Electric vehicle cluster scheduling method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of electric vehicles, and discloses an electric vehicle cluster scheduling method and device, electronic equipment and a storage medium. The method comprises the following steps: for an electric vehicle cluster consisting of a plurality of electric vehicles, taking the minimum value of the sum of variances of charge states of all the electric vehicles in the electric vehicle cluster as a target function; establishing a dispatching optimization model by taking the power dispatched by the electric vehicle cluster as the total power to be dispatched and the sum of the power dispatched by all the electric vehicles as the power dispatched by the electric vehicle cluster as constraint conditions; the scheduling optimization model is used for determining a first charge state of each electric vehicle in the electric vehicle cluster, and determining a first vehicle power allocated to each electric vehicle according to the first charge state and a to-be-scheduled total power; and sending the first automobile power allocated to each electric automobile to the corresponding electric automobile in the electric automobile cluster to complete scheduling of the electric automobile cluster so as to realize efficient utilization of batteries of the electric automobiles.

Description

Technical Field

[0001] This application relates to the field of electric vehicle technology, and in particular to an electric vehicle cluster scheduling method, apparatus, electronic device, and storage medium. Background Technology

[0002] With increasing environmental awareness and the development of renewable energy, electric vehicles are gradually emerging as a clean energy mode of transportation. Electric vehicles require a full charge to operate normally, but the limited number and uneven distribution of charging stations cause inconvenience for users during long-distance travel or when fast charging is needed. To address this issue, a large number of charging stations can be installed, covering more cities and highways. This trend provides more convenient charging services for electric vehicle users and promotes the development of the electric vehicle market. Furthermore, through the application of internet and IoT technologies, electric vehicle charging equipment (i.e., charging stations) can be remotely monitored and managed. This not only allows users to conveniently check the real-time usage and detailed charging speed of charging stations, but also enables fault detection and repair of charging stations through intelligent management systems, improving the reliability and stability of charging equipment, thereby further promoting the development of electric vehicle charging technology.

[0003] The number of electric vehicles (EVs) connected to the grid for charging has increased significantly with their widespread adoption, exacerbating the overall load pressure on the power grid. To cope with this rising pressure, the power grid needs to upgrade its equipment and improve its management and control technologies to enhance its ability to handle various load conditions. EVs, as high-capacity power sources with both charging and discharging capabilities, can effectively utilize their energy storage capacity through certain technological means while charging continues. Therefore, without harming the interests of EV users, the charging and discharging energy of EVs can be rationally allocated as a mobile power source with a certain capacity, contributing to grid-connected peak-shaving plans and thus improving the economic benefits for both the power grid and EV users.

[0004] Current scheduling methods for electric vehicle (EV) clusters primarily consider the rated capacity of the clusters and allocate power accordingly, supporting peak shaving and valley filling of the power grid based on their charging and discharging power. However, this allocation method leads to discrepancies in the state of charge (SOC) of batteries within different EV clusters and individual EVs. When an EV's battery reaches its power constraint first, it will stop operating, causing the charging and discharging behavior of the EV cluster to be limited by individual circumstances, thus affecting the overall power output capacity of the EV cluster. Therefore, the current scheduling strategy for EV clusters is not reasonable. Summary of the Invention

[0005] The purpose of this application is to provide a power scheduling method, device, electronic device and storage medium for electric vehicles, which can more rationally schedule electric vehicle clusters, thereby achieving efficient utilization of electric vehicle batteries in the cluster.

[0006] To address the aforementioned technical problems, embodiments of this application provide an electric vehicle cluster scheduling method, comprising the following steps: For an electric vehicle cluster consisting of several electric vehicles, a scheduling optimization model is established with the minimum sum of the variances of the states of charge (SOCs) of all electric vehicles in the cluster as the objective function, and with the total power to be scheduled as the power to be scheduled and the sum of the power scheduled by all electric vehicles as the power scheduled by the electric vehicle cluster as constraints; wherein, the scheduling optimization model is used to determine the first SOC of each electric vehicle in the electric vehicle cluster, and to determine the first vehicle power allocated to each electric vehicle based on the first SOC and the total power to be scheduled; the first vehicle power allocated to each electric vehicle is sent to the corresponding electric vehicle in the electric vehicle cluster, thereby completing the scheduling of the electric vehicle cluster.

