Method for cloud control of vehicle charging and method for controlling vehicle charging

Abstract

The invention relates to a cloud control method for vehicle charging. The method comprises the following steps: predicting estimated charging setting parameters of a vehicle (1) in each charging time period based on behavior habit data of a user (S1); planning a charging plan of the vehicle in each charging time period at least based on the predicted estimated charging setting parameter and the power supply related information, so as to optimize the charging cost of the vehicle (S2); on the basis of the planned charging plan, charging control parameters of the vehicle during the respective charging periods are determined, and the determined charging control parameters are sent to the vehicle and / or a charging device (4) for charging the vehicle (S3). According to the application, the charging plan of the vehicle in each charging time period can be automatically planned and the corresponding charging control parameters can be set based on the behavior habit data of the user and the power supply related information; therefore, the charging cost of the vehicle is reduced as much as possible under the condition that the vehicle battery can reach the final charge state conforming to the vehicle using habit of the user when the charging of the vehicle is finished.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicle charging, and in particular to a method for cloud-based control of vehicle charging, a cloud server, a method for controlling vehicle charging, a vehicle, a charging device, and a computer program product for at least assisting in implementing the steps of the method described in the present application. Background Art

[0002] With the development and popularization of electric vehicle charging technology, users can charge electric vehicles using charging piles installed in home garages or office buildings. After connecting to the charging pile, the electric vehicle will start the charging process until the battery is fully charged and the charging process will automatically stop. However, on the one hand, keeping the battery fully charged for a long time will shorten the service life of the battery. On the other hand, when the power supply network adopts time-of-use electricity prices, it is impossible to fully utilize the valley electricity price period to charge the vehicle, making the charging cost of the vehicle relatively high. In this regard, users can also manually control the start and stop of the charging process in the corresponding application, but this manual control method will greatly affect the user experience of vehicle charging.

[0003] Therefore, there is room for improvement in the current vehicle charging control method. Summary of the invention

[0004] The purpose of the present application is to provide a method for cloud control of vehicle charging, a cloud server, a method for controlling vehicle charging, a vehicle, a charging device, and a computer program product to at least partially solve the problems in the prior art.

[0005] According to a first aspect of the present application, a method for cloud control of vehicle charging is provided, and the method may include:

[0006] - Predict the estimated charging setting parameters of the vehicle in each charging time period based on the user's behavior habit data;

[0007] - planning a charging plan for the vehicle in each charging time period based at least on the predicted estimated charging setting parameters and the power supply related information to optimize the charging cost of the vehicle; and

[0008] - Determining charging control parameters of the vehicle in each charging time period based on the planned charging plan, and sending the determined charging control parameters to the vehicle and / or a charging device for charging the vehicle.

[0009] The core concept of the present application includes at least: in the application scenario of charging a vehicle using a charging device with fixed usage rights in, for example, a home garage or office building, the charging plan for the vehicle in each charging time period can be automatically planned based on the user's behavioral habit data and power supply related information and the corresponding charging control parameters can be set, so as to reduce the charging cost of the vehicle as much as possible while ensuring that the vehicle battery can reach the final charge state that conforms to the user's vehicle usage habits when the vehicle finishes charging.

[0010] According to an optional embodiment of the present application, when planning the charging plan of the vehicle in each charging time period based at least on the predicted estimated charging setting parameters and power supply related information, a predetermined battery health maintenance principle can be additionally introduced as an influencing factor for planning the charging plan to optimize the battery health status of the vehicle.

[0011] According to another optional embodiment of the present application, the user's behavioral habit data may include the user's driving behavior data and / or the user's charging behavior data, wherein the user's driving behavior data includes historical data on the vehicle's departure time, and / or historical data on the vehicle's driving distance, and / or the user's itinerary planning information for a future time period, etc., and the user's charging behavior data includes historical data on the charging start time, and / or historical data on the final state of charge of the battery when the vehicle finishes charging, and / or historical data on the charging power set by the user, etc.

[0012] According to another optional embodiment of the present application, the estimated charging setting parameters may include the charging end time of one or more future charging and discharging cycles and the final state of charge of the battery when the vehicle finishes charging.

