Electric vehicle charging and discharging control method, device, equipment, medium and product

Abstract

The invention relates to the technical field of new energy automobiles, and discloses an electric automobile charging and discharging control method, device and equipment, a medium and a product. The home arrival moment and the home arrival moment charge state of the electric vehicle are obtained; obtaining an electric vehicle travel plan, wherein the travel plan comprises a departure moment and a destination; estimating the electric quantity required by the travel plan according to the home location, the destination and the home leaving moment; if the state of charge at the home arrival moment exceeds a set threshold value, in a set optimization period, optimization solution is carried out on the time period of charging the electric vehicle and supplying power to the home by taking minimization of power consumption cost and battery loss as targets; wherein the power consumption cost is the sum of the power supply quantity of the power grid for the electric vehicle and the family in the period, and the battery loss is calculated according to the family power supply quantity of the electric vehicle in the period, the travel power consumption of the electric vehicle and battery delivery parameters. The problem of accelerated attenuation of the battery due to the fact that the health state of the battery is not considered in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to a method, device, equipment, medium and product for controlling charging and discharging of electric vehicles. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the increasing popularity and application of electric vehicles, electric vehicle charge and discharge control technology has become an important research direction in the field of new energy vehicles. Electric vehicle charge and discharge control not only affects the user experience and economic benefits, but also has a significant impact on grid load balance and battery life.

[0004] At present, the research on electric vehicle charging and discharging control technology mainly focuses on maximizing user economic benefits and balancing the load of the power grid. When users have charging needs, the optimized scheduling is based on the electricity cost or the load balance of the power grid, without considering the health status of the battery. Frequent charging and discharging and deep discharge may cause the health status of the battery to decay faster and shorten the service life of the electric vehicle battery. Summary of the Invention

[0005] To overcome the above-mentioned deficiencies of the prior art, the present invention provides a method, device, equipment, medium and product for controlling the charging and discharging of an electric vehicle, so as to achieve a balance between household electricity costs and battery damage.

[0006] To achieve the above objectives, one or more embodiments of the present invention provide a method for controlling charging and discharging of an electric vehicle, comprising the following steps: Obtain the time when the electric vehicle arrives home and the state of charge at the time of arrival; Obtaining a travel plan for the electric vehicle, wherein the travel plan includes a departure time and a destination; Estimate the amount of electricity required for the travel plan based on the home location, destination, and departure time; If the state of charge exceeds a set threshold when arriving home, the time period during which the electric vehicle charges and supplies power to the home is optimized within a set optimization cycle, with the goal of minimizing electricity costs and battery loss. The electricity cost is the sum of the power supplied by the grid to the electric vehicle and the home during that cycle, and the battery loss is calculated based on the power supplied by the electric vehicle to the home during that cycle, the electric vehicle's travel power consumption, and the battery's factory parameters.

[0007] In some embodiments, the objective function is a weighted sum of electricity cost and battery loss, the sum of the weights of the two is 1, and the weights are adjustable.

[0008] In some embodiments, user preference settings are received, and the weights of electricity cost and battery loss are determined based on the user preferences; the user preference settings include an economic mode, a protection mode, and an adaptive mode; in the economic mode, the weight of electricity cost is higher than the weight of battery loss, in the protection mode, the weight of electricity cost is lower than the weight of battery loss, and in the adaptive mode, the weights of electricity cost and battery loss are adaptively adjusted based on the peak and valley differences in electricity prices and the battery health status.

[0009] In some embodiments, if no new travel plan is received after the electric vehicle is connected to the charging system, the next travel plan is determined based on the historical travel plans set by the user.

[0010] In some embodiments, historical household electricity usage data is also obtained and analyzed to obtain time series data of the household's electricity usage behavior. The time series data is used to estimate the amount of electricity supplied to the household by the power grid / electric vehicle.

[0011] In some embodiments, the objective function is: ;in, 、 Respectively represent the electricity cost and battery loss in this scheduling period, represents the weight coefficient; ; ;in, hour, ; Constraints include: ; ;

[0012] Where T represents the scheduling period, Indicates the real-time electricity price, represents the amount of electricity required for household consumption at time t, represents the charge capacity of the electric vehicle at time t, 、 They represent the start and end time of the electric vehicle supplying power to the home, 、 Respectively represent the start time and end time of electric vehicle charging, 、 They represent the arrival time and departure time of the electric car respectively. Indicates the loss cost corresponding to the unit loss of battery, Indicates the amount of electricity required for this travel plan. Represents the battery cycle life coefficient, and Respectively represent the minimum and maximum protection power of electric vehicles, represents the conditional threshold for electric vehicles to power homes, Indicates the minimum power of the electric car when leaving home. Indicates the remaining power of the electric car when it arrives home.

