A method and system for charging an electric vehicle based on a mobile charging station

Abstract

The application discloses a kind of electric vehicle charging method and system based on mobile charging station, to enhance the load regulation capability and renewable energy consumption capability of distribution network, improve the flexibility of distribution network to respond to extreme weather, improve the charging service quality of electric vehicle;Platform comprehensively considers the driving time and charging station queuing condition of existing charging electric vehicle, based on existing charging pile condition deployment mobile charging station, provide the charging scheme that can be selected according to battery capacity for electric vehicle user, so as to provide better charging experience for electric vehicle user;In addition, platform control mobile charging station formulates charging time, realizes the load regulation effect of distribution network load and new energy consumption.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle charging technology, specifically relating to an electric vehicle charging method and system based on a mobile charging station. Background Technology

[0002] In recent years, with the increasing frequency of extreme weather events caused by climate change, renewable energy generation and electric vehicle transportation have been seen as solutions to the growing energy crisis caused by environmental problems. However, both methods have also placed significant pressure on the stability of the power distribution network. On the one hand, the peak periods for renewable energy generation and load demand often occur at different times, increasing the difficulty of load regulation and renewable energy absorption in the distribution network. On the other hand, the cluster charging behavior of electric vehicles exacerbates the burden on the distribution network and may even lead to overload crises during peak load periods. Therefore, flexible response strategies are needed to address these events.

[0003] Electric vehicles (EVs) have been widely promoted in many countries due to their advantages such as zero carbon emissions, low noise, and high energy efficiency. In 2011, 55,000 EVs were sold globally, and this number is projected to reach 7 million by 2021. Despite this rapid growth in the number of EVs, the limited availability of fixed charging stations restricts their driving range. Consumer anxiety about range hinders the acceptance of EVs. The construction of fixed charging stations is affected by objective factors such as urban land scarcity and long-term profitability, becoming a key obstacle to the widespread adoption of EVs.

[0004] To address these issues, researchers have proposed battery swapping and wireless charging technologies, but these technologies are still in their early stages and have limited application. However, mobile charging technology is relatively mature. Companies such as Volkswagen, Tesla, and Swiss Energy have launched mobile charging station products to provide users with convenient charging services. Mobile charging stations are equipped with several energy storage devices and charging equipment, enabling them to provide charging services for electric vehicles within their service area. They can also select deployment locations based on the charging needs of electric vehicles in the region, driving the charging station to convenient charging locations to provide charging services for electric vehicles.

[0005] Compared to fixed charging stations, mobile charging stations offer several significant advantages. Firstly, charging piles must be installed in areas with sufficient installation and parking space, and their power output must not exceed the grid's distribution capacity. However, users' lifestyles and work patterns lead to highly random electric vehicle charging behavior, making it difficult to find consistently suitable deployment locations for fixed charging stations in urban areas. Mobile charging platforms, on the other hand, can predict the location and queuing time of electric vehicles based on the location and travel behavior of those being charged, thus finding suitable mobile charging station deployment sites in high-density areas and providing users with flexible charging services. By adjusting the deployment locations of mobile charging stations, compared to the passive service model of fixed charging piles, mobile charging stations improve the utilization rate of charging facilities, save costs, and reduce user waiting time. Furthermore, the charging and power supply of mobile charging stations can help the power system avoid peak loads from electric vehicle charging behavior, thereby reducing the load demand on the distribution network. In recent years, domestic and international scholars have conducted extensive research on the application of mobile charging stations, but most of these studies have not considered the application of mobile charging stations to regulate load and achieve charging economy and efficiency from a regional perspective, nor have they considered how to use mobile charging stations to enhance the resilience of the distribution network. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide an electric vehicle charging method and system based on mobile charging stations to address the shortcomings of the prior art, and to solve the technical problems of flexible charging services when the distribution location of electric vehicles changes dynamically, electric vehicle charging scheme planning considering queuing time, and power distribution network load regulation based on mobile charging stations.

[0007] The present invention adopts the following technical solution:

[0008] A method for charging electric vehicles based on a mobile charging station includes the following steps:

[0009] S1. Establish a database to obtain electric vehicle power and location information within the region, obtain charging pile information within the region, obtain historical charging information within the region, and obtain historical daily load curves of the regional power system.

