Electric vehicle charging and discharging control method based on norm

Abstract

The invention relates to a norm-based electric vehicle charging and discharging control method and device, computer equipment, a computer readable storage medium and a computer program product, which can be applied to the technical field of automatic control. The method comprises the steps of collecting basic parameters of a battery of the electric vehicle and object behavior data of the electric vehicle; determining target parameters of the electric vehicle according to the basic parameters of the battery and the object behavior data; according to the target parameters, constructing a multi-target processing model containing L1 norm constraints; according to the multi-target processing model, initial charging and discharging information of the electric vehicle is determined; according to the real-time feedback signal of the power grid and the real-time state of the battery, the initial charging and discharging information is updated, and dynamic charging and discharging information of the electric vehicle is obtained; and performing charging and discharging control on the electric vehicle according to the dynamic charging and discharging information. By adopting the method, the charging and discharging efficiency of the electric vehicle can be improved.

Description

Technical Field

[0001] This application relates to the field of automation control technology, and particularly to a norm-based electric vehicle charging and discharging control method, device, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] With the development of new energy technology, electric vehicles are gradually becoming popular as an environmentally friendly and efficient means of transportation. In the electric vehicle technology system, the charging and discharging control system is an important part that affects the user experience and the operation of the power grid. Therefore, the research and application of charging and discharging control technology have received wide attention.

[0003] Traditional technologies usually control the charging and discharging of electric vehicles through a fixed charging mode; however, charging and discharging control in this way lacks flexibility and cannot adapt to complex and changing vehicle usage environments and grid demands, resulting in low charging and discharging efficiency of electric vehicles. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a norm-based electric vehicle charging and discharging control method, device, computer equipment, computer-readable storage medium, and computer program product that can improve the charging and discharging efficiency of electric vehicles.

[0005] In a first aspect, this application provides a norm-based electric vehicle charging and discharging control method. The method includes:

[0006] Collect the basic parameters of the battery of the electric vehicle and the object behavior data of the electric vehicle;

[0007] Determine the target parameters of the electric vehicle according to the basic parameters of the battery and the object behavior data;

[0008] Construct a multi-objective processing model including L1 norm constraints according to the target parameters;

[0009] Determine the initial charging and discharging information of the electric vehicle according to the multi-objective processing model;

[0010] Update the initial charging and discharging information according to the real-time feedback signal of the power grid and the real-time state of the battery to obtain the dynamic charging and discharging information of the electric vehicle;

[0011] Control the charging and discharging of the electric vehicle according to the dynamic charging and discharging information.

[0012] In one of the embodiments, the collecting the basic parameters of the battery of the electric vehicle and the object behavior data of the electric vehicle includes:

[0013] Collect the type parameter, internal resistance parameter, and self-discharge rate parameter of the battery as the basic parameters of the battery;

[0014] Collect the object travel mileage demand data and time pattern data of the electric vehicle as the object behavior data.

[0015] In one embodiment, the determining the target parameters of the electric vehicle according to the basic parameters of the battery and the object behavior data includes:

[0016] Perform data fusion processing on the basic parameters of the battery and the object behavior data to obtain the fusion parameters of the electric vehicle;

[0017] Perform cleaning processing and standardization processing on the fusion parameters to obtain the target parameters.

[0018] In one embodiment, the multi-objectives corresponding to the multi-objective processing model include the objective of minimizing the full life cycle cost of the electric vehicle and the objective of suppressing the power grid power fluctuation.

[0019] In one embodiment, the determining the initial charge and discharge information of the electric vehicle according to the multi-objective processing model includes:

[0020] Determine the particle swarm model;

[0021] Solve the multi-objective processing model through the particle swarm model to obtain the initial charge and discharge information.

[0022] In one embodiment, the controlling the charge and discharge of the electric vehicle according to the dynamic charge and discharge information includes:

[0023] Send the dynamic charge and discharge information to the battery management system of the electric vehicle;

[0024] Through the battery management system, control the charge and discharge power of the electric vehicle according to the dynamic charge and discharge information.

[0025] In a second aspect, the present application also provides an electric vehicle charge and discharge control device based on a norm. The device includes:

[0026] A data acquisition module for acquiring the basic parameters of the battery of the electric vehicle and the object behavior data of the electric vehicle;

[0027] A parameter determination module for determining the target parameters of the electric vehicle according to the basic parameters of the battery and the object behavior data;

[0028] A model construction module for constructing a multi-objective processing model including L1 norm constraints according to the target parameters;

[0029] An information determination module, configured to determine initial charge and discharge information of the electric vehicle according to the multi-objective processing model;

[0030] An information update module, configured to update the initial charge and discharge information according to the real-time feedback signal of the power grid and the real-time state of the battery to obtain the dynamic charge and discharge information of the electric vehicle;

[0031] A vehicle control module, configured to control the charge and discharge of the electric vehicle according to the dynamic charge and discharge information.

[0032] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the following steps are implemented:

[0033] Collect basic parameters of the battery of the electric vehicle and object behavior data of the electric vehicle;

[0034] Determine target parameters of the electric vehicle according to the basic parameters of the battery and the object behavior data;

[0035] Construct a multi-objective processing model including L1 norm constraints according to the target parameters;

[0036] Determine initial charge and discharge information of the electric vehicle according to the multi-objective processing model;

[0037] Update the initial charge and discharge information according to the real-time feedback signal of the power grid and the real-time state of the battery to obtain the dynamic charge and discharge information of the electric vehicle;

[0038] Control the charge and discharge of the electric vehicle according to the dynamic charge and discharge information.

[0039] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the following steps are implemented:

[0040] Collect basic parameters of the battery of the electric vehicle and object behavior data of the electric vehicle;

[0041] Determine target parameters of the electric vehicle according to the basic parameters of the battery and the object behavior data;

[0042] Construct a multi-objective processing model including L1 norm constraints according to the target parameters;

[0043] Determine initial charge and discharge information of the electric vehicle according to the multi-objective processing model;

[0044] Update the initial charge and discharge information according to the real-time feedback signal of the power grid and the real-time state of the battery to obtain the dynamic charge and discharge information of the electric vehicle;

[0045] Perform charge and discharge control on the electric vehicle according to the dynamic charge and discharge information.