[0013] A second aspect of the present invention provides an electric vehicle charge and discharge control device, comprising: A home status acquisition module is configured to acquire the time when the electric vehicle arrives home and the state of charge at the time of arrival; a travel plan acquisition module configured to acquire a travel plan of the electric vehicle, including a departure time and a destination; a required power estimation module, configured to estimate the power required for the travel plan based on the home location, destination, and departure time; The charge and discharge control module is configured to determine the battery's state of charge when arriving home. If the state of charge exceeds a set threshold, the module optimizes the time period for charging the electric vehicle and supplying power to the home within a set optimization cycle, with the goal of minimizing electricity costs and battery loss. The electricity cost is the sum of the power supplied by the grid to the electric vehicle and the home during the cycle, and the battery loss is calculated based on the power supplied by the electric vehicle to the home during the cycle, the electric vehicle's travel power consumption, and the battery's factory parameters.

[0014] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the method described.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the method described above when the program is executed by a processor.

[0016] A fifth aspect of the present invention provides a computer program product, comprising a computer program, and when the computer program is executed by a processor, the method described is implemented.

[0017] One or more of the above technical solutions divide electric vehicle discharge into driving consumption and home power supply. This increases the battery's depth of discharge when the state of charge exceeds a set threshold, enabling more efficient energy consumption in frequently idle electric vehicles and avoiding the loss of battery life caused by frequent charging and discharging. Furthermore, by simultaneously incorporating electricity costs and battery loss into the objective function, a balance is struck between user economic costs and battery loss, resolving the problem of accelerated battery degradation caused by prior art failure to consider battery health. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0019] Figure 1 Example scenarios for application of one or more embodiments of the present invention; Figure 2 A flow chart of a method for controlling charging and discharging an electric vehicle according to one or more embodiments of the present invention; Figure 3 A module framework diagram of an electric vehicle charging and discharging control device provided by one or more embodiments of the present invention. DETAILED DESCRIPTION

[0020] The following describes embodiments of the present application in more detail with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are for illustrative purposes only and are not intended to limit the scope of protection of the present application.

[0021] In the description of the embodiments of the present application, the term “including” and similar terms should be understood as open inclusion, that is, “including but not limited to.” The term “based on” should be understood as “at least partially based on.”

[0022] As described in the background technology, electric vehicle charging and discharging control technology is mainly studied around maximizing user economic benefits and balancing the load of the power grid. When a user has a charging demand, it optimizes the scheduling based on the electricity cost or the load balance of the power grid, without considering the health status of the battery. Frequent charging and discharging and deep discharge may cause the battery health status to decay faster, shortening the service life of the electric vehicle battery. For example, due to mileage anxiety, users may charge when there is still a lot of power left in the battery, which leads to frequent charging and discharging. In order to solve the above problems, household electricity consumption and electric vehicle charging can be considered in a coordinated manner. After the user connects the electric vehicle to the charging system, according to the battery status of the electric vehicle, when the remaining power is large, it can continue to discharge to a more appropriate discharge depth by supplying power to the home. According to the user's travel plan, the electric vehicle can be charged to meet the user's needs while meeting the lowest comprehensive cost of electricity and battery loss, thereby extending the battery life while meeting the user's travel needs.

[0023] Figure 1An example scenario for one or more embodiments of the present invention is illustrated. In this scenario, energy scheduling optimization is performed on a household basis, linking the household power supply system and the electric vehicle's charge-discharge conversion system to achieve coordinated control of household power supply and electric vehicle charging and discharging. Specifically, a connection is established between the household power supply system and the electric vehicle's charging system. The household power supply system is connected to the power grid system and the electric vehicle's charge-discharge conversion system, respectively, with on-off switches provided on both connections. The electric vehicle's charging system includes a charge-discharge conversion system. Both the on-off switch and the charge-discharge conversion system are connected to an intelligent controller. The intelligent controller can switch between charging and discharging the electric vehicle by controlling the charge-discharge conversion system. By controlling the two on-off switches, the grid system switches power to the household, while the electric vehicle switches power to the household. As an example scenario, in the initial scenario, the household power supply system is connected to the grid system. While the intelligent controller controls the electric vehicle's discharge, it switches the connection between the grid system and the household power supply system to a connection between the charge-discharge control system and the household power supply system, switching power from the grid to the electric vehicle.

