Electric vehicle energy storage charging and discharging optimization scheduling method based on V2G feasible region

Abstract

The invention discloses an electric vehicle energy storage charging and discharging optimization scheduling method based on a V2G feasible region, particularly relates to the technical field of electric vehicle energy management, and is used for solving the problem of scheduling failure caused by instability of energy flow interaction when regenerative braking energy recovery and V2G discharging of an existing electric vehicle are carried out at the same time. Determining whether a feasible region initial condition is satisfied based on a multi-source prediction algorithm by acquiring a regenerative braking signal and residual electric quantity data; when the conditions are met, axle brake distribution characteristics are analyzed through dynamic modeling and a stochastic differential equation, and a real-time characteristic curve of vehicle discharge and power grid adaptation is identified based on power grid load data; determining an energy flow identification result of regenerative braking and V2G discharge according to the characteristic curve and the dynamic change characteristics; and when the identification result is in a preset coupling interval, calculating a feasible region constraint condition, scheduling the output power of the driving system and the bidirectional inverter, and dynamically distributing the regenerative braking recovery power and the power grid discharge power.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle energy management, and more specifically, to an electric vehicle energy storage charging and discharging optimization scheduling method based on a V2G feasible domain. Background Art

[0002] V2G technology is an important energy management method. Through V2G technology, electric vehicles can achieve two-way energy flow between charging and discharging, which not only supports the peak-to-valley shifting of the power grid, but also improves the flexibility of energy utilization of electric vehicles. In addition, regenerative braking energy recovery, as an important function of electric vehicles, can convert the mechanical energy during vehicle braking into electrical energy and store it in the battery, thereby improving energy utilization. However, the parallel operation of V2G technology and regenerative braking energy recovery needs to be carried out under complex dynamic conditions, involving the coordinated management of multiple factors such as vehicle status, battery health, and power grid load.

[0003] In the prior art, when electric vehicles are performing regenerative braking energy recovery and V2G discharge simultaneously, scheduling failures are easily caused by the instability of energy flow interaction, making it difficult to effectively ensure the stability and efficiency of the charging and discharging process. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an electric vehicle energy storage charging and discharging optimization scheduling method based on the V2G feasible domain to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible domain includes the following steps:

[0007] Obtain the real-time braking signal of the regenerative braking system and the remaining power data of the battery management system, and analyze whether the initial determination conditions of the feasible domain are met based on the multi-source prediction algorithm;

[0008] When the initial determination conditions of the feasible domain are met, the axle braking distribution characteristics are analyzed through vehicle dynamics modeling and stochastic differential equations to evaluate the dynamic change characteristics of energy distribution during vehicle braking; the dynamic characteristics of the power grid are analyzed through power grid load data modeling to identify the real-time characteristic curve of vehicle discharge and power grid adaptation;

[0009] Based on the dynamic change characteristics of energy distribution during vehicle braking and the real-time characteristic curve of vehicle discharge and grid adaptation, the energy flow identification result when the vehicle is in parallel with regenerative braking and grid discharge is determined;

[0010] When the energy flow identification result is in the preset coupling interval, the feasible domain constraint conditions that meet the vehicle driving safety and grid power demand are determined;

[0011] Based on the constraints of the feasible domain and the dynamic change characteristics of energy distribution during vehicle braking, the output power of the on-board drive system and the bidirectional inverter is dispatched, and the recovery power generated by regenerative braking and the grid discharge power are dynamically allocated.

[0012] In a preferred embodiment, the real-time braking signal of the regenerative braking system and the remaining power data of the battery management system are obtained, and whether the initial determination conditions of the feasible domain are met is analyzed based on a multi-source prediction algorithm, specifically including:

[0013] Acquiring real-time braking signals of the regenerative braking system, including collecting real-time data of brake pressure and wheel speed from brake pressure sensors and wheel speed sensors;

[0014] Obtaining the remaining power data of the battery management system, including obtaining the remaining power information and battery health status information of the vehicle battery through the battery management system;

[0015] A multi-source prediction algorithm is used to integrate and analyze the real-time braking signal, remaining power information and battery health status information to determine whether the initial conditions of the feasible domain for interaction between the vehicle and the power grid are met.

[0016] In a preferred embodiment, when the initial determination conditions of the feasible region are met, the wheel axle braking distribution characteristics are analyzed by vehicle dynamics modeling and stochastic differential equations to evaluate the dynamic change characteristics of energy distribution during vehicle braking, specifically including:

[0017] Based on the real-time driving status information of the vehicle, wheel speed, brake pressure and vehicle posture data are collected. The vehicle posture data includes the pitch angle and roll angle of the vehicle;

[0018] The wheel speed, brake pressure and vehicle posture data are calculated through the vehicle dynamics model to obtain the preliminary distribution of wheel axle braking energy;

[0019] Combined with the stochastic differential equation model, the preliminary distribution of wheel axle braking energy is dynamically optimized to eliminate the interference of road friction coefficient changes and braking unevenness on energy distribution;

[0020] The dynamic coefficient of brake energy distribution is calculated to quantify the dynamic change characteristics of energy distribution between axles during vehicle braking.

[0021] In a preferred embodiment, the dynamic coefficient of braking energy distribution is calculated to quantify the dynamic change characteristics of energy distribution between wheel axles during vehicle braking, specifically:

[0022] Calculate the dynamic coefficient of brake energy distribution:

[0023] Among them, C d Indicates the dynamic coefficient of brake energy distribution, F front Indicates the front wheel braking force, F rear Represents the rear wheel braking force, w f and w b are the weight factors of the front and rear wheels respectively, and ω f and ω b Both are greater than 0, and Δμ represents the difference in friction coefficient between the front and rear wheels.

[0024] In a preferred embodiment, when the initial determination conditions of the feasible domain are met, the dynamic characteristics of the power grid are analyzed by power grid load data modeling to identify the real-time characteristic curve of vehicle discharge and power grid adaptation, specifically including:

[0025] Collect grid load data, including local grid voltage, frequency change rate and load fluctuation characteristics;

[0026] Preprocess the collected grid load data, including normalization, noise filtering and data alignment, to eliminate the interference of different sampling frequencies and noise on the analysis results;

[0027] A dynamic change model of power grid load is constructed based on the time series model. The time series model uses the historical data and real-time data of the power grid load to obtain the dynamic characteristics of the power grid load through fitting;

[0028] The real-time adaptability of the grid load to the vehicle discharge is analyzed using the grid load dynamic change model and vehicle discharge parameters, and the real-time characteristic curve of vehicle discharge and grid adaptation is output.