[0046] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0047] Collect the basic parameters of the battery of the electric vehicle and the object behavior data of the electric vehicle;

[0048] Determine the target parameters of the electric vehicle according to the basic parameters of the battery and the object behavior data;

[0049] Construct a multi-objective processing model including L1 norm constraints according to the target parameters;

[0050] Determine the initial charge and discharge information of the electric vehicle according to the multi-objective processing model;

[0051] Update the initial charge and discharge information according to the real-time feedback signal of the power grid and the real-time state of the battery to obtain the dynamic charge and discharge information of the electric vehicle;

[0052] Perform charge and discharge control on the electric vehicle according to the dynamic charge and discharge information.

[0053] The above-mentioned norm-based electric vehicle charging and discharging control method, device, computer device, computer-readable storage medium and computer program product collect the basic parameters of the battery of the electric vehicle and the object behavior data of the electric vehicle; determine the target parameters of the electric vehicle according to the basic parameters of the battery and the object behavior data; construct a multi-objective processing model containing L1 norm constraints according to the target parameters; determine the initial charging and discharging information of the electric vehicle according to the multi-objective processing model; update the initial charging and discharging information according to the real-time feedback signal of the power grid and the real-time state of the battery to obtain the dynamic charging and discharging information of the electric vehicle; and perform charging and discharging control on the electric vehicle according to the dynamic charging and discharging information. This solution collects the basic parameters of the battery and the object behavior data of the electric vehicle to determine the target parameters, constructs a multi-objective processing model containing L1 norm constraints according to the target parameters, and adopts a multi-objective processing model containing L1 norm constraints to simultaneously consider minimizing the full-life cycle cost and suppressing the power grid power fluctuation, which is beneficial to achieving the balance between economic benefits and grid stability; by generating the initial charging and discharging information and dynamically updating it according to the real-time feedback signal of the power grid and the real-time state of the battery, it is beneficial to adapt to the changes in the power grid load and battery performance; finally, precise control is performed according to the dynamic charging and discharging information to avoid frequent oscillation of the charging and discharging power in the same period, reduce the negative impact on the battery life, extend the battery service life, thereby effectively improving the charging and discharging efficiency of the electric vehicle, reducing the overall cost of users, and at the same time improving the operation efficiency and stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0055] Figure 1 It is a schematic flowchart of a norm-based electric vehicle charging and discharging control method in an embodiment;

[0056] Figure 2 It is a schematic flowchart of the steps of collecting the basic parameters of the battery and the object behavior data in an embodiment;

[0057] Figure 3 It is a schematic flowchart of a norm-based electric vehicle charging and discharging control method in another embodiment;

[0058] Figure 4 It is a structural block diagram of a norm-based electric vehicle charging and discharging control device in an embodiment;

[0059] Figure 5 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0060] To make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant regulations.

[0062] In an exemplary embodiment, as Figure 1 shown, a norm-based electric vehicle charging and discharging control method is provided. In this embodiment, it is exemplified that the method is applied to an electric vehicle charging and discharging control system (such as a terminal or a server); it can be understood that the method can also be applied to a terminal or a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be but not limited to various personal computers, laptop computers, smart phones, tablet computers, etc.; the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. In this embodiment, the method includes the following steps:

[0063] Step S101, collect the basic parameters of the battery of the electric vehicle and the object behavior data of the electric vehicle.

[0064] Step S102, determine the target parameters of the electric vehicle according to the basic parameters of the battery and the object behavior data.

[0065] Step S103, construct a multi-objective processing model including L1 norm constraints according to the target parameters.

[0066] Step S104, determine the initial charging and discharging information of the electric vehicle according to the multi-objective processing model.

[0067] Step S105, update the initial charging and discharging information according to the real-time feedback signal of the power grid and the real-time state of the battery to obtain the dynamic charging and discharging information of the electric vehicle.

[0068] Step S106, perform charging and discharging control on the electric vehicle according to the dynamic charging and discharging information.

[0069] Among them, the electric vehicle can be a vehicle that uses a battery as a power source.

[0070] Among them, the basic parameters of the battery can be parameters that describe the basic characteristics of the battery. For example, the basic parameters of the battery can be core parameters such as battery type (such as lithium-ion battery, nickel-metal hydride battery), internal resistance, self-discharge rate, etc.

[0071] Among them, the object behavior data can be data that describes the usage of the electric vehicle. For example, the object behavior data can be behavior data such as the user's travel mileage demand and time pattern.

[0072] Among them, the target parameters can be key indicators required for optimizing the charge and discharge control of the electric vehicle. For example, the target parameters can be the starting SOC (state of charge), ending SOC, maximum SOC, minimum SOC, battery capacity, upper limit of charging power, lower limit of charging power, etc. of the electric vehicle.

[0073] Among them, the L1 norm constraint can be a mathematical constraint condition used to limit the change range of variables. For example, the L1 norm constraint can be a mathematical expression used to avoid the oscillation of charge and discharge power in the same time period.

[0074] Among them, the multi-objective processing model can be a model that simultaneously considers multiple optimization objectives (which can be a mathematical model or a neural network model). For example, the multi-objective processing model can be a comprehensive optimization model that simultaneously considers the minimization of the total life cycle cost of the electric vehicle and the suppression of grid power fluctuations. That is, the multi-objectives corresponding to the multi-objective processing model include the objective of minimizing the total life cycle cost of the electric vehicle and the objective of suppressing grid power fluctuations.

[0075] Among them, the objective of minimizing the total life cycle cost can be an optimization objective to reduce the total usage cost of the electric vehicle. For example, the objective of minimizing the total life cycle cost can be an economic objective achieved by optimizing the net value of charging cost minus discharge revenue.

