Coordinated dispatching control system of battery car charging power and power grid state

Abstract

The application provides a battery car charging power and power grid state coordinated scheduling control system, and belongs to the technical field of battery car charging. The system comprises a battery car charging network, an energy storage subsystem and a charging regulation subsystem. The charging regulation subsystem comprises a data acquisition module, a health degree evaluation module, a power grid quality evaluation module and a scheduling decision module. The data acquisition module is used for acquiring battery state data, power grid operation data and charging tasks. The health degree evaluation module evaluates the battery state according to the battery state data. The power grid quality evaluation module constructs a multi-dimensional evaluation vector based on the operation data, simulates the load through an overload simulation strategy and evaluates the power grid quality. The scheduling decision module generates a charging list and a scheduling instruction according to the power grid quality evaluation result and the battery health evaluation result, and controls the switching device and the charging power in combination with the charging task. The system realizes the dynamic adaptation of the charging power and the power grid carrying capacity through the cooperation of multiple modules, and takes into account the charging safety and the power grid stability.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging technology, and more specifically to a coordinated scheduling and control system for electric vehicle charging power and power grid status. Background Technology

[0002] Electric bicycles have rapidly gained popularity as a green mode of transportation globally, and their large-scale charging activities have become an important part of urban power grids. With the continued expansion of electric bicycle ownership, centralized charging facilities are being deployed extensively in residential areas, commercial centers, and other locations, resulting in significant peak characteristics in the spatiotemporal distribution of charging loads. The dynamic fluctuations of these loads and the inherent regulation capabilities of the power grid are increasingly challenging to match, while the heterogeneous health status of electric bicycle batteries further complicates charging safety management.

[0003] Traditional charging networks suffer from several problems: First, grid condition assessment often relies on preset fixed thresholds or offline calculation models, making it difficult to adapt to real-time operating conditions during dynamic load changes. Second, battery health monitoring and power scheduling control functions are separated into different subsystems, making it impossible to coordinate and optimize charging strategies based on the actual degree of battery degradation. These limitations result in limited utilization of charging resources and an inability to effectively guarantee power supply security during peak hours. Summary of the Invention

[0004] This invention provides a coordinated scheduling and control system for electric vehicle charging power and grid status, which solves the problems in the prior art such as the lack of dynamic load capacity assessment of the power grid leading to scheduling risks and the difficulty in predicting sudden load impact conditions causing system instability.

[0005] To achieve the above objectives, embodiments of the present invention provide a coordinated scheduling and control system for electric vehicle charging power and grid status, comprising: an electric vehicle charging network, an energy storage subsystem, and a charging control subsystem. The electric vehicle charging network includes several charging interfaces and switching devices for controlling the power on and off of the charging interfaces. The charging control subsystem includes: a data acquisition module for connecting the electric vehicle's BMS system to the electric vehicle charging network to collect battery status data and the operating data of the electric vehicle charging network, and to obtain the charging tasks of the electric vehicle; a health assessment module for assessing the battery status based on the battery status data using a preset health assessment model to obtain a health assessment result; and a grid quality control subsystem. The quantity assessment module is used to construct a multi-dimensional assessment vector based on real-time collected operating data according to a preset assessment cycle, and to perform load simulation on the electric vehicle charging network through a preset overload simulation strategy to obtain load parameters. It is also used to assess the quality of the electric vehicle charging network based on the response data of the electric vehicle charging network under simulated load, combined with the multi-dimensional assessment vector, through a preset assessment strategy to obtain a quality assessment result. The scheduling decision module is used to generate a charging list and a scheduling instruction for each charging task according to a preset generation strategy based on the quality assessment result and the health assessment result of each electric vehicle battery. The scheduling instruction is used to control the on / off state of the switching device and adjust the charging power of the charging interface.

[0006] Optionally, the energy storage subsystem is connected to the switching device, and the energy storage subsystem is used to: power the charging interface through the switching device during the evaluation period; and receive and store the electrical energy from the electric vehicle charging network outside the evaluation period.

[0007] Optionally, the health assessment model is constructed based on the extended Kalman filter algorithm, and the health assessment model is configured with equivalent circuit models for different types of batteries, as well as state equations and observation equations corresponding to each equivalent circuit model.

[0008] Optionally, the health assessment model for battery status assessment includes: matching the corresponding equivalent circuit model and the corresponding state equation and observation equation according to the type information of the battery to be assessed; and performing iterative calculations using an extended Kalman filter algorithm based on the state data and the state equation and observation equation, outputting the predicted values ​​of the battery's SOC and SOH as the assessment result.

[0009] Optionally, the power grid quality assessment module includes a simulated charging load device, which is connected to the electric vehicle charging network. The simulated charging load device is used to simulate load scenarios where different numbers and types of electric vehicle batteries are charged simultaneously by adjusting the output power.

[0010] Optionally, the overload simulation strategy includes: constructing a load growth model based on the number of electric vehicles currently connected to the electric vehicle charging network and the real-time status data of each electric vehicle; simulating the load change curve of the electric vehicle charging network when different numbers of electric vehicles are connected using the simulated charging load device according to the load growth model, and outputting the simulated load power corresponding to each node in the change curve; loading the simulated load power into the electric vehicle charging network using the simulated charging load device to perform load simulation; and obtaining the load parameters of the electric vehicle charging network based on the load simulation results.