[0029] In a preferred embodiment, a dynamic change model of power grid load is constructed based on a time series model. The time series model uses historical data and real-time data of power grid load to obtain the dynamic characteristics of power grid load by fitting, specifically:

[0030] The autoregressive integrated moving average model is used to fit the power grid load data, and the formula is:

[0031] Y t =φ1Y t-1 +φ2Y t-2 +…+φ p Y t-p +∈ t +θ1∈ t-1 +…+θ q ∈ t-q ;

[0032] Among them, Y trepresents the grid load value at time t, φ1, φ2, …, φ p represents the autoregressive coefficient, ∈ t represents the error term at time t, θ1, θ2, …, θ q represents the moving average coefficient, p and q represent the autoregressive order and the moving average order respectively;

[0033] The time series model uses historical data to predict the dynamic changes of load and provide the dynamic characteristics of power grid load.

[0034] In a preferred embodiment, the real-time adaptability of the grid load to the vehicle discharge is analyzed by using the grid load dynamic change model and the vehicle discharge parameters, and a real-time characteristic curve of vehicle discharge and grid adaptation is output, specifically:

[0035] Conduct adaptability analysis on the dynamic change data of grid load generated by time series model and vehicle discharge parameters;

[0036] Analyze the real-time adaptability of the grid load to vehicle discharge and calculate the adaptability index: Among them, S represents the adaptability index, P i It represents the difference between the vehicle discharge power and the grid load power at the i-th sampling point, W i represents the load priority weight of the i-th sampling point, and n represents the total number of load data sampling points in the sampling period;

[0037] The adaptability index generated by the adaptability analysis generates a real-time characteristic curve of vehicle discharge and grid adaptation.

[0038] In a preferred embodiment, based on the dynamic change characteristics of energy distribution during vehicle braking and the real-time characteristic curve of vehicle discharge and grid adaptation, the energy flow identification result when the vehicle is in parallel with regenerative braking and grid discharge is determined, specifically including:

[0039] The dynamic coefficient of brake energy distribution and the adaptability index are input into the energy flow identification model. The energy flow identification model is based on the threshold judgment rule and is calculated according to the following rules:

[0040] When the dynamic coefficient of braking energy distribution is greater than the corresponding preset threshold and the adaptability index is greater than the corresponding preset threshold, the energy flow identification result is a coordinated state;

[0041] When the dynamic coefficient of braking energy distribution is greater than the corresponding preset threshold but the adaptability index is less than or equal to the corresponding preset threshold, the energy flow identification result is regenerative braking priority;

[0042] When the dynamic coefficient of braking energy distribution is less than or equal to the corresponding preset threshold and the adaptability index is greater than the corresponding preset threshold, the energy flow identification result is grid discharge priority;

[0043] When the dynamic coefficient of braking energy distribution and the adaptability index are both less than or equal to the corresponding preset thresholds, the energy flow identification result is a non-cooperative state;

[0044] The energy flow identification result is output according to the calculation result of the energy flow identification model, and the energy flow identification result is used to characterize the coordinated state of the regenerative braking energy and the grid discharge power under the current working condition.

[0045] In a preferred embodiment, when the energy flow identification result is in a preset coupling interval, the feasible domain constraint conditions that meet the vehicle driving safety and the power demand of the power grid are determined, specifically including:

[0046] Determine whether the energy flow identification result is within a preset coupling interval, where the preset coupling interval is used to characterize the coordinated state range of the regenerative braking energy and the grid discharge power;

[0047] When the energy flow identification result is in the preset coupling interval, the vehicle driving state parameters are obtained, including vehicle speed, vehicle attitude angle and wheel slip rate, which are used to describe the dynamic safety state of the vehicle driving;

[0048] Obtaining grid power status parameters, including real-time grid voltage, frequency change rate, and load fluctuation characteristics, to describe the dynamic characteristics of grid power demand;

[0049] Based on the vehicle driving state parameters and the power state parameters of the power grid, the feasible domain constraints are calculated through a multi-objective optimization algorithm. The multi-objective optimization algorithm uses the vehicle driving safety threshold and the power demand threshold of the power grid as constraints to dynamically adjust the energy flow allocation ratio.

[0050] Output feasible domain constraints to limit the distribution range of vehicle regenerative braking energy recovery and grid discharge power.

[0051] In a preferred embodiment, based on the constraints of the feasible domain and the dynamic change characteristics of energy distribution during vehicle braking, the output power of the vehicle drive system and the bidirectional inverter is scheduled, and the recovery power generated by regenerative braking and the grid discharge power are dynamically allocated, specifically including:

[0052] According to the constraints of the feasible region and the dynamic coefficient of braking energy distribution, the maximum allocation ratio of the recovery power generated by regenerative braking and the maximum allocation ratio of the grid discharge power are calculated;

[0053] Based on the calculation results, the regenerative braking recovery power provided by the vehicle drive system and the grid discharge power output by the bidirectional inverter are respectively determined;

[0054] The calculation results of regenerative braking recovery power and grid discharge power are transmitted to the vehicle drive system and bidirectional inverter as dynamic allocation instructions to adjust their actual output power and ensure that power is dynamically allocated in proportion.

[0055] The technical effects and advantages of the electric vehicle energy storage charging and discharging optimization scheduling method based on the V2G feasible domain of the present invention are as follows:

[0056] 1. By introducing the initial judgment conditions of the feasible domain, the coordinated scheduling process of electric vehicle regenerative braking energy recovery and V2G discharge operation is limited to a reasonable range that meets the vehicle driving safety and grid power requirements, thereby effectively avoiding the scheduling failure caused by energy flow interference. The dynamic change characteristics of energy distribution during vehicle braking are analyzed by vehicle dynamics modeling and stochastic differential equations. At the same time, the real-time characteristic curves of vehicle discharge and grid adaptation are identified based on grid load data modeling, realizing the accurate identification and dynamic management of the coordinated state of energy flow. This method can adapt to complex dynamic working conditions and improve the stability and efficiency of charging and discharging scheduling.

[0057] 2. Through the power allocation optimization algorithm based on the constraints of the feasible domain, the output power of the vehicle drive system and the bidirectional inverter is dynamically scheduled, so that the recovery power generated by regenerative braking and the power of the grid discharge are reasonably allocated in proportion to meet the actual needs of the vehicle and the grid. The dynamic allocation strategy can flexibly adjust the energy allocation scheme of regenerative braking and grid discharge according to the energy flow identification results and real-time working conditions, thereby further improving the utilization efficiency of electric energy while ensuring the safety of vehicle driving, reducing the risk of grid load fluctuations, and providing technical support for the efficient coordination of electric vehicles and grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the electric vehicle energy storage charging and discharging optimization scheduling method based on the V2G feasible domain of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Example: Figure 1 The present invention provides an electric vehicle energy storage charging and discharging optimization scheduling method based on the V2G feasible domain, which includes the following steps:

[0061] The real-time braking signal of the regenerative braking system and the remaining power data of the battery management system are obtained, and whether the initial determination conditions of the feasible domain are met is analyzed based on the multi-source prediction algorithm.