[0076] Among them, the objective of suppressing grid power fluctuations can be an optimization objective to reduce the impact of the charge and discharge process of the electric vehicle on the grid. For example, the objective of suppressing grid power fluctuations can be to ensure the smooth charge and discharge power of the vehicle within the same electricity price period through the L1 norm constraint and avoid frequent power changes.

[0077] Among them, the initial charge and discharge information can be a charge and discharge plan calculated according to the multi-objective processing model. For example, the initial charge and discharge information can be a time-power comparison table that includes the charging power and discharging power of the electric vehicle in each time period.

[0078] Among them, the real-time grid feedback signal can be dynamic information on the grid operation state. For example, the real-time grid feedback signal can be information such as electricity price fluctuations and load demand.

[0079] Among them, the real-time state of the battery can be the dynamic working parameters during the operation of the battery. For example, the real-time state of the battery can be parameters such as the current SOC value, temperature, and internal resistance.

[0080] Among them, the dynamic charge-discharge information can be the charge-discharge scheme adjusted according to the real-time situation. For example, the dynamic charge-discharge information can be the charge-discharge power scheme dynamically adjusted according to the grid feedback signal and the battery state through the Deep Deterministic Policy Gradient (DDPG) framework.

[0081] Among them, the charge-discharge control can be the control of the charging and discharging processes of the electric vehicle battery. For example, the charge-discharge control can be the process of the optimized charge-discharge strategy being sent to the Battery Management System (BMS) in real time through the vehicle-mounted controller via the bus for execution.

[0082] Optionally, when the electric vehicle charge-discharge control system actually executes the charge-discharge control method, it first collects the basic parameters of the electric vehicle battery and the object behavior data of the electric vehicle through the vehicle sensor network, user application (APP), and vehicle networking platform; the basic parameters of the battery include core parameters such as battery type (such as lithium-ion battery, nickel-metal hydride battery), internal resistance, and self-discharge rate, while the object behavior data includes behavior data such as the user's travel mileage demand and time pattern. The electric vehicle charge-discharge control system cleans and standardizes the collected data to achieve multi-source data fusion and ensure parameter accuracy. Then, the electric vehicle charge-discharge control system determines the target parameters of the electric vehicle based on the basic parameters of the battery and the object behavior data, including key indicators such as the starting SOC (State of Charge), ending SOC, maximum SOC, minimum SOC, battery capacity, upper limit of charging power, and lower limit of charging power. The electric vehicle charge-discharge control system constructs a multi-objective processing model with L1 norm constraints, which simultaneously considers the objective of minimizing the total life cycle cost of the electric vehicle and the objective of suppressing grid power fluctuations. The electric vehicle charge-discharge control system uses an improved particle swarm optimization algorithm to solve the multi-objective processing model to determine the initial charge-discharge information of the electric vehicle. The electric vehicle charge-discharge control system then updates the initial charge-discharge information through the Deep Deterministic Policy Gradient (DDPG) framework according to the real-time grid feedback signal and the real-time state of the battery to obtain the dynamic charge-discharge information of the electric vehicle. Finally, the electric vehicle charge-discharge control system sends the dynamic charge-discharge information to the Battery Management System (BMS) in real time through the CAN bus to control the charging and discharging of the electric vehicle.

[0083] In the above-mentioned norm-based electric vehicle charging and discharging control method, the basic parameters of the electric vehicle's battery and the object behavior data of the electric vehicle are collected; according to the basic parameters of the battery and the object behavior data, the target parameters of the electric vehicle are determined; according to the target parameters, a multi-objective processing model including L1 norm constraints is constructed; according to the multi-objective processing model, the initial charging and discharging information of the electric vehicle is determined; according to the real-time feedback signal of the power grid and the real-time state of the battery, the initial charging and discharging information is updated to obtain the dynamic charging and discharging information of the electric vehicle; according to the dynamic charging and discharging information, the charging and discharging control of the electric vehicle is carried out. This solution collects the basic parameters of the electric vehicle's battery and the object behavior data to determine the target parameters, constructs a multi-objective processing model including L1 norm constraints according to the target parameters, and uses the multi-objective processing model including L1 norm constraints to simultaneously consider the minimization of the full life cycle cost and the suppression of the power grid power fluctuation, which is beneficial to achieving the balance between economic benefits and power grid stability; by generating the initial charging and discharging information and dynamically updating it according to the real-time feedback signal of the power grid and the real-time state of the battery, it is beneficial to adapt to the changes in the power grid load and the battery performance; finally, precise control is carried out according to the dynamic charging and discharging information, avoiding frequent oscillations of the charging and discharging power in the same period, reducing the negative impact on the battery life, extending the battery service life, thereby effectively improving the charging and discharging efficiency of the electric vehicle, reducing the overall cost of users, and at the same time improving the operation efficiency and stability of the power grid.

[0084] In an exemplary embodiment, referring to Figure 2 , the basic parameters of the electric vehicle's battery and the object behavior data of the electric vehicle are collected, and the specific contents are as follows:

[0085] Step S201, collect the type parameter, internal resistance parameter and self-discharge rate parameter of the battery as the basic parameters of the battery;

[0086] Step S202, collect the object travel mileage demand data and time law data of the electric vehicle as the object behavior data.

[0087] Among them, the type parameter can be a technical feature describing the type of the battery. For example, the type parameter can be the type identification of different types of batteries such as lithium-ion batteries and nickel-metal hydride batteries.

[0088] Among them, the internal resistance parameter can be a technical parameter describing the internal resistance characteristics of the battery. For example, the internal resistance parameter can be the internal resistance value of the battery expressed in ohms.

[0089] Among them, the self-discharge rate parameter can be a parameter describing the ratio of the self-loss of the battery power in the non-use state. For example, the self-discharge rate parameter can be the natural loss rate of the battery capacity per unit time expressed as a percentage.

[0090] Among them, the object travel mileage demand data can be data describing the daily driving distance requirements of electric vehicle users.

[0091] Among them, the time pattern data can be data describing the travel time habits of electric vehicle users.