[0024] For safety reasons, the power supply of electric vehicles should not be too large, so they can selectively power some household appliances. Based on this, the home power supply system includes multiple power supply sub-circuits, each of which is equipped with an on-off sub-switch. When the power grid system / electric vehicle charging and discharging system is connected to the home power supply system, it can also selectively control the on and off of one or more of the multiple power supply sub-circuits. For example, it can be divided into non-adjustable loads and adjustable loads, where non-adjustable loads include equipment that needs to be powered every day, such as lights and refrigerators, and adjustable loads include electrical equipment with fixed power modes, such as washing machines, as well as high-power electrical equipment such as air conditioners and water heaters. Based on this, the home power supply system includes non-adjustable load power supply circuits and adjustable load power supply circuits. Under normal conditions, electric vehicles can be limited to only power non-adjustable loads. Under special conditions, such as power outages, all loads can be powered by electric vehicles.

[0025] To enhance the flexibility of user control, the intelligent controller is connected to a terminal device, which may be a vehicle-mounted terminal, a mobile phone, a tablet or other user terminal, and is capable of receiving user-defined settings. The customized settings may include restrictions on the power supply range when the electric vehicle supplies power to the home.

[0026] The charge and discharge conversion system is connected to the battery management system (BMS) to obtain the battery state of charge (SOC) and battery state of health (SOH); the home power supply system is also connected to the smart meter to obtain household electricity consumption data, including daily electricity consumption, including off-peak hours, normal hours, and peak hours.

[0027] Based on this, a "car-home-grid" energy network is constructed. The purpose of the present invention is to optimize the charging time period of electric vehicles and the time period when electric vehicles power the home with the goal of minimizing electricity costs and battery loss, based on the user's electricity consumption behavior, electric vehicle battery status, and user travel plans. The intelligent controller obtains the user's daily usage data, accepts user-defined requirements, receives real-time electricity prices, and makes judgments on the charging and discharging of electric vehicle batteries. When the intelligent control system determines that the electric vehicle battery charging state has been entered, it sends a command to the charge-discharge conversion system to run in the forward direction and start using the grid electricity to charge the electric vehicle battery. When the intelligent control system determines that the electric vehicle battery is now in the state of powering the home power system, it sends a command to the charge-discharge conversion system to run in the reverse direction and start using the electric vehicle battery to power the home power system circuit in the reverse direction.

[0028] Figure 2 A flow chart of a method for controlling electric vehicle charging and discharging in coordination with household electricity consumption, provided by one or more embodiments of the present invention, is shown. The method is applied to the controller and includes the following steps: S101: Obtaining the arrival time and charge state of the electric vehicle at the time of arrival; S102: Obtaining a travel plan for the electric vehicle, the travel plan including a departure time and a destination; S103: estimating the amount of electricity required for the travel plan based on the home location, destination, and departure time; S104: If the state of charge exceeds a set threshold when arriving home, within a set optimization cycle, the time period for charging the electric vehicle and supplying power to the home is optimized with the goal of minimizing electricity costs and battery loss. The electricity cost is the sum of the power supplied by the grid to the electric vehicle and the home during the cycle, and the battery loss is calculated based on the power supplied by the electric vehicle to the home during the cycle, the electric vehicle's travel power consumption, and the battery's factory parameters.

[0029] This method divides EV discharge into driving consumption and home power supply. This increases the battery's depth of discharge when the state of charge exceeds a set threshold, effectively consuming energy from frequently idle EVs and avoiding the loss of battery life caused by frequent charging and discharging. Furthermore, by incorporating both electricity costs and battery loss into the objective function, it balances user economic costs with battery loss, resolving the problem of accelerated battery degradation caused by prior art failure to consider battery health.

[0030] In step S101, considering that when a user has a charging demand, the user usually connects the electric vehicle to the charging system after parking the vehicle at home, in this embodiment, the time when the electric vehicle is connected to the charging system is recorded as the time when the electric vehicle arrives home, and the current charge state of the electric vehicle is obtained and recorded as the charge state at the time of arrival at home.