[0013] According to another optional embodiment of the present application, based on a predetermined battery health maintenance principle, the final state of charge of the battery when the vehicle finishes charging can be set to no higher than a predetermined state of charge threshold, and / or the idle time period from the moment the battery reaches the final state of charge to the moment charging ends can be reduced, and / or the battery charging power can be reduced on the premise that the set final state of charge can be reached when the vehicle finishes charging, and so on.

[0014] According to another optional embodiment of the present application, the power supply related information may include time-of-use electricity price information, wherein the charging plan of the vehicle in each charging time period is planned at least based on the predicted estimated charging setting parameters and the time-of-use electricity price information, and the charging control parameters of the vehicle are determined based on the planned charging plan, wherein the charging control parameters include the battery charging power in the battery charging process and / or the start and / or termination of the battery charging process, etc., wherein the charging cost of charging the vehicle's battery to the final charge state is calculated based on the time-of-use electricity price information and the change process of the vehicle's battery charging power over time.

[0015] According to another optional embodiment of the present application, the power supply related information may include peak shaving request information and / or valley filling request information, wherein the charging plan of the vehicle in each charging time period is planned based on the peak shaving request information and / or valley filling request information, and the charging control parameters of the vehicle are determined based on the planned charging plan, the charging control parameters include the battery charging power in the battery charging process and / or the start and / or termination of the battery charging process, etc., wherein the compensation income obtained by the vehicle through the peak shaving auxiliary service and / or valley filling auxiliary service is calculated based on the change process of the battery charging power of the vehicle over time and the peak shaving request information and / or valley filling request information.

[0016] According to another optional embodiment of the present application, the charging fee of the vehicle can be calculated based on the calculated charging cost and the calculated compensation income, and the charging fee of the vehicle can be optimized on the premise that the vehicle can reach the set final state of charge when the charging is finished.

[0017] According to another optional embodiment of the present application, the estimated charging setting parameters of the vehicle in each charging time period can be predicted by a prediction model based on the user's behavioral habit data, wherein the prediction model includes, for example, a machine learning model, a linear and logistic regression model and / or a neural network model.

[0018] According to another optional embodiment of the present application, actual charging setting parameters of the vehicle are obtained in a corresponding charging time period, and the prediction model is optimized based on the deviation between the estimated charging setting parameters and the actual charging setting parameters, for example, through a self-learning algorithm.

[0019] According to a second aspect of the present application, a cloud server is provided, which is configured to execute the cloud control method for vehicle charging according to the present application.

[0020] According to a third aspect of the present application, a method for controlling vehicle charging is provided, and the method may include:

[0021] In response to the charging control parameters received from the cloud server according to the present application, the charging process of the vehicle can be controlled based at least on the received charging control parameters.

[0022] According to a fourth aspect of the present application, a vehicle is provided, which may include the following components:

[0023] - an on-board communication unit configured to receive charging control parameters from a cloud server according to the present application; and

[0024] - An on-board control unit configured to execute the method for controlling vehicle charging according to the present application.

[0025] According to a fifth aspect of the present application, a charging device is provided, and the charging device may include the following components:

[0026] - a pile-end communication unit configured to receive charging control parameters from a cloud server according to the present application; and

[0027] A pile end control unit, which is configured to be used in the method for controlling vehicle charging according to the present application.

[0028] According to a sixth aspect of the present application, a computer program product, such as a computer-readable program carrier, is provided, which contains or stores computer program instructions, and when the computer program instructions are executed by a processor, they at least assist in implementing the steps of the method described in the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The principles, features and advantages of the present application may be better understood by describing the present application in more detail below with reference to the accompanying drawings. The accompanying drawings show:

[0030] Figure 1 A flowchart showing a method for cloud-based control of vehicle charging according to an exemplary embodiment of the present application is shown;

[0031] Figure 2 A schematic diagram of a charging scenario according to an exemplary embodiment of the present application is shown;

[0032] Figure 3 A schematic diagram showing a curve of battery state of charge changing over time during a vehicle charging process according to an exemplary embodiment of the present application; and

[0033] Figure 4 A flowchart of a method for controlling vehicle charging according to an exemplary embodiment of the present application is shown. DETAILED DESCRIPTION

[0034] In order to make the technical problems, technical solutions and beneficial technical effects to be solved by the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and multiple exemplary embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the scope of protection of the present application.