[0092] Optionally, during the process of collecting the basic parameters of the battery of the electric vehicle and the object behavior data of the electric vehicle, the electric vehicle charge and discharge control system first uses the on-vehicle sensor system to collect the type parameters of the battery in real time, such as identifying the battery as a lithium-ion battery or a nickel-metal hydride battery; at the same time, measures the internal resistance parameter of the battery through the internal detection circuit; also obtains the self-discharge rate parameter by periodically monitoring and recording the capacity loss rate of the battery in the non-use state. The electric vehicle charge and discharge control system performs data standardization processing on these collected battery type parameters, internal resistance parameters, and self-discharge rate parameters to form a set of basic parameters of the battery. At the same time, the electric vehicle charge and discharge control system collects the object travel mileage demand data of the electric vehicle through the user application program (APP) and the on-vehicle trip record system, such as the travel demand that the user drives 30 kilometers per day on average; and obtains the time pattern data by long-term recording and analyzing the pattern of the user's travel time points. The electric vehicle charge and discharge control system performs data cleaning on these collected object travel mileage demand data and time pattern data, removes outliers, and forms a set of object behavior data of the electric vehicle.

[0093] The technical solution provided by this embodiment is beneficial to determining the basic characteristics and performance limitations of the battery by accurately collecting the battery type parameters, internal resistance parameters, and self-discharge rate parameters; at the same time, by collecting the object travel mileage demand data and time pattern data, it provides a true and reliable parameter basis for the subsequent optimization of the charge and discharge strategy, thus facilitating the improvement of the accuracy of charge and discharge control.

[0094] In an exemplary embodiment, according to the basic parameters of the battery and the object behavior data, the target parameters of the electric vehicle are determined, which specifically includes the following content: performing data fusion processing on the basic parameters of the battery and the object behavior data to obtain the fusion parameters of the electric vehicle; performing cleaning processing and standardization processing on the fusion parameters to obtain the target parameters.

[0095] Among them, the data fusion processing can be a technical processing process for integrating and analyzing multi-source data. For example, the data fusion processing can be a process of comprehensively analyzing and integrating the collected parameters such as battery type, internal resistance, self-discharge rate, and the behavior data such as user travel mileage demand and time pattern through vehicle sensors, user application programs (APPs), and vehicle networking platforms.

[0096] Among them, the fusion parameters of the electric vehicle can be a comprehensive parameter set obtained through data fusion processing. For example, the fusion parameters of the electric vehicle can be a preliminary data set formed by integrating battery basic parameters and user behavior data, including various unfiltered and non-standardized original fusion information.

[0097] Among them, the cleaning process can be a process of identifying and processing outliers, error values or missing values in the data. For example, the cleaning process can be a technical step of removing outliers in battery parameters or user behavior data to ensure data quality and reliability.

[0098] Among them, the standardization process can be a process of converting data with different dimensions and ranges into a unified standard. For example, the standardization process can be converting data with different units such as battery internal resistance parameters and self-discharge rate parameters into a dimensionless standard format to make it suitable for subsequent model calculation and analysis.

[0099] Among them, the target parameters can be a set of high-quality parameters after cleaning and standardization processes. For example, the target parameters can be a processed and standardized data set including starting SOC, ending SOC, maximum SOC, minimum SOC, battery capacity, upper limit of charging power, lower limit of charging power, etc.

[0100] Optionally, when determining the target parameters of the electric vehicle, the electric vehicle charging and discharging control system first performs data fusion processing on the basic parameters of the battery and the object behavior data. Specifically, the electric vehicle charging and discharging control system integrates and matches basic parameters such as battery type parameters (such as lithium-ion battery, nickel-metal hydride battery), internal resistance parameters (such as 0.05 ohms), and self-discharge rate parameters (such as 0.5% / month) with behavior data such as object travel mileage demand data (such as driving 30 kilometers per day on average) and time pattern data through a data multi-source fusion algorithm to obtain the fusion parameters of the electric vehicle. The electric vehicle charging and discharging control system adopts multi-source heterogeneous data fusion technology to ensure the effective integration of data from different sources (on-vehicle sensors, user applications, and vehicle networking platforms). Subsequently, the electric vehicle charging and discharging control system performs cleaning processing on the obtained fusion parameters, identifies and removes abnormal data points through an outlier detection algorithm, such as extreme travel mileage records or abnormal battery parameter values; at the same time, fills in the missing values in the data using a time series data interpolation method to ensure data integrity. The electric vehicle charging and discharging control system performs standardization processing on the cleaned data, converting parameters with different dimensions and units (such as internal resistance unit in ohms and self-discharge rate unit in percentage) into a unified dimensionless standard format to obtain a standardized target parameter set including starting SOC, ending SOC, maximum SOC, minimum SOC, battery capacity, upper limit of charging power, and lower limit of charging power, etc.

[0101] The technical solution provided in this embodiment is conducive to integrating multi-source heterogeneous data and realizing the correlation analysis of battery characteristics and user habits by performing data fusion processing on battery basic parameters and object behavior data; by performing cleaning and standardization processing on the fusion parameters, it is conducive to eliminating abnormal data and unifying the expression forms of parameters with different dimensions, improving the data quality and usability; this series of data processing processes provides high-quality target parameters for subsequent model construction and improves the overall cooperation efficiency.

[0102] In an exemplary embodiment, according to the multi-objective processing model, the initial charge and discharge information of the electric vehicle is determined, which specifically includes the following contents: determining the particle swarm model; through the particle swarm model, performing a solution process on the multi-objective processing model to obtain the initial charge and discharge information.

[0103] Among them, the particle swarm model can be an optimization algorithm framework based on swarm intelligence. For example, the particle swarm model can include key elements such as population size, particle position, particle velocity, local optimal solution, and global optimal solution.

[0104] Among them, the solution process can be a process of performing mathematical calculations on the multi-objective processing model by applying an algorithm. For example, the solution process can be an operation of searching for the optimal value of the optimization objective function under the constraint conditions through a specific iterative calculation method, including steps such as particle position update, fitness calculation, and global optimal solution update.