[0010] S2. Based on the historical daily load curve of the regional power system obtained in step S1, plan and predict the daily load. Consider the economic efficiency of peak-valley regulation for the predicted load curve. With the goal of the lowest charging cost, establish a charging time planning model for the mobile charging station. Solve the charging time planning model for the mobile charging station to obtain the battery charging plan for the mobile charging station. Control the mobile energy storage station to charge according to the charging plan on the same day.

[0011] S3. Based on the historical charging information and electric vehicle power and location information obtained in step S1, predict the distribution of electric vehicle charging demand for the next day. With the shortest charging time for electric vehicles in the corresponding area as the objective, establish a mobile charging station deployment model. Solve the mobile charging station deployment model to obtain the mobile charging station deployment plan for the corresponding area. The next day, control the mobile charging station to deploy according to the deployment plan.

[0012] S4. On the next day, establish a dynamic charging planning model based on step S3, and determine the optimal charging scheme based on the charging needs of electric vehicles.

[0013] Specifically, in step S2, the constraints of the mobile charging station charging time planning model include electric vehicle charging planning constraints and battery energy storage driving path constraints.

[0014] Furthermore, the specific constraints for electric vehicle charging planning are as follows:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022] in, For electric vehicles To the charging station The shortest driving route, Shortest path route Total driving distance; For electric vehicles To the charging station The shortest driving route considering traffic congestion. The shortest route Total driving distance, The shortest route The total travel time on the road is long; For electric vehicles To the charging station Driving and waiting in line take time. For charging stations While existing electric vehicles are charging and other electric vehicles are queuing, vehicles are at charging stations. The waiting time in the queue For electric vehicles The set of shortest driving routes to all charging stations This represents the driving distance for the corresponding shortest driving route. For electric vehicles The set of shortest driving times to each charging station This is the set of driving routes with the corresponding shortest driving time. Electric vehicles that take into account road congestion The collection of charging times to each charging station.

[0023] Furthermore, the driving path constraints for battery energy storage are specifically as follows:

[0024]

[0025]

[0026]

[0027] in, For electric vehicles The percentage of remaining battery power; Indicates electric vehicles Total driving range on a full battery. For electric vehicles The driving distance of the shortest driving path charging solution. For electric vehicles The driving distance of the shortest driving time charging solution For electric vehicles The driving distance of the shortest charging time charging scheme.

[0028] Specifically, in step S3, the objective function for deploying the mobile charging station is as follows:

[0029]

[0030] in, y represents the total charging time for all electric vehicles in the region; y represents the number of electric vehicles charging in the corresponding region. For electric vehicles The charging time.

[0031] Furthermore, the constraints for deploying mobile charging stations are as follows:

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040] in, For the site A binary variable indicating whether a mobile charging station will be selected for deployment. For electric vehicles The charging station selection vector, For electric vehicles Choose a charging station binary variables, For charging stations Average queuing time The mutual influence duration coefficient of vehicles charging at the station. For electric vehicles The charging time To determine the number of fixed charging stations, The number of mobile charging station deployment points, A vector consisting of binary variables representing the deployment locations of mobile charging stations. The total number of mobile charging stations. The shortest route The total travel time on the road is long.

[0041] Specifically, step S4 involves the following: When an electric vehicle is searching for the shortest path to charge, the city map is a topological map that only considers distance. A topological graph that simultaneously considers distance and traffic conditions Divide the city roads into multiple locations, and form a matrix. A matrix is ​​used to find the shortest path between two points in a city, taking traffic congestion into account. and This is the predecessor matrix of the shortest path; based on the matrix above, the point is calculated... Shortest driving route set and the corresponding driving distance set and the shortest driving time set and the corresponding driving distance set and the shortest driving time set .

[0042] Furthermore, the objective function for mobile charging station charging planning is as follows:

[0043]

[0044] in, It is the power objective function of the mobile charging station. This represents the average daily power output of a conventional generator. This refers to the total number of mobile charging stations.

[0045] Furthermore, the constraints on mobile charging station planning and power distribution network load regulation are as follows:

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053] in, This represents the total power capacity of mobile charging stations in the corresponding area. For time period The average power absorbed by mobile charging stations from the power grid. For time period The power supplied to electric vehicles by mobile charging stations; The time interval for mobile charging stations to charge from the power grid; The time interval during which a mobile charging station discharges power to a charging vehicle; and Time periods The load of the power system and the amount of new energy generation.