[0035] Figure 1 The following is a flowchart of a method for cloud-based control of vehicle charging according to an exemplary embodiment of the present application. The following exemplary embodiments describe the method according to the present application in more detail.

[0036] like Figure 1As shown, the method for cloud-based control of vehicle charging may include steps S1 to S3. In step S1, the estimated charging setting parameters of the vehicle in each charging time period may be predicted based on the user's behavioral habit data. The method for cloud-based control of vehicle charging according to the present application is executed in the cloud server 2 of the vehicle manufacturer, which is particularly suitable for application scenarios where electric vehicles are charged in charging equipment with fixed usage rights installed in home garages or office building parking lots, that is, the charging equipment 4 is dedicated to a user or a user group (such as family members, company members, etc.). Figure 2 A schematic diagram of a charging scenario according to an exemplary embodiment of the present application is shown. After the vehicle 1 is electrically connected to a charging device 4 with fixed usage rights installed in, for example, a home garage or an office building parking lot, the vehicle 1 absorbs electrical energy from the power supply network 3 through the charging device 4 (the direction of electrical energy flow is shown by a thick line with an arrow) to charge the battery of the vehicle 1.

[0037] According to the regulatory requirements for electric vehicles, during the operation of the vehicle 1, real-time monitoring data such as battery information, vehicle status information, vehicle location information, energy consumption data and / or charging data, namely RTM data, must be collected through the on-board TBOX device, and encrypted and transmitted to the cloud server 2 of the vehicle manufacturer through the wireless communication network. Here, the behavioral habit data may include the user's driving behavior data, such as historical data on the vehicle departure time and / or historical data on the vehicle's travel distance, etc. These data can reflect the user's vehicle usage habits for the vehicle 1, wherein, based on the historical data on the vehicle departure time, the charging end time of the vehicle 1 in one or more future charging and discharging cycles can be predicted, that is, the battery needs to be charged to the set final state of charge before the vehicle 1 departs, and based on the historical data on the vehicle's travel distance, the amount of electricity required for daily driving of the vehicle 1 can be predicted, thereby setting the final state of charge of the battery when the vehicle ends charging according to the required amount of electricity. In addition, the user's driving behavior data may also include the user's itinerary planning information for the future time period, which can be obtained from the user's mobile terminal or social network account. Based on the itinerary planning information, the prediction accuracy of the vehicle's travel distance in the future time period can be further improved, thereby improving the prediction accuracy of the set final state of charge.

[0038] The user's behavioral habit data may also include the user's charging behavior data - for example, historical data about the charging start time, and / or historical data about the final state of charge of the battery when the vehicle finishes charging, and / or historical data about the charging power set by the user, etc. These data can reflect the user's charging habits for the vehicle 1, wherein the charging start time of the vehicle 1 in one or more future charging and discharging cycles can be predicted based on the historical data about the charging start time, the final state of charge of the battery when the vehicle finishes charging, that is, the charge amount of the vehicle battery, can be predicted based on the historical data about the final state of charge of the battery when the vehicle finishes charging, and the charging power set by the user can be predicted based on the historical data about the charging power that meets the user's habits.

[0039] The user's behavior habit data can be input into the prediction model, and the prediction model can predict the estimated charging setting parameters of the vehicle 1 in each charging time period based on the user's behavior habit data, especially including the charging end time of one or more future charging and discharging cycles and the final charge state of the battery when the vehicle ends charging. Exemplarily, the prediction model may include a machine learning model, a linear and logistic regression model, and / or a neural network model, etc. In order to improve the prediction accuracy of the prediction model, the actual charging setting parameters of the vehicle 1 can be obtained in the corresponding charging time period, and the prediction model can be optimized based on the deviation between the estimated charging setting parameters and the actual charging setting parameters, for example, through a self-learning algorithm. It can be understood that as the prediction accuracy of the prediction model gradually improves, the predicted estimated charging setting parameters will become closer and closer to the actual charging setting parameters of the corresponding time period.