[0007] An embodiment of this application also provides an electric vehicle cluster scheduling device, comprising: a model building module, used for establishing a scheduling optimization model for an electric vehicle cluster consisting of several electric vehicles, with the minimum of the sum of the variances of the states of charge of all electric vehicles in the cluster as the objective function, and with the scheduled power of the electric vehicle cluster as the total power to be scheduled and the sum of the scheduled power of all electric vehicles as the scheduled power of the electric vehicle cluster as the constraint condition; wherein, the scheduling optimization model is used to determine the first state of charge of each electric vehicle in the electric vehicle cluster, and determine the first vehicle power allocated to each electric vehicle based on the first state of charge and the total power to be scheduled; and a cluster scheduling module, used to send the first vehicle power allocated to each electric vehicle to the corresponding electric vehicle in the electric vehicle cluster, thereby completing the scheduling of the electric vehicle cluster.

[0008] Embodiments of this application also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described electric vehicle cluster scheduling method.

[0009] Embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described electric vehicle cluster scheduling method.

[0010] The electric vehicle cluster scheduling method provided in the embodiments of this application, for a number of electric vehicles, uses the minimum sum of the variances of the states of charge of all electric vehicles as the objective function, and the minimum sum of the variances of the states of charge of all electric vehicles in the electric vehicle cluster as the objective function, with the power scheduled by the electric vehicle cluster as the total power to be scheduled and the sum of the power scheduled by all electric vehicles as the power scheduled by the electric vehicle cluster as the constraint condition, to establish a scheduling optimization model. This scheduling optimization model is used to determine the first state of charge of each electric vehicle in the electric vehicle cluster, and based on the first state of charge and the total power to be scheduled, determine the first vehicle power allocated to each electric vehicle, and then send the first vehicle power allocated to each electric vehicle to the corresponding electric vehicle in the electric vehicle cluster, thereby completing the scheduling of the electric vehicle cluster. These electric vehicles can be considered as an electric vehicle cluster. This application uses a scheduling optimization model to determine the state of charge (SOC) of each electric vehicle's battery in real time. Based on the SOC, the dispatchable power of each electric vehicle is determined, enabling real-time optimization of the power scheduling strategy for the electric vehicle cluster. This allows each electric vehicle to schedule more power when its battery SOC is high and less power when its SOC is low, ensuring that the SOC of each electric vehicle in the cluster remains consistent. Consequently, the SOC of the entire electric vehicle cluster is also consistent with the SOC of each individual electric vehicle, preventing discrepancies between the cluster and individual vehicles' battery states. This more rational scheduling of the electric vehicle cluster ensures optimal battery utilization and improves the cluster's output power. Furthermore, scheduling electric vehicles based on SOC not only maximizes battery energy utilization but also prevents overcharging or over-discharging, extending battery life and further enhancing the electric vehicle's output power.

[0011] In some optional embodiments, the method further includes: There are multiple electric vehicle clusters composed of the plurality of electric vehicles; the method further includes: establishing a two-layer scheduling optimization model with the minimum sum of the variances of the states of charge (SOCs) of all electric vehicle clusters as the objective function, and with constraints including the SOC of each electric vehicle cluster being the average SOC of all electric vehicles in the corresponding electric vehicle cluster, the sum of the scheduled power of all electric vehicle clusters being the total power to be scheduled, and the sum of the scheduled power of all electric vehicles in each electric vehicle cluster being the power scheduled by the corresponding electric vehicle cluster; wherein the two-layer scheduling optimization model is used to determine the second SOC of each electric vehicle cluster, and based on the second SOC and the total power to be scheduled, determine the cluster power allocated to each electric vehicle cluster; and to determine the third SOC of each electric vehicle in each electric vehicle cluster, and based on the third SOC and the cluster power allocated to each electric vehicle cluster, determine the second vehicle power allocated to each electric vehicle in each electric vehicle cluster; and sending the second vehicle power allocated to each electric vehicle to the corresponding electric vehicle in the corresponding electric vehicle cluster, thereby completing the scheduling of the electric vehicle clusters.

[0012] In some optional embodiments, the multiple electric vehicle clusters are obtained by the following steps: dividing a number of electric vehicles into the multiple electric vehicle clusters according to the starting charging time and charging duration of each electric vehicle on the same day.

[0013] In some optional embodiments, the step of dividing several electric vehicles into multiple electric vehicle clusters based on the starting charging time and charging duration of each electric vehicle on the same day includes: dividing the total duration of the day into multiple time periods and obtaining the charging probability density of each electric vehicle in each time period; and dividing several electric vehicles into multiple electric vehicle clusters based on the charging probability density of each electric vehicle in each time period.