[0054] Secondly, embodiments of the present invention provide an electric vehicle charging system based on a mobile charging station, comprising:

[0055] The data module establishes a database to obtain information on the battery level and location of electric vehicles within the region, information on charging piles within the region, historical charging information within the region, and historical daily load curves of the regional power system.

[0056] The first charging module plans and predicts the daily load based on the historical daily load curve of the regional power system obtained from the data module. Considering the economic efficiency of peak-valley regulation for the predicted load curve, and aiming at the lowest charging cost, it establishes a charging time planning model for the mobile charging station. The charging time planning model of the mobile charging station is solved to obtain the battery charging plan of the mobile charging station, and the mobile energy storage station is controlled to charge according to the charging plan on the same day.

[0057] The deployment module, based on the historical charging information and electric vehicle power and location information obtained from the data module, predicts the distribution of electric vehicle charging demand for the next day. With the shortest charging time for electric vehicles in the corresponding area as the objective, a mobile charging station deployment model is established. The mobile charging station deployment model is solved to obtain the mobile charging station deployment plan for the corresponding area. The mobile charging station is then deployed according to the deployment plan the next day.

[0058] The second charging module, within the next day, establishes a dynamic charging planning model based on the deployment module and determines the optimal charging scheme according to the charging needs of electric vehicles.

[0059] Compared with the prior art, the present invention has at least the following beneficial effects:

[0060] An electric vehicle charging method based on mobile charging stations takes into account the rapid growth in the number of electric vehicles and the difficulty in constructing fixed charging piles. It utilizes the flexibility and convenience of deploying mobile charging stations and the peak-shaving capability of energy storage power supply to provide convenient charging services for electric vehicle users. The energy storage capacity of mobile charging stations provides the ability to absorb new energy and regulate load. The platform controls the mobile charging stations to discharge during peak load periods and charge during off-peak periods and peak periods of new energy power generation. The Floyd algorithm and mixed integer linear programming (MILP) algorithm are used to provide diversified services for the distribution network and electric vehicle users, thereby improving the quality of electric vehicle charging services and the power supply, economy and stability of the power system.

[0061] Furthermore, considering the remaining battery capacity of electric vehicles and the queuing situation at charging stations, the remaining battery capacity is used as a constraint on the driving route for charging, and the queuing time at charging stations is included in the calculation of travel time. Users are provided with a variety of travel plans, such as the shortest driving distance, the shortest driving time, and the shortest charging time, so as to provide electric vehicle users with more reasonable and convenient charging planning services.

[0062] Furthermore, based on the distribution of charging vehicles in the entire region, and considering the deployable locations and number of mobile charging stations, a mixed-integer linear programming model is established with the goal of minimizing the overall charging time for electric vehicles in the region. The mobile charging station deployment scheme is then implemented to maximize the overall charging efficiency of the mobile charging station area.

[0063] Furthermore, urban roads are simplified into a topological model, and road traffic conditions are incorporated. The Floyd algorithm is used to generate the predecessor matrix of the shortest path, and the matrix calculation results provide key data for electric vehicle travel planning, thereby ensuring the practical feasibility of charging planning schemes.

[0064] Furthermore, the load curve of the urban power distribution network is adjusted by utilizing the charging and discharging of mobile charging stations. By considering the charging and discharging power, time period, and capacity constraints of mobile charging piles, a model is established with the goal of minimizing the sum of squares of the minimum load and average load in each time period of the city. This provides a power distribution network load charging and discharging adjustment scheme that minimizes the peak-to-valley difference of the load curve, thereby achieving power distribution network load adjustment and reducing the charging cost of mobile charging stations.

[0065] It is understandable that the beneficial effects of the second aspect mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0066] In summary, this invention establishes an electric vehicle mobile charging station service platform and uses a commercial optimization solver to optimize electric vehicle charging services and mobile charging station deployment, thereby providing charging services with shorter time consumption and shorter distances, as well as load regulation and renewable energy absorption capacity of the power system.

[0067] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0068] Figure 1 A schematic diagram of the charging planning process for a charging vehicle using the Floyd algorithm;

[0069] Figure 2 A map of a city that includes charging stations and electric vehicle distribution points;

[0070] Figure 3 A schematic diagram of three driving route options for a city that includes charging stations and electric vehicle deployment points;

[0071] Figure 4 A city map containing the deployment points of fixed charging stations and mobile charging stations, as well as the distribution points of electric vehicles;

[0072] Figure 5 A schematic diagram of the deployment points of mobile charging stations in a city, including fixed charging stations, mobile charging station deployment points, and electric vehicle distribution points;

[0073] Figure 6 This is a daily load curve for a certain city.