[0011] Optionally, the quality assessment results include load parameters and several power grid quality levels, with each power grid quality level corresponding to a preset comprehensive power grid quality score range.

[0012] Optionally, the response data of the electric vehicle charging network when a simulated load is applied is obtained, and multidimensional parameters reflecting the grid quality are extracted from the multidimensional evaluation vector and the response data; the deviation of each dimension parameter in the multidimensional parameters from the corresponding threshold is calculated to obtain the corresponding deviation rate; the dimensional deviation rates are weighted to obtain a comprehensive grid quality score; and the grid quality level is determined based on the comprehensive score.

[0013] Optionally, the generation strategy includes: extracting key evaluation parameters from battery status evaluation results, electric vehicle charging network quality evaluation results, and charging tasks, and determining the constraint coefficients of each key evaluation parameter; calculating the execution adaptability of each charging task according to the determined constraint coefficients through preset association rules; and generating scheduling instructions and charging lists according to the calculated execution adaptability through preset scheduling rules.

[0014] Optionally, the scheduling instructions include charging instructions and power-off instructions. The charging instructions include the time node for controlling the switching device to turn on and the initial charging power of the charging interface. The scheduling rules include: comparing the execution adaptability of each charging task with a preset threshold; for charging tasks with an execution adaptability not lower than the preset threshold, sorting them from high to low according to the execution adaptability and including them in the charging list; determining the time node for controlling the switching device to turn on based on the current load parameters of the electric vehicle charging network and the charging time in each charging task; and determining the initial charging power of the corresponding charging interface based on the SOH prediction value of each electric vehicle battery to generate the corresponding charging instructions; for charging tasks with an execution adaptability lower than the preset threshold, generating the corresponding power-off instructions to control the switching device to remain in the off state.

[0015] This invention provides a collaborative scheduling and control system for electric vehicle charging power and grid status. Through a grid quality assessment module, the system dynamically simulates the load and performs real-time quality assessment of the charging network, accurately identifying the grid safety margin. Combined with a health assessment module, it performs multi-dimensional closed-loop monitoring of battery health status, achieving adaptive matching between charging power and battery degradation. By leveraging a scheduling decision module to optimize the generation of charging lists and coordinate scheduling commands, the system significantly improves grid resource utilization while ensuring charging safety, effectively suppressing the impact of load fluctuations on the distribution system. Simultaneously, through a bidirectional buffering mechanism in the energy storage subsystem, the system's robustness in responding to sudden operating conditions is enhanced, ultimately achieving a triple optimization goal of charging efficiency, battery life, and grid stability. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0017] Figure 1 This is the overall control diagram of the collaborative scheduling and control system provided in the embodiments of the present invention;

[0018] Figure 2 This is a flowchart of the energy storage subsystem provided in an embodiment of the present invention;

[0019] Figure 3 This is a flowchart of the battery health assessment process provided in an embodiment of the present invention;

[0020] Figure 4 This is a scheduling decision flowchart provided in an embodiment of the present invention. Detailed Implementation

[0021] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0022] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0023] As mentioned above, with the rapid growth in demand for large-scale charging of electric vehicles, existing charging control systems suffer from several problems, including: lack of assessment of the grid's dynamic carrying capacity leading to scheduling safety risks; disconnect between battery health status and charging power control causing safety hazards; and difficulty in predicting sudden load shocks leading to system instability. Therefore, developing a scheduling control system that deeply coordinates grid status and charging behavior is particularly urgent.

[0024] To address this issue, this invention provides a collaborative scheduling and control system for electric vehicle charging power and grid status. The system proactively applies programmable load simulation and constructs a multi-dimensional evaluation strategy through a grid quality assessment module to accurately quantify the grid's real-time carrying capacity. Combined with a health assessment module's multi-dimensional diagnosis of battery degradation and a scheduling decision module's dynamic weight optimization based on the quality and health assessment results, it generates a charging list and power commands that precisely match the grid safety margin and battery capacity. The system utilizes a bidirectional buffering mechanism of an energy storage subsystem to promptly mitigate load impacts, effectively resolving the coupling problem between grid scheduling risks and battery safety hazards, and achieving a synergistic leap in charging resource utilization, system safety boundaries, and battery lifespan.

[0025] The following is combined Figures 1-4 This invention is described in detail.

[0026] like Figure 1 As shown, this embodiment of the invention provides a coordinated scheduling and control system for electric vehicle charging power and grid status, including: an electric vehicle charging network, an energy storage subsystem, and a charging control subsystem. The electric vehicle charging network includes several charging interfaces and switching devices for controlling the power on and off of the charging interfaces. The charging control subsystem includes: a data acquisition module for connecting the electric vehicle's BMS system to the electric vehicle charging network to collect battery status data and the operation data of the electric vehicle charging network, as well as to obtain the charging tasks of the electric vehicle; a health assessment module for assessing the battery status based on the battery status data using a preset health assessment model to obtain a health assessment result; and a grid quality assessment module. The estimation module is used to construct a multi-dimensional evaluation vector based on real-time collected operating data according to a preset evaluation cycle, and to perform load simulation on the electric vehicle charging network through a preset overload simulation strategy to obtain load parameters. It is also used to evaluate the quality of the electric vehicle charging network based on the response data of the electric vehicle charging network under simulated load, combined with the multi-dimensional evaluation vector, through a preset evaluation strategy to obtain a quality evaluation result. The scheduling decision module is used to generate a charging list and a scheduling instruction for each charging task according to a preset generation strategy based on the quality evaluation result and the health evaluation result of each electric vehicle battery. The scheduling instruction is used to control the on / off state of the switching device and adjust the charging power of the charging interface.