[0031] In step S102, the user sets a daily use plan for the electric vehicle through the terminal device. The daily use plan includes daily use time and round-trip addresses, as well as temporary use plans and round-trip addresses. For example, for office workers with regular work and rest schedules, the daily use plan can set the workday, departure time, and work location. The weekend travel plan is a temporary use plan, which can be a travel plan, and also sets the date, departure time, and destination address. The purpose of setting the time and address is to set the power demand and charging deadline. For example, if the user makes an appointment to go somewhere after 8 o'clock tomorrow, the system will complete the charging of the electric vehicle battery before 8 o'clock tomorrow and ensure that the power meets the travel plan.

[0032] If no new travel plans are received after the electric vehicle is connected to the charging system, the next travel plan is determined based on the user's previous travel plans. For example, if the user does not set a new travel plan on Friday, the system will assume that the user will not use the vehicle for the weekend and will use the following Monday as the next travel plan. Of course, users can enter travel plans through the terminal device at any time, and the system can adaptively modify the constraints to adjust the subsequent optimization scheduling results.

[0033] In step S103, the system calculates the amount of electricity required to complete the planned trip based on factors such as the distance between the home and destination, road conditions, weather conditions, and the vehicle's energy consumption characteristics. This calculation takes into account factors such as the electric vehicle's energy consumption model, road gradient, and expected travel speed to ensure accurate estimates. For example, for a 30-kilometer commute, the system might estimate 5 kWh of electricity required, but in inclement weather or congested traffic, this figure could increase to 6-7 kWh.

[0034] As a specific implementation method, the method for predicting the amount of electricity required for a travel plan is as follows: plan the optimal driving route based on the home location and destination; divide the driving route into multiple sub-path segments based on the road slope, road type and road conditions, and each sub-path segment has relatively consistent road slope, road type and road conditions; based on the energy consumption rate prediction model, predict the energy consumption rate of each sub-path segment according to the slope, predicted congestion index and predicted vehicle speed of each sub-path segment, where the energy consumption rate is defined as the energy consumption per kilometer. It can be understood that the predicted congestion index can be obtained through the navigation system, and the predicted vehicle speed can be the average speed of recent users; obtain weather forecast information, and if it is bad weather, determine the energy consumption rate increment based on the weather type and severity to obtain the corrected energy consumption rate of each sub-path segment; multiply the distance of each sub-path segment by the energy consumption rate to obtain the energy consumption of the segment; add up the energy consumption of all sub-path segments to obtain the total energy consumption required to complete the entire travel plan.

[0035] Among them, the energy consumption rate prediction model training method is: (1) Obtaining historical travel data and weather data of the vehicle, wherein the historical travel data includes travel path, real-time vehicle speed, real-time energy consumption rate, and congestion index of the road section; it is understood that the travel path, real-time vehicle speed, real-time energy consumption rate, and real-time congestion index are obtained through the navigation system during the use of the vehicle.

[0036] (2) The travel path is divided according to the road slope to obtain multiple sub-path segments, each of which has a relatively consistent road slope. It can be understood that the relative consistency here does not necessarily require the values ​​to be completely consistent, as long as the difference in values ​​meets the set tolerance, and the length of each sub-path segment must meet the set threshold.

[0037] (3) Taking each sub-path segment as a sample, the average slope, average vehicle speed, congestion index of the road segment, and average energy consumption rate are obtained to obtain a training data set. After obtaining the training data set, based on historical weather conditions, sample data corresponding to severe weather conditions such as strong winds, rain and fog are identified and selected as a correction data set. (4) Using the average slope, average vehicle speed, and congestion index of the road section as input and the average energy consumption rate as output, an energy consumption rate prediction model is trained based on a deep learning model. It is understood that as the number of vehicle uses increases and the training data set obtained becomes richer, the energy consumption rate prediction model will become increasingly accurate. Of course, in the early stages of vehicle use, a simple estimate based solely on the mileage can be made.