[0074] Figure 7 The daily load curve of the charging and discharging load was adjusted for the use of mobile charging stations in a certain city. Detailed Implementation

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0077] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0078] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0079] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0080] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0081] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0082] This invention provides an electric vehicle charging method based on mobile charging stations to enhance the load regulation capacity and renewable energy consumption capacity of the power distribution network, improve the resilience of the power distribution network in the face of extreme weather, and improve the charging service quality of electric vehicles. The platform comprehensively considers the driving time of existing electric vehicles and the queuing situation at charging stations, and deploys mobile charging stations based on the existing charging pile situation to provide electric vehicle users with charging solutions that can be selected according to battery capacity, thereby providing electric vehicle users with a better charging experience. In addition, the platform controls the mobile charging stations to set charging times, thereby realizing the load regulation function of power distribution network load and renewable energy consumption.

[0083] This invention discloses a method for charging electric vehicles based on a mobile charging station, comprising the following steps:

[0084] S1. Establish a database to obtain electric vehicle power and location information within the region, obtain charging pile information within the region, obtain historical charging information within the region, and obtain historical daily load curves of the regional power system.

[0085] S2. Based on the historical daily load curve obtained in step S1, plan and predict the daily load. Consider the economic efficiency of peak-valley regulation for the predicted load curve. With the goal of the lowest charging cost, establish a charging time planning model for the mobile charging station. Solve the model to obtain the battery charging plan for the mobile charging station. Control the mobile energy storage station to charge according to the charging plan on the same day.

[0086] The constraints of electric vehicle charging planning are as follows:

[0087] (1)

[0088] (2)

[0089] (3)

[0090] (4)

[0091] (5)

[0092] (6)

[0093] (7)

[0094] in, For electric vehicles To the charging station The shortest driving route, Shortest path route Total driving distance; For electric vehicles To the charging station The shortest driving route considering traffic congestion. The shortest route Total driving distance, The shortest route Total time spent; For electric vehicles To the charging station Driving and waiting in line take time. Electric vehicles that take into account road congestion The collection of charging times to each charging station.

[0095] Based on the algorithm results, any electric vehicle can be calculated. Shortest path parameters to charging station a and Shortest driving path parameters , and Shortest charging time parameter , and Electric vehicles that are being charged must meet the driving path constraints of battery energy storage.

[0096] The driving path constraints for electric vehicle battery energy storage are as follows:

[0097] (8)

[0098] (9)

[0099] (10)

[0100] in, For electric vehicles The percentage of remaining battery power; Indicates electric vehicles The total driving distance on a full charge is indicated by order numbers a, b, and c, which represent the three optimal charging station options for the electric vehicle. When a user plans to charge their electric vehicle, the system will offer three charging options: shortest driving distance, shortest driving time, and shortest queuing time.

[0101] S3. Based on the historical charging information and electric vehicle distribution information obtained in step S1, predict the distribution of electric vehicle charging demand for the next day. With the shortest charging time for electric vehicles in the area as the target, establish a mobile charging station deployment model, solve the model to obtain the mobile charging station deployment plan for the area, and control the mobile charging station to be deployed according to the deployment plan the next day.

[0102] The constraints for deploying mobile charging stations are as follows:

[0103] (11)

[0104] (12)

[0105] (13)

[0106] (14)

[0107] (15)

[0108] (16)

[0109] (17)

[0110] (18)

[0111] in, It is used to represent a site A binary variable indicating whether a mobile charging station has been selected for deployment; 0 indicates that the station has not been selected, and 1 indicates that the station has been selected. It is an electric car Charging station selection vector; It is used to represent electric vehicles Choose a charging station The binary variable, 0 indicates that the charging station has not been selected for charging, and 1 indicates that the charging station has been selected for charging; Indicates charging station Average queuing time; It is the mutual influence duration coefficient between charging vehicles at the station; It is an electric car The charging time It refers to the number of fixed charging stations. This refers to the number of mobile charging station deployment points.

[0112] The objective function for deploying mobile charging stations is as follows:

[0113] (19)

[0114] in, y represents the total charging time for all electric vehicles in the region; y represents the number of electric vehicles charging in the region.