[0027] The electric vehicle charging network is a charging infrastructure network consisting of multiple charging interfaces, connecting lines, and related monitoring equipment. It provides charging services for electric vehicles, with each charging interface capable of independently powering a single vehicle. The energy storage subsystem includes energy storage battery packs and a charge / discharge control module. It possesses energy storage and release functions and can adjust energy supply and demand during quality checks of the electric vehicle charging network to ensure stable operation. The operational data of the electric vehicle charging network refers to parameters reflecting the network's operating status collected by monitoring equipment during the charging process, such as line voltage, line current, line loss, and load rate. Charging tasks consist of user-submitted instructions containing charging requirements, typically including the desired charging amount, charging cut-off time, and acceptable charging power range. The multi-dimensional evaluation vector is a set of multi-dimensional parameters constructed based on real-time operational data of the charging network. Each dimension corresponds to a characteristic parameter reflecting grid quality (such as voltage fluctuation amplitude and current stability coefficient), used to comprehensively characterize the grid's operating status. Response data refers to various feedback data generated by the charging network in response to simulated loads, including voltage changes, current fluctuations, and line loss changes. This data is used to analyze the grid's adaptability to load changes. A charging list is an ordered list containing multiple charging tasks, prioritized according to certain criteria. It specifies the execution order and related parameters of each charging task, providing a basis for charging scheduling.

[0028] The electric vehicle charging power and grid status coordinated scheduling control system provided in this invention achieves multi-dimensional breakthroughs in addressing the pain points of existing technologies by constructing a closed-loop coordinated mechanism of "health assessment - grid load simulation - quality assessment - scheduling decision": This system monitors the grid operating status in real time through the grid quality assessment module, and combines this with the precise understanding of the battery status by the health assessment module. The scheduling decision module dynamically generates charging schemes that adapt to the grid load capacity and battery health status, enabling the charging power to be adjusted in real time according to grid load fluctuations and battery status changes. This achieves a dynamic balance between charging demand and grid carrying capacity, significantly reducing the risk of grid failures caused by sudden changes in charging load. This system mitigates risks; simultaneously, through a health assessment module, it customizes charging curves for batteries in different health states, rationally plans the charging sequence based on the time requirements of the charging task, avoids wasting charging resources, improves charging efficiency, and extends battery cycle life; in addition, this system introduces an energy storage subsystem as a buffer regulation unit, providing auxiliary power supply during the operation of the grid quality assessment module to maintain grid stability. At the same time, the grid quality assessment module predicts the grid response under different charging loads in advance through overload simulation, and the scheduling decision module formulates a peak-shifting charging strategy accordingly, distributing the concentrated charging load to the off-peak period of grid load, reducing the peak load of the grid, and significantly improving the stability and reliability of grid operation.

[0029] like Figure 2As shown, preferably, the energy storage subsystem is connected to the switching device, and the energy storage subsystem is used to: power the charging interface through the switching device during the evaluation period; and receive and store the electrical energy from the electric vehicle charging network outside the evaluation period.

[0030] The assessment cycle refers to a fixed time interval preset by the power grid quality assessment module, used to periodically simulate loads and assess the quality of the electric vehicle charging network, ensuring that the assessment results reflect real-time dynamic changes in the power grid. The energy storage subsystem establishes a physical connection with the switching device through electrical lines, both supplying power to the switching device to drive its on / off actions and enabling energy interaction with the charging interface and the power grid through the switching device's switching mechanism. Outside of the assessment cycle, the energy storage subsystem switches to energy storage mode, connecting to the electric vehicle charging network through the switching device, absorbing redundant power from the power grid and storing it in the energy storage battery pack, to be released when needed.

[0031] Specifically, the connection design between the energy storage subsystem and the switching device serves a dual purpose: Firstly, during the evaluation period of the power grid quality assessment module, the energy storage subsystem provides stable power to the charging interface through the switching device. At this time, the power supply is not affected by real-time load fluctuations in the power grid, ensuring that the charging interface can obtain continuous and stable power input when the simulated charging load device is tested under different load scenarios. This allows for accurate collection of power grid response data to load changes, improving the reliability of the quality assessment results. Secondly, outside the evaluation period, the energy storage subsystem switches to energy storage mode and connects to the electric vehicle charging network through the switching device. It actively absorbs and stores surplus power from the power grid, reducing power waste during off-peak hours and reserving energy for charging demand during peak hours.