[0105] Optionally, when the electric vehicle charging and discharging control system determines the initial charging and discharging information of the electric vehicle, it first determines the key parameters and configuration information of the particle swarm model. Specifically, the electric vehicle charging and discharging control system sets the population size of the particle swarm model to a preset number (such as 100) of particles. The position of each particle is represented as the charging and discharging power decision variables of the electric vehicle at each time period, including var_ev_p[t] (the average charging power of the electric vehicle at time period t), var_ev_p_in[t] (the average charging power of the electric vehicle at time period t), and var_ev_p_out[t] (the average discharging power of the electric vehicle at time period t), etc. The electric vehicle charging and discharging control system sets the search space range of the particle position according to the battery performance parameters, ensuring that the position is restricted between the charging power lower limit (ev_p_min) and the charging power upper limit (ev_p_max), and establishes a particle velocity update mechanism to control the particle search direction through the inertia weight and individual learning factor. Subsequently, the electric vehicle charging and discharging control system determines the termination condition of the particle swarm model as a preset number of maximum iterations (such as 500 times) or the fitness function value continuously changing less than the set threshold of 1e-5. Then, the electric vehicle charging and discharging control system applies the configured particle swarm model to solve the multi-objective processing model. The multi-objective processing model comprehensively considers multiple constraint conditions such as minimizing the charging cost, maximizing the discharging revenue, minimizing the power fluctuation (achieved through the L1 norm constraint), as well as the battery SOC range constraint, power range constraint, and mutual exclusion constraint, etc., to obtain the initial charging and discharging information of the electric vehicle, such as the optimal charging and discharging power scheme within 24 hours, realizing the multi-objective optimization of minimizing the charging cost, maximizing the discharging revenue, and smooth power change.

[0106] The technical solution provided in this embodiment is beneficial to efficiently search for the global optimal solution by introducing the particle swarm optimization algorithm to solve the multi-objective processing model under the condition of comprehensively considering multiple constraint objectives such as charging cost, discharging revenue, and power fluctuation; by utilizing the swarm intelligence characteristics of the particle swarm, it is beneficial to avoid falling into the local optimum and improve the quality and stability of the solution; thus, it is beneficial to obtain more accurate initial charging and discharging information of the electric vehicle and achieve the comprehensive optimization goal of the electric vehicle charging and discharging strategy.

[0107] In an exemplary embodiment, the charging and discharging of the electric vehicle is controlled according to the dynamic charging and discharging information, which specifically includes the following: sending the dynamic charging and discharging information to the battery management system of the electric vehicle; and through the battery management system, controlling the charging and discharging power of the electric vehicle according to the dynamic charging and discharging information.

[0108] Among them, the battery management system can be a dedicated control unit for monitoring and controlling the operating status of an electric vehicle battery pack. For example, the battery management system can be an execution system that receives charge and discharge strategy instructions sent via a CAN (Controller Area Network) bus and adjusts the battery charge and discharge power in real time to ensure that the battery operates within a safe range.

[0109] Among them, the charge and discharge power can be a parameter describing the energy transfer rate when the electric vehicle battery is charging or discharging. For example, the charge and discharge power can be the instantaneous power value of battery charging or discharging expressed in kilowatts (kW).

[0110] Optionally, after obtaining the dynamic charge and discharge information of the electric vehicle, the electric vehicle charge and discharge control system first uses the wireless communication module to send the dynamic charge and discharge information to the battery management system of the electric vehicle. For example, a formatted time-power comparison table (including the charging power and discharging power for each period) is transmitted to the in-vehicle controller of the electric vehicle. This time-power comparison table is stored in the EEPROM memory (electrically erasable programmable read-only memory) of the in-vehicle controller as a charge and discharge instruction set for real-time execution. Subsequently, the in-vehicle controller sends the dynamic charge and discharge information to the battery management system of the electric vehicle in the form of a standardized data frame via the CAN bus. After receiving the dynamic charge and discharge information, the battery management system of the electric vehicle verifies the charge and discharge instructions based on the built-in protection logic to ensure that all charge and discharge operations are within the safe operating range of the battery.

[0111] The technical solution provided in this embodiment is conducive to the accurate execution and closed-loop control of the charge and discharge strategy by directly sending the dynamic charge and discharge information to the battery management system of the electric vehicle and having the battery management system execute the control; thus, it is conducive to realizing the full-process intelligent control of the electric vehicle charge and discharge process on the premise of ensuring the safe operation of the battery.

[0112] The following uses an application example to illustrate the norm-based electric vehicle charge and discharge control method provided in this application. This application example is illustrated by applying this method to an electric vehicle charge and discharge control system (such as a terminal or a server).

[0113] This application example generates a dynamic charge and discharge strategy by establishing an electric vehicle multi-objective optimization model, integrating the minimization of the full-life cycle cost and the goal of suppressing grid power fluctuations, and combining an improved dynamic inertia weight particle swarm algorithm with L1 norm constraints. Furthermore, a supporting edge computing device and cloud collaborative architecture are proposed to achieve the efficient solution, real-time update, and cross-vehicle collaborative optimization of the strategy. This application example significantly improves the economy of electric vehicles, battery life, and grid stability, and supports dynamic adaptation to battery performance degradation and user behavior changes.

[0114] This application example includes the following steps:

[0115] 1. Parameter acquisition and preprocessing:

[0116] Collect core parameters such as battery type (e.g., lithium-ion battery, nickel-metal hydride battery), internal resistance, self-discharge rate, as well as behavioral data such as users' travel mileage requirements and time patterns. Achieve multi-source data fusion through vehicle sensors, user applications (APPs), and the vehicle networking platform, and clean and standardize abnormal data to ensure parameter accuracy.

[0117] 2. Construction of multi-objective optimization model:

[0118] Define the following optimization objectives and construct a mathematical model:

[0119] Objective 1: Minimize the total life cycle cost;

[0120] Objective 2: Suppress the power fluctuation of the power grid.