[0115] S4. The next day, the system establishes a dynamic charging planning model. Before charging, electric vehicle owners can send their charging needs to the service platform. The system will calculate and provide the best available charging solution based on the electric vehicle's charging needs.

[0116] To find the shortest path to each charging station, electric vehicle charging must take into account road congestion along the route. The pathfinding problem can be solved using... Floyd An algorithm addresses this issue. When an electric vehicle searches for the shortest path to charge, the city map can be viewed as a topological map that only considers distance. A topological graph that simultaneously considers distance and traffic conditions Divide the city roads into multiple locations, and form a matrix. A matrix is ​​used to find the shortest path between two points in a city, taking traffic congestion into account. and It is the predecessor matrix of the shortest path. The matrix is ​​generated as follows: Figure 1 As shown.

[0117] Based on the matrix above, the point Shortest driving route set and the corresponding driving distance set and the shortest driving time set and the corresponding driving distance set and the shortest driving time set All of them can be calculated.

[0118] The constraints on mobile charging station charging planning and power distribution network load regulation are as follows:

[0119] (20)

[0120] (twenty one)

[0121] (twenty two)

[0122] (twenty three)

[0123] (twenty four)

[0124] (25)

[0125] (26)

[0126] in, This represents the total electrical capacity of mobile charging stations in the area; assuming a day is divided into... Each period, It is a time period The average power absorbed by mobile charging stations from the power grid. It is a time period The power supplied to electric vehicles by mobile charging stations; It refers to the time interval during which the mobile charging station charges from the power grid; It is the time interval during which a mobile charging station discharges power to a charging vehicle. and They are time periods The load of the power system and the amount of new energy generation; It is the average daily power generation of conventional generators such as thermal power generators.

[0127] The objective function for mobile charging station charging planning is as follows:

[0128] (27)

[0129] in, This is the power objective function for mobile charging stations. The square of the expression is used to ensure that the curve for each period is as close as possible to the average load curve.

[0130] In another embodiment of the present invention, an electric vehicle charging system based on a mobile charging station is provided. This system can be used to implement the above-mentioned electric vehicle charging method based on a mobile charging station. Specifically, the electric vehicle charging system based on a mobile charging station includes a data module, a first charging module, a deployment module, and a second charging module.

[0131] The data module establishes a database, obtains electric vehicle power and location information within the region, obtains charging pile information within the region, obtains historical charging information within the region, and obtains historical daily load curves of the regional power system.

[0132] The first charging module plans and predicts the daily load based on the historical daily load curve of the regional power system obtained from the data module. Considering the economic efficiency of peak-valley regulation for the predicted load curve, and aiming at the lowest charging cost, it establishes a charging time planning model for the mobile charging station. The charging time planning model of the mobile charging station is solved to obtain the battery charging plan of the mobile charging station, and the mobile energy storage station is controlled to charge according to the charging plan on the same day.

[0133] The deployment module, based on the historical charging information and electric vehicle power and location information obtained from the data module, predicts the distribution of electric vehicle charging demand for the next day. With the shortest charging time for electric vehicles in the corresponding area as the objective, a mobile charging station deployment model is established. The mobile charging station deployment model is solved to obtain the mobile charging station deployment plan for the corresponding area. The mobile charging station is then deployed according to the deployment plan the next day.

[0134] The second charging module, within the next day, establishes a dynamic charging planning model based on the deployment module and determines the optimal charging scheme according to the charging needs of electric vehicles.

[0135] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of an electric vehicle charging method based on a mobile charging station, including:

[0136] A database is established to obtain information on the electric vehicle (EV) power level and location, charging pile information, historical charging information, and historical daily load curves of the regional power system. Based on these historical daily load curves, the daily load is predicted. Considering the economic efficiency of peak-valley regulation, a mobile charging station charging time planning model is established with the goal of minimizing charging costs. This model is solved to obtain the battery charging plan for the mobile charging stations, and the mobile energy storage stations are controlled to charge according to the plan on the same day. Based on historical charging information and the EV power level and location information within the region, the distribution of EV charging demand for the next day is predicted. A mobile charging station deployment model is established with the goal of minimizing the charging time for EVs in the corresponding region. This model is solved to obtain the deployment scheme for mobile charging stations in the corresponding region, and the mobile charging stations are deployed according to the scheme the next day. Within the next day, a dynamic charging planning model is established to determine the optimal charging scheme based on the EV charging demand.