[0062] When the initial judgment conditions of the feasible domain are met, the wheel axle braking distribution characteristics are analyzed through vehicle dynamics modeling and stochastic differential equations to evaluate the dynamic change characteristics of energy distribution during vehicle braking; the dynamic characteristics of the power grid are analyzed through power grid load data modeling to identify the real-time characteristic curve of vehicle discharge and power grid adaptation.

[0063] Based on the dynamic change characteristics of energy distribution during vehicle braking and the real-time characteristic curve of vehicle discharge and grid adaptation, the energy flow identification result when the vehicle is in parallel with regenerative braking and grid discharge is determined.

[0064] When the energy flow identification result is in the preset coupling interval, the feasible domain constraints that meet the vehicle driving safety and grid power requirements are determined.

[0065] Based on the constraints of the feasible domain and the dynamic change characteristics of energy distribution during vehicle braking, the output power of the on-board drive system and the bidirectional inverter is dispatched, and the recovery power generated by regenerative braking and the grid discharge power are dynamically allocated.

[0066] Obtain the real-time braking signal of the regenerative braking system and the remaining power data of the battery management system, and analyze whether the initial determination conditions of the feasible domain are met based on the multi-source prediction algorithm, including:

[0067] Get the real-time braking signal of the regenerative braking system, including collecting real-time data of brake pressure and wheel speed from the brake pressure sensor and wheel speed sensor:

[0068] Real-time data of brake pressure and wheel speed are collected from the vehicle's regenerative braking system. The brake pressure sensor installed in the vehicle's braking system is used to monitor the pressure changes in the braking system in real time, and the collected pressure signal is converted into an electrical signal. The wheel speed sensor installed at the wheel is used to obtain the vehicle's wheel speed data in real time, and the speed data is saved in the form of a digital signal.

[0069] Real-time data of brake pressure and wheel speed are key parameters for judging the vehicle's current braking status and energy recovery.

[0070] Obtain the remaining power data of the battery management system, including obtaining the remaining power information of the vehicle battery and the battery health status information through the battery management system:

[0071] The battery management system monitors the battery's operating status in real time through the voltage, current and temperature sensors inside the battery and calculates the battery's remaining power value. The battery management system calculates and evaluates the battery's health status information, including the battery's remaining service life parameters and battery capacity decay rate, as the basis for battery status evaluation.

[0072] A multi-source prediction algorithm is used to integrate and analyze real-time braking signals, remaining power information, and battery health status information to determine whether the initial conditions of the feasible domain for interaction between the vehicle and the power grid are met:

[0073] The collected brake pressure data, wheel speed data, and the remaining power information and battery health status information output by the battery management system are normalized to eliminate the differences between different data types and units.

[0074] Feature extraction based on multi-source prediction algorithm extracts key feature parameters from normalized data, such as brake pressure peak, wheel speed change rate, remaining power percentage and battery health status level, to form a comprehensive feature vector.

[0075] Using the decision model of the multi-source prediction algorithm, the feature vector is input into the judgment module, and the initial judgment result of the feasible domain of interaction between the vehicle and the power grid is obtained by calculation, including whether the necessary conditions for energy flow interaction are met.

[0076] The necessary conditions for energy flow interaction refer to the basic requirements that electric vehicles must meet when interacting with the power grid (such as regenerative braking energy recovery and discharging to the grid), including vehicle status requirements, grid status requirements, and communication and control requirements.

[0077] Vehicle status requirements include:

[0078] Regenerative braking capability: The vehicle should have effective regenerative braking function, which can convert mechanical energy into electrical energy and recover it during braking;

[0079] Battery remaining capacity: The remaining capacity of the battery should be higher than the set minimum threshold to ensure that the energy demand for normal vehicle driving can be met after discharging to the grid;

[0080] Battery health status: The battery should be in good health, with sufficient capacity and performance to support frequent charge and discharge operations.

[0081] Grid status requirements include:

[0082] Grid demand: The grid should have a current demand to accept vehicle discharge, such as requiring additional power support during peak hours;

[0083] Grid parameters: Grid parameters such as voltage and frequency should be within the permitted range to ensure the safety and stability of energy interaction.

[0084] Communications and control requirements include:

[0085] Information exchange: There should be a reliable communication channel between the vehicle and the power grid to exchange status information and control instructions in real time;

[0086] Control strategy: There should be a clear control strategy to ensure coordination and optimization during the energy interaction process.

[0087] Among them, the multi-source prediction algorithm refers to an algorithm that integrates data from multiple different sources to predict vehicle energy demand and allocation. These data sources may include vehicle speed, acceleration, road slope, traffic conditions, driver behavior, battery status, etc. By integrating and analyzing these multi-dimensional data, the algorithm can more accurately predict the vehicle's energy needs, thereby optimizing energy management strategies and improving the vehicle's energy efficiency and battery life.

[0088] The steps to implement the multi-source prediction algorithm are as follows:

[0089] Data collection: Collect relevant data from various sensors and systems of the vehicle, such as brake pressure, wheel speed, remaining battery power and health status.

[0090] Data preprocessing: Clean, normalize and extract features of the collected data to eliminate differences between different data types and units and ensure data quality and consistency.

[0091] Feature fusion: Fuse the preprocessed multi-source data to form a comprehensive feature vector. This can be achieved through methods such as splicing, weighted averaging, or deep learning models.

[0092] Model training: Using historical data, a machine learning or deep learning algorithm (such as support vector machine, random forest, neural network, etc.) is used to train the prediction model so that it can learn the relationship between vehicle energy demand and multi-source characteristics.

[0093] Real-time prediction: In practical applications, multi-source data collected in real time is input into the trained model to predict the vehicle's energy demand or allocation strategy.

[0094] Decision-making and control (i.e., decision-making model): Based on the prediction results, the vehicle's energy management strategy is adjusted, such as the allocation of regenerative braking energy recovery and grid discharge power, to optimize vehicle performance and energy utilization efficiency.

[0095] Based on the results of the multi-source prediction algorithm, it is determined whether the vehicle currently meets the basic requirements for interacting with the power grid. The determination basis includes:

[0096] Real-time braking status: whether there is sufficient regenerative braking energy recovery capability; battery status: whether there is sufficient remaining power and health status to support the grid discharge needs.

[0097] If the above conditions are met (sufficient regenerative braking energy recovery capability and sufficient remaining power and health status to support the grid discharge demand), it is marked as feasible, that is, the initial determination conditions of the feasible domain are met. Otherwise, it enters the standby state or re-determines after adjusting the strategy.

[0098] Among them, the basic requirements refer to the minimum conditions that need to be met when the vehicle interacts with the power grid, including:

[0099] Regenerative braking capability: The vehicle should be able to effectively recover braking energy and convert it into electrical energy;

[0100] Battery status: The battery should have sufficient remaining charge and health status to support the need to discharge to the grid.

