar to the step S102 in the first embodiment, and will not be described herein.

Step S403, determining a first constraint condition according to the current position data, the current driving state data, the first target position data, and the fixed position data corresponding to all the electric vehicles.

In this embodiment, step S403 is substantially the same as or similar to step S103 in the first embodiment, and will not be described herein.

In this embodiment, the execution sequence of step S401, step S402 and step S403 is not limited, and may be reasonably selected according to the actual application requirement.

And step S404, performing iterative computation by using a preset genetic algorithm according to the intelligent scheduling fitness function, the first constraint condition, the first coded data and the second coded data to obtain scheduling recommendation data.

In this embodiment, step S404 is substantially the same as or similar to step S104 in the first embodiment, and will not be described herein.

Step S405, according to the scheduling recommendation data, scheduling notification is sent to electric vehicles corresponding to all the entity power stations.

In this example, the dispatch notification is used to notify the physical power station to which all electric vehicles that require de-physical power stations to charge and/or change power are assigned.

Alternatively, it may be preferable that the dispatch notification is sent only to the electric vehicle to which the physical power station is assigned, and the dispatch notification is not sent to the electric vehicle to which the virtual power station is assigned, the data transmission amount may be reduced, and the driver of the electric vehicle who does not need to be charged and/or replaced is avoided from being stirred.

The embodiment of the application can be seen that the embodiment of the application not only completes the scheduling requirement of the charging and power changing scheme of the electric vehicle and gives high-efficiency recommended scheduling data, but also can send scheduling notification to the electric vehicle corresponding to the entity power station, thereby being convenient for users to use, avoiding disturbing the electric vehicle which does not need to be charged and changed, and being beneficial to improving user experience.

Example five

As shown in fig. 5, fig. 5 is a schematic block diagram of a charging and power-changing intelligent scheduling device for an electric vehicle according to a fifth embodiment of the present application, where the device includes:

the encoding module 501 is configured to determine the first encoded data and the second encoded data according to the number of electric vehicles and the number of physical power stations located in the specified geographic area.

In this embodiment, the designated area generally has a plurality of roads on which the electric vehicles can travel and a plurality of physical power stations on which the electric vehicles can be charged and/or replaced. Specific areas, terrains, climates and other conditions of the designated region range are not limited, and can be reasonably selected according to actual application requirements. For example, the specified geographic area may be a fixed area determined based on the geographic locations of the plurality of physical power stations or a variable area determined based on the real-time geographic locations of the plurality of electric vehicles. The encoding module 501 is configured to obtain geographic data of a specified geographic area, where the geographic data is related geographic data capable of identifying the specified geographic area.

In the present embodiment, parameters such as the type of electric vehicle, the brand, the type of power battery, the number of people on a nuclear load, and the maximum full load mass are not limited. For commercial vehicles such as an electric heavy truck, an electric mixer truck, an electric tractor and the like, the electric vehicle in the embodiment refers to an electric vehicle executing the current transportation plan in a specified region range, and the geographic position of a first destination of the current transportation plan executed by the electric vehicle can be represented by first target position data, that is, the first target position data is used for representing the position of the first destination of the electric vehicle executing the current transportation plan. The encoding module 501 is used to obtain the number of all electric vehicles.

In this embodiment, the physical power station is built at a fixed position within a specified geographical area, and is provided with one or more charging and/or power exchanging devices, so that charging and/or power exchanging services can be provided for the electric vehicle, i.e. the number of power exchanging batteries and/or charging guns of the physical power station is greater than 1. The encoding module 501 is used to obtain the number of all physical power stations.

In this embodiment, the first encoded data is used to identify all electric vehicles respectively, and the specific identification mode is not limited, and may be reasonably selected according to actual application requirements. For example, 5 electric vehicles may be identified by numerals 1, 2, 3, 4, 5, or 5 electric vehicles may be identified by letters A, B, C, D, E. The encoding module 501 is used to complete the identification of all electric vehicles.

In this embodiment, the second encoded data is used to represent the electric vehicle originally planned to be received by each entity power station and 1 virtual power station, and the specific representation mode is not limited, and may be reasonably selected according to the actual application requirements. In this embodiment, the electric vehicle that the virtual power station plans to receive is the electric vehicle that can reach the first destination where the present transportation plan is executed without charging and power exchange. The encoding module 501 is used to complete the identification of the electric vehicles originally planned to be received by each physical power station and 1 virtual power station.

The fitness function construction module 502 is configured to construct an intelligent scheduling fitness function.

In this embodiment, the fitness function construction module 502 is at least configured to calculate and obtain the earliest time for all electric vehicles to complete charging and/or power exchange according to current position data and current driving state data of the electric vehicles, and fixed position data and current service state data corresponding to all the entity power stations.