[0014] In some optional embodiments, the initial charging time satisfies the following formula:

[0015]

[0016] Among them, f start μ represents the probability density of the initial charging time. start σ represents the expected value of the initial charging time. start The standard deviation of the initial charging time is represented.

[0017] In some optional embodiments, the objective function is expressed by the following formula:

[0018]

[0019] Where N represents the number of electric vehicle clusters, and Mi represents the number of electric vehicles in electric vehicle cluster i. This represents the state of charge of electric vehicle j in the electric vehicle cluster i at time t+1.

[0020] In some alternative embodiments, the Calculated using the following formula:

[0021]

[0022] in, C i,j =S OH,i,j C nom E i,j C represents the power input of electric vehicle j in the electric vehicle cluster i. i,j η represents the current battery capacity of electric vehicle j in the electric vehicle cluster i. i,j This represents the energy conversion efficiency of electric vehicle j within the electric vehicle cluster i. Let S represent the power of electric vehicle j in the electric vehicle cluster i, T represent one cycle of the power cycle, and S represent the power of electric vehicle j in the cluster i. OH,i,j C represents the battery health status of electric vehicle j in the electric vehicle cluster i. nom This represents the standard capacity of electric vehicle j in the electric vehicle cluster i. Attached Figure Description

[0023] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0024] Figure 1 This is a flowchart of an electric vehicle cluster scheduling method according to an embodiment of this application;

[0025] Figure 2 This is a schematic diagram of electric vehicle cluster partitioning according to an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of an application system structure according to an embodiment of this application;

[0027] Figure 4 This is a schematic diagram of an energy management system according to an embodiment of this application;

[0028] Figure 5 This is a cluster scheduling example diagram provided according to an embodiment of this application;

[0029] Figure 6This is a schematic diagram of an electric vehicle cluster scheduling device according to an embodiment of this application;

[0030] Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0032] One embodiment of this application relates to a method for scheduling electric vehicle clusters. The specific process of the electric vehicle cluster scheduling method in this embodiment can be described as follows: Figure 1 As shown, it includes:

[0033] Step 101: For an electric vehicle cluster consisting of several electric vehicles, a scheduling optimization model is established with the minimum sum of the variances of the states of charge of all electric vehicles in the cluster as the objective function, and the power scheduled by the electric vehicle cluster as the total power to be scheduled and the sum of the power scheduled by all electric vehicles as the power scheduled by the electric vehicle cluster as the constraints. The scheduling optimization model is used to determine the first state of charge of each electric vehicle in the electric vehicle cluster, and to determine the first vehicle power allocated to each electric vehicle based on the first state of charge and the total power to be scheduled.

[0034] Step 102: The first vehicle power allocated to each electric vehicle is sent to the corresponding electric vehicle in the electric vehicle cluster to complete the scheduling of the electric vehicle cluster.

[0035] In this embodiment, for a number of electric vehicles, a scheduling optimization model is established with the minimum sum of the variances of the states of charge of all electric vehicles as the objective function, and with the minimum sum of the variances of the states of charge of all electric vehicles in the electric vehicle cluster as the objective function. The scheduling optimization model is used to determine the first state of charge of each electric vehicle in the electric vehicle cluster, and to determine the first vehicle power allocated to each electric vehicle based on the first state of charge and the total power to be scheduled. Then, the first vehicle power allocated to each electric vehicle is sent to the corresponding electric vehicle in the electric vehicle cluster, thereby completing the scheduling of the electric vehicle cluster. These electric vehicles can be considered as an electric vehicle cluster. This application uses a scheduling optimization model to determine the state of charge (SOC) of each electric vehicle's battery in real time. Based on the SOC, the dispatchable power of each electric vehicle is determined, enabling real-time optimization of the power scheduling strategy for the electric vehicle cluster. This allows each electric vehicle to schedule more power when its battery SOC is high and less power when its SOC is low, ensuring that the SOC of each electric vehicle in the cluster remains consistent. Consequently, the SOC of the entire electric vehicle cluster is also consistent with the SOC of each individual electric vehicle, preventing discrepancies between the cluster and individual vehicles' battery states. This more rational scheduling of the electric vehicle cluster ensures optimal battery utilization and improves the cluster's output power. Furthermore, scheduling electric vehicles based on SOC not only maximizes battery energy utilization but also prevents overcharging or over-discharging, extending battery life and further enhancing the electric vehicle's output power.