[0101] Sufficient remaining power means that the remaining power of the battery should be higher than the set minimum threshold to ensure that the vehicle's own energy needs can still be met after discharging to the grid. This threshold can be determined based on factors such as vehicle type, usage and battery capacity.

[0102] Sufficient regenerative braking energy recovery capability means that the vehicle's regenerative braking system can recover sufficient energy during braking and effectively store it in the battery. This capability can be evaluated through the design parameters and actual performance indicators of the regenerative braking system.

[0103] When the initial determination conditions of the feasible region are met, the axle braking distribution characteristics are analyzed through vehicle dynamics modeling and stochastic differential equations to evaluate the dynamic change characteristics of energy distribution during vehicle braking, including:

[0104] Based on the real-time driving status information of the vehicle, the wheel speed, brake pressure and vehicle posture data are collected. The vehicle posture data includes the pitch angle and roll angle of the vehicle.

[0105] The wheel speed is collected in real time by a speed sensor installed on the wheel axle, and the speed data reflects the movement state of the vehicle during braking.

[0106] The brake pressure is collected by the brake pressure sensor installed in the hydraulic pipeline of the brake system. The brake pressure data is used to describe the braking force applied during the vehicle braking process.

[0107] The vehicle attitude data includes the pitch angle and roll angle of the vehicle, which are obtained through the attitude sensor installed at the center of the vehicle chassis. The pitch angle reflects the longitudinal tilt state of the vehicle, and the roll angle reflects the lateral tilt state of the vehicle.

[0108] The wheel speed, brake pressure and vehicle posture data are calculated through the vehicle dynamics model to obtain the preliminary distribution of wheel axle braking energy:

[0109] The vehicle dynamics model is used to describe the mechanical behavior of the vehicle during braking, involving the relationship between wheel speed, brake pressure and vehicle posture data. In the dynamics model, parameters such as the vehicle's center of gravity, wheelbase and wheel friction characteristics are introduced.

[0110] According to the vehicle dynamics model, the braking force distribution acting on the front and rear wheels during vehicle braking is calculated. The specific calculation formula is as follows: Among them, F front Indicates the front wheel braking force; F rear Indicates the rear wheel braking force; P b Indicates brake pressure, collected by brake pressure sensor; A f and A r are the front wheel braking area and the rear wheel braking area respectively; μ f and μ r are the friction coefficients of the front and rear wheels respectively; θ pitch represents the pitch angle of the vehicle, which is collected by the attitude sensor; L represents the wheelbase of the vehicle, which is the geometric parameter of the vehicle.

[0111] Through the above calculations, the preliminary distribution of the wheel axle braking force is obtained, and this distribution data will be used in the subsequent optimization analysis steps.

[0112] Combined with the stochastic differential equation model, the preliminary distribution of wheel axle braking energy is dynamically optimized and analyzed to eliminate the interference of road friction coefficient changes and braking unevenness on energy distribution:

[0113] In order to consider the interference of road friction coefficient change and braking unevenness on energy distribution, a stochastic differential equation is introduced to dynamically optimize the wheel axle braking energy distribution. The stochastic differential equation is as follows: dF = α·F·dt+β·σ·dW; where dF represents the dynamic change of braking force, which is used to describe the dynamic change characteristics of braking force over time; α represents the time constant that characterizes the attenuation of braking force, and its value is determined by the response characteristics of the braking system, and its unit is the reciprocal of seconds; F represents the braking force value at the current moment, which comes from the calculation result of the dynamic model; dt represents the time increment, which is used to simulate the continuous change process of braking force; β represents the sensitivity factor of road friction coefficient change, which is used to measure the sensitivity of braking force to the change of road friction coefficient. The higher the value, the more significant the response of braking force to the change of friction coefficient; σ represents the random disturbance intensity, which is used to describe the influence of external random factors (such as uneven road surface or sensor noise) on the change of braking force. The larger the value, the more significant the effect of random disturbance on the system; dW represents the Brownian motion term, which describes random disturbance and obeys the standard normal distribution, which is used to simulate the unpredictable random factors in the process of braking force change.

[0114] The axle braking force distribution value obtained by preliminary calculation is substituted into the stochastic differential equation. The braking force is iteratively optimized in combination with the actual road friction coefficient (collected by road surface sensors) and the random disturbance model to eliminate the influence of dynamic interference on the braking force distribution.

[0115] The dynamic coefficient of brake energy distribution is calculated to quantify the dynamic characteristics of energy distribution between axles during vehicle braking:

[0116] Finally, the dynamic coefficient of braking energy distribution is calculated to quantify the dynamic change characteristics of energy distribution between axles. The calculation formula is: Among them, C d Indicates the dynamic coefficient of braking energy distribution; w f and w b are the weight factors of the front and rear wheels, respectively, reflecting the influence of the vehicle's center of gravity on the front and rear wheel braking force distribution, and w f and w b Both are greater than 0; Δμ represents the difference in friction coefficient between the front and rear wheels, which is used to correct the impact of uneven road surface on energy distribution.

[0117] For front-wheel drive vehicles, the weight of the front wheels can be increased (for example, by increasing the front-wheel priority allocation ratio). For rear-wheel drive vehicles, the weight of the rear wheels can be appropriately increased to make the model more in line with actual working conditions.

[0118] The dynamic coefficient of brake energy distribution reflects the proportional relationship between the front and rear wheel brake energy distribution. This coefficient is used as a quantitative indicator of the dynamic characteristics of the vehicle's brake energy distribution. The larger the dynamic coefficient of brake energy distribution, the more the brake force distribution between the front and rear wheels is inclined to the front wheels during the vehicle's braking process, while the participation of the rear wheels is relatively low. In front-wheel drive vehicles, this is usually a normal phenomenon that conforms to the design characteristics of the vehicle; in rear-wheel drive or four-wheel drive vehicles, an excessively large dynamic coefficient of brake energy distribution may indicate uneven brake force distribution, which needs to be evaluated in combination with the specific drive form and working conditions.

[0119] When the initial determination conditions of the feasible domain are met, the dynamic characteristics of the power grid are analyzed through power grid load data modeling to identify the real-time characteristic curve of vehicle discharge and power grid adaptation, including:

[0120] Collect grid load data, including local grid voltage, frequency change rate and load fluctuation characteristics:

[0121] The voltage data of the local area of ​​the power grid is collected by the monitoring equipment installed on the power grid side. The collection equipment records the voltage value of each sampling cycle. The local voltage data of the power grid is used to reflect the impact of load changes on the stability of the power supply of the power grid.

[0122] Real-time detection of grid frequency fluctuations, calculation of frequency change rate as a key parameter of load dynamic characteristics. Frequency change rate reflects the degree of interference of load fluctuations on grid frequency stability and is an important data for dynamic analysis.