[0137] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory). This computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0138] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the electric vehicle charging method based on a mobile charging station in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0139] A database is established to obtain information on the electric vehicle (EV) power level and location, charging pile information, historical charging information, and historical daily load curves of the regional power system. Based on these historical daily load curves, the daily load is predicted. Considering the economic efficiency of peak-valley regulation, a mobile charging station charging time planning model is established with the goal of minimizing charging costs. This model is solved to obtain the battery charging plan for the mobile charging stations, and the mobile energy storage stations are controlled to charge according to the plan on the same day. Based on historical charging information and the EV power level and location information within the region, the distribution of EV charging demand for the next day is predicted. A mobile charging station deployment model is established with the goal of minimizing the charging time for EVs in the corresponding region. This model is solved to obtain the deployment scheme for mobile charging stations in the corresponding region, and the mobile charging stations are deployed according to the scheme the next day. Within the next day, a dynamic charging planning model is established to determine the optimal charging scheme based on the EV charging demand.

[0140] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0141] Please see Figure 1 The flowchart of the Floyd algorithm for electric vehicle charging planning is presented. To find the shortest path to each charging station, the road congestion along the route must be considered. The pathfinding problem can be solved using the Floyd algorithm; when electric vehicles are searching for the shortest path to charge, the city map can be viewed as a topological graph that only considers distance. A topological graph that simultaneously considers distance and traffic conditions Then, the road is divided into multiple points based on the distance; matrix At some point, it is used to find the shortest distance from one point in the city to other points; matrix Used to find the shortest travel time from a point to another location, taking into account distance and traffic conditions; matrix and The predecessor matrix represents the shortest path from each point; based on the matrix, the set of shortest driving paths from each point to each charging station is generated. and the corresponding set of driving distances and the corresponding set of driving time Furthermore, by considering the queuing delays at each charging station, the set of travel times for electric vehicles to each charging station, taking into account queuing conditions, is derived. .

[0142] To verify the effectiveness of the method proposed in this invention, a charging platform test was conducted in a portion of a city's urban area. This area included several fixed and mobile charging stations, as well as several electric vehicles awaiting charging, to verify the effectiveness of the aforementioned model.

[0143] Example 1: Assume there are six charging stations in the area. An electric vehicle needs to charge and sends a charging request to the platform. The platform provides a travel route for the vehicle. The electric vehicle and the charging stations are marked with hollow red dots and solid red dots on the map, as shown below. Figure 2 As shown.

[0144] Example 2: Assume there are six fixed charging stations, four mobile charging station deployment sites, two mobile charging stations, and thirty-six electric vehicles waiting to be charged in this area. The platform deploys mobile charging stations considering the distribution of electric vehicles to minimize the charging time for electric vehicles in this area. In the diagram, green dots represent optional deployment points for mobile charging stations, solid red dots represent fixed charging piles, and hollow red dots represent parking locations for electric vehicles. Figure 4 As shown.

[0145] Example 3: Assuming the regional electricity load meets the electricity load curve of a certain city, and assuming the capacity of mobile charging stations, the mobile charging stations are used for charging and discharging to regulate the regional load. In the figure, the blue line represents the city's daily electricity load (DS), while the orange line represents electricity not absorbed from renewable energy sources. Figure 6 As shown.

[0146] The specific execution steps are as follows:

[0147] For Example 1, the original system data is input, and the Floyd algorithm described above is used to solve the electric vehicle charging route planning problem to solve the planned route for the trip.

[0148] For Example 2, the original system data is input, and the comprehensive optimization model is used to solve the optimal mobile charging station deployment problem:

[0149]

[0150] St.(8)-(18)

[0151] For Example 3, the original system data is input, and a comprehensive optimization model is used to solve the optimal charging and discharging problem of mobile charging stations that considers peak shaving and valley filling of the distribution network and absorption of new energy sources:

[0152]

[0153] St.(20)-(26)

[0154] The calculation results of Example 1 are in Figure 3 The diagram shows that light-colored routes represent the shortest driving paths, gray routes represent the routes with the shortest driving time, and black routes represent the routes with the shortest charging time. By considering traffic congestion and charging station queues, the charging service platform offers users three charging options: the shortest driving path route, the shortest driving time route, and the shortest charging time route. This caters to the charging needs of different users and significantly reduces charging time during long queues and traffic congestion, providing a better charging service.