[0032] The preferred embodiment of this invention proposes a technical solution that avoids real-time load interference simulation testing of the power grid by providing independent power supply during the evaluation period, thus enabling more accurate acquisition of load parameters and response data. Simultaneously, it utilizes an energy storage subsystem to achieve spatiotemporal transfer of electrical energy, reducing power grid transmission losses and lowering user charging costs. Through this "time-sharing, on-demand switching" mode, the energy storage subsystem is no longer simply a backup power source, but becomes a core hub connecting power grid assessment, charging scheduling, and energy optimization, significantly enhancing the flexibility and economy of the entire collaborative dispatch and control system.

[0033] like Figure 3 As shown, preferably, the health assessment model is constructed based on the extended Kalman filter algorithm, and the health assessment model is configured with equivalent circuit models for different types of batteries, as well as state equations and observation equations corresponding to each equivalent circuit model.

[0034] More preferably, the health assessment model for assessing battery status includes: matching the corresponding equivalent circuit model and the corresponding state equation and observation equation according to the type information of the battery to be assessed; performing iterative calculations using an extended Kalman filter algorithm based on the state data and the state equation and observation equation, and outputting the predicted values ​​of the battery's SOC and SOH as the assessment result.

[0035] The Extended Kalman Filter (EKF) algorithm is an improved version of the Kalman filter, suitable for state estimation of nonlinear systems. By performing a Taylor series expansion of the nonlinear function and ignoring higher-order terms, it approximates the nonlinear problem as a linear one, enabling real-time prediction and correction of the system state with high estimation accuracy and real-time performance. The equivalent circuit model is a circuit model used to simulate the electrochemical characteristics of a battery. Through combinations of circuit components such as resistors, capacitors, and power supplies, it equivalently reflects the voltage and current response relationships of the battery during charging and discharging. Different types of batteries, such as lead-acid batteries and lithium batteries, require different equivalent circuit models due to differences in their electrochemical characteristics. The state equation is a mathematical equation describing the state changes of a battery system. In the health assessment model, state parameters such as SOC and SOH are used as variables to reflect the changes in state parameters with time and inputs such as charging and discharging current. This is the core basis for predicting the state in the EKF algorithm. The observation equation refers to the mathematical equation establishing the relationship between observable battery parameters such as terminal voltage and battery state parameters such as SOC and SOH. The system state is inferred from the measured observable parameters, providing a basis for state correction in the EKF algorithm. Iterative computation refers to the operation method in the extended Kalman filter algorithm that continuously corrects the state estimate through a "prediction-update" loop calculation process. Each iteration optimizes the prediction result based on new observation data until it converges to a stable state estimate, ensuring the accuracy of SOC and SOH predictions.

[0036] Specifically, the health assessment model uses the extended Kalman filter algorithm as its core framework, pre-configuring dedicated equivalent circuit models for different battery types. Each model corresponds to experimentally validated state and observation equations. For example, for lithium batteries, a second-order RC equivalent circuit model including polarization resistance and polarization capacitance is used, with its state equation focusing on reflecting the voltage hysteresis characteristics caused by lithium-ion diffusion. For lead-acid batteries, a simplified first-order RC model is used, with its state equation highlighting the impact of plate sulfation on internal resistance. This precise type-model matching solves the problem of traditional single models being unable to adapt to multiple battery types. The evaluation process follows the logic of type identification, model invocation, and iterative optimization: First, the type information of the battery to be evaluated is obtained through the data acquisition module, and the model automatically matches the corresponding equivalent circuit model and the corresponding state equation and observation equation; then, the real-time battery state data obtained by the data acquisition module is input into the model, and state prediction is performed based on the state equation to obtain preliminary estimates of SOC and SOH; then, the measured terminal voltage is compared with the theoretical voltage under the predicted state through the observation equation, the residual is calculated and the Kalman gain is updated to correct the preliminary estimate; finally, the prediction error is continuously reduced through multiple rounds of iterative calculation until the estimated values ​​of SOC and SOH converge to a stable range, and the final predicted value is output as the health assessment result.

[0037] For example, taking an electric vehicle equipped with a ternary lithium battery as an example, its battery status assessment process is as follows: The data acquisition module first obtains the battery type identifier "ternary lithium battery" from the BMS system. The health assessment model automatically matches the second-order RC equivalent circuit model and the corresponding state equation and observation equation. Then, real-time data is input: charging current 10A, terminal voltage 48V, temperature 25℃. Based on the state equation, the initial prediction is that the SOC is 85% and the SOH is 92%. Then, the residual between the measured terminal voltage of 48V and the theoretical predicted voltage of 48.5V is calculated through the observation equation. After updating the Kalman gain, the SOC is corrected to 83% and the SOH to 91%. After 5 iterations, the final converged assessment result is output: SOC is 82% and SOH is 90%. This provides the scheduling decision module with an accurate basis for the battery's current sufficient charge but slightly degraded health status, thereby guiding the charging interface to use a medium-low power mode for charging.

[0038] The health assessment model proposed in the preferred embodiment of this invention aims to solve the problems of poor model universality and low assessment accuracy for different types of batteries in existing battery state assessment systems. By constructing an adaptive assessment model based on the extended Kalman filter algorithm, it can accurately assess the SOC and SOH of different types of electric vehicle batteries. This not only provides a reliable basis for charging power adjustment and battery protection, but also provides a basis for subsequent charging power adjustment, avoiding battery life degradation caused by overcharging and fast charging, and achieving a balance between battery protection and charging efficiency.