[0123] The load monitoring device collects load fluctuation characteristic data, including real-time load power changes, load on and off status, etc. This data is used to describe the dynamic behavior of the grid load and directly affects the compatibility of the vehicle and the grid.

[0124] The collected grid load data is preprocessed, including normalization, noise filtering and data alignment, to eliminate the interference of different sampling frequencies and noise on the analysis results:

[0125] The collected grid load data has different dimensions due to different data sources, so it needs to be converted to the same magnitude through normalization. Normalization ensures that the local voltage, frequency change rate and load fluctuation characteristics of the grid have the same weight, which is convenient for subsequent analysis.

[0126] The grid load data is filtered for noise, and the data is converted from the time domain to the frequency domain through Fourier transform to remove high-frequency noise. Noise filtering can eliminate the impact of sensor errors and environmental interference on the data and improve the accuracy of the analysis results.

[0127] The normalized voltage data, frequency change rate, and load fluctuation characteristic data are aligned on a unified time axis to ensure that the timestamps of the data are consistent, which facilitates subsequent modeling and analysis.

[0128] A dynamic change model of power grid load is constructed based on the time series model. The time series model uses the historical data and real-time data of the power grid load and obtains the dynamic characteristics of the power grid load through fitting:

[0129] The power grid load dynamic change model combines historical data and real-time data. Historical data is obtained through long-term monitoring records of the power grid, and real-time data comes from currently collected data.

[0130] The autoregressive integrated moving average model (ARI MA model) is used to fit the power grid load data, and the formula is:

[0131] Y t =φ1Y t-1 +φ2Y t-2 +…+φ p Y t-p +∈ t +θ1∈ t-1 +…+θ q ∈ t-q ;

[0132] Among them, Y t represents the grid load value at time t; φ1, φ2, …, φ p represents the autoregressive coefficient; ∈ t represents the error term at time t; θ1, θ2, …, θ q represents the moving average coefficient; p and q represent the autoregressive order and the moving average order, respectively.

[0133] The time series model uses historical data to predict the dynamic changes of load and provide the dynamic characteristics of power grid load for subsequent analysis.

[0134] The real-time adaptability of the grid load to the vehicle discharge is analyzed by using the grid load dynamic change model and vehicle discharge parameters, and the real-time characteristic curve of vehicle discharge and grid adaptation is output:

[0135] The dynamic change data of the grid load generated by the time series model is used to perform adaptability analysis with the vehicle discharge parameters (including the vehicle's output power, discharge voltage and load demand).

[0136] Analyze the real-time adaptability of the grid load to vehicle discharge and calculate the adaptability index: Among them, S represents the adaptability index, which is used to quantify the matching degree between the vehicle discharge power and the grid load demand. The larger the value, the more suitable the vehicle discharge power is for the current grid load demand, and vice versa, the poorer the adaptability is; P i represents the difference between the vehicle discharge power and the grid load power at the i-th sampling point; W iIt represents the load priority weight of the i-th sampling point, reflecting the importance of different loads to the grid stability or vehicle discharge demand; n represents the total number of load data sampling points in the sampling period.

[0137] The results of the adaptability analysis generate a real-time characteristic curve of vehicle discharge and grid adaptation. The curve describes the degree of adaptation of the vehicle discharge power under different grid load conditions for subsequent optimization and scheduling.

[0138] Based on the dynamic change characteristics of energy distribution during vehicle braking and the real-time characteristic curve of vehicle discharge and grid adaptation, the energy flow identification results when the vehicle is in parallel with regenerative braking and grid discharge are determined, including:

[0139] The dynamic coefficient of brake energy distribution and the adaptability index are input into the energy flow identification model. The energy flow identification model is based on the threshold judgment rule and is calculated according to the following rules:

[0140] The dynamic coefficient of brake energy distribution and the adaptability index are input into the energy flow identification model. The dynamic coefficient of brake energy distribution and the adaptability index are calculated and normalized through the above steps to ensure that the parameters are coordinated and calculated within a uniform magnitude range.

[0141] When the dynamic coefficient of braking energy distribution is greater than the corresponding preset threshold and the adaptability index is greater than the corresponding preset threshold, the energy flow identification result is "cooperative state"; priority matching energy flow parallel operation: At this time, the front wheel braking force of the vehicle is dominant, and the vehicle discharge power is highly adapted to the grid demand, indicating that both regenerative braking and discharge operations have good conditions, so priority matching energy flow parallel operation. Priority matching parallel operation makes full use of regenerative braking energy and supports the grid.

[0142] When the dynamic coefficient of braking energy distribution is greater than the corresponding preset threshold but the adaptability index is less than or equal to the corresponding preset threshold, the energy flow identification result is "regenerative braking priority"; regenerative braking energy recovery is prioritized: regenerative braking energy is biased towards the front wheel, but the grid discharge power is poorly matched with the load demand. At this time, priority should be given to regenerative braking energy recovery to reduce the waste of grid discharge resources. Inefficient matching of energy flow is avoided, which is in line with the technical application scenario.

[0143] When the dynamic coefficient of braking energy distribution is less than or equal to the corresponding preset threshold and the adaptability index is greater than the corresponding preset threshold, the energy flow identification result is "grid discharge priority"; priority is given to matching grid discharge operation: the front and rear wheel braking force distribution of the vehicle is balanced, the regenerative braking energy distribution is relatively stable, and the vehicle discharge power is highly matched with the grid demand, and the grid discharge operation is prioritized at this time. This ensures that the load demand of the grid is met first, in line with the goal of stable grid operation.

[0144] When the dynamic coefficient of braking energy distribution and the adaptability index are both less than or equal to the corresponding preset thresholds, the energy flow identification result is "non-cooperative state"; reduce the priority of energy flow parallel operation: the vehicle regenerative braking energy distribution is balanced, but the discharge matching is insufficient, indicating that the energy flow synergy conditions are not met under the current working conditions. At this time, the priority of energy flow parallel operation should be reduced. Avoid forcibly performing energy flow operations in a non-cooperative state to reduce invalid energy distribution.

[0145] Among them, when the dynamic coefficient of brake energy distribution is greater than its corresponding preset threshold, it means that the energy distribution under the current working condition of the vehicle tends to be dominated by the front wheel braking force. At this time, the regenerative braking energy recovery may be preferentially distributed to the front wheels, resulting in insufficient energy recovery capacity of the rear wheels. When the dynamic coefficient of brake energy distribution is less than or equal to the preset threshold, it means that the front and rear wheel braking forces are more evenly distributed under the current working condition of the vehicle, and the regenerative braking energy recovery effect is more stable.