[0155] The calculation results of Example 2 are in Figure 5 The diagram shows the selected deployment locations of mobile charging stations, circled in circles, and the darker lines representing the shortest driving routes. The charging platform considers the charging needs and charging times of electric vehicles to select the optimal deployment points for charging, minimizing user time, and provides the charging routes for each vehicle under these conditions. This comprehensive selection of mobile charging station deployment locations reduces charging time for electric vehicle users in the region.

[0156] The calculation results of Example 3 are in Figure 7 The results show that the dark curve represents the distribution network load curve before load adjustment, while the light curve represents the distribution network load curve after load adjustment using mobile charging stations. By rationally planning the charging and discharging time of mobile charging stations, the adjusted distribution network load curve is calculated. The results show that using mobile charging stations can effectively achieve the purpose of peak shaving and valley filling of the distribution network load, reduce the load pressure of the distribution network, and improve the distribution network's ability to absorb new energy.

[0157] In summary, the electric vehicle charging method and system based on a mobile charging station of the present invention has the following characteristics:

[0158] 1. By taking into account traffic congestion and charging station queues, the system provides users with multiple charging options, including the shortest driving route, the shortest driving time route, and the shortest charging time route. This can greatly reduce charging time for users in situations with long charging queues and traffic congestion, providing them with a better charging service.

[0159] 2. By considering the charging needs and charging time of electric vehicles, the optimal deployment point for deploying charging vehicles is selected to minimize user time, thereby reducing the charging time for electric vehicle users in the region through the deployment of mobile charging stations.

[0160] 3. By rationally planning the charging and discharging time of mobile charging stations, the load curve of the distribution network was adjusted, the load pressure of the distribution network was reduced, and the renewable energy absorption capacity of the distribution network was improved.

[0161] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0162] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0163] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0164] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0165] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0166] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0167] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM). Only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0168] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0169] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0170] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0171] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for charging electric vehicles based on a mobile charging station, characterized in that, Includes the following steps: S1. Establish a database to obtain electric vehicle power and location information within the region, obtain charging pile information within the region, obtain historical charging information within the region, and obtain historical daily load curves of the regional power system. S2. Based on the historical daily load curve of the regional power system obtained in step S1, plan and predict the daily load. Consider the economic efficiency of peak-valley regulation for the predicted load curve. With the goal of the lowest charging cost, establish a charging time planning model for the mobile charging station. Solve the charging time planning model for the mobile charging station to obtain the battery charging plan for the mobile charging station. Control the mobile energy storage station to charge according to the charging plan on the same day. S3. Based on the historical charging information and electric vehicle power and location information obtained in step S1, predict the distribution of electric vehicle charging demand for the next day. With the shortest charging time for electric vehicles in the corresponding area as the objective, establish a mobile charging station deployment model. Solve the mobile charging station deployment model to obtain the mobile charging station deployment plan for the corresponding area. The next day, control the mobile charging station to deploy according to the deployment plan. S4. The following day, based on step S3, establish a dynamic charging planning model and determine the optimal charging scheme according to the charging needs of electric vehicles. Specifically, when the electric vehicle is searching for the shortest path to charge, the city map is a topological map that only considers distance. A topological graph that simultaneously considers distance and traffic conditions Divide the city roads into multiple locations, and form a matrix. A matrix is ​​used to find the shortest path between two points in a city, taking traffic congestion into account. and This is the predecessor matrix of the shortest path; based on the matrix above, the point is calculated... Shortest driving route set and the corresponding driving distance set and the shortest driving time set and the corresponding driving distance set and the shortest driving time set The objective function for mobile charging station charging planning is as follows: in, It is the power objective function of the mobile charging station. This represents the average daily power generation of a conventional generator. This refers to the total number of mobile charging stations.

2. The electric vehicle charging method based on a mobile charging station according to claim 1, characterized in that, In step S2, the constraints of the mobile charging station charging time planning model include electric vehicle charging planning constraints and battery energy storage driving path constraints.