[0121] 3. Solution by intelligent algorithm:

[0122] Use an improved particle swarm algorithm (dynamic inertia weight and adaptive mutation mechanism) to solve the optimization model, specifically including:

[0123] 1) Initialize the parameters of the particle swarm (population size, inertia weight range);

[0124] 2) Calculate the fitness of the particles (based on the objective function value);

[0125] 3) Update the velocity and position of the particles (introduce mutation operations to avoid local optima);

[0126] 4) Dynamically adjust the inertia weight to balance the global search and local convergence capabilities;

[0127] 5) Output the optimal charging and discharging strategy.

[0128] 4. Dynamic adjustment of adaptive reinforcement learning strategy:

[0129] 1) Deploy a deep deterministic policy gradient (DDPG) framework, receive real-time grid feedback signals (such as electricity price fluctuations, load demands) and battery status, and dynamically adjust the charging and discharging power;

[0130] 2) Update the parameters of the policy network through online learning to adapt to long-term battery performance degradation and changes in user habits.

[0131] 5. Collaboration between edge computing devices and the cloud:

[0132] Edge computing device: Integrated with a neural processing unit (NPU) acceleration chip to run lightweight artificial intelligence (AI) models (compressed long short-term memory (LSTM) and deep Q-network (DQN)) in real time;

[0133] Cloud platform: Stores historical data and trains complex models, regularly sends updates to the edge side, and supports cross-vehicle collaborative optimization.

[0134] 6. Policy storage and execution:

[0135] The optimized charging and discharging policy is stored in the non-volatile memory of the vehicle-mounted controller and is sent to the battery management system (BMS) for execution in real time through the Controller Area Network (CAN) bus to ensure the dynamic update and stability of the policy.

[0136] 7. Computing device and storage medium:

[0137] Computing device: Includes a processor, a memory, and a communication module; the processor is used to run optimization algorithms, the memory stores parameters and policies, and the communication module enables interaction with the power grid and user terminals.

[0138] Storage medium: A computer-readable storage medium stores program code that implements all steps of the above method when executed by a processor.

[0139] The process of this application example can be referred to Figure 3 , including: collecting electric vehicle battery parameters and user charging and discharging demand parameters; constructing an optimization objective function with L1 norm constraint to minimize charging and discharging costs and suppress power fluctuations; using an improved particle swarm optimization algorithm to solve the charging and discharging policy; dynamically adjusting the adaptive reinforcement learning policy; collaborating between the edge computing device and the cloud; generating an optimal charging and discharging plan for the entire life cycle and storing it.

[0140] II. Technical problems (invention objectives) to be solved by this application example:

[0141] This application example aims to solve many problems existing in the existing charging and discharging strategies of electric vehicles, including the negative impact of frequent charging and discharging on battery life, the high full-life cycle cost, and the damage to grid stability. By establishing an electric vehicle optimization model, combining the L1 norm constraint to suppress power fluctuations, and using an improved intelligent optimization algorithm, this application example can generate a charging and discharging strategy that takes into account both economy and battery life, thereby optimizing the use experience of electric vehicles, reducing the overall cost of users, and improving the operating efficiency and stability of the grid.

[0142] III. The complete technical solution provided by this application example:

[0143] Step 1. Parameter collection and preprocessing:

[0144] To ensure the maximum benefit of electric vehicle owners, relevant optimization parameters need to be provided before the optimization of electric vehicles. The optimization parameters are constant known quantities and do not change during the optimization process.

[0145] The optimization parameters mainly include:

[0146] ev_soc_init: The initial state of charge (SOC) of the vehicle;

[0147] ev_soc_end: The end state of charge (SOC) of the vehicle;

[0148] ev_soc_max: The maximum state of charge (SOC) of the vehicle;

[0149] ev_soc_min: The minimum state of charge (SOC) of the vehicle;

[0150] ev_capacity: The battery capacity of the vehicle;

[0151] ev_p_max: The upper limit of the charging power. The positive direction of the charging power is charging. When the electric vehicle discharges, the power is negative. Therefore, the upper limit of the charging power is the maximum charging power;

[0152] ev_p_min: The lower limit of the charging power. The positive direction of the charging power is charging. When the electric vehicle discharges, the power is negative. Therefore, the lower limit of the charging power is -1 × the maximum discharge power;

[0153] ev_price_in[t]: The inflow (charging) price of the electric vehicle, t ~ [0, T), where t and T represent time values, and ~ means belonging to;

[0154] ev_price_out[t]: The outflow (discharge) price of the electric vehicle, t ~ [0, T).

[0155] To describe the electric vehicle optimization model, it is necessary to describe the state variables that can be adjusted during the charging and discharging process of the electric vehicle.

[0156] The decision variables mainly include:

[0157] var_ev_soc[t]: State of Charge (SOC) of the electric vehicle at the end of time period t, t ∈ [0, T);

[0158] var_ev_p[t]: Average charging power of the electric vehicle in time period t, t ∈ [0, T);

[0159] var_ev_p_in[t]: Average charging power of the electric vehicle in time period t, t ∈ [0, T);

[0160] var_ev_p_out[t]: Average discharging power of the electric vehicle in time period t, t ∈ [0, T);

[0161] var_ev_abs_delt_p[t]: Amplitude of power fluctuation of the electric vehicle between time period t and t - 1, t ∈ [0, T);

[0162] var_ev_p_status[t]: Power status (Boolean variable) of the electric vehicle in time period t, t ∈ [0, T);

[0163] var_fee: Charging cost.

[0164] Step 2: Construction of the multi-objective optimization model:

[0165] In the multi-objective optimization framework, the L1 norm constraint is combined with the minimization of the life cycle cost to form a comprehensive optimization model. Specifically, the optimization objective can be expressed as:

[0166]

[0167] In the formula: buy_cost is the charging cost of the electric vehicle, and sell_benift is the discharging income of the electric vehicle.