[0146] When the adaptability index is greater than the corresponding preset threshold, it means that the vehicle discharge power can better meet the grid load demand. At this time, vehicle discharge is prioritized to support grid stability. When the adaptability index is less than or equal to the preset threshold, it means that the vehicle discharge power is less compatible with the grid load demand. At this time, the discharge priority is reduced and the power allocation strategy may need to be adjusted.

[0147] The energy flow identification result is output according to the calculation result of the energy flow identification model. The energy flow identification result is used to characterize the coordinated state of the regenerative braking energy and the grid discharge power under the current working condition:

[0148] According to the judgment rules of the energy flow identification model, the energy flow identification results are output. The identification results include: the priority state of the current regenerative braking energy distribution; the adaptability state of the grid discharge power; and the coordination priority of regenerative braking and grid discharge.

[0149] Judgment rules of the energy flow identification model: Based on the output results of the energy flow identification model, a series of thresholds and conditions are set to determine whether the current energy flow state meets the preset working mode or safety requirements. For example, when the regenerative braking power of the vehicle exceeds a certain set value and the grid load demand reaches a certain level, the system determines that it is suitable for V2G discharge operation.

[0150] Energy flow identification results: Based on the above models and judgment rules, the output results are used to indicate the current energy flow status, such as "regenerative braking energy recovery only", "V2G discharge only", "regenerative braking and V2G discharge in parallel" or "no energy flow", etc. This result will serve as the basis for subsequent energy management and scheduling strategies.

[0151] Among them, the energy flow identification model is used to analyze and determine the state and characteristics of energy flow in the process of electric vehicle regenerative braking energy recovery and discharge to the grid (V2G). Through the real-time collection of vehicle operation data (such as vehicle speed, acceleration, braking signal, etc.) and grid status data (such as voltage, frequency, load, etc.), the energy flow pattern is identified and classified using data-driven algorithms (such as deep learning models), thereby determining the type and characteristics of the current energy flow.

[0152] The output recognition results are used to characterize the coordinated state of the vehicle's regenerative braking energy and the grid discharge power under the current operating conditions, such as: the proportional distribution of energy recovery and discharge; the dynamic adjustment of the regenerative braking energy priority; and whether the current power scheduling strategy needs to be changed.

[0153] It is worth noting that the preset threshold of the dynamic coefficient of brake energy distribution is used to determine whether the front and rear wheel brake force distribution is reasonable. This threshold is set according to the vehicle type (front drive, rear drive, four-wheel drive) and driving conditions (such as road friction coefficient, braking demand), and is usually determined through vehicle dynamics tests. The preset threshold of the adaptability index is used to quantify the adaptability of the vehicle discharge power to the grid load demand. This threshold is set according to the dynamic load characteristics of the grid, voltage stability and the capacity range of the vehicle output power, and is adjusted through real-time grid load fluctuations and vehicle discharge capacity analysis.

[0154] When the energy flow identification result is in the preset coupling interval, the feasible domain constraints that meet the vehicle driving safety and grid power requirements are determined, including:

[0155] Determine whether the energy flow identification result is in the preset coupling interval. The preset coupling interval is used to characterize the coordinated state range of the regenerative braking energy and the grid discharge power:

[0156] The preset coupling interval represents an effective range for the coordinated operation of the regenerative braking energy and the grid discharge power.

[0157] The specific definitions are as follows:

[0158] Collaborative interval: the corresponding energy flow identification result is "collaborative state"; non-collaborative interval: the corresponding energy flow identification result is "regenerative braking priority", "grid discharge priority" or "non-collaborative state".

[0159] It is determined whether the energy flow identification result is in the cooperative interval. If yes, the energy flow identification result is in the preset coupling interval.

[0160] When the energy flow identification result is in the preset coupling interval, the vehicle driving state parameters are obtained, including vehicle speed, vehicle attitude angle and wheel slip rate, which are used to describe the dynamic safety state of the vehicle:

[0161] Vehicle speed: obtained through the vehicle speed sensor and used to represent the real-time running speed of the vehicle.

[0162] Vehicle attitude angle: obtained through the vehicle-mounted inertial measurement unit, including pitch angle and roll angle, which are used to describe the dynamic attitude of the vehicle.

[0163] Wheel slip rate: calculated by vehicle speed and wheel speed, the formula is as follows: Among them, S r is the wheel slip rate, V t Indicates the vehicle speed, V w Indicates the wheel speed.

[0164] The above parameters are collected by vehicle sensors and stored in the vehicle controller as input for subsequent calculation of feasible domain constraints.

[0165] Obtain the power state parameters of the power grid, including the real-time voltage, frequency change rate and load fluctuation characteristics of the power grid side, which are used to describe the dynamic characteristics of the power demand of the power grid:

[0166] The real-time voltage is collected by the power grid monitoring equipment; the frequency change rate is calculated by the frequency sensor; the load fluctuation characteristics, including the load change rate and the load on state, are collected by the load monitoring equipment.

[0167] The above parameters are collected by the grid-side monitoring equipment and transmitted to the vehicle controller through the communication module for subsequent optimization calculations.

[0168] Based on the vehicle driving state parameters and the power state parameters of the power grid, the feasible domain constraints are calculated through a multi-objective optimization algorithm. The multi-objective optimization algorithm takes the vehicle driving safety threshold and the power demand threshold of the power grid as constraints and dynamically adjusts the energy flow allocation ratio:

[0169] The multi-objective optimization algorithm takes the following data as input: vehicle driving state parameters, including vehicle speed, vehicle attitude angle and wheel slip rate; grid power state parameters, including real-time voltage, frequency change rate and load fluctuation characteristics.

[0170] The goal of the multi-objective optimization algorithm is to meet the vehicle driving safety threshold and the grid power demand threshold, and dynamically adjust the distribution ratio of regenerative braking energy and grid discharge power; the optimization goal can be expressed as: Among them, W v is the weight of the vehicle driving safety parameter, which indicates the importance of the vehicle driving state in the optimization objective and is used to balance the priority of vehicle driving safety and grid power demand; S r is the wheel slip rate, which indicates the slip rate of the wheel under the current working condition and reflects the wheel grip and the dynamic safety status of the vehicle; S this the vehicle driving safety threshold, which indicates the safety critical value of the vehicle slip rate and is used to determine whether the vehicle dynamic stability meets the safety requirements; W g is the weight of the power parameter of the power grid, which indicates the importance of the power demand of the power grid in the optimization objective and is used to balance the relationship between the load demand of the power grid and the driving safety of the vehicle; P d P is the real-time power demand of the power grid, which indicates the power demand on the current power grid side and is collected in real time by the power grid load monitoring equipment; g The vehicle discharge power indicates the actual discharge power released by the vehicle to the grid, which is regulated and output by the vehicle controller in real time.

[0171] By optimizing the calculation results, the distribution ratio of regenerative braking energy and grid discharge power is dynamically adjusted to ensure that the constraints of driving safety and power demand are met.