[0039] Preferably, the power grid quality assessment module includes a simulated charging load device, which is connected to the electric vehicle charging network. The simulated charging load device is used to simulate load scenarios where different numbers and types of electric vehicle batteries are charged simultaneously by adjusting the output power.

[0040] Further preferably, the overload simulation strategy includes: constructing a load growth model based on the number of electric vehicles currently connected to the electric vehicle charging network and the real-time status data of each electric vehicle; simulating the load change curve of the electric vehicle charging network when different numbers of electric vehicles are connected using the simulated charging load device, and outputting the simulated load power corresponding to each node in the change curve; loading the simulated load power into the electric vehicle charging network using the simulated charging load device to perform load simulation; and obtaining the load parameters of the electric vehicle charging network based on the load simulation results.

[0041] The simulated charging load device is an electronic device with adjustable output power. It uses power electronic conversion technology to simulate the charging characteristics of different electric vehicle batteries, such as charging current waveforms and power change curves. It can accurately reproduce the load state when multiple electric vehicles are charging simultaneously, providing a controllable simulated load source for testing the grid's carrying capacity. The load scenario refers to the load combination state formed when different numbers and types of electric vehicles are simultaneously connected to the charging network. The load growth model is used to predict the growth trend of the charging network load over a future period. Model parameters include the load growth rate and the time of peak load occurrence. Load parameters are key parameters reflecting the grid load characteristics obtained through load simulation, including peak load, average load, load fluctuation frequency, and load duration. These are core input data for grid quality assessment.

[0042] Specifically, the implementation process of the overload simulation strategy is as follows: First, a load growth model is constructed: Based on the number of currently connected electric vehicles and real-time status data of each vehicle, such as battery type, current charging power, and estimated charging time, obtained by the data acquisition module, the model predicts the load growth trend for different numbers of electric vehicles and the corresponding load. The model outputs a correlation curve between the number of connected vehicles and the load power. Next, a simulated load is generated and loaded: According to the load growth model, the output power of the simulated charging load device is increased in a gradient manner. For example, the load is first simulated when 5 electric vehicles are connected, then increased to 10 vehicles, until the preset maximum number of simulated vehicles is reached. Simultaneously, the load change curves at each order of magnitude and the simulated load power of each node are recorded. Then, load parameters are extracted: After the load simulation is completed, the system automatically analyzes the load response of the electric vehicle charging network during the simulation, extracting key load parameters such as peak load, load fluctuation coefficient, and overload duration. These parameters will be used as input to the power grid quality assessment module to determine the stability of the power grid under different load pressures.

[0043] The preferred embodiment of this invention provides a power grid quality assessment module that can comprehensively test the operating status of the charging network under various load scenarios without affecting actual charging. The output load parameters, combined with subsequent power grid quality level assessments, form a complete characterization of the power grid status. This provides crucial information for the scheduling decision-making module to formulate strategies such as peak-shaving charging and power regulation, ultimately achieving both stable control of the power grid load and ensuring charging safety. Breaking through the limitations of traditional passive load bearing, this invention actively simulates different load scenarios to pre-determine the power grid's load-bearing limits, solving the problem of unpredictable sudden overloads during actual charging.

[0044] Preferably, the quality assessment results include load parameters and several power grid quality levels, with each power grid quality level corresponding to a preset comprehensive power grid quality score range.

[0045] Further preferably, the evaluation strategy includes: acquiring response data of the electric vehicle charging network when a simulated load is applied; extracting multidimensional parameters reflecting grid quality from the multidimensional evaluation vector and the response data; calculating the deviation between each dimension parameter and its corresponding threshold in the multidimensional parameters to obtain the corresponding deviation rate; weighting the dimension deviation rates to obtain a comprehensive grid quality score; and determining the grid quality level based on the comprehensive score.

[0046] The power grid quality level is a rating system based on a comprehensive power grid quality score. Each level corresponds to the overall performance of the power grid in terms of safety, stability, and efficiency, and is used to intuitively reflect the current operating status of the power grid. The comprehensive power grid quality score range is a preset score range for each power grid quality level, such as 90-100 points for Level I and 70-89 points for Level II, serving as the quantitative basis for judging the power grid quality level. Multidimensional parameters are multidimensional characteristic parameters reflecting power grid quality extracted from multidimensional evaluation vectors and simulated load response data. They cover parameters such as voltage deviation and harmonic distortion rate, achieving a comprehensive characterization of the power grid status. The deviation rate refers to the degree of deviation between the measured value of a certain dimension parameter and a preset threshold. The calculation formula is: (measured value - threshold) / threshold × 100%. Preset weights are weighting coefficients set according to the importance of each dimension parameter to power grid quality, such as a weight of 0.3 for voltage fluctuation amplitude and 0.2 for line loss rate.