[0036] The implementation details of the electric vehicle cluster scheduling method in this embodiment are described below. The following content is only for the convenience of understanding and is not necessary for implementing this solution.

[0037] In step 101, for a group of electric vehicles, i.e., an electric vehicle cluster, during the charging and discharging phase, the maximum output power of the electric vehicle cluster depends on the electric vehicle with the highest State of Charge (SOC) within the cluster. When the SOC of that electric vehicle is full and charging can no longer continue, the electric vehicle cluster's maximum power output will also cease. Similarly, during the battery discharge phase, the maximum output power of the entire electric vehicle cluster depends on the electric vehicle with the lowest SOC. Therefore, to extend the duration of maximum power output as much as possible, the consistency of the State of Charge is used as the objective function, defined as the minimum of the sum of the variances of the State of Charge of all electric vehicles in the electric vehicle cluster. Thus, in this embodiment, for the electric vehicle cluster, the minimum of the sum of the variances of the State of Charge of all electric vehicles in the cluster is used as the objective function, and the power scheduled by the electric vehicle cluster is the total power to be scheduled, and the sum of the power scheduled by all electric vehicles is the power scheduled by the electric vehicle cluster are used as constraints to establish a scheduling optimization model. The scheduling optimization model is used to determine the first state of charge of each electric vehicle and, based on the first state of charge and the total power to be scheduled, to determine the first vehicle power allocated to each electric vehicle.

[0038] In some embodiments, these electric vehicles can form multiple electric vehicle clusters. In this case, a two-level scheduling optimization model is established with the minimum sum of the variances of the states of charge (SOCs) of all electric vehicle clusters as the objective function, and the constraints being the average SOC of all electric vehicles in the corresponding electric vehicle cluster for each cluster, the sum of the scheduled power of all electric vehicle clusters as the total power to be scheduled, and the sum of the scheduled power of all electric vehicles in each electric vehicle cluster as the power scheduled by the corresponding electric vehicle cluster. The two-level scheduling optimization model is used to determine the second SOC of each electric vehicle cluster, and based on the second SOC and the total power to be scheduled, determine the cluster power allocated to each electric vehicle cluster; and to determine the third SOC of each electric vehicle in each electric vehicle cluster, and based on the third SOC and the cluster power allocated to each electric vehicle cluster, determine the second vehicle power allocated to each electric vehicle in each electric vehicle cluster.

[0039] The total power to be dispatched refers to the current total power available for dispatch by the power grid. This can be the total power when electric vehicles are charging or discharging. Assuming it's a low-peak electricity consumption period, the power grid can charge electric vehicles. The dispatch optimization model allocates this total power to each electric vehicle cluster and each electric vehicle within each cluster based on the current available power, allowing the power grid to charge the corresponding electric vehicles according to the allocated power. Conversely, assuming it's a high-peak electricity consumption period, where electric vehicles need to charge the power grid, the model allocates this total power to each electric vehicle cluster and each electric vehicle within each cluster based on the current required power, allowing each electric vehicle to charge the power grid according to the allocated power.

[0040] It is understandable that the total power to be scheduled changes in real time, and the state of charge of electric vehicles also changes in real time. Therefore, the final cluster power allocated to each electric vehicle cluster, as well as the second vehicle power allocated to each electric vehicle in each electric vehicle cluster, also changes in real time.

[0041] The objective function in this embodiment is specifically expressed by the following formula:

[0042]

[0043] Where N represents the number of electric vehicle clusters, and Mi represents the number of electric vehicles in electric vehicle cluster i. This represents the state of charge of electric vehicle j in electric vehicle cluster i at time t+1.

[0044] By combining the state of charge and state of health of each electric vehicle battery, and using the ampere-hour integral method for determining the state of charge, the real-time battery state of charge is calculated. Calculated using the following formula:

[0045]

[0046] in, C i,j =S OH,i,j C nom E i,j C represents the power input of electric vehicle j in electric vehicle cluster i. i,j η represents the current battery capacity of electric vehicle j in electric vehicle cluster i. i,j This represents the energy conversion efficiency of electric vehicle j within electric vehicle cluster i. Let S represent the power of electric vehicle j in electric vehicle cluster i, T represent one cycle of the power cycle, and S represent the power of electric vehicle j in electric vehicle cluster i. OH,i,j C represents the battery health status of electric vehicle j in electric vehicle cluster i.nom This represents the standard capacity of electric vehicle j in electric vehicle cluster i.

[0047] In its implementation, the aforementioned two-layer scheduling optimization model consists of an upper layer (EV cluster control layer) and a lower layer (EV control layer). The upper layer determines the second state of charge (SOC) of each EV cluster and, based on the SOC and the total power to be scheduled, determines the cluster power allocated to each EV cluster, ensuring that the EV clusters participating in the scheduling meet the total power requirement to be scheduled. The lower layer determines the third SOC of each EV in each EV cluster and, based on the third SOC and the cluster power allocated to each EV cluster, determines the second vehicle power allocated to each EV in each EV cluster, ensuring that the EVs participating in the scheduling in each EV cluster meet the cluster power requirement allocated to that EV cluster. Furthermore, to ensure the safe and stable operation of each EV cluster, the battery SOC of the EVs in the EV cluster must operate within a reasonable range.

[0048] Therefore, the constraints of the two-level scheduling optimization model mainly fall into two categories: electric vehicle cluster constraints and electric vehicle constraints. Electric vehicle cluster constraints include electric vehicle cluster power constraints and electric vehicle cluster state of charge constraints, while electric vehicle constraints include both electric vehicle power constraints and electric vehicle state of charge constraints.

[0049] For electric vehicle cluster power constraints, the total power to be scheduled is distributed to each electric vehicle cluster and then allocated to each electric vehicle within that cluster. In other words, the sum of the power scheduled by all electric vehicle clusters equals the total power to be scheduled, as shown in the following formula:

[0050]

[0051] in, This represents the total power to be scheduled at time t. This represents the cluster power of electric vehicle cluster i at time t.

[0052] For the state of charge (SOC) constraint of electric vehicle clusters, where the SOC of each electric vehicle cluster is the average of the SOCs of all electric vehicles in the cluster, the following formula applies:

[0053]

[0054] in, This represents the second charge state of electric vehicle cluster i.

[0055] For electric vehicle power constraints, the sum of the power of an electric vehicle cluster is obtained by adding up the power of each electric vehicle within the cluster. That is, the sum of the power dispatched by all electric vehicles in each electric vehicle cluster is the power dispatched by the corresponding electric vehicle cluster. The overall power of the electric vehicle cluster is constrained by the rated capacity of the converter, as shown in the following formula:

[0056]

[0057]

[0058] Among them, P i，min P represents the lower limit of the battery power of electric vehicles within the electric vehicle cluster. i，max This represents the upper limit of the battery power of electric vehicles within the electric vehicle cluster.

[0059] For the state of charge (SOC) constraint of electric vehicles, the SOC interval of all electric vehicles within the same electric vehicle cluster is the same, as shown in the following formula:

[0060]

[0061] In the formula, This represents the lower limit of the battery state of charge of electric vehicle i. This represents the upper limit of the battery state of charge of electric vehicle i.

[0062] The two-level scheduling optimization model in this embodiment can be solved using the commercial gurobi solver. Taking two clusters of 100 electric vehicles each as an example, the gurobi solver can be called through MATLAB to perform optimization. The solution speed of this model is less than 10 seconds.

[0063] In some embodiments, the above-mentioned multiple electric vehicle clusters are obtained by the following division method: based on the starting charging time and charging duration of each electric vehicle on the same day, several electric vehicles are divided into multiple electric vehicle clusters, that is, electric vehicles with the same starting charging time and charging duration on the same day are grouped into the same electric vehicle cluster, thus obtaining multiple electric vehicle clusters.

[0064] In one example, the total duration of the day is divided into multiple time periods, and the charging probability density of each electric vehicle in each time period is obtained. Based on the charging probability density of each electric vehicle in each time period, several electric vehicles are divided into multiple electric vehicle clusters.

[0065] The initial charging time satisfies the following formula:

[0066]

[0067] Among them, fstart μ represents the probability density of the initial charging time. start σ represents the expected value of the initial charging time. start This represents the standard deviation of the initial charging time.

[0068] Assuming the electric vehicle is charging normally, the charging time follows the formula:

[0069]

[0070] Where D represents the electricity consumed by the electric vehicle, and η e p represents the charging efficiency of an electric vehicle under normal conditions. e This represents the power output of an electric vehicle during charging.

[0071] For example, the total duration of the day is divided into five time periods: 0-td1, td1-td2, td2-td3, td3-td4, and td4-24. The probability density of electric vehicle charging within each time period is calculated by dividing the probability of charging within that period by the length of that period. Using the equal area method, the area expressions for the five regions are:

[0072]

[0073] like Figure 2 The diagram showing the division of the electric vehicle clusters shows that the intersection points are 0.6, 1.2, 1.6, and 2.5 respectively.

[0074] In step 102, after obtaining the first vehicle power allocated to each electric vehicle through the scheduling optimization model, the first vehicle power is sent to the corresponding electric vehicle in the electric vehicle cluster, thereby completing the scheduling of the electric vehicle cluster.

[0075] In some embodiments, if several electric vehicles are divided into multiple electric vehicle clusters, the cluster power allocated to each electric vehicle cluster is sent to the corresponding electric vehicle cluster. Then, the second vehicle power allocated to each electric vehicle in each electric vehicle cluster is sent to the corresponding electric vehicle in the corresponding electric vehicle cluster, thereby completing the power scheduling of several electric vehicles in multiple electric vehicle clusters.

[0076] In one embodiment, the electric vehicle cluster scheduling method of this embodiment can be applied to, for example... Figure 3 In the system architecture shown, the scheduling of electric vehicle clusters is achieved through system integration. During the production process of electric vehicle battery pack manufacturers, the essence of the production activity is still to connect a large number of battery packs in series and parallel to form a large-volume battery pack with sufficient capacity to meet usage requirements. Therefore, the battery of the electric vehicle shown in this embodiment is a battery pack.

[0077] Figure 3 The cluster controller in this application primarily issues power commands to the electric vehicles within the electric vehicle cluster. Specifically, it sends the first power allocated to each electric vehicle to the corresponding vehicle, completing the power scheduling of several electric vehicles. Based on the overall information of the electric vehicle cluster, the cluster controller makes decisions, rationally calculates the optimal power output value for each electric vehicle in the cluster, and then rationally arranges the output of each electric vehicle cluster. This cluster control framework has a high degree of intelligence, strong robustness, and excellent adaptability to different situations. Each electric vehicle cluster identifies the battery status of its individual electric vehicles based on the received power, moving from point to surface, from electric vehicle to electric vehicle cluster, and further to the system level, ensuring the safe and stable operation of the entire system and achieving optimal energy allocation.

[0078] The energy management system is a key device for ensuring the safe and stable operation and internal energy distribution of electric vehicle clusters. It also serves as the interface between the power grid and the battery energy management system. A schematic diagram of the energy management system for an electric vehicle cluster is shown below. Figure 4 As shown, the energy management system acquires the power of the electric vehicle cluster in real time and issues commands to each different electric vehicle, thereby enabling each electric vehicle cluster to allocate and execute tasks according to the commands.

[0079] The power scheduling process for several electric vehicles can be as follows: Figure 5 As shown, within an electric vehicle cluster, the battery state of charge (SBC) of each electric vehicle is sent from its battery management system to the energy management system within the cluster, and then from the energy management system of each cluster to the cluster controller. Simultaneously, the dispatch center (e.g., the power grid) sends the total power to be dispatched to the cluster controller in real time. The power of the electric vehicle batteries within each electric vehicle cluster is obtained through a scheduling optimization model. These instructions are then sent to the energy management system of each electric vehicle cluster, which in turn allocates the power of individual electric vehicle batteries. The power signal is sent to the electric vehicle battery to achieve power control. Specifically, the power signal sent from the dispatch center to the cluster controller... It will be updated periodically, and the update time will vary depending on the application scenario of the electric vehicle cluster. After the new power is updated, the power of the electric vehicle battery in each electric vehicle cluster will be obtained through the scheduling optimization model.

[0080] The electric vehicle cluster scheduling method in this embodiment can optimize the charging and discharging plan of the electric vehicle cluster in real time based on battery status to achieve efficient utilization of battery energy. By rationally allocating charging and discharging tasks, overcharging or over-discharging of the battery is avoided, maximizing battery life and improving the energy utilization efficiency of the entire cluster. The battery status-based scheduling strategy can intelligently arrange vehicle charging tasks according to the remaining battery capacity and charging needs of different vehicles. By rationally scheduling charging pile resources, load balancing among charging piles is achieved, avoiding load concentration and reducing the pressure on charging piles, thereby improving charging efficiency. The battery status-based scheduling strategy can rationally plan the routes and task allocation of electric vehicles. Optimizing the vehicle scheduling order and task arrangement based on factors such as battery capacity and remaining driving range of different vehicles reduces vehicle idling mileage and waiting time, improving vehicle operating efficiency and driving range. The battery status-based scheduling strategy can rationally arrange vehicle charging time and location according to user needs and preferences, providing personalized and efficient charging services. This will significantly reduce user waiting time, improve user satisfaction and experience, and enhance the acceptability of the vehicle-sharing model. By rationally scheduling the charging and discharging plans of electric vehicle clusters, coordinating the use of charging piles, smoothing grid load, delaying grid upgrades, achieving peak shaving and valley filling, and optimizing energy allocation, it will contribute to building a smart and sustainable energy system.

[0081] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this patent.

[0082] Another embodiment of this application relates to an electric vehicle cluster scheduling device. The implementation details of this electric vehicle cluster scheduling device are described below. The following implementation details are provided for ease of understanding and are not essential for implementing this solution. A schematic diagram of the electric vehicle power scheduling device in this embodiment can be seen as follows: Figure 6 As shown, it includes: a model building module 601 and a cluster scheduling module 602.

[0083] Specifically, the model building module 601 is used to establish a scheduling optimization model for an electric vehicle cluster consisting of several electric vehicles, with the objective function being the minimum sum of the variances of the states of charge of all electric vehicles in the cluster, and the constraints being the total power to be scheduled for the electric vehicle cluster and the sum of the power scheduled for all electric vehicles. The scheduling optimization model is used to determine the first state of charge of each electric vehicle in the electric vehicle cluster, and to determine the first vehicle power allocated to each electric vehicle based on the first state of charge and the total power to be scheduled.

[0084] The cluster scheduling module 602 is used to send the first vehicle power allocated to each electric vehicle to the corresponding electric vehicle in the electric vehicle cluster, thereby completing the scheduling of the electric vehicle cluster.

[0085] In one example, if there are multiple electric vehicle clusters consisting of several electric vehicles, the model building module 601 is further used to establish a two-level scheduling optimization model with the minimum of the sum of the variances of the states of charge of all electric vehicle clusters as the objective function, and with the state of charge of each electric vehicle cluster as the average of the states of charge of all electric vehicles in the corresponding electric vehicle cluster, the sum of the power scheduled by all electric vehicle clusters as the total power to be scheduled, and the sum of the power scheduled by all electric vehicles in each electric vehicle cluster as the power scheduled by the corresponding electric vehicle cluster as the constraints. The two-level scheduling optimization model is used to determine the second state of charge of each electric vehicle cluster, and based on the second state of charge and the total power to be scheduled, determine the cluster power allocated to each electric vehicle cluster, and determine the third state of charge of each electric vehicle in each electric vehicle cluster, and based on the third state of charge and the cluster power allocated to each electric vehicle cluster, determine the second vehicle power allocated to each electric vehicle in each electric vehicle cluster.

[0086] The power scheduling module 602 is also used to send the second vehicle power allocated to each electric vehicle in each electric vehicle cluster to the corresponding electric vehicle in the corresponding electric vehicle cluster, thereby completing the scheduling of the electric vehicle cluster.

[0087] It is not difficult to see that this embodiment is a device embodiment corresponding to the above method embodiments, and this embodiment can be implemented in conjunction with the above method embodiments. The relevant technical details and technical effects mentioned in the above embodiments are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above embodiments.

[0088] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units are absent in this embodiment.

[0089] Another embodiment of this application relates to an electronic device, such as... Figure 7 As shown, it includes: at least one processor 701; and a memory 702 communicatively connected to the at least one processor 701; wherein the memory 702 stores instructions executable by the at least one processor 701, the instructions being executed by the at least one processor 701 to enable the at least one processor 701 to execute the power scheduling method for electric vehicles in the above embodiments.

[0090] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0091] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0092] Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0093] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for scheduling electric vehicle clusters, characterized in that, include: For an electric vehicle cluster consisting of several electric vehicles, an optimization model is established with the minimum of the sum of the variances of the states of charge of all electric vehicles in the cluster as the objective function, and the power scheduled by the electric vehicle cluster as the total power to be scheduled and the sum of the power scheduled by all electric vehicles as the power scheduled by the electric vehicle cluster as the constraints. The scheduling optimization model is used to determine the first state of charge of each electric vehicle in the electric vehicle cluster, and to determine the first vehicle power allocated to each electric vehicle based on the first state of charge and the total power to be scheduled. The first vehicle power allocated to each electric vehicle is sent to the corresponding electric vehicle in the electric vehicle cluster to complete the scheduling of the electric vehicle cluster.

2. The electric vehicle cluster scheduling method according to claim 1, characterized in that, The electric vehicle cluster consisting of the aforementioned electric vehicles may be multiple, and the method further includes: A two-level scheduling optimization model is established with the minimum sum of variances of the states of charge of all electric vehicle clusters as the objective function, and the constraints being the average state of charge of all electric vehicles in the corresponding electric vehicle cluster as the state of charge of each electric vehicle cluster, the sum of the power scheduled by all electric vehicle clusters as the total power to be scheduled, and the sum of the power scheduled by all electric vehicles in each electric vehicle cluster as the power scheduled by the corresponding electric vehicle cluster. The two-layer scheduling optimization model is used to determine the second state of charge of each electric vehicle cluster, and to determine the cluster power allocated to each electric vehicle cluster based on the second state of charge and the total power to be scheduled, and to determine the third state of charge of each electric vehicle in each electric vehicle cluster, and to determine the second vehicle power allocated to each electric vehicle in each electric vehicle cluster based on the third state of charge and the cluster power allocated to each electric vehicle cluster. The second vehicle power allocated to each electric vehicle is sent to the corresponding electric vehicle in the corresponding electric vehicle cluster to complete the scheduling of the electric vehicle cluster.

3. The electric vehicle cluster scheduling method according to claim 2, characterized in that, Multiple electric vehicle clusters are obtained through the following steps: Based on the starting charging time and charging duration of each electric vehicle on that day, several electric vehicles are divided into multiple electric vehicle clusters.

4. The electric vehicle cluster scheduling method according to claim 3, characterized in that, The process of dividing several electric vehicles into multiple electric vehicle clusters based on the starting charging time and charging duration of each electric vehicle on that day includes: The total duration of the day is divided into multiple time periods, and the charging probability density of each electric vehicle in each time period is obtained. Based on the charging probability density of each electric vehicle in each time period, several electric vehicles are divided into multiple electric vehicle clusters.

5. The electric vehicle cluster scheduling method according to claim 4, characterized in that, The initial charging time satisfies the following formula: Among them, f start μ represents the probability density of the initial charging time. start σ represents the expected value of the initial charging time. start The standard deviation of the initial charging time is represented.

6. The electric vehicle cluster scheduling method according to claim 2, characterized in that, The objective function is expressed by the following formula: Where N represents the number of electric vehicle clusters, and Mi represents the number of electric vehicles in electric vehicle cluster i. This represents the state of charge of electric vehicle j in the electric vehicle cluster i at time t+1.

7. The electric vehicle cluster scheduling method according to claim 6, characterized in that, The Calculated using the following formula: in, C i,j =S OH,i,j C nom E i,j C represents the power input of electric vehicle j in the electric vehicle cluster i. i,j η represents the current battery capacity of electric vehicle j in the electric vehicle cluster i. i,j This represents the energy conversion efficiency of electric vehicle j within the electric vehicle cluster i. Let S represent the power of electric vehicle j in the electric vehicle cluster i, T represent one cycle of the power cycle, and S represent the power of electric vehicle j in the cluster i. OH,i,j C represents the battery health status of electric vehicle j in the electric vehicle cluster i. nom This represents the standard capacity of electric vehicle j in the electric vehicle cluster i.

8. A power dispatching device for an electric vehicle, characterized in that, include: The model building module is used to establish a scheduling optimization model for an electric vehicle cluster consisting of several electric vehicles, with the minimum of the sum of the variances of the states of charge of all electric vehicles in the cluster as the objective function, and the power scheduled by the electric vehicle cluster as the total power to be scheduled and the sum of the power scheduled by all electric vehicles as the power scheduled by the electric vehicle cluster as the constraints. The scheduling optimization model is used to determine the first state of charge of each electric vehicle in the electric vehicle cluster, and to determine the first vehicle power allocated to each electric vehicle based on the first state of charge and the total power to be scheduled. The cluster scheduling module is used to send the first vehicle power allocated to each electric vehicle to the corresponding electric vehicle in the electric vehicle cluster, thereby completing the scheduling of the electric vehicle cluster.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the electric vehicle cluster scheduling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the electric vehicle cluster scheduling method as described in any one of claims 1 to 7.

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