[0168] 1. Optimization objective:

[0169] As described in the background, the optimization objective of the electric vehicle is to maximize the operation income of the vehicle owner. At the same time, in order to avoid the frequent change of the charging and discharging power of the vehicle affecting the battery life, the L1 norm (L1 norm) is added to the optimization objective to ensure that the vehicle power is as stable as possible during the same electricity price period. The multi-objective optimization model is expressed as:

[0170]

[0171] In the formula: buy_cost is the charging cost of the electric vehicle, and sell_benift is the discharging income of the electric vehicle.

[0172] The specific optimization objectives are as follows:

[0173] (1) The charging cost is

[0174]

[0175] (2) The discharging revenue is

[0176]

[0177] (3) The L1 norm is

[0178]

[0179] 2. Constraints:

[0180] The constraints for electric vehicle optimization are used to describe the internal relationships of the vehicle's decision variables on the one hand and to constrain the charging and discharging behavior boundaries of the vehicle on the other hand. The specific constraints are as follows:

[0181] (1) Initial state of charge (SOC) constraint:

[0182] The optimized initial state of charge (SOC) should be the specified initial state of charge (SOC):

[0183]

[0184] (2) Final state of charge (SOC) constraint:

[0185] The optimized final state of charge (SOC) should be the specified final state of charge (SOC):

[0186]

[0187] (3) State of charge (SOC) range constraint:

[0188] Minimum state of charge (SOC) <= State of charge (SOC) during optimization process <= Maximum state of charge (SOC):

[0189]

[0190] (4) State of charge (SOC) continuity constraint:

[0191] The final state of charge (SOC) at the previous moment should be equal to the initial state of charge (SOC) at the current moment:

[0192]

[0193] (5) Electric vehicle power range constraint:

[0194] Lower limit of charging power <= Electric vehicle power <= Upper limit of charging power:

[0195]

[0196] (6)Constraint on the charging power range of electric vehicles:

[0197] 0 <= Electric vehicle charging power <= Upper limit of charging power:

[0198]

[0199] (7)Constraint on the discharging power range of electric vehicles:

[0200] 0 <= Electric vehicle discharging power <= -1 × Lower limit of charging power:

[0201]

[0202] (8)Mutual exclusion constraint on electric vehicle power:

[0203] An electric vehicle can only be in a charging or discharging state:

[0204]

[0205] (9)L1 norm constraint:

[0206] The L1 norm constraint is mainly used to avoid power oscillations in the same time period:

[0207] For t ∈ [1, T):

[0208]

[0209]

[0210] For t = 0:

[0211]

[0212]

[0213] Step 3: Implementation of the improved particle swarm optimization algorithm:

[0214] 1. Initialize the particle swarm (population size = 100, position range is the charging and discharging power constraint interval);

[0215] 2. Calculate the particle fitness (based on the multi-objective weighted function);

[0216] 3. Update the particle velocity and position;

[0217] 4. Introduce an adaptive mutation mechanism: If the particle falls into a local optimum, randomly perturb its position with a 10% probability;

[0218] 5. Iterate until convergence (maximum number of iterations = 500, fitness change threshold = 1e-5).

[0219] Step 4. Dynamically adjust the adaptive reinforcement learning strategy:

[0220] Deploy a Deep Deterministic Policy Gradient (DDPG) framework, receive grid load signals and battery status in real time, and dynamically adjust the charging and discharging power; achieve online learning through a prioritized experience replay mechanism and soft update of the target network to adapt to long-term battery performance degradation.

[0221] Step 5. Collaborate between the edge computing device and the cloud:

[0222] 1. Hardware configuration:

[0223] Main processor: Advanced neural network processing unit (computing power of 1.6 trillion operations per second), supporting 8-bit integer quantization inference;

[0224] Coprocessor: Quad-core central processing unit (main frequency 2.0 GHz), responsible for data preprocessing and communication protocol processing;

[0225] Storage module: Random access memory (running lightweight models) + general flash storage;

[0226] Communication interface: Support for 5G New Radio (below 6 GHz band), vehicle-to-everything communication (dedicated short-range communication protocol), and Controller Area Network bus, with a communication delay < 20 milliseconds.

[0227] 2. Deployment of lightweight artificial intelligence (AI) models:

[0228] Model compression and quantization technology: Convert the model from 32-bit floating-point numbers to 8-bit integer (INT8) format, reducing the volume by 75%;

[0229] Inference acceleration: Use a neural network computing architecture to achieve operator fusion, with a single inference time < 30 milliseconds.

[0230] Step 6. Policy storage and real-time execution:

[0231] 1. Store the optimized charging and discharging strategy generated in the electrically erasable programmable read-only memory (EEPROM) of the vehicle-mounted controller in the format of a time-power comparison table;

[0232] 2. Send the policy to the battery management system via the Controller Area Network (CAN) bus to adjust the charging and discharging power in real time;

[0233] 3. Support dynamic update: When changes in battery parameters or electricity prices are detected, trigger the algorithm to recalculate and overwrite the old policy.

[0234] Step Seven: Computing Device and Storage Medium:

[0235] Storage Medium: A computer-readable storage medium (such as a Secure Digital (SD) card or a cloud database) stores program code and configuration files; the code includes a data preprocessing module, an optimization algorithm module, and a policy execution module, and supports Over-the-Air (OTA) remote upgrade.

[0236] The technical solution provided by this application example, by collecting the basic battery parameters and object behavior data of an electric vehicle, determines the target parameters. According to the target parameters, a multi-objective processing model with L1 norm constraint is constructed. Using the multi-objective processing model with L1 norm constraint, considering both the minimization of the full life cycle cost and the suppression of grid power fluctuations, is conducive to achieving the balance between economic benefits and grid stability; through the generated initial charging and discharging information, and dynamically updating according to the real-time feedback signal of the grid and the real-time state of the battery, is conducive to adapting to the changes in grid load and battery performance; finally, precise control is performed according to the dynamic charging and discharging information, avoiding frequent oscillations of the charging and discharging power in the same period, reducing the negative impact on the battery life, extending the battery life, thereby effectively improving the charging and discharging efficiency of the electric vehicle, reducing the overall cost of users, and at the same time improving the operation efficiency and stability of the grid.