[0047] Specifically, the quality assessment results adopt a two-factor structure of load parameters and quality level, including both specific data reflecting load pressure and intuitive level indicators. The assessment strategy centers on the quantitative analysis of multi-dimensional parameters, transforming raw data into quality levels through four steps: First, extracting multi-dimensional parameters: Based on the multi-dimensional assessment vector, which includes voltage fluctuation amplitude, current stability coefficient, etc., from real-time operating data, combined with response data collected when simulated load is applied, a multi-dimensional assessment parameter set is formed. Then, calculating the deviation rate: The deviation rate is calculated based on the difference between the measured value and the threshold. Next, a weighted comprehensive score is calculated: The deviation rate of each dimension parameter is converted into a single score (e.g., 0% deviation rate corresponds to 100 points, 50% deviation rate corresponds to 50 points), and then weighted and summed according to preset weights (e.g., voltage parameter total weight 0.3, current parameter 0.2, line parameter 0.2, load parameter 0.3), resulting in a comprehensive power grid quality score. Finally, determining the quality level: The comprehensive score is compared with a preset range to match the corresponding power grid quality level, ultimately outputting an assessment result containing both load parameters and quality level.

[0048] For example, taking a scenario simulating 20 electric bicycles charging simultaneously in an electric bicycle charging network, the evaluation strategy implementation process is as follows: First, extract multi-dimensional parameters from real-time operating data and simulated response data, including voltage fluctuation amplitude ±6%, current stability coefficient 0.85, harmonic distortion rate 6%, line loss rate 8%, peak load 75kW, etc.; then calculate the deviation rate. For example, if the voltage fluctuation amplitude threshold is ±5%, its deviation rate is (6-5) / 5×100%=20%, and the current stability coefficient threshold is 0.9, its deviation rate is (0.9) / 5×100%=20%. 9-0.85) / 0.9×100%≈5.56%; then convert the deviation rate into individual scores, 20% deviation corresponds to 80 points, 5.56% deviation corresponds to 94 points, and calculate the weighted score according to the following weights: voltage parameters 0.3, current parameters 0.2, line parameters 0.2, and load parameters 0.3, to obtain a comprehensive score of 82 points; finally, compare with the preset range: 70-89 points is Level II, determine the power grid quality level as Level II, and output the load parameters: peak load 75kW, load fluctuation frequency 0.3 times / minute, forming a complete quality assessment result.

[0049] This invention, in its preferred embodiment, constructs a comprehensive quality assessment process, aiming to address the problems of single indicators and a disconnect between assessment results and actual dispatch needs in existing power grid quality assessment methods. Traditional assessments often focus only on isolated parameters such as voltage and current, lacking a comprehensive judgment of the overall power grid status, and the assessment results are difficult to directly guide charging dispatch. This solution constructs an assessment system of "multi-dimensional parameter quantification + grade classification," combining power grid operation data with load simulation results to output a comprehensive assessment result including load parameters and quality levels, providing a clear and operable basis for dispatch decisions.

[0050] like Figure 4 As shown, preferably, the generation strategy includes: extracting key evaluation parameters from battery status evaluation results, electric vehicle charging network quality evaluation results, and charging tasks, and determining the constraint coefficients of each key evaluation parameter; calculating the execution adaptability of each charging task according to the determined constraint coefficients through preset association rules; and generating scheduling instructions and charging lists according to the calculated execution adaptability through preset scheduling rules.

[0051] Further preferably, the scheduling instruction includes a charging instruction and a power-off instruction. The charging instruction includes the time node for controlling the switching device to turn on and the initial charging power of the charging interface. The scheduling rules include: comparing the execution adaptability of each charging task with a preset threshold; for charging tasks with an execution adaptability not lower than the preset threshold, sorting them from high to low execution adaptability and including them in the charging list; determining the time node for controlling the switching device to turn on based on the current load parameters of the electric vehicle charging network and the charging time in each charging task; and determining the initial charging power of the corresponding charging interface based on the SOH prediction value of each electric vehicle battery to generate the corresponding charging instruction; for charging tasks with an execution adaptability lower than the preset threshold, generating the corresponding power-off instruction to control the switching device to remain in the off state.

[0052] The key assessment parameters are a set of core parameters extracted from health assessment results, quality assessment results, and charging tasks. These include predicted SOC, predicted SOH, grid quality level, current capacity, voltage fluctuation amplitude, charging power demand, remaining charging time, and charging cutoff time. These are fundamental indicators for measuring the feasibility of charging tasks. Constraint coefficients are weighted coefficients set based on the impact of each key assessment parameter on charging scheduling. For example, a low SOC increases the charging urgency coefficient, and insufficient grid capacity increases the load limitation coefficient. These coefficients quantify the constraint strength of parameters on execution suitability. Execution suitability refers to a comprehensive score measuring the feasibility of a charging task under current conditions. A higher score indicates a better match between the task and grid capacity and battery status, and thus a higher priority for execution. The charging list is a list of charging tasks sorted by execution suitability, clearly defining the execution order, time window, and power parameters of each task. This serves as the basis for charging scheduling. Association rules include: calculating execution suitability using the following formula:

[0053] F=ω1·S B +ω2·S G +ω3·S T

[0054] Where F represents the execution adaptability; ω1, ω2, ω3 represent weight coefficients, with default values ​​of ω1 = 0.3, ω2 = 0.4, and ω3 = 0.3; S B Indicates battery health score; S G Indicates the grid capacity; ST This indicates that the task is urgent.