The current position data of the electric vehicle is used for representing the geographic position of the electric vehicle at the current moment, and the value of the data changes along with the real-time movement track of the electric vehicle. The fitness function construction module 502 is configured to obtain current location data of the electric vehicle.

The current driving state data is at least used to characterize the battery available power, i.e. the current driving state data of the specific electric vehicle is at least used to characterize the remaining available power of the battery powering the specific electric vehicle at the current moment. The fitness function construction module 502 is configured to obtain current driving state data.

The fixed location data is used to characterize a fixed geographical location, i.e. the fixed location data of a particular physical power station is used to characterize a specific geographical location of that physical power station within a specified geographical range. The fitness function construction module 502 is configured to obtain fixed location data.

The current service state data is at least used for representing waiting time of charging and/or changing electricity, namely the current service state data of the specific entity power station is at least used for representing waiting time length for starting charging and/or waiting time length for starting changing electricity after the electric vehicle runs to the specific entity power station at the current moment. The fitness function construction module 502 is configured to obtain current service status data.

The constraint construction module 503 is configured to determine a first constraint condition according to current position data, current driving state data, first target position data corresponding to all electric vehicles, and fixed position data corresponding to all entity power stations.

In this embodiment, the first constraint is used to characterize the electric vehicles that can reach the corresponding first destination without charging or changing electricity, and all the physical power stations that each electric vehicle can reach without charging or changing electricity. Constraint building block 503 is configured to obtain at least a first constraint.

And the optimal solution algorithm calculating module 504 is configured to perform iterative calculation according to the intelligent scheduling fitness function, the first constraint condition, the first encoded data and the second encoded data by using a preset genetic algorithm, so as to obtain scheduling recommendation data.

In this embodiment, the preset genetic algorithm is an optimal solution algorithm, and is configured to iteratively calculate the scheduling recommendation data according to the first encoded data and the second encoded data by using the intelligent scheduling fitness function and the first constraint condition. The optimal solution algorithm calculating module 504 is at least configured to calculate using a genetic algorithm to obtain recommended scheduling data.

Optionally, the constraint construction module 503 is further configured to determine a second constraint condition of the preset genetic algorithm according to the first target position data corresponding to all electric vehicles and the fixed position data corresponding to all entity power stations; the second constraint condition is used for representing all entity power stations which start under the full power state of each electric vehicle and can reach the first destination;

correspondingly, the optimal solution algorithm calculating module 504 is further configured to perform iterative calculation according to the intelligent scheduling fitness function, the first constraint condition, the second constraint condition, the first encoded data and the second encoded data by using a preset genetic algorithm, so as to obtain scheduling recommendation data.

Further, the constraint construction module 503 is further configured to determine a third constraint condition of the genetic algorithm according to the first target position data and the second target position data corresponding to all electric vehicles, and the fixed position data corresponding to all entity power stations; wherein the second target location data is used to characterize the location of a second destination at which the next transportation plan is continued after the electric vehicle arrives at the first destination; the third constraint condition is used for representing all entity power stations which start under the full power state and sequentially reach the first destination and the second destination of each electric vehicle, and the midway charging and/or power changing times are not more than 1;

Correspondingly, the optimal solution algorithm calculating module 504 is further configured to perform iterative calculation by using a preset genetic algorithm according to the intelligent scheduling fitness function, the first constraint condition, the second constraint condition, the third constraint condition, the first encoded data and the second encoded data, so as to obtain scheduling recommendation data.

Optionally, the intelligent scheduling fitness function is used for calculating and obtaining earliest time for all electric vehicles to complete charging and/or power change according to current position data, current running state data, fixed position data corresponding to all entity power stations, current service state data and running terrain data of the electric vehicles; wherein the driving terrain data is used for representing the terrain distribution of a driving route between the current geographic position of the electric vehicle and the fixed geographic position of the entity power station.

Optionally, the device further comprises a recommending module, which is used for obtaining charging and changing plan data according to the scheduling recommending data, the current position data and the current running state data corresponding to the electric vehicle, and the fixed position data and the current service state data corresponding to all the entity power stations; the charging and battery replacement plan data are at least used for indicating that the electric vehicle reaches a recommended entity power station, and at least one of charging and battery replacement modes is selected.

Further, when it is determined that the time interval value of the time distance from which charging can be started to start to perform power conversion is equal to or greater than the first preset duration threshold after the target vehicle arrives at the target power station, the charging plan data is further used for indicating that the target vehicle performs power conversion after the charging duration of the target power station is the second preset duration threshold. The target vehicle is an electric vehicle requiring power change.