[0237] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps do not necessarily execute in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily execute at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0238] Based on the same inventive concept, an embodiment of the present application further provides a norm-based electric vehicle charging and discharging control device for implementing the above-mentioned norm-based electric vehicle charging and discharging control method. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the norm-based electric vehicle charging and discharging control device provided below can refer to the limitations on the norm-based electric vehicle charging and discharging control method in the above text, and will not be repeated here.

[0239] In an exemplary embodiment, as Figure 4 shown, a norm-based electric vehicle charging and discharging control device is provided. The norm-based electric vehicle charging and discharging control device 400 may include:

[0240] A data acquisition module 401, configured to acquire the basic parameters of the battery of the electric vehicle and the object behavior data of the electric vehicle;

[0241] A parameter determination module 402, configured to determine the target parameters of the electric vehicle according to the basic parameters of the battery and the object behavior data;

[0242] A model construction module 403, configured to construct a multi-objective processing model including L1 norm constraints according to the target parameters;

[0243] An information determination module 404, configured to determine the initial charging and discharging information of the electric vehicle according to the multi-objective processing model;

[0244] An information update module 405, configured to update the initial charging and discharging information according to the real-time feedback signal of the power grid and the real-time state of the battery to obtain the dynamic charging and discharging information of the electric vehicle;

[0245] A vehicle control module 406, configured to control the charging and discharging of the electric vehicle according to the dynamic charging and discharging information.

[0246] In an exemplary embodiment, the data acquisition module 401 is further configured to acquire the type parameter, internal resistance parameter and self-discharge rate parameter of the battery as the basic parameters of the battery; acquire the object travel mileage demand data and time pattern data of the electric vehicle as the object behavior data.

[0247] In an exemplary embodiment, the parameter determination module 402 is further configured to perform data fusion processing on the basic parameters of the battery and the object behavior data to obtain the fusion parameters of the electric vehicle; perform cleaning processing and standardization processing on the fusion parameters to obtain the target parameters.

[0248] In an exemplary embodiment, the multi-objectives corresponding to the multi-objective processing model include the objective of minimizing the full life cycle cost of the electric vehicle and the objective of suppressing the power grid power fluctuation.

[0249] In an exemplary embodiment, the information determination module 404 is further configured to determine a particle swarm model; and solve the multi-objective processing model through the particle swarm model to obtain initial charge and discharge information.

[0250] In an exemplary embodiment, the vehicle control module 406 is further configured to send the dynamic charge and discharge information to the battery management system of the electric vehicle; and control the charge and discharge power of the electric vehicle according to the dynamic charge and discharge information through the battery management system.

[0251] Each module in the above-mentioned norm-based electric vehicle charge and discharge control device can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor in the computer device in hardware form or be independent of it, or can be stored in the memory in the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above-mentioned modules.

[0252] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as Figure 5 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. The computer program, when executed by the processor, implements a norm-based electric vehicle charge and discharge control method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0253] Those skilled in the art can understand, Figure 5The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0254] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0255] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0256] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0257] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., and are not limited thereto.

[0258] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0259] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A norm-based electric vehicle charging and discharging control method, characterized in that: The method comprises: Collecting basic parameters of a battery of an electric vehicle and object behavior data of the electric vehicle; Determining target parameters of the electric vehicle according to the basic parameters of the battery and the object behavior data; According to the target parameters, a multi-target processing model including L1 norm constraints is constructed; Determining initial charging and discharging information of the electric vehicle according to the multi-objective processing model; According to the real-time feedback signal of the power grid and the real-time status of the battery, the initial charge and discharge information is updated to obtain dynamic charge and discharge information of the electric vehicle; The charging and discharging of the electric vehicle is controlled according to the dynamic charging and discharging information.

2. The method according to claim 1, characterized in that The collecting of basic parameters of the battery of the electric vehicle and object behavior data of the electric vehicle includes: Collecting type parameters, internal resistance parameters and self-discharge rate parameters of the battery as basic parameters of the battery; The object travel mileage demand data and time regularity data of the electric vehicle are collected as the object behavior data.

3. The method according to claim 1, characterized in that The step of determining target parameters of the electric vehicle according to the basic parameters of the battery and the object behavior data includes: Performing data fusion processing on the basic parameters of the battery and the object behavior data to obtain fusion parameters of the electric vehicle; The fusion parameters are cleaned and standardized to obtain the target parameters.

4. The method according to claim 1, characterized in that: The multi-objectives corresponding to the multi-objective processing model include the objective of minimizing the full life cycle cost of the electric vehicle and the objective of suppressing power fluctuations of the power grid.

5. The method according to claim 1, characterized in that Determining the initial charge and discharge information of the electric vehicle according to the multi-objective processing model includes: Determine the particle swarm model; The multi-objective processing model is solved by the particle swarm model to obtain the initial charge and discharge information.

6. The method according to any one of claims 1 to 5, characterized in that The step of controlling the charging and discharging of the electric vehicle according to the dynamic charging and discharging information comprises: Sending the dynamic charging and discharging information to the battery management system of the electric vehicle; The battery management system controls the charging and discharging power of the electric vehicle according to the dynamic charging and discharging information.

7. A charge and discharge control device for electric vehicles based on norm, characterized in that: The device comprises: A data acquisition module, used to collect basic parameters of the battery of the electric vehicle and object behavior data of the electric vehicle; A parameter determination module, used for determining target parameters of the electric vehicle according to the basic parameters of the battery and the object behavior data; A model building module, used for building a multi-objective processing model including L1 norm constraints according to the objective parameters; An information determination module, used to determine the initial charge and discharge information of the electric vehicle according to the multi-objective processing model; An information updating module, used for updating the initial charging and discharging information according to the real-time feedback signal of the power grid and the real-time status of the battery, so as to obtain dynamic charging and discharging information of the electric vehicle; The vehicle control module is used to control the charging and discharging of the electric vehicle according to the dynamic charging and discharging information.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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

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