[0055] Battery health score S B It can be represented as:

[0056] S B =α×SOC N +b×SOH N

[0057] Among them, SOC N The normalized state of charge (SOH) is equal to the SOC value multiplied by 100. N The standardized health status is represented by SOH value multiplied by 100; α and b represent sub-weights, α = 0.6 and b = 0.4.

[0058] Power grid carrying capacity S G It can be represented as:

[0059] S G =c×G L +d×R U

[0060] Among them, G L The power grid is classified into different levels: Level I = 100, Level II = 80, Level III = 60, Level IV = 40; R U R represents the margin utilization rate score. U =100×(1-charging power demand / current capacity margin); c,d represent sub-weights, c=0.5, d=0.5.

[0061] Mission Emergency S T It can be represented as:

[0062] S T =min(100,(T) S / T R ()×80+20)

[0063] Among them, T S T represents the standard charging time. R Indicates the remaining charging time.

[0064] The timing for controlling the switching device to turn on is determined by calculating the difference between the current load and the upper limit of the power grid, combined with the suitability ranking and power demand of each charging task, using a time window allocation algorithm. The initial charging power of the charging interface is dynamically set based on the predicted SOH value of the corresponding electric vehicle battery using a piecewise function, which can be expressed as:

[0065]

[0066] Where P represents the initial charging power, P rThis indicates the rated power.

[0067] Specifically, the strategy generation process is as follows: First, extract SOC and SOH from the battery health assessment results, extract grid quality level and current carrying capacity from the grid quality assessment results, and extract charging power demand, remaining charging time, and deadline from the charging task, integrating them to form key assessment parameters; then, assign dynamic constraint coefficients to each parameter based on the degree of influence of the parameters on charging scheduling; finally, quantify and fuse the parameters through association rules, calculate the battery health score, grid carrying capacity score, and task urgency score according to preset weights, and obtain the execution adaptability by weighted summation; then, compare the adaptability with preset thresholds, sort the tasks that meet the criteria according to adaptability to generate a charging list, combine the current grid load parameters and task time requirements, determine the time node for switching device conduction through a time window allocation algorithm, determine the initial power of the charging interface according to a piecewise function, and generate charging instructions; generate power-off instructions for tasks that do not meet the criteria, and control the switching device to remain disconnected.

[0068] Taking a lithium battery electric vehicle as an example, its SOC is 60%, SOH is 80%, the charging power requirement is 6kW, the remaining charging time is 2 hours, the standard time is 3 hours, the current grid load is 40kW, the upper limit of the load capacity is 60kW, and the quality level is II. In the strategy generation process, the battery health score is calculated as 0.6×60+0.4×80=68 points according to the execution adaptability calculation formula; the grid load capacity score is calculated as 0.5×80+0.5×(1-6 / 20)×100=85 points according to the load capacity calculation formula; the task urgency score is calculated as (3 / 2)×80+20=140, which is taken as 100 points; and the execution adaptability score is calculated as 0.3×68+0.4×85+0.3×100=83.4 points, which is higher than the threshold of 60 points. In the scheduling rules, since the grid has a usable capacity of 20kW to accommodate the task, the conduction time node is set to the current time after sorting by adaptability. The initial power is determined based on SOH=80% and the piecewise function.

[0069] =6×(0.5+80 / 180)≈5.67kW, generating a charging command.

[0070] The preferred embodiments of this invention construct a complete scheduling decision-making technical solution. By integrating multi-dimensional parameters of battery health, grid status, and charging tasks, it achieves quantitative evaluation of execution suitability using dynamic constraint coefficients and association rules. This not only solves the problem of grid overload or battery damage caused by traditional scheduling relying on manual experience and single-factor decision-making, but also achieves precise matching between charging tasks and grid carrying capacity and battery characteristics through time window allocation algorithms and SOH graded power adjustment mechanisms. This not only improves charging efficiency and grid stability, but also prioritizes the execution of high-urgency, high-suitability tasks through suitability threshold screening and dynamic instruction generation, while avoiding the negative impact of low-suitability tasks on the grid and batteries, thus balancing the scientific nature, safety, and flexibility of scheduling.

[0071] The electric vehicle charging power and grid status coordinated scheduling control system provided by this invention achieves multi-dimensional technological breakthroughs by constructing a closed-loop coordinated system of "health assessment - grid load simulation - quality assessment - scheduling decision": It can accurately assess the SOC and SOH of different types of batteries based on the extended Kalman filter algorithm, and significantly improve charging efficiency and effectively extend battery cycle life through customized charging curves; it can also predict the grid carrying capacity in advance by using simulated charging load devices and overload simulation strategies, and effectively control the voltage fluctuation amplitude by combining multi-dimensional parameter assessment, thus significantly enhancing grid stability; at the same time, by executing adaptive quantification calculation and dynamic scheduling rules, it optimizes the charging task sequencing and power allocation, significantly improves the utilization rate of charging interfaces, and can automatically cut off low-adaptability tasks to prevent overload risks. Ultimately, it achieves safe and efficient electric vehicle charging process and stable and economical grid operation, providing a systematic solution to the contradiction between large-scale electric vehicle charging and grid carrying capacity.