[0038] Energy consumption rate increments affected by weather are obtained through a mapping table lookup. A dataset corresponding to severe weather conditions is obtained and the average slope, average vehicle speed, and congestion index of the road section are input into the energy consumption rate prediction model to obtain the theoretical energy consumption rate. If the absolute value of the actual energy consumption rate increment relative to the theoretical energy consumption rate is less than a set threshold, the relevant dataset is excluded. This excludes weather conditions with minimal adverse effects on driving. Energy consumption rate increments are categorized by weather type (rain, snow, fog, wind) and severity, such as light rain, moderate rain, and heavy rain (differentiated by precipitation volume), and the average is calculated to create a mapping table for weather, severity, and energy consumption rate increments. Another specific implementation method predicts the energy required for the next trip plan based on historical destinations, the routes taken to reach each destination, and the actual energy consumption. If multiple historical energy consumption records exist for the same destination, the maximum value is selected. The maximum actual energy consumption typically corresponds to the worst environmental or road conditions. If the destination corresponding to the next travel plan is a historical destination, the required power is estimated based on the maximum actual power consumption of the historical destination; if the destination corresponding to the next travel plan is not a historical destination, the power consumption is predicted according to the previous implementation method.

[0039] In step S104, the optimization period is the time period between the current and next arrival time, that is, the time period between two consecutive times when the electric vehicle is connected to the charging system. Based on this, no matter how often the user uses the vehicle, it is possible to ensure that the battery power within the period is reasonably released, and the required power is charged before the next departure, while coordinating the balance between economy and battery damage.

[0040] Specifically, the objective function is a weighted sum of electricity cost and battery loss, where the sum of these two factors equals 1. The weights are adjustable. Users can adjust the weights of these two factors based on their preferences. For example, a user who prioritizes economy can increase the weight of electricity cost, while a user who prioritizes battery life can increase the weight of battery loss.

[0041] For example, an economic mode, a protection mode, and an adaptive mode can be set. The economic mode maximizes electricity cost savings and allows deeper discharge. For example, the weights of electricity cost and battery loss are set to 0.8 and 0.2 respectively. The protection mode minimizes battery loss and prioritizes shallow charging and shallow discharge. For example, the weights of electricity cost and battery loss are set to 0.4 and 0.6 respectively. The adaptive mode means that if the peak-valley difference in electricity prices is large, it will automatically lean towards the economic mode. That is, based on the electricity price in the current period, the difference between the total electricity cost of charging when the electricity price is the lowest and the charging electricity cost when the electricity price is the highest is estimated. The weight coefficient is adaptively adjusted according to the difference. The larger the difference, the greater the weight of the electricity cost and the smaller the weight of the battery loss. At the same time, under the adaptive model, the battery health status, such as the SOH value, is also monitored in real time. If the SOH value is lower than the set threshold, the protection mode is adopted.

[0042] Constraints include electric vehicle power balance constraints, charge and discharge time constraints, and upper and lower power limit constraints. Specifically, the charge and discharge time constraints include that the time period when the electric vehicle supplies power to the home is before the time period when the electric vehicle is charged, and the end time of the electric vehicle charging is before the time of leaving home; the upper and lower power limit constraints include that the state of charge of the electric vehicle after supplying power to the home meets the minimum protection power of the battery, and the power of the electric vehicle when charging is completed is lower than the maximum protection power. Ensure that the electric vehicle has enough power to complete the next trip when leaving home, while avoiding excessive charge and discharge of the battery and extending the battery life. The charge and discharge time constraints can ensure that travel needs are met, and the upper and lower power limit constraints can ensure that the battery charge and discharge power is maintained in the optimal range within each scheduling cycle (such as maintaining the SOC value between 30% and 80%), which is conducive to battery health maintenance.

[0043] To optimize battery charging and discharging times based on household electricity demand, it's necessary to understand the household's electricity usage patterns. Specifically, historical household electricity usage data is obtained and analyzed to generate a time series of household electricity usage behavior. This data is then used to estimate the amount of electricity supplied to the household by the grid and / or electric vehicles.

[0044] Exemplarily, the objective function is: ;in, 、 Respectively represent the electricity cost and battery loss cost in this scheduling cycle, represents the weight coefficient; ; in, hour, ; Constraints include: ; ;

[0045] Where T represents the scheduling period, Indicates the real-time electricity price, represents the amount of electricity required for household consumption at time t, represents the charge capacity of the electric vehicle at time t, 、 They represent the start and end time of the electric vehicle supplying power to the home, 、 Respectively represent the start time and end time of electric vehicle charging, 、 They represent the arrival time and departure time of the electric car respectively. Indicates the end of the electric vehicle's power supply to the home. Indicates the loss cost corresponding to the unit loss of battery, Indicates the amount of electricity required for this travel plan. Represents the battery cycle life coefficient, and Respectively represent the minimum and maximum protection power of electric vehicles, represents the conditional threshold for electric vehicles to power homes, Indicates the minimum power of the electric car when leaving home. Represents the remaining power of the electric car when it arrives home. With the goal of minimizing the above objective function, 、 、 、 Perform optimization solution.

[0046] This objective function simultaneously considers electricity price fluctuations, battery characteristics, and user needs to determine the optimal charging timeframe, ensuring that the vehicle receives power during off-peak hours and maintains the optimal battery charge and discharge range. This optimization process ensures that the vehicle has sufficient charge for the next trip.

[0047] In order to prevent electric vehicle batteries from aging faster due to high-power discharge and ensure that basic household electricity needs are met, electric vehicles can be limited to supplying power only to non-adjustable loads.

[0048] After obtaining the optimization results, charge and discharge control is performed according to the optimization results to control the connection and disconnection between the home power supply system and the power grid / charge and discharge conversion system, as well as the charge and discharge switching of electric vehicles, to achieve automated charge and discharge control.

[0049] Based on the above method, one or more embodiments of the present invention further provide an electric vehicle charging and discharging control device comprising a home arrival status acquisition module 201, a travel plan acquisition module 202, a required power estimation module 203, and a charge and discharge control module 204. The home arrival status acquisition module 201 is used to acquire the electric vehicle's arrival time and charge state at the time of arrival; the travel plan acquisition module 202 is used to acquire the electric vehicle's travel plan, including the departure time and destination; the required power estimation module 203 is used to estimate the power required for the travel plan based on the home's location, destination, and departure time; the charge and discharge control module 204 determines the battery charge state at the time of arrival. If the charge state at the time of arrival exceeds a set threshold, within a set optimization cycle, the time period for the electric vehicle to charge and supply power to the home is optimized with the goal of minimizing electricity cost and battery loss; wherein the electricity cost is the sum of the power supplied by the power grid to the electric vehicle and the home during the cycle, and the battery loss is calculated based on the power supplied by the electric vehicle to the home during the cycle, the electric vehicle's travel power consumption, and the battery's factory parameters.

[0050] The charge-discharge control device also includes a user preference module for obtaining user preferences for charging strategies, such as economy mode, battery protection mode, or balanced mode. Under different modes, the system adjusts the weighting of electricity cost and battery loss in the optimization objective to meet different user needs. Users can set preferences through a mobile app or the home energy management system interface, and the system automatically adjusts the charge-discharge strategy accordingly.

[0051] The device also has an emergency travel processing module to handle users' temporary travel needs.

[0052] The device also includes a data analysis and reporting module that collects and analyzes historical charge and discharge data to generate electricity cost savings and battery health reports. This module uses a visual interface to demonstrate the economic benefits and battery life extensions of charge and discharge strategies, helping users understand the effectiveness of the system.

[0053] During system operation, the Home Status Acquisition Module first records vehicle return information. The Travel Plan Acquisition Module then predicts future travel needs. Finally, the Charge and Discharge Control Module generates and executes the optimal charge and discharge strategy. This entire process is highly automated, requiring minimal user intervention. This ensures vehicle availability while optimizing electricity costs and battery loss.

[0054] One or more embodiments of the present invention further provide an electronic device that can be used to implement the electric vehicle charging and discharging control method in the above embodiments. The electronic device includes one or more processors, one or more memories coupled to the processors, and a communication module coupled to the processors.

[0055] The memory may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, at least one of the following: read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, hard disk, compact disc (CD), digital video disc (DVD), or other magnetic and / or optical storage. Examples of volatile memories include, but are not limited to, at least one of the following: random access memory (RAM) or other volatile memories that do not persist during a power outage. A computer program may be stored in the ROM. When the processor executes the computer program, the above-described electric vehicle charging and discharging control method is implemented.

[0056] In some embodiments, the program may be tangibly embodied in a computer-readable medium, which may be included in a device (such as a memory) or other storage device accessible by the device. The program may be loaded from the computer-readable medium into RAM for execution. The computer-readable medium may include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, or a hard disk. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the above-described electric vehicle charging and discharging control method.

[0057] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a server or terminal, the computer program instructions fully or partially generate the processes or functions described in the embodiments of the present application. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible by the server or terminal, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, hard disk, or magnetic tape), an optical medium (e.g., a digital video disk (DVD), etc.), or a semiconductor medium (e.g., a solid-state drive).