[0172] Output feasible region constraints to limit the distribution range of vehicle regenerative braking energy recovery and grid discharge power:

[0173] The feasible domain constraints include the maximum allocation ratio of regenerative braking energy recovery and the maximum allocation ratio of grid discharge power. The optimized feasible domain constraints will be transmitted to the vehicle controller as control instructions.

[0174] Based on the constraints of the feasible domain and the dynamic characteristics of energy distribution during vehicle braking, the output power of the vehicle drive system and the bidirectional inverter is dispatched, and the recovery power generated by regenerative braking and the grid discharge power are dynamically allocated, including:

[0175] According to the constraints of the feasible region and the dynamic coefficient of braking energy distribution, the maximum allocation ratio of the recovery power generated by regenerative braking and the maximum allocation ratio of the grid discharge power are calculated:

[0176] The feasible domain constraints are obtained through the above steps and are used to limit the distribution range of the vehicle in regenerative braking energy recovery and grid discharge operations. The dynamic coefficient of braking energy distribution is used to quantify the dynamic distribution ratio of the front and rear wheel braking force during vehicle braking.

[0177] According to the constraints of the feasible domain and the dynamic coefficient of braking energy distribution, the distribution ratio of the recovery power generated by regenerative braking and the grid discharge power is determined.

[0178] The distribution ratio of the recovery power generated by regenerative braking is the ratio of the value of the feasible domain constraint condition to the sum of the value of the feasible domain constraint condition and the dynamic coefficient of braking energy distribution. The distribution ratio of the grid discharge power is one minus the distribution ratio of the recovery power generated by regenerative braking.

[0179] Through the above calculation method, the maximum allocation ratio of regenerative braking recovery power to grid discharge power is obtained to ensure that the allocation ratio meets the energy flow requirements.

[0180] Based on the calculation results, the regenerative braking recovery power provided by the vehicle drive system and the grid discharge power output by the bidirectional inverter are determined respectively:

[0181] The vehicle's maximum power output represents the maximum output power that the vehicle can provide in its current state, and is determined in real time by the operating conditions of the vehicle's power system.

[0182] The recovery power generated by regenerative braking and the grid discharge power are calculated based on the allocation ratio: the recovery power generated by regenerative braking is the allocation ratio of the recovery power generated by regenerative braking multiplied by the maximum power output that the vehicle can provide; the grid discharge power is the allocation ratio of the grid discharge power multiplied by the maximum power output that the vehicle can provide.

[0183] The calculation results of regenerative braking recovery power and grid discharge power are transmitted to the vehicle drive system and bidirectional inverter as dynamic allocation instructions to adjust their actual output power and ensure that the power is dynamically allocated in proportion:

[0184] According to the calculation results, a dynamic allocation instruction is generated, including: the power setting value that the regenerative braking system needs to recover; the power setting value that the bidirectional inverter needs to output to the power grid.

[0185] The dynamic allocation instructions are transmitted to the regenerative braking system and the bidirectional inverter respectively through the vehicle's internal communication network.

[0186] The regenerative braking system dynamically adjusts the actual recovered power according to the instructions received; the bidirectional inverter dynamically adjusts the power output to the grid according to the instructions, ensuring that the vehicle's regenerative braking recovery power and the grid discharge power are dynamically distributed according to the distribution ratio.

[0187] During the dynamic allocation process, the actual power output of the regenerative braking system and the bidirectional inverter can be monitored in real time. If the deviation between the actual output power and the allocation instruction target value exceeds the allowable range, the allocation instruction is adjusted in real time to ensure that the actual output is consistent with the allocation ratio.

[0188] It is worth noting that the V2G feasible domain can be understood as all possible states or operating ranges of electric vehicles participating in V2G operations (such as charging and discharging) under the conditions of meeting the needs of the electric vehicles themselves and the constraints of the power grid.

[0189] To better understand the V2G feasible domain, in V2G technology, electric vehicles not only act as electricity consumers, but can also feed back stored electricity to the grid under certain conditions to provide auxiliary services such as peak load regulation and frequency regulation. Therefore, determining the feasibility range of electric vehicles participating in V2G operations at different times and states is crucial to optimizing their interaction with the grid. For example, researchers may analyze factors such as the state of charge (SOC) of electric vehicles, travel needs of users, and load conditions of the grid to determine under what conditions electric vehicles can participate in V2G operations without affecting user experience or grid stability. This is actually defining the "feasible domain" for electric vehicles to participate in V2G operations.

[0190] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0191] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0192] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0193] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0194] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0195] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0196] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0197] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0198] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0199] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An electric vehicle energy storage charging and discharging optimization scheduling method based on V2G feasible domain, characterized in that: The steps include: Obtain the real-time braking signal of the regenerative braking system and the remaining power data of the battery management system, and analyze whether the initial determination conditions of the feasible domain are met based on the multi-source prediction algorithm; When the initial determination conditions of the feasible domain are met, the axle braking distribution characteristics are analyzed through vehicle dynamics modeling and stochastic differential equations to evaluate the dynamic change characteristics of energy distribution during vehicle braking; the dynamic characteristics of the power grid are analyzed through power grid load data modeling to identify the real-time characteristic curve of vehicle discharge and power grid adaptation; Based on the dynamic change characteristics of energy distribution during vehicle braking and the real-time characteristic curve of vehicle discharge and grid adaptation, the energy flow identification result when the vehicle is in parallel with regenerative braking and grid discharge is determined; When the energy flow identification result is in the preset coupling interval, the feasible domain constraint conditions that meet the vehicle driving safety and grid power demand are determined; Based on the constraints of the feasible domain and the dynamic change characteristics of energy distribution during vehicle braking, the output power of the on-board drive system and the bidirectional inverter is dispatched, and the recovery power generated by regenerative braking and the grid discharge power are dynamically allocated.

2. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 1 is characterized in that: Obtain the real-time braking signal of the regenerative braking system and the remaining power data of the battery management system, and analyze whether the initial determination conditions of the feasible domain are met based on the multi-source prediction algorithm, including: Acquiring real-time braking signals of the regenerative braking system, including collecting real-time data of brake pressure and wheel speed from brake pressure sensors and wheel speed sensors; Obtaining the remaining power data of the battery management system, including obtaining the remaining power information and battery health status information of the vehicle battery through the battery management system; A multi-source prediction algorithm is used to integrate and analyze the real-time braking signal, remaining power information and battery health status information to determine whether the initial conditions of the feasible domain for interaction between the vehicle and the power grid are met.

3. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 1 is characterized in that: When the initial determination conditions of the feasible region are met, the axle braking distribution characteristics are analyzed through vehicle dynamics modeling and stochastic differential equations to evaluate the dynamic change characteristics of energy distribution during vehicle braking, including: Based on the real-time driving status information of the vehicle, wheel speed, brake pressure and vehicle posture data are collected. The vehicle posture data includes the pitch angle and roll angle of the vehicle; The wheel speed, brake pressure and vehicle posture data are calculated through the vehicle dynamics model to obtain the preliminary distribution of wheel axle braking energy; Combined with the stochastic differential equation model, the preliminary distribution of wheel axle braking energy is dynamically optimized to eliminate the interference of road friction coefficient changes and braking unevenness on energy distribution; The dynamic coefficient of brake energy distribution is calculated to quantify the dynamic change characteristics of energy distribution between axles during vehicle braking.

4. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 3 is characterized in that: The dynamic coefficient of brake energy distribution is calculated to quantify the dynamic change characteristics of energy distribution between axles during vehicle braking, specifically: Calculate the dynamic coefficient of brake energy distribution: Among them, C d Indicates the dynamic coefficient of brake energy distribution, F front Indicates the front wheel braking force, F rear Represents the rear wheel braking force, w f and w b are the weight factors of the front and rear wheels respectively, and w f and w b Both are greater than 0, and Δμ represents the difference in friction coefficient between the front and rear wheels.

5. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 1 is characterized in that: When the initial determination conditions of the feasible domain are met, the dynamic characteristics of the power grid are analyzed through power grid load data modeling to identify the real-time characteristic curve of vehicle discharge and power grid adaptation, including: Collect grid load data, including local grid voltage, frequency change rate and load fluctuation characteristics; Preprocess the collected grid load data, including normalization, noise filtering and data alignment, to eliminate the interference of different sampling frequencies and noise on the analysis results; A dynamic change model of power grid load is constructed based on the time series model. The time series model uses the historical data and real-time data of the power grid load to obtain the dynamic characteristics of the power grid load through fitting; The real-time adaptability of the grid load to the vehicle discharge is analyzed using the grid load dynamic change model and vehicle discharge parameters, and the real-time characteristic curve of vehicle discharge and grid adaptation is output.

6. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 5 is characterized in that: A dynamic change model of power grid load is constructed based on the time series model. The time series model uses the historical data and real-time data of the power grid load and obtains the dynamic characteristics of the power grid load through fitting, which are as follows: The autoregressive integrated moving average model is used to fit the power grid load data, and the formula is: Y t =φ1Y t-1 +φ2Y t-2 +…+φ p Y t-p +∈ t +θ1∈ t-1 +…+θ q ∈ t-q ; Among them, Y t represents the grid load value at time t, φ1, φ2, …, φ p represents the autoregressive coefficient, ∈ t represents the error term at time t, θ1, θ2, …, θ q represents the moving average coefficient, p and q represent the autoregressive order and the moving average order respectively; The time series model uses historical data to predict the dynamic changes of load and provide the dynamic characteristics of power grid load.

7. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 5 is characterized in that: The real-time adaptability of the grid load to the vehicle discharge is analyzed by using the grid load dynamic change model and vehicle discharge parameters, and the real-time characteristic curve of vehicle discharge and grid adaptation is output, specifically: Conduct adaptability analysis on the dynamic change data of grid load generated by time series model and vehicle discharge parameters; Analyze the real-time adaptability of the grid load to vehicle discharge and calculate the adaptability index: Among them, S represents the adaptability index, P i It represents the difference between the vehicle discharge power and the grid load power at the i-th sampling point, W i represents the load priority weight of the i-th sampling point, and n represents the total number of load data sampling points in the sampling period; The adaptability index generated by the adaptability analysis generates a real-time characteristic curve of vehicle discharge and grid adaptation.

8. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 1 is characterized in that: Based on the dynamic change characteristics of energy distribution during vehicle braking and the real-time characteristic curve of vehicle discharge and grid adaptation, the energy flow identification results when the vehicle is in parallel with regenerative braking and grid discharge are determined, including: The dynamic coefficient of brake energy distribution and the adaptability index are input into the energy flow identification model. The energy flow identification model is based on the threshold judgment rule and is calculated according to the following rules: When the dynamic coefficient of braking energy distribution is greater than the corresponding preset threshold and the adaptability index is greater than the corresponding preset threshold, the energy flow identification result is a coordinated state; When the dynamic coefficient of braking energy distribution is greater than the corresponding preset threshold but the adaptability index is less than or equal to the corresponding preset threshold, the energy flow identification result is regenerative braking priority; When the dynamic coefficient of braking energy distribution is less than or equal to the corresponding preset threshold and the adaptability index is greater than the corresponding preset threshold, the energy flow identification result is grid discharge priority; When the dynamic coefficient of braking energy distribution and the adaptability index are both less than or equal to the corresponding preset thresholds, the energy flow identification result is a non-cooperative state; The energy flow identification result is output according to the calculation result of the energy flow identification model, and the energy flow identification result is used to characterize the coordinated state of the regenerative braking energy and the grid discharge power under the current working condition.

9. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 1 is characterized in that: When the energy flow identification result is in the preset coupling interval, the feasible domain constraints that meet the vehicle driving safety and grid power requirements are determined, including: Determine whether the energy flow identification result is within a preset coupling interval, where the preset coupling interval is used to characterize the coordinated state range of the regenerative braking energy and the grid discharge power; When the energy flow identification result is in the preset coupling interval, the vehicle driving state parameters are obtained, including vehicle speed, vehicle attitude angle and wheel slip rate, which are used to describe the dynamic safety state of the vehicle driving; Obtaining grid power status parameters, including real-time grid voltage, frequency change rate, and load fluctuation characteristics, to describe the dynamic characteristics of grid power demand; Based on the vehicle driving state parameters and the power state parameters of the power grid, the feasible domain constraints are calculated through a multi-objective optimization algorithm. The multi-objective optimization algorithm takes the vehicle driving safety threshold and the power demand threshold of the power grid as constraints to dynamically adjust the energy flow allocation ratio. Output feasible domain constraints to limit the distribution range of vehicle regenerative braking energy recovery and grid discharge power.

10. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 1 is characterized in that: Based on the constraints of the feasible domain and the dynamic characteristics of energy distribution during vehicle braking, the output power of the on-board drive system and the bidirectional inverter is dispatched, and the recovery power generated by regenerative braking and the grid discharge power are dynamically allocated, including: According to the constraints of the feasible region and the dynamic coefficient of braking energy distribution, the maximum allocation ratio of the recovery power generated by regenerative braking and the maximum allocation ratio of the grid discharge power are calculated; Based on the calculation results, the regenerative braking recovery power provided by the vehicle drive system and the grid discharge power output by the bidirectional inverter are respectively determined; The calculation results of regenerative braking recovery power and grid discharge power are transmitted to the vehicle drive system and bidirectional inverter as dynamic allocation instructions to adjust their actual output power and ensure that power is dynamically allocated in proportion.

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