[0040] In step S2, a charging plan for the vehicle 1 in each charging time period may be planned based at least on the predicted estimated charging setting parameters and power supply related information to optimize the charging cost of the vehicle 1. Figure 2 As shown, the cloud server 2 can obtain the power supply related data from the power grid server 31, which especially includes time-sharing electricity price information, and the time-sharing electricity price information varies according to the time period, for example, different electricity prices are divided according to different time periods (such as peak power period, valley power period, etc.). Since the electricity price during the peak power period is higher than the electricity price during the valley power period, the battery of the vehicle 1 is charged as much as possible during the valley power period on the premise that the set final state of charge can be reached when the vehicle finishes charging. Then, the charging cost of charging the battery of the vehicle 1 to the final state of charge can be calculated based on the time-sharing electricity price information and the change process of the battery charging power of the vehicle 1 over time.

[0041] In the case where the vehicle 1 signs a peak shaving and valley filling auxiliary service with the power supply network 3, the power supply related information may also include peak shaving request information and / or valley filling request information obtained from the power grid server 31, so as to control the vehicle 1 to participate in the peak shaving auxiliary service and / or valley filling auxiliary service of the power grid based on the instructions of the power grid server 31 during the charging process. Specifically, during the peak load period, the battery charging process can be stopped and / or the battery charging power can be reduced based on the peak shaving request information received from the power grid server 31; during the low load period, the battery charging process can be started and / or the battery charging power can be increased based on the valley filling request information received from the power grid server 31. Then, the compensation income obtained through the peak shaving auxiliary service and / or valley filling auxiliary service can be calculated based on the peak shaving request information and / or valley filling request information. The charging cost of the vehicle 1 can be calculated based on the calculated charging cost and the calculated compensation income, and the charging cost of the vehicle 1 can be optimized on the premise that the vehicle can reach the set final state of charge when the charging is finished. Here, the charging cost is the calculated charging cost minus the calculated compensation income, and the charging plan can be formulated with the minimization of the charging cost as the objective function. For example, according to the "Implementation Rules for the Management of Electric Power Auxiliary Services in East China", the compensation standard for adjustable loads participating in peak-shaving auxiliary services is 0.8 yuan / kWh, and the compensation standard for participating in valley-filling auxiliary services is 0.24 yuan / kWh. If vehicle 1 participates in peak-shaving auxiliary services during peak load periods, the compensation income per kilowatt-hour is higher than the peak-valley electricity price difference of time-of-use electricity prices; if vehicle 1 participates in valley-filling auxiliary services during low load periods, vehicle 1 can obtain compensation for valley-filling auxiliary services while using the electricity price during the valley electricity period.

[0042] In step S3, the charging control parameters of the vehicle 1 in each charging time period can be determined based on the planned charging plan, and the determined charging control parameters are sent to the vehicle 1 and / or the charging device 4 used to charge the vehicle 1. Here, the charging control parameters may include, for example, the battery charging power in the battery charging process, the start and / or termination of the battery charging process, etc. Exemplarily, the charging plan of the vehicle 1 in each charging time period can be planned at least based on the predicted estimated charging setting parameters and the time-of-use electricity price information, and the battery charging power in the battery charging process and / or the start and / or termination of the battery charging process, etc. can be determined based on the planned charging plan, so as to control the battery of the vehicle 1 to charge as much as possible during the valley period on the premise that the vehicle can reach the set final charge state when the vehicle finishes charging. For example, the charging plan of the vehicle 1 in each charging time period can also be planned based on the peak shaving request information and / or valley filling request information, and the battery charging power and / or the start and / or termination of the battery charging process in the battery charging process can be determined based on the planned charging plan, so as to control the vehicle 1 to participate in the peak shaving auxiliary service and / or valley filling auxiliary service of the power grid during the charging process and obtain corresponding compensation benefits. The charging control parameters determined in the cloud server 2 can be sent to the vehicle 1 and / or the charging device 4, so that the on-board control unit 12 and / or the pile end control unit 42 can control the battery of the vehicle 1 to execute the planned charging plan based on the received charging control parameters.