[0058] In addition, although adopting specific order to describe each operation, this should be understood as requiring such operation to be carried out with shown specific order or with sequential order, or requiring all illustrated operations to be carried out to obtain desired result.Under certain environment, multitasking and parallel processing may be advantageous.Similarly, although comprising some specific implementation details in the above discussion, these should not be interpreted as limiting the scope of the application.Some features described in the context of independent embodiment can also be implemented in a single implementation in combination.On the contrary, the various features described in the context of independent implementation also can be implemented in a plurality of implementations individually or in the mode of any suitable subcombination.

[0059] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Claims

1. A method for controlling charging and discharging of an electric vehicle, characterized in that: The following steps are involved: Obtain the time when the electric vehicle arrives home and the state of charge at the time of arrival; Obtaining a travel plan for the electric vehicle, wherein the travel plan includes a departure time and a destination; Estimate the amount of electricity required for the travel plan based on the home location, destination, and departure time; If the state of charge exceeds a set threshold when arriving home, the time period during which the electric vehicle charges and supplies power to the home is optimized within a set optimization cycle, with the goal of minimizing electricity costs and battery loss. The electricity cost is the sum of the power supplied by the grid to the electric vehicle and the home during that cycle, and the battery loss is calculated based on the power supplied by the electric vehicle to the home during that cycle, the electric vehicle's travel power consumption, and the battery's factory parameters.

2. The electric vehicle charging and discharging control method according to claim 1, wherein: The objective function is the weighted sum of electricity cost and battery loss, the sum of their weights is 1, and the weights are adjustable.

3. The electric vehicle charging and discharging control method according to claim 2, wherein: Receive user preference settings and determine the weights of electricity cost and battery loss based on the user preferences; the user preference settings include economic mode, protection mode and adaptive mode; in economic mode, the weight of electricity cost is higher than the weight of battery loss, in protection mode, the weight of electricity cost is lower than the weight of battery loss, and in adaptive mode, the weights of electricity cost and battery loss are adaptively adjusted according to the peak and valley differences in electricity prices and the battery health status.

4. The electric vehicle charge and discharge control method according to claim 1, wherein: If no new travel plan is received after the electric vehicle is connected to the charging system, the next travel plan is determined based on the historical travel plan set by the user.

5. The electric vehicle charging and discharging control method according to claim 1, wherein: The household's historical electricity usage data is also obtained and analyzed to obtain time series data of the household's electricity usage behavior. The time series data is used to estimate the amount of electricity supplied to the household by the power grid / electric vehicle.

6. The electric vehicle charging and discharging control method according to claim 5, characterized in that: The objective function is: ;in, 、 Respectively represent the electricity cost and battery loss in this scheduling period, represents the weight coefficient; ； ;in, hour, ; Constraints include: ； ； Where T represents the scheduling period, Indicates the real-time electricity price, represents the amount of electricity required for household consumption at time t, represents the charge capacity of the electric vehicle at time t, 、 They represent the start and end time of the electric vehicle supplying power to the home, 、 Respectively represent the start time and end time of electric vehicle charging, 、 They represent the time when the electric car arrives home and the time when it leaves home, Indicates the loss cost corresponding to the unit loss of battery, Indicates the amount of electricity required for this travel plan. Represents the battery cycle life coefficient, and Respectively represent the minimum and maximum protection power of electric vehicles, represents the conditional threshold for electric vehicles to power homes, Indicates the minimum power of the electric car when leaving home. Indicates the remaining power of the electric car when it arrives home.

7. An electric vehicle charging and discharging control device, characterized in that: include: A home status acquisition module is configured to acquire the time when the electric vehicle arrives home and the state of charge at the time of arrival; a travel plan acquisition module configured to acquire a travel plan of the electric vehicle, including a departure time and a destination; a required power estimation module, configured to estimate the power required for the travel plan based on the home location, destination, and departure time; The charge and discharge control module is configured to determine the battery's state of charge when arriving home. If the state of charge exceeds a set threshold, the module optimizes the time period for charging the electric vehicle and supplying power to the home within a set optimization cycle, with the goal of minimizing electricity costs and battery loss. The electricity cost is the sum of the power supplied by the grid to the electric vehicle and the home during the cycle, and the battery loss is calculated based on the power supplied by the electric vehicle to the home during the cycle, the electric vehicle's travel power consumption, and the battery's factory parameters.

8. An electronic device, characterized in that: The electronic device comprises a processor and a memory, wherein computer instructions are stored in the memory. When the computer instructions are executed by the processor, the electronic device executes the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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