3. The electric vehicle charging method based on a mobile charging station according to claim 2, characterized in that, The specific constraints of electric vehicle charging planning are as follows: in, For electric vehicles To the charging station The shortest driving route, Shortest path route Total driving distance; For electric vehicles To the charging station The shortest driving route considering traffic congestion. The shortest route Total driving distance, The shortest route The total travel time on the road is long; For electric vehicles To the charging station Driving and waiting in line take time. For charging stations While existing electric vehicles are charging and other electric vehicles are queuing, vehicles are at charging stations. The waiting time in the queue For electric vehicles The set of shortest driving routes to all charging stations This represents the driving distance for the corresponding shortest driving route. For electric vehicles The set of shortest driving times to each charging station This is the set of driving routes with the corresponding shortest driving time. Electric vehicles that take into account road congestion The collection of charging times to each charging station.

4. The electric vehicle charging method based on a mobile charging station according to claim 2, characterized in that, The specific driving path constraints for battery energy storage are as follows: in, For electric vehicles The percentage of remaining battery power; Indicates electric vehicles Total driving range on a full battery. For electric vehicles The driving distance of the shortest driving path charging solution. For electric vehicles The driving distance of the shortest driving time charging solution For electric vehicles The driving distance of the shortest charging time charging scheme.

5. The electric vehicle charging method based on a mobile charging station according to claim 1, characterized in that, In step S3, the objective function for deploying the mobile charging station is as follows: in, y represents the total charging time for all electric vehicles in the region; y represents the number of electric vehicles charging in the corresponding region. For electric vehicles The charging time.

6. The electric vehicle charging method based on a mobile charging station according to claim 5, characterized in that, The constraints for deploying mobile charging stations are as follows: in, For the site A binary variable indicating whether a mobile charging station will be selected for deployment. For electric vehicles The charging station selection vector, For electric vehicles Choose a charging station binary variables, For charging stations Average queuing time The mutual influence duration coefficient of vehicles charging at the station. For electric vehicles The charging time To determine the number of fixed charging stations, The number of mobile charging station deployment points, A vector consisting of binary variables representing the deployment locations of mobile charging stations. The total number of mobile charging stations. The shortest route The total travel time on the road is long.

7. The electric vehicle charging method based on a mobile charging station according to claim 1, characterized in that, The constraints on mobile charging station charging planning and power distribution network load regulation are as follows: in, This represents the total power capacity of mobile charging stations in the corresponding area. For time period The average power absorbed by mobile charging stations from the power grid. For time period The power supplied to electric vehicles by mobile charging stations; The time interval for mobile charging stations to charge from the power grid; The time interval during which a mobile charging station discharges power to a charging vehicle; and Time periods The load of the power system and the amount of new energy generation.

8. An electric vehicle charging system based on a mobile charging station, characterized in that, include: The data module establishes a database to obtain information on the battery level and location of electric vehicles within the region, information on charging piles within the region, historical charging information within the region, and historical daily load curves of the regional power system. The first charging module plans and predicts the daily load based on the historical daily load curve of the regional power system obtained from the data module. Considering the economic efficiency of peak-valley regulation for the predicted load curve, and aiming at the lowest charging cost, it establishes a charging time planning model for the mobile charging station. The charging time planning model of the mobile charging station is solved to obtain the battery charging plan of the mobile charging station, and the mobile energy storage station is controlled to charge according to the charging plan on the same day. The deployment module, based on the historical charging information and electric vehicle power and location information obtained from the data module, predicts the distribution of electric vehicle charging demand for the next day. With the shortest charging time for electric vehicles in the corresponding area as the objective, a mobile charging station deployment model is established. The mobile charging station deployment model is solved to obtain the mobile charging station deployment plan for the corresponding area. The mobile charging station is then deployed according to the deployment plan the next day. The second charging module, within the next day, establishes a dynamic charging planning model based on the deployment module. It determines the optimal charging solution based on the charging needs of the electric vehicles. Specifically, when the electric vehicle is searching for the shortest path to charge, the city map is a topological map that only considers distance. A topological graph that simultaneously considers distance and traffic conditions Divide the city roads into multiple locations, and form a matrix. A matrix is ​​used to find the shortest path between two points in a city, taking traffic congestion into account. and This is the predecessor matrix of the shortest path; based on the matrix above, the point is calculated... Shortest driving route set and the corresponding driving distance set and the shortest driving time set and the corresponding driving distance set and the shortest driving time set The objective function for mobile charging station charging planning is as follows: in, It is the power objective function of the mobile charging station. This represents the average daily power generation of a conventional generator. This refers to the total number of mobile charging stations.

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