[0043] For example, according to Figure 3 The curve diagram of the battery state of charge changing with time during the vehicle charging process according to an exemplary embodiment of the present application is shown. At time t1, the user drives the vehicle 1 to the home garage or office building and connects the vehicle to the charging device 4, thereby starting the charging process. Since the vehicle 1 has just finished its journey, the battery state of charge SOC0 at time t1 is at a low level. Although it is currently in the peak power period, in order to ensure that the vehicle 1 has a certain amount of power stored to cope with emergency needs, the vehicle 1 is still charged until the preset first state of charge threshold SOC1 (for example, 30%) is reached at time t2, and the battery charging process can be controlled to be suspended. Waiting starts from time t2 until, for example, at time t3 (i.e., the peak power period ends and the valley power period begins), the battery charging process can be controlled to be restarted. Therefore, the battery of vehicle 1 is charged again from time t3. For example, at time t4, the cloud server 2 receives a valley filling service request from the power grid server 31. Therefore, during the time period from time t4 to t5, while using the valley electricity price, compensation for valley filling auxiliary services can be obtained, until the preset second state of charge threshold SOC2 (for example, 60%) is reached at time t5.

[0044] Optionally, when planning the charging plan of the vehicle 1 in each charging time period based at least on the predicted estimated charging setting parameters and power supply related information, a predetermined battery health maintenance principle can be additionally introduced as an influencing factor for planning the charging plan to optimize the battery health state of the vehicle 1. Based on the predetermined battery health maintenance principle, the final state of charge of the battery when the vehicle ends charging can be set to not higher than a predetermined state of charge threshold (e.g., 80%) to prevent the battery of the vehicle 1 from being charged to an excessively high state of charge; the idle time between the moment when the battery reaches the final state of charge and the moment when the vehicle ends charging (i.e., the moment when the user drives the vehicle to depart) can be reduced as much as possible, thereby avoiding the battery of the vehicle 1 from being in a high state of charge for a long time as much as possible; the battery charging power can also be reduced as much as possible while ensuring that the vehicle reaches the set final state of charge when charging ends, considering that the battery life can be effectively extended by slow charging.

[0045] For example, according to Figure 3 The curve diagram shown shows that, since it is predicted according to the user's behavior habit data that the user will drive the vehicle 1 to start at time t7, in order to avoid the battery of the vehicle 1 being in a high state of charge for a long time as much as possible, the battery charging process can be controlled to be suspended at time t5 and start waiting, and the battery charging process can be controlled to be restarted at time t6, and the battery is charged at a lower charging power through slow charging, until the final state of charge SOC3 (for example, 80%) predicted according to the user's behavior habit data is reached at time t7. At time t7, the user drives the vehicle 1 to start, so the battery state of charge of the vehicle 1 begins to gradually decrease after time t7, thereby completing a battery charge and discharge cycle.

[0046] Considering that each user has different car usage frequency and charging habits, for example, users who use the car frequently need to charge once a day, while users who use the car less frequently may charge once every five days, and the frequency of users' car usage on weekdays and weekends is also different, a charging plan for a charging and discharging cycle can be formulated based on the user's behavioral habit data, and the number of days included in the charging cycle can be adjusted according to the user's behavioral habits. In addition, as the prediction accuracy of the prediction model gradually improves, the predicted estimated charging setting parameters will be closer and closer to the actual charging setting parameters of the corresponding time period, and charging plans for multiple charging and discharging cycles can be formulated, such as charging plans for the next few dozen days.

[0047] According to an embodiment of the present application, in an application scenario where a vehicle is charged using a charging device with fixed usage permissions in, for example, a home garage or office building, the vehicle's charging plan for each charging time period can be automatically planned and corresponding charging control parameters can be set based on the user's behavioral habit data and power supply-related information, so that the vehicle's charging costs can be reduced as much as possible while ensuring that the vehicle reaches a final charge state that is consistent with the user's vehicle usage habits when charging is finished. In addition, the vehicle's battery health can be optimized to extend the battery life as much as possible.

[0048] exist Figure 2 The schematic diagram of the charging scenario also shows a schematic block diagram of a cloud server 2 of a vehicle manufacturer, where the cloud server 2 is configured to execute the method for cloud-based control of vehicle charging according to the present application.

[0049] The above describes a method for cloud control of vehicle charging executed in the cloud server 2, in which the determined charging control parameters are sent to the vehicle 1 and / or the charging device 4. After the vehicle 1 and / or the charging device 4 receive the charging control parameters, the method for controlling vehicle charging is executed in the vehicle 1 and / or the charging device 4 to complete the battery charging process of the vehicle 1.

[0050] Figure 4 FIG. 2 shows a flowchart of a method for controlling vehicle charging according to an exemplary embodiment of the present application. Figure 4 As shown, the method may include step S1'. In step S1', in response to the charging control parameters received from the cloud server 2, the charging process of the vehicle 1 may be controlled at least based on the received charging control parameters. Here, the charging control parameters may include, for example, the battery charging power in the battery charging process, the start and / or termination of the battery charging process, etc. In some charging design architectures, the on-board control unit 12 of the vehicle 1 may control the battery of the vehicle 1 to charge according to the planned charging plan based on the received charging control parameters; in other charging design architectures, the pile end control unit 42 of the charging device 4 may also control the battery of the vehicle 1 to charge according to the planned charging plan based on the received charging control parameters; in other charging design architectures, the on-board control unit 12 of the vehicle 1 and the pile end control unit 42 of the charging device 4 may also cooperate with each other based on the received charging control parameters to control the battery of the vehicle 1 to charge according to the planned charging plan.

[0051] In addition, it should be noted that the step numbers described herein do not necessarily represent a chronological order, but are merely a figure mark. The order can be changed according to specific circumstances as long as the technical purpose of the present application can be achieved.

[0052] exist Figure 2The schematic diagram of the charging scenario also shows a schematic block diagram of a vehicle 1, and the vehicle 1 may include the following components:

[0053] - an on-board communication unit 11 configured to receive charging control parameters from a cloud server 2 according to the present application; and

[0054] An on-board control unit 12 configured to execute the method for controlling vehicle charging according to the present application.

[0055] exist Figure 2 The schematic diagram of the charging scenario also shows a schematic block diagram of a charging device 4, and the charging device 4 may include the following components:

[0056] - a terminal communication unit 41 configured to receive charging control parameters from a cloud server 2 according to the present application; and

[0057] A pile end control unit 42, which is configured for the method for controlling vehicle charging according to the present application.

[0058] It should be understood that, in this document, the expressions "first", "second", "third", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance, nor should they be understood as implicitly indicating the number of the indicated technical features.

[0059] If an embodiment includes an “and / or” relationship between a first feature and a second feature, it should be interpreted as follows: according to one embodiment, the embodiment has both the first feature and the second feature, and according to another embodiment, the embodiment has either only the first feature or only the second feature.

[0060] Although specific embodiments have been described above, these embodiments are not intended to limit the scope of the present disclosure, even when only a single embodiment is described with respect to specific features. The feature examples provided in the present disclosure are intended to be illustrative, not restrictive, unless otherwise stated. In specific implementations, multiple features may be combined with each other, where technically feasible, according to actual needs. Various substitutions, changes, and modifications may also be conceived without departing from the spirit and scope of the present application.

Claims

1. A method for cloud control of vehicle charging, the method comprising: Predicting estimated charging setting parameters of the vehicle (1) in each charging time period based on the user's behavior habit data; Planning a charging plan for the vehicle (1) in each charging time period based at least on the predicted estimated charging setting parameters and power supply related information, so as to optimize the charging cost of the vehicle (1); as well as Based on the planned charging plan, charging control parameters of the vehicle (1) in each charging time period are determined, and the determined charging control parameters are sent to the vehicle (1) and / or a charging device (4) for charging the vehicle (1).

2. The method according to claim 1, wherein: When planning a charging plan for a vehicle (1) in each charging time period based at least on the predicted estimated charging setting parameters and power supply related information, a predetermined battery health maintenance principle is additionally introduced as an influencing factor for planning the charging plan to optimize the battery health status of the vehicle (1).

3. The method according to claim 1, wherein: The user's behavior habit data includes the user's driving behavior data and / or the user's charging behavior data, wherein the user's driving behavior data includes historical data on the vehicle's departure time, and / or historical data on the vehicle's driving distance, and / or the user's itinerary planning information for a future time period, and the user's charging behavior data includes historical data on the charging start time, and / or historical data on the final state of charge of the battery when the vehicle finishes charging, and / or historical data on the charging power set by the user.

4. The method according to any one of claims 1 to 3, wherein: The estimated charging setting parameters include the charging end time of one or more future charging and discharging cycles and the final state of charge of the battery when the vehicle finishes charging.

5. The method according to claim 2, wherein: Based on a predetermined battery health maintenance principle, the final state of charge of the battery when the vehicle finishes charging is set to be no higher than a predetermined state of charge threshold, and / or the idle time period from the time when the battery reaches the final state of charge to the time when charging ends is reduced, and / or the battery charging power is reduced under the premise that the set final state of charge can be reached when the vehicle finishes charging.

6. The method according to any one of claims 1 to 3, wherein: The power supply related information includes time-of-use electricity price information, wherein a charging plan for the vehicle (1) in each charging time period is planned at least based on the predicted estimated charging setting parameters and the time-of-use electricity price information, and a charging control parameter of the vehicle (1) is determined based on the planned charging plan, wherein the charging control parameter includes a battery charging power in a battery charging process and / or the start and / or termination of a battery charging process, wherein a charging cost for charging the battery of the vehicle (1) to a final state of charge is calculated based on the time-of-use electricity price information and a process of changing the battery charging power of the vehicle (1) over time.

7. The method according to claim 6, wherein: The power supply related information includes peak shaving request information and / or valley filling request information, wherein a charging plan for the vehicle (1) in each charging time period is planned based on the peak shaving request information and / or valley filling request information, and a charging control parameter of the vehicle (1) is determined based on the planned charging plan, wherein the charging control parameter includes a battery charging power in a battery charging process and / or the start and / or stop of a battery charging process, wherein the compensation income obtained by the vehicle (1) through a peak shaving auxiliary service and / or a valley filling auxiliary service is calculated based on a change process of the battery charging power of the vehicle (1) over time and the peak shaving request information and / or valley filling request information.

8. The method according to any one of claims 1 to 3, wherein: The charging fee of the vehicle (1) is calculated based on the calculated charging cost and the calculated compensation income, and the charging fee of the vehicle (1) is optimized on the premise that the vehicle can reach a set final state of charge when charging is completed.

9. The method according to any one of claims 1 to 3, wherein: Predicting estimated charging setting parameters of the vehicle (1) in each charging time period based on the user's behavior habit data through a prediction model, wherein the prediction model includes, for example, a machine learning model, a linear and logistic regression model, and / or a neural network model; and / or The actual charging setting parameters of the vehicle (1) are obtained during the corresponding charging time period, and the prediction model is optimized based on the deviation between the estimated charging setting parameters and the actual charging setting parameters, for example, by a self-learning algorithm.

10. A cloud server (2), configured to execute the method according to any one of claims 1 to 8.

11. A method for controlling vehicle charging, the method comprising: In response to charging control parameters received from the cloud server (2) according to claim 10, a charging process of the vehicle (1) is controlled based at least on the received charging control parameters.

12. A vehicle (1), comprising the following components: an on-board communication unit (11) configured to receive a charging control parameter from the cloud server (2) according to claim 10; and An on-board control unit (12) is configured to execute the method according to claim 11.

13. A charging device (4), comprising the following components: A pile-end communication unit (41) configured to receive charging control parameters from the cloud server (2) according to claim 10; and A pile end control unit (42) is configured for use in the method according to claim 11.

14. A computer program product, such as a computer-readable program carrier, comprising or storing computer program instructions, which, when executed by a processor, at least assist in implementing the steps of the method according to any one of claims 1 to 9 and 11.

Images