[0072] The above description is merely a preferred embodiment of the technical solution of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A coordinated scheduling and control system for electric vehicle charging power and grid status, characterized in that, include: The electric vehicle charging network includes an energy storage subsystem and a charging control subsystem. The electric vehicle charging network includes several charging interfaces and a switching device for controlling the power supply to and from the charging interfaces. The charging control subsystem includes: The data acquisition module is used to connect the BMS system of the electric vehicle to the electric vehicle charging network to collect battery status data and the operation data of the electric vehicle charging network, as well as to obtain the charging task of the electric vehicle. The health assessment module is used to assess the battery status based on the battery status data using a preset health assessment model, and obtain a health assessment result. The power grid quality assessment module is used to construct a multi-dimensional assessment vector based on real-time collected operational data according to a preset assessment cycle, and to perform load simulation on the electric vehicle charging network using a preset overload simulation strategy to obtain load parameters. It is also used to assess the quality of the electric vehicle charging network based on the response data of the electric vehicle charging network under simulated load, combined with the multi-dimensional assessment vector, using a preset assessment strategy to obtain a quality assessment result. The quality assessment result includes load parameters and several power grid quality levels. The power grid quality assessment module includes a simulated charging load device connected to the electric vehicle charging network. The simulated charging load device is used to adjust... The output power is used to simulate load scenarios where different numbers and types of electric bicycle batteries are charged simultaneously. The overload simulation strategy includes: constructing a load growth model based on the number of electric bicycles currently connected to the electric bicycle charging network and the real-time status data of each electric bicycle; according to the load growth model, simulating the load change curve of the electric bicycle charging network when different numbers of electric bicycles are connected through the simulated charging load device, and outputting the simulated load power corresponding to each node in the change curve; loading the simulated load power into the electric bicycle charging network through the simulated charging load device to perform load simulation; and obtaining the load parameters of the electric bicycle charging network based on the load simulation results. The scheduling decision module is used to generate a charging list and a scheduling instruction for each charging task based on the load parameters and grid quality level in the quality assessment results and the health assessment results of each electric vehicle battery through a preset generation strategy. The scheduling instructions are used to control the on / off state of the switching device and adjust the charging power of the charging interface.

2. The collaborative scheduling and control system according to claim 1, characterized in that, The energy storage subsystem is connected to the switching device, and the energy storage subsystem is used for: During the evaluation period, the charging interface is powered on via the switching device; Outside of the evaluation period, electrical energy is received from and stored by the electric vehicle charging network.

3. The collaborative scheduling and control system according to claim 1, characterized in that, The health assessment model is constructed based on the extended Kalman filter algorithm. The health assessment model is configured with equivalent circuit models for different types of batteries, as well as state equations and observation equations corresponding to each equivalent circuit model.

4. The collaborative scheduling control system according to claim 3, characterized in that, The health assessment model evaluates the battery status by including: Based on the type information of the battery to be evaluated, the corresponding equivalent circuit model and the corresponding state equation and observation equation are matched; Based on the state data, combined with the state equation and observation equation, iterative calculations are performed using the extended Kalman filter algorithm to output the predicted values ​​of the battery's SOC and SOH as the evaluation results.

5. The collaborative scheduling and control system according to claim 1, characterized in that, Each power grid quality level corresponds to a preset comprehensive power grid quality score range.

6. The collaborative scheduling control system according to claim 5, characterized in that, The evaluation strategy includes: Obtain the response data of the electric vehicle charging network when a simulated load is applied, and extract multidimensional parameters reflecting the grid quality from the multidimensional evaluation vector and the response data; Calculate the deviation between each dimension parameter and its corresponding threshold in the multidimensional parameters to obtain the corresponding deviation rate; The deviation rates are weighted to obtain a comprehensive power grid quality score; The power grid quality level is determined based on a comprehensive score.

7. The collaborative scheduling and control system according to claim 1, characterized in that, The generation strategy includes: Key evaluation parameters are extracted from the battery status assessment results, the electric vehicle charging network quality assessment results, and the charging tasks, and the constraint coefficients of each key evaluation parameter are determined. Based on the determined constraint coefficients, the execution adaptability of each charging task is calculated using preset association rules. Based on the calculated execution adaptability, scheduling instructions and charging lists are generated through preset scheduling rules.

8. The collaborative scheduling control system according to claim 7, characterized in that, The scheduling instructions include charging instructions and power-off instructions. The charging instructions include the time point for controlling the switching device to turn on and the initial charging power of the charging interface. The scheduling rules include: Compare the execution adaptability of each charging task with the preset threshold; For charging tasks with an execution adaptability of not less than a preset threshold, sort them from high to low according to the execution adaptability and include them in the charging list. Combine the current load parameters of the electric vehicle charging network and the charging time in each charging task to determine the time node for the control switching device to be turned on. Based on the SOH prediction value of each electric vehicle battery, determine the initial charging power of the corresponding charging interface to generate the corresponding charging instruction. For charging tasks where the compatibility is below a preset threshold, a corresponding power-off command is generated to control the switching device to remain in the off state.

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