Further, the first preset duration threshold is greater than the second preset duration threshold.

Further, the device also comprises an updating module, which is used for updating the current position data and the current running state data corresponding to all electric vehicles and the current service state data corresponding to all entity power stations according to the preset time interval value.

And updating the charging and changing plan data according to the updated current position data and current running state data corresponding to all electric vehicles and the updated current service state data corresponding to all entity power stations.

Optionally, the device further comprises a notification module, which is used for sending the scheduling notification to the electric vehicles corresponding to all the entity power stations according to the scheduling recommendation data.

As can be seen from the above embodiments of the present invention, by using the intelligent scheduling device for charging and replacing electric power for an electric vehicle in this embodiment, the corresponding intelligent scheduling method for charging and replacing electric power for an electric vehicle in the foregoing method embodiments may be implemented, and the specific beneficial effects of the corresponding method embodiments are not described herein.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.

Claims

1. An intelligent scheduling method for charging and power changing of an electric vehicle is characterized by comprising the following steps:

2. The method according to claim 1, wherein the method further comprises:

determining a second constraint condition of the preset genetic algorithm according to the first target position data corresponding to all the electric vehicles and the fixed position data corresponding to all the entity power stations; wherein the second constraint is used for representing all the entity power stations from which each electric vehicle starts in a full power state and can reach the first destination;

correspondingly, performing iterative computation by using a preset genetic algorithm according to the intelligent scheduling fitness function, the first constraint condition, the first encoded data and the second encoded data, and obtaining scheduling recommendation data includes:

and carrying out iterative computation by utilizing the preset genetic algorithm according to the intelligent scheduling fitness function, the first constraint condition, the second constraint condition, the first encoded data and the second encoded data to obtain the scheduling recommendation data.

3. The method according to claim 2, wherein the method further comprises:

determining a third constraint condition of the genetic algorithm according to the first target position data and the second target position data corresponding to all the electric vehicles and the fixed position data corresponding to all the entity power stations; wherein the second target location data is used to characterize the location of a second destination at which the next transportation plan is continued after the electric vehicle arrives at the first destination; the third constraint condition is used for representing all the entity power stations which start at the full power state and sequentially reach the first destination and the second destination, and the midway charging and/or power changing times are not more than 1;

correspondingly, the performing iterative computation by using the preset genetic algorithm according to the intelligent scheduling fitness function, the first constraint condition, the second constraint condition, the first encoded data and the second encoded data, and obtaining the scheduling recommendation data includes:

and carrying out iterative computation by utilizing the preset genetic algorithm according to the intelligent scheduling fitness function, the first constraint condition, the second constraint condition, the third constraint condition, the first encoded data and the second encoded data to obtain the scheduling recommendation data.

4. The method according to claim 1, wherein the intelligent scheduling fitness function is configured to calculate and obtain earliest time for all electric vehicles to complete charging and/or power change according to the current position data, the current driving state data, the fixed position data, the current service state data, and driving topography data of all the entity power stations of the electric vehicles; wherein the travel terrain data is used to characterize a terrain distribution of a travel route between a current geographic location of the electric vehicle to a fixed geographic location of the physical power station.

5. The method according to claim 1, wherein the method further comprises:

according to the scheduling recommendation data, the current position data and the current running state data corresponding to the electric vehicle, the fixed position data and the current service state data corresponding to all the entity power stations, and charging and changing plan data are obtained; and the charging and replacing plan data are at least used for indicating that at least one of charging and replacing modes is selected after the electric vehicle reaches the recommended entity power station.

6. The method of claim 5, wherein when the time interval value for which the charging can be started is equal to or greater than a first preset duration threshold value after determining that the target vehicle arrives at the target power station, the charging plan data is further used for indicating that the target vehicle is subjected to the power change after the charging duration of the target power station is a second preset duration threshold value; the target vehicle is an electric vehicle needing power conversion.

7. The method of claim 6, wherein the first preset duration threshold is greater than the second preset duration threshold.

8. The method of claim 5, wherein the method further comprises:

updating the current position data and the current running state data corresponding to all the electric vehicles and the current service state data corresponding to all the entity power stations according to preset time interval values;

and updating the charging and replacing plan data according to the updated current position data and the updated current running state data corresponding to all the electric vehicles and the updated current service state data corresponding to all the entity power stations.

9. The method according to claim 1, wherein the method further comprises:

and sending scheduling notification to the electric vehicles corresponding to all the entity power stations according to the scheduling recommendation data.

10. An electric vehicle charging and battery-changing intelligent scheduling device, characterized in that the device comprises: