Charging scheduling optimization method and device, electronic equipment, storage medium and vehicle

Abstract

The invention relates to the technical field of intelligent network connection, in particular to a charging scheduling optimization method and device, electronic equipment, a storage medium and a vehicle, and the method comprises the steps: collecting the battery data of the vehicle, the user preference data, the current state data of a charging pile, and the current operation data of a power grid; inputting the battery data, the user preference data, the current state data and the current operation data into a pre-constructed multi-target optimization model to output the charging duration, the charging cost, the charging power and the charging temperature of vehicle charging; and determining at least one charging strategy for vehicle charging based on the charging duration, the charging cost, the charging power and the charging temperature, and controlling the vehicle to be charged until a certain charging stop condition is met. Therefore, the problems that the charging efficiency is low due to the fact that the battery characteristic difference is not considered in the related technology are solved; in addition, local power grid overload is easily caused in the concentrated charging period, power grid load unbalance is caused, and the average charging cost of users is increased.

Description

Technical Field

[0001] This application relates to the field of intelligent connected vehicle technology, and in particular to an optimization method, device, electronic device, storage medium, and vehicle for charging scheduling. Background Technology

[0002] In related technologies, long short-term neural network models can be used to construct charging demand prediction models to predict charging demand in future time periods. This includes building various optimized charging and discharging strategies, battery charging efficiency evaluation models, and battery charging scheduling models for charging stations. Furthermore, when multiple batteries are charging simultaneously, the globally optimal charging power allocation scheme can be solved. Alternatively, vehicle battery SOC (State of Charge) can be monitored in real time. When SOC ≤ a preset threshold, the vehicle is inserted at the front of the charging queue; otherwise, it is sorted according to a first-come, first-served principle, with emergency vehicles jumping the queue and regular vehicles following. A multi-stress factor model can then be used to quantify the battery capacity degradation rate, thereby generating a scheduling strategy with the goal of minimizing charging costs, user waiting time, and battery degradation costs.

[0003] However, the relevant technologies do not take into account the differences in battery characteristics, resulting in low charging efficiency. In addition, concentrated charging periods can easily cause local grid overload, resulting in grid load imbalance and increasing the average charging cost for users, which urgently needs to be improved. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, storage medium, and vehicle for optimizing charging scheduling, in order to solve the problems in related technologies, such as low charging efficiency due to failure to consider differences in battery characteristics; in addition, concentrated charging periods are prone to causing local grid overload, resulting in grid load imbalance and increased average charging costs for users.

[0005] The first aspect of this application provides an optimization method for vehicle charging scheduling, comprising the following steps: collecting vehicle battery data, user preference data of the vehicle, current status data of charging piles, and current operation data of the power grid; inputting the battery data, user preference data, current status data, and current operation data into a pre-constructed multi-objective optimization model to output the charging duration, charging cost, charging power, and charging temperature of the vehicle; determining at least one charging strategy for the vehicle based on the charging duration, charging cost, charging power, and charging temperature, and controlling the vehicle to charge according to the at least one charging strategy until the vehicle meets a preset charging stop condition.

[0006] Optionally, in one embodiment of this application, before inputting the battery data, user preference data, current state data, and current operating data into a pre-constructed multi-objective optimization model, the method further includes: collecting target battery data of the target vehicle, target user preference data of the target vehicle, current target state data of the target charging pile, and current target operating data of the target power grid; determining the target charging duration and target charging cost of the target vehicle based on the target battery data, target user preference data, current target state data, and current target operating data, and constructing a constraint function for the multi-objective optimization model based on the target charging duration and target charging cost; constructing a first constraint condition for the multi-objective optimization model based on the target battery data; constructing a second constraint condition for the multi-objective optimization model based on the current target state data; constructing a third constraint condition for the multi-objective optimization model based on the current target operating data; and constructing the multi-objective optimization model based on the constraint function, the first constraint condition, the second constraint condition, and the third constraint condition.

[0007] Optionally, in one embodiment of this application, the step of inputting the battery data, user preference data, current status data, and current operating data into a pre-constructed multi-objective optimization model to output the charging time, charging cost, charging power, and charging temperature of the vehicle includes: determining the initial charging time, initial charging cost, initial charging power, and initial charging temperature of the vehicle based on the first, second, and third constraints of the multi-objective optimization model; and filtering the initial charging time, initial charging cost, initial charging power, and initial charging temperature to obtain the charging time, charging cost, charging power, and charging temperature.

[0008] Optionally, in one embodiment of this application, determining at least one charging strategy for charging the vehicle based on the charging time, the charging cost, the charging power, and the charging temperature includes: obtaining a charging time shorter than a preset time, and determining the corresponding charging cost, charging power, and charging temperature based on the charging time shorter than the preset time to obtain a first charging strategy; obtaining a charging cost shorter than a preset cost, and determining the corresponding charging time, charging power, and charging temperature based on the charging cost shorter than the preset cost to obtain a second charging strategy; obtaining a charging temperature shorter than a preset temperature, and determining the corresponding charging time, charging cost, and charging power based on the charging temperature shorter than the preset temperature to obtain a third charging strategy; and obtaining the charging strategy based on the first charging strategy, the second charging strategy, and the third charging strategy.

[0009] Optionally, in one embodiment of this application, before inputting the battery data, user preference data, current status data, and current running data into a pre-constructed multi-objective optimization model, the method further includes: determining whether the battery data, user preference data, current status data, and current running data all satisfy preset data conditions; if at least one of the battery data, user preference data, current status data, and current running data does not satisfy the preset data conditions, then generating a corresponding processing action according to the preset data conditions until the battery data, user preference data, current status data, and current running data all satisfy the preset data conditions, and allowing the input of battery data, user preference data, current status data, and current running data that satisfy the preset data conditions into the multi-objective optimization model; if the battery data, user preference data, current status data, or current running data all satisfy the preset data conditions, then allowing the input of battery data, user preference data, current status data, and current running data into the multi-objective optimization model.

[0010] A second aspect of this application provides an optimization device for vehicle charging scheduling, comprising: a first acquisition module for acquiring vehicle battery data, user preference data of the vehicle, current status data of charging piles, and current operation data of the power grid; an output module for inputting the battery data, user preference data, current status data, and current operation data into a pre-constructed multi-objective optimization model to output the charging duration, charging cost, charging power, and charging temperature of the vehicle; and a control module for determining at least one charging strategy for the vehicle based on the charging duration, charging cost, charging power, and charging temperature, and controlling the vehicle to charge according to the at least one charging strategy until the vehicle meets a preset charging stop condition.

[0011] Optionally, in one embodiment of this application, it further includes: a second acquisition module, configured to acquire target battery data of the target vehicle, target user preference data of the target vehicle, current target state data of the target charging pile, and current target operation data of the target power grid before inputting the battery data, user preference data, current state data, and current operation data into the pre-constructed multi-objective optimization model; a first construction module, configured to determine the target charging duration and target charging cost of the target vehicle based on the target battery data, target user preference data, current target state data, and current target operation data, so as to construct the constraint function of the multi-objective optimization model according to the target charging duration and target charging cost; a second construction module, configured to construct the first constraint condition of the multi-objective optimization model based on the target battery data; a third construction module, configured to construct the second constraint condition of the multi-objective optimization model based on the current target state data; a fourth construction module, configured to construct the third constraint condition of the multi-objective optimization model based on the current target operation data; and a fifth construction module, configured to construct the multi-objective optimization model based on the constraint function, the first constraint condition, the second constraint condition, and the third constraint condition.

[0012] Optionally, in one embodiment of this application, the output module includes: a determining unit, configured to determine the initial charging duration, initial charging cost, initial charging power, and initial charging temperature of the vehicle charging based on the first constraint, second constraint, and third constraint of the multi-objective optimization model; and a filtering unit, configured to filter the initial charging duration, initial charging cost, initial charging power, and initial charging temperature to obtain the charging duration, charging cost, charging power, and charging temperature.

[0013] Optionally, in one embodiment of this application, the control module includes: a first generation unit, configured to acquire a charging duration shorter than a preset duration, and determine the corresponding charging cost, charging power, and charging temperature based on the charging duration shorter than the preset duration to obtain a first charging strategy; a second generation unit, configured to acquire a charging cost shorter than a preset cost, and determine the corresponding charging duration, charging power, and charging temperature based on the charging cost shorter than the preset cost to obtain a second charging strategy; a third generation unit, configured to acquire a charging temperature shorter than a preset temperature, and determine the corresponding charging duration, charging cost, and charging power based on the charging temperature shorter than the preset temperature to obtain a third charging strategy; and a fourth generation unit, configured to obtain the charging strategy based on the first charging strategy, the second charging strategy, and the third charging strategy.

[0014] Optionally, in one embodiment of this application, it further includes: a judgment module, configured to determine whether the battery data, user preference data, current state data, and current running data all satisfy preset data conditions before inputting the battery data, user preference data, current state data, and current running data into a pre-constructed multi-objective optimization model; a first input module, configured to generate a corresponding processing action according to the preset data conditions when at least one of the battery data, user preference data, current state data, and current running data does not satisfy the preset data conditions, until the battery data, user preference data, current state data, and current running data all satisfy the preset data conditions, and allow the input of battery data, user preference data, current state data, and current running data that satisfy the preset data conditions into the multi-objective optimization model; and a second input module, configured to allow the input of battery data, user preference data, current state data, and current running data into the multi-objective optimization model when the battery data, user preference data, current state data, or current running data all satisfy the preset data conditions.

[0015] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle charging scheduling optimization method as described in the above embodiments.

[0016] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described optimized method for vehicle charging scheduling.

[0017] A fifth aspect of this application provides a vehicle that includes the electronic equipment described above.

[0018] A sixth aspect of this application provides a computer program product, including a computer program that, when executed, implements the above-described optimized method for vehicle charging scheduling.

[0019] This application embodiment can input collected vehicle battery data, vehicle user preference data, charging pile current status data, and power grid current operation data into a pre-constructed multi-objective optimization model. This model then outputs the vehicle's charging time, charging cost, charging power, and charging temperature, thereby determining the vehicle's charging strategy and controlling the charging process until certain charging stop conditions are met. By comprehensively collecting data from the vehicle, charging pile, and power grid, it provides rich evidence for accurate decision-making. Utilizing the multi-objective optimization model, it simultaneously considers multiple factors such as charging time, cost, power, and temperature, outputting a comprehensive optimal result. This allows for flexible adaptation to different scenarios and needs, achieving intelligent and automated charging processes and improving charging experience and efficiency. Therefore, it solves the problems in related technologies, such as low charging efficiency due to failure to consider differences in battery characteristics; and the increased average charging cost for users due to the potential for local power grid overload during peak charging periods.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of an optimization method for vehicle charging scheduling provided according to an embodiment of this application; Figure 2 This is a schematic diagram of a charging power optimization curve provided according to an embodiment of this application; Figure 3 This is a block diagram of an optimized vehicle charging scheduling system according to an embodiment of this application; Figure 4 This is a block diagram of a vehicle charging scheduling optimization device provided according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0022] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0023] The following description, with reference to the accompanying drawings, outlines an optimization method, apparatus, electronic device, storage medium, and vehicle for charging scheduling according to embodiments of this application. Addressing the issues mentioned in the background art, such as low charging efficiency due to the lack of consideration for differences in battery characteristics, and the potential for local grid overload and grid load imbalance during peak charging periods, which increases the average charging cost for users, this application provides an optimization method for vehicle charging scheduling. In this method, collected vehicle battery data, user preference data, current charging pile status data, and current grid operation data are input into a pre-constructed multi-objective optimization model. This model then outputs the charging duration, charging cost, charging power, and charging temperature, thereby determining the vehicle charging strategy and controlling vehicle charging until certain charging stop conditions are met. By comprehensively collecting data from the vehicle, charging pile, and grid, it provides rich evidence for accurate decision-making. Utilizing the multi-objective optimization model, it simultaneously considers multiple factors such as charging duration, cost, power, and temperature, outputting a comprehensive optimal result. This method can flexibly adapt to different scenarios and needs, achieving intelligent and automated charging processes and improving charging experience and efficiency. This solves the problems in related technologies, such as low charging efficiency due to failure to consider differences in battery characteristics; in addition, concentrated charging periods can easily cause local grid overload, resulting in grid load imbalance and increasing the average charging cost for users.

[0024] Specifically, Figure 1 This is a flowchart of an optimization method for vehicle charging scheduling provided according to an embodiment of this application.

[0025] like Figure 1 As shown, the optimized method for vehicle charging scheduling includes the following steps: In step S101, the vehicle's battery data, the vehicle's user preference data, the current status data of the charging pile, and the current operation data of the power grid are collected.

[0026] It is understood that, in the embodiments of this application, the vehicle's battery data may include, but is not limited to, battery management system data, such as SOC, SOH (State of Health), temperature, etc.; the vehicle's user preference data may include, but is not limited to, expected pick-up time and cost sensitivity (level 1-5), which can be set through the vehicle's HMI (Human Machine Interface) or mobile APP (Application), supporting voice input natural language commands (such as "fully charged before 8 am tomorrow, cost not exceeding 50 yuan"), etc., and this application does not impose specific limitations; the current status data of the charging pile may include, but is not limited to, the real-time power data of the charging pile, with an accuracy of ±1%, and is acquired using a Hall effect sensor with a range of 0-250kW and a bandwidth of DC to 100Hz; the interface compatibility data is identified through control guide circuit impedance measurement, such as CCS (Combined Charging System), CHAdeMO (CHArge de MOve), GB-T (Recommended National Standard of This application does not impose specific restrictions on interface data such as those from China (recommended national standards); the current operating data of the power grid may include, but is not limited to, regional load factor (0-100%), which can be obtained from the distribution automation system with 15-minute refresh data; time-of-use electricity price data (15-minute granular update), accessing the power trading platform API (Application Programming Interface), predicting the electricity price curve for the next 24 hours, etc., which can be specifically set by those skilled in the art according to the actual situation, and this application does not impose specific restrictions.

[0027] In some embodiments, the present application may first collect vehicle battery data, vehicle user preference data, current status data of charging piles, and current operation data of the power grid.

[0028] Optionally, in one embodiment of this application, before inputting battery data, user preference data, current state data, and current operating data into the pre-constructed multi-objective optimization model, the method further includes: collecting target battery data of the target vehicle, target user preference data of the target vehicle, current target state data of the target charging pile, and current target operating data of the target power grid; determining the target charging duration and target charging cost of the target vehicle based on the target battery data, target user preference data, current target state data, and current target operating data, so as to construct a constraint function for the multi-objective optimization model based on the target charging duration and target charging cost; constructing a first constraint condition for the multi-objective optimization model based on the target battery data; constructing a second constraint condition for the multi-objective optimization model based on the current target state data; constructing a third constraint condition for the multi-objective optimization model based on the current target operating data; and constructing the multi-objective optimization model based on the constraint function, the first constraint condition, the second constraint condition, and the third constraint condition.

[0029] In some embodiments, before inputting battery data, user preference data, current status data, and current operating data into a pre-constructed multi-objective optimization model, this application embodiment can first collect target battery data of the target vehicle, target user preference data of the target vehicle, current target status data of the target charging pile, and current target operating data of the target power grid, thereby determining the target charging duration and target charging cost of the target vehicle, thus constructing the constraint function of the multi-objective optimization model, and constructing the first constraint condition, second constraint condition, and third constraint condition of the multi-objective optimization model based on the target battery data, current target status data, and current target operating data, respectively, thereby constructing the multi-objective optimization model.

[0030] The expression for the multi-objective optimization model can be, but is not limited to, as follows: , , in, These are the weighting coefficients for time and cost. ; Total charging time For the total cost, Power grid power; This represents the upper limit of the power grid; The battery has reached its final charge level; To meet the user's power needs; Battery temperature, not exceeding .

[0031] The expression for the first constraint condition can be, but is not limited to, as follows: , The expression for the second constraint can be, but is not limited to, as follows: , The expression for the third constraint can be, but is not limited to, as follows: , Optionally, in one embodiment of this application, before inputting battery data, user preference data, current status data, and current running data into a pre-built multi-objective optimization model, the method further includes: determining whether the battery data, user preference data, current status data, and current running data all meet preset data conditions; if at least one of the battery data, user preference data, current status data, and current running data does not meet the preset data conditions, then generating a corresponding processing action according to the preset data conditions until the battery data, user preference data, current status data, and current running data all meet the preset data conditions, and allowing the input of battery data, user preference data, current status data, and current running data that meet the preset data conditions into the multi-objective optimization model; if the battery data, user preference data, current status data, or current running data all meet the preset data conditions, then allowing the input of battery data, user preference data, current status data, and current running data into the multi-objective optimization model.

[0032] In some embodiments, before inputting battery data, user preference data, current status data, and current running data into a pre-built multi-objective optimization model, this application can first determine whether the battery data, user preference data, current status data, and current running data all meet certain data conditions. If not, a corresponding processing action is generated based on the certain data conditions. If all data conditions are met, then the battery data, user preference data, current status data, and current running data that meet the certain data conditions are allowed to be input into the multi-objective optimization model. These certain data conditions can be set by those skilled in the art according to actual circumstances, and this application does not impose specific limitations.

[0033] For example, embodiments of this application can normalize battery data, user preference data, current status data, and current running data to obtain battery data, user preference data, current status data, and current running data that meet certain data conditions, and input the battery data, user preference data, current status data, and current running data that meet certain data conditions into a multi-objective optimization model.

[0034] In some embodiments, this application can allow battery data, user preference data, current status data, and current running data to be directly input into a multi-objective optimization model when the battery data, user preference data, current status data, or current running data all meet certain data conditions.

[0035] In step S102, battery data, user preference data, current status data, and current operating data are input into a pre-built multi-objective optimization model to output the charging time, charging cost, charging power, and charging temperature of the vehicle.

[0036] As one possible approach, embodiments of this application can utilize a pre-built multi-objective optimization model to output the charging time, charging cost, charging power, and charging temperature of the vehicle.

[0037] Optionally, in one embodiment of this application, battery data, user preference data, current status data, and current operating data are input into a pre-built multi-objective optimization model to output the charging time, charging cost, charging power, and charging temperature of the vehicle. This includes: determining the initial charging time, initial charging cost, initial charging power, and initial charging temperature of the vehicle based on the first, second, and third constraints of the multi-objective optimization model; and filtering the initial charging time, initial charging cost, initial charging power, and initial charging temperature to obtain the charging time, charging cost, charging power, and charging temperature.

[0038] In some embodiments, the present application embodiments can determine the initial charging time, initial charging cost, initial charging power, and initial charging temperature of a vehicle by using the first, second, and third constraints of a multi-objective optimization model, and then filter the initial charging time, initial charging cost, initial charging power, and initial charging temperature to obtain the charging time, charging cost, charging power, and charging temperature.

[0039] For example, embodiments of this application can construct a feasible solution space based on the first, second, and third constraints of a multi-objective optimization model to exclude duration, cost, power, and temperature that violate the first, second, and third constraints. A genetic algorithm, such as NSGA-II (Non-dominated Sorting Genetic Algorithm II), is then used to obtain a non-dominated solution set. For example, the charging power curve is adjusted in real time with a step size of 5 minutes. This application does not impose specific limitations on this, thereby obtaining the corresponding charging duration, charging cost, charging power, and charging temperature.

[0040] In addition, the improvements to NSGA-II (population size 50, mutation rate 0.1) in this application can be achieved by incorporating a simulated annealing mechanism to enhance global search capabilities and effectively avoid the risk of getting trapped in local optima; and by using a real number encoding method to improve the convergence efficiency and accuracy of the algorithm. The specific settings can be made by those skilled in the art according to the actual situation, and this application does not impose any specific limitations.

[0041] In step S103, based on charging time, charging cost, charging power and charging temperature, at least one charging strategy for vehicle charging is determined, and the vehicle is controlled to charge according to at least one charging strategy until the vehicle meets the preset charging stop conditions.

[0042] In some embodiments, the present application can determine a charging strategy for the vehicle based on charging time, charging cost, charging power, and charging temperature, and then control the vehicle to charge according to the charging strategy until the vehicle meets certain charging stop conditions. These certain charging stop conditions can be set by those skilled in the art according to actual conditions, and the present application does not impose specific limitations.

[0043] It should be noted that, in this embodiment of the application, the output range of charging power can be 3kW-240kW (suitable for home / supercharging piles); according to actual test data, it only takes 22 seconds (including communication delay) to increase from 20kW to 150kW, thus making the switching time ≤30 seconds.

[0044] In addition, the embodiments of this application can visualize the generated charging strategy and display it on the user interaction terminal. The user interaction terminal can display content such as: time / cost comparison, three-dimensional prediction graph of charging progress (time-power-temperature), and bar chart comparing energy saving with historical records. The specific settings can be made by those skilled in the art according to the actual situation, and this application does not impose specific limitations.

[0045] For example, embodiments of this application can be implemented according to... Figure 2 The charging strategy shown will continue until certain charging stop conditions are met. Figure 2 In the diagram, the horizontal axis represents time, ranging from 0 to 24 hours; the vertical axis represents charging power, ranging from 0 to 240 kW; the straight line represents the traditional charging process, and the wavy line represents the charging process of this application; the shaded area represents peak electricity price periods.

[0046] It should be noted that the embodiments of this application have been verified by simulation and actual testing: the average charging time is shortened by 22%; user expenses are reduced by 18%-35%; the load peak-valley difference is reduced by 40%; and the SOH decay rate is reduced by 30% (comparative test after 100 cycles), etc. This application does not impose specific limitations.

[0047] Optionally, in one embodiment of this application, determining at least one charging strategy for vehicle charging based on charging time, charging cost, charging power, and charging temperature includes: obtaining a charging time shorter than a preset time, and determining the corresponding charging cost, charging power, and charging temperature based on the charging time shorter than the preset time to obtain a first charging strategy; obtaining a charging cost shorter than a preset cost, and determining the corresponding charging time, charging power, and charging temperature based on the charging cost shorter than the preset cost to obtain a second charging strategy; obtaining a charging temperature shorter than a preset temperature, and determining the corresponding charging time, charging cost, and charging power based on the charging temperature shorter than the preset temperature to obtain a third charging strategy; and obtaining a charging strategy based on the first charging strategy, the second charging strategy, and the third charging strategy.

[0048] It is understood that in the embodiments of this application, the first charging strategy can be understood as the fastest charging strategy. For example, if the charging time is less than a certain time, a corresponding strategy is generated. The certain time can be set by those skilled in the art according to the actual situation, and this application does not impose any specific restrictions.

[0049] In some embodiments, the present application first obtains a charging time of less than a certain duration, and then determines the corresponding charging cost, charging power and charging temperature based on the charging time of less than a certain duration, thereby obtaining a first charging strategy.

[0050] The second charging strategy can be understood as an economical charging strategy. For example, if the charging cost is less than a certain cost, a corresponding strategy is generated. The certain cost can be set by those skilled in the art according to the actual situation, and this application does not impose specific restrictions.

[0051] In some embodiments, the present application can first obtain a charging cost that is less than a certain cost, and then determine the corresponding charging time, charging power and charging temperature based on the charging cost that is less than a certain cost, thereby obtaining a second charging strategy.

[0052] The third charging strategy can be understood as a maintenance charging strategy. For example, when the charging temperature is lower than a certain temperature, a corresponding strategy is generated. The certain temperature can be set by those skilled in the art according to the actual situation, and this application does not impose any specific restrictions.

[0053] It should be noted that in the maintenance charging strategy of this application embodiment, the charging cutoff voltage is reduced by 0.1V and the constant current stage rate is limited to below 0.5C.

[0054] In some embodiments, the present application can first obtain a charging temperature below a certain temperature, and then determine the corresponding charging time, charging cost and charging power based on the charging temperature below a preset temperature, thereby obtaining a third charging strategy.

[0055] Furthermore, in the embodiments of this application, a charging strategy can be obtained based on a first charging strategy, a second charging strategy, and a third charging strategy.

[0056] The optimized vehicle charging scheduling system proposed in this application will now be described with reference to a specific embodiment.

[0057] in, Figure 3 This is a block diagram of an optimized vehicle charging scheduling system according to an embodiment of this application.

[0058] like Figure 3 As shown, the vehicle charging scheduling optimization system includes a data acquisition layer 301, an analysis layer 302, and an execution layer 303.

[0059] The data acquisition layer 301 includes an on-board terminal module 3011, a charging pile sensing module 3012, and a power grid status monitoring module 3013.

[0060] Among them, the vehicle terminal module 3011 is used to collect vehicle battery data, such as SOC, SOH, temperature, etc.; vehicle user preference data, such as expected pick-up time, cost sensitivity (level 1-5), etc., which are not specifically limited in this application.

[0061] The charging pile sensing module 3012 is used to collect the current status data of the charging pile, such as the real-time power data of the charging pile and the compatibility data of the interface. This application does not impose specific limitations.

[0062] The power grid status monitoring module 3013 is used to collect current operating data of the power grid, such as regional load factor (0-100%), time-of-use electricity price data, etc. This application does not impose specific restrictions.

[0063] Analysis layer 302 is used to output the charging time, charging cost, charging power and charging temperature of the vehicle using a pre-built multi-objective optimization model.

[0064] The execution layer 303 is used to display the user interaction terminal interface, which may include three charging strategies, such as the fastest charging strategy, the economical charging strategy, and the maintenance charging strategy, etc. This application does not impose specific limitations.

[0065] In addition, such as Figure 3 As shown, arrows can be used to indicate the direction of data flow and the transmission of control commands.

[0066] Example 1: Home charging station scenario This application embodiment can automatically read the vehicle identification number (VIN) and obtain the vehicle's battery data after the user plugs in the charging gun. The battery data can be 82kWh capacity, SOH 92%, and the current operating data of the power grid can be detected, such as the current grid load rate (65%) and time-of-use electricity price (0.48 yuan / kWh). Then, through a pre-built multi-objective optimization model, three charging strategies are generated: Strategy A (fastest charging strategy): fully charged in 2 hours, cost 39.8 yuan; Strategy B (economic charging strategy): fully charged in 4 hours, cost 28.6 yuan; Strategy C (maintenance charging strategy): fully charged in 6 hours, with temperature controlled below 38℃.

[0067] Furthermore, in this embodiment of the application, after determining that the charging strategy for vehicle charging is Strategy B, the power is dynamically adjusted: charging at 40kW for the first 2 hours (when electricity prices are low), and then reducing to 20kW for the next 2 hours (to avoid peak grid hours).

[0068] Example 2: Public Supercharging Station Scenario In this embodiment, the charging pile can simultaneously connect to 8 vehicle types (including 3 taxis and 5 private cars). According to the fastest charging strategy, the SOC of the taxis is prioritized to be replenished to 80%; and according to the economic charging strategy, the SOC of the private cars is replenished to a certain value, such as 80%. The specific settings can be made by those skilled in the art according to the actual situation, and this application does not impose specific limitations.

[0069] In this embodiment of the application, when the total power is limited to 600kW, the power allocation scheme of each charging pile is shown in Table 1. Table 1 is a power dynamic allocation schematic table provided according to the embodiment of the application.

[0070] Table 1

[0071] The vehicle charging scheduling optimization method proposed in this application can input collected vehicle battery data, vehicle user preference data, current status data of charging piles, and current operation data of the power grid into a pre-constructed multi-objective optimization model. This model then outputs the charging duration, charging cost, charging power, and charging temperature of the vehicle, thereby determining the charging strategy and controlling vehicle charging until certain charging stop conditions are met. By comprehensively collecting data from the vehicle, charging pile, and power grid, it provides rich evidence for accurate decision-making. Using a multi-objective optimization model, it simultaneously considers multiple factors such as charging duration, cost, power, and temperature, outputting a comprehensive optimal result. This method can flexibly adapt to different scenarios and needs, achieving intelligent and automated charging processes and improving charging experience and efficiency. Therefore, it solves the problems in related technologies, such as low charging efficiency due to failure to consider differences in battery characteristics; and the potential for local power grid overload and load imbalance during concentrated charging periods, leading to increased average charging costs for users.

[0072] Next, with reference to the accompanying drawings, an optimized vehicle charging scheduling apparatus according to an embodiment of this application is described.

[0073] Figure 4 This is a block diagram of an optimization device for vehicle charging scheduling provided according to an embodiment of this application.

[0074] like Figure 4 As shown, the vehicle charging scheduling optimization device 10 includes: a first acquisition module 100, an output module 200, and a control module 300.

[0075] The first acquisition module 100 is used to collect vehicle battery data, vehicle user preference data, current status data of charging piles, and current operation data of the power grid.

[0076] The output module 200 is used to input battery data, user preference data, current status data and current operation data into a pre-built multi-objective optimization model to output the charging time, charging cost, charging power and charging temperature of the vehicle.

[0077] The control module 300 is used to determine at least one charging strategy for vehicle charging based on charging time, charging cost, charging power and charging temperature, and control the vehicle to charge according to at least one charging strategy until the vehicle meets the preset charging stop conditions.

[0078] Optionally, in one embodiment of this application, it further includes: a second acquisition module, a first construction module, a second construction module, a third construction module, a fourth construction module, and a fifth construction module.

[0079] The second acquisition module is used to acquire target battery data of the target vehicle, target user preference data of the target vehicle, current target status data of the target charging pile, and current target operation data of the target power grid before inputting battery data, user preference data, current status data, and current operation data into the pre-built multi-objective optimization model.

[0080] The first construction module is used to determine the target charging duration and target charging cost of the target vehicle based on target battery data, target user preference data, current target status data, and current target operation data, so as to construct the constraint function of the multi-objective optimization model based on the target charging duration and target charging cost.

[0081] The second building module is used to construct the first constraint condition for a multi-objective optimization model based on the target battery data.

[0082] The third building module is used to construct the second constraint condition of the multi-objective optimization model based on the current target state data.

[0083] The fourth building module is used to construct the third constraint condition of the multi-objective optimization model based on the current target running data.

[0084] The fifth building module is used to construct a multi-objective optimization model based on constraint functions, the first constraint condition, the second constraint condition, and the third constraint condition.

[0085] Optionally, in one embodiment of this application, the output module 200 includes: a determining unit and a filtering unit.

[0086] The determining unit is used to determine the initial charging time, initial charging cost, initial charging power, and initial charging temperature of the vehicle based on the first, second, and third constraints of the multi-objective optimization model.

[0087] The filtering unit is used to filter the initial charging time, initial charging cost, initial charging power, and initial charging temperature to obtain the charging time, charging cost, charging power, and charging temperature.

[0088] Optionally, in one embodiment of this application, the control module 300 includes: a first generation unit, a second generation unit, a third generation unit, and a fourth generation unit.

[0089] The first generation unit is used to obtain a charging time shorter than a preset time, and based on the charging time shorter than the preset time, to determine the corresponding charging cost, charging power and charging temperature, so as to obtain a first charging strategy.

[0090] The second generation unit is used to obtain a charging cost that is less than a preset cost, and based on the charging cost that is less than the preset cost, to determine the corresponding charging time, charging power and charging temperature, so as to obtain a second charging strategy.

[0091] The third generation unit is used to obtain a charging temperature that is lower than a preset temperature, and based on the charging temperature that is lower than the preset temperature, to determine the corresponding charging time, charging cost and charging power, so as to obtain a third charging strategy.

[0092] The fourth generation unit is used to obtain a charging strategy based on the first charging strategy, the second charging strategy, and the third charging strategy.

[0093] Optionally, in one embodiment of this application, it further includes: a judgment module, a first input module, and a second input module.

[0094] The judgment module is used to determine whether the battery data, user preference data, current status data, and current running data all meet the preset data conditions before inputting them into the pre-built multi-objective optimization model.

[0095] The first input module is used to generate corresponding processing actions according to the preset data conditions when at least one of the battery data, user preference data, current status data, and current running data does not meet the preset data conditions, until the battery data, user preference data, current status data, and current running data all meet the preset data conditions, and allows the battery data, user preference data, current status data, and current running data that meet the preset data conditions to be input into the multi-objective optimization model.

[0096] The second input module is used to allow inputting battery data, user preference data, current status data, and current running data into the multi-objective optimization model when the battery data, user preference data, current status data, or current running data all meet preset data conditions.

[0097] It should be noted that the explanation of the above-mentioned method for optimizing vehicle charging scheduling also applies to the vehicle charging scheduling optimization device of this embodiment, and will not be repeated here.

[0098] The vehicle charging scheduling optimization device proposed in this application can input collected vehicle battery data, vehicle user preference data, charging pile current status data, and power grid current operation data into a pre-constructed multi-objective optimization model. This model then outputs the vehicle charging duration, charging cost, charging power, and charging temperature, thereby determining the vehicle charging strategy and controlling vehicle charging until certain charging stop conditions are met. By comprehensively collecting data from the vehicle, charging pile, and power grid, it provides rich evidence for accurate decision-making. Using a multi-objective optimization model, it simultaneously considers multiple factors such as charging duration, cost, power, and temperature, outputting a comprehensive optimal result. This allows for flexible adaptation to different scenarios and needs, achieving intelligent and automated charging processes and improving charging experience and efficiency. Therefore, it solves the problems in related technologies, such as low charging efficiency due to failure to consider differences in battery characteristics; and the potential for local power grid overload and load imbalance during concentrated charging periods, leading to increased average charging costs for users.

[0099] Figure 5 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. The electronic device may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0100] When the processor 502 executes the program, it implements the vehicle charging scheduling optimization method provided in the above embodiments.

[0101] Furthermore, electronic devices also include: Communication interface 503 is used for communication between memory 501 and processor 502.

[0102] The memory 501 is used to store computer programs that can run on the processor 502.

[0103] Memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0104] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0105] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0106] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0107] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described vehicle charging scheduling optimization method.

[0108] This application also provides a vehicle that includes the electronic devices described above.

[0109] This application also provides a computer program product, including a computer program that, when executed, implements the above-described vehicle charging scheduling optimization method.

[0110] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0111] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0112] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0113] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). In addition, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically by optically scanning paper or other media, then editing, interpreting or otherwise processing them as necessary, and then storing them in computer memory.

[0114] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0115] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0116] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0117] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for optimizing vehicle charging scheduling, characterized in that, The method comprises the following steps: collecting battery data of a vehicle, user preference data of the vehicle, current state data of a charging pile, and current operation data of a power grid; inputting the battery data, the user preference data, the current state data, and the current operation data into a pre-constructed multi-objective optimization model to output charging duration, charging cost, charging power, and charging temperature of charging of the vehicle; based on the charging duration, the charging cost, the charging power, and the charging temperature, determining at least one charging strategy for charging of the vehicle, and controlling the vehicle to charge according to the at least one charging strategy until the vehicle meets a preset charging stop condition.

2. The method of claim 1, wherein, Before inputting the battery data, the user preference data, the current state data, and the current operation data into the pre-constructed multi-objective optimization model, the method further comprises: collecting target battery data of a target vehicle, target user preference data of the target vehicle, current target state data of a target charging pile, and current target operation data of a target power grid; based on the target battery data, the target user preference data, the current target state data, and the current target operation data, determining charging target duration and charging target cost of the target vehicle, so as to construct a constraint function of the multi-objective optimization model according to the charging target duration and the charging target cost; based on the target battery data, constructing a first constraint condition of the multi-objective optimization model; based on the current target state data, constructing a second constraint condition of the multi-objective optimization model; based on the current target operation data, constructing a third constraint condition of the multi-objective optimization model; based on the constraint function, the first constraint condition, the second constraint condition, and the third constraint condition, constructing the multi-objective optimization model.

3. The method of claim 2, wherein, The inputting the battery data, the user preference data, the current state data, and the current operation data into the pre-constructed multi-objective optimization model to output charging duration, charging cost, charging power, and charging temperature of charging of the vehicle comprises: based on the first constraint condition, the second constraint condition, and the third constraint condition of the multi-objective optimization model, determining initial charging duration, initial charging cost, initial charging power, and initial charging temperature of charging of the vehicle; screening the initial charging duration, the initial charging cost, the initial charging power, and the initial charging temperature to obtain the charging duration, the charging cost, the charging power, and the charging temperature.

4. The method of claim 1, wherein, The determining at least one charging strategy for charging of the vehicle based on the charging duration, the charging cost, the charging power, and the charging temperature comprises: obtaining charging duration less than a preset duration, and based on the charging duration less than the preset duration, determining corresponding charging cost, charging power, and charging temperature to obtain a first charging strategy; obtaining charging cost less than a preset cost, and based on the charging cost less than the preset cost, determining corresponding charging duration, charging power, and charging temperature to obtain a second charging strategy; acquire a charging temperature less than the preset temperature, and determine a corresponding charging duration, charging cost and charging power based on the charging temperature less than the preset temperature, to obtain a third charging strategy; obtain the charging strategy based on the first charging strategy, the second charging strategy and the third charging strategy.

5. The method of claim 1, wherein, Before inputting the battery data, the user preference data, the current state data and the current operation data into the pre-constructed multi-objective optimization model, further comprising: determining whether the battery data, the user preference data, the current state data and the current operation data all meet a preset data condition; if at least one of the battery data, the user preference data, the current state data and the current operation data does not meet the preset data condition, generating a corresponding processing action according to the preset data condition until the battery data, the user preference data, the current state data and the current operation data all meet the preset data condition, and allowing the battery data, the user preference data, the current state data and the current operation data meeting the preset data condition to be input into the multi-objective optimization model; if the battery data, the user preference data, the current state data or the current operation data all meet the preset data condition, allowing the battery data, the user preference data, the current state data and the current operation data to be input into the multi-objective optimization model.

6. An apparatus for optimizing vehicle charging schedule, characterized by, comprising: a first acquisition module, configured to acquire battery data of a vehicle, user preference data of the vehicle, current state data of a charging pile and current operation data of a power grid; an output module, configured to input the battery data, the user preference data, the current state data and the current operation data into a pre-constructed multi-objective optimization model, to output a charging duration, a charging cost, a charging power and a charging temperature of the vehicle charging; a control module, configured to determine at least one charging strategy of the vehicle charging based on the charging duration, the charging cost, the charging power and the charging temperature, and control the vehicle to charge according to the at least one charging strategy until the vehicle meets a preset charging stop condition.

7. The apparatus of claim 6, wherein, further comprising: a second acquisition module, configured to acquire target battery data of a target vehicle, target user preference data of the target vehicle, current target state data of a target charging pile and current target operation data of a target power grid before inputting the battery data, the user preference data, the current state data and the current operation data into the pre-constructed multi-objective optimization model; a first construction module, configured to determine a charging target duration and a charging target cost of the target vehicle based on the target battery data, the target user preference data, the current target state data and the current target operation data, to construct a constraint function of the multi-objective optimization model according to the charging target duration and the charging target cost; a second construction module, configured to construct a first constraint condition of the multi-objective optimization model based on the target battery data; a third constructing module, configured to construct a second constraint condition of the multi-objective optimization model based on the current target state data; a fourth constructing module, configured to construct a third constraint condition of the multi-objective optimization model based on the current target operation data; a fifth constructing module, configured to construct the multi-objective optimization model based on the constraint function, the first constraint condition, the second constraint condition and the third constraint condition.

8. An electronic device, comprising: comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the optimization method for vehicle charging scheduling according to any one of claims 1-5.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the optimization method for vehicle charging scheduling according to any one of claims 1-5.

10. A vehicle characterized by comprising: The vehicle comprises the electronic device according to claim 8.

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