Charging management method, charging management system, and electronic device

Abstract

The application provides a charging management method, a charging management system and an electronic device, comprising: obtaining user charging information of a vehicle to be charged, and determining a user behavior coefficient; collecting state information of a charging environment, and determining an environment state coefficient based on the state information; obtaining safety information of a charging facility, and determining a facility safety coefficient based on the safety information; determining a charging adaptation degree of the vehicle to be charged under real-time working parameters based on the user behavior coefficient, the environment state coefficient and the real-time working parameters of the vehicle to be charged; and correcting a reference charging current based on the charging adaptation degree and the facility safety coefficient to obtain a target charging current of the vehicle to be charged. In this way, the reference charging current can be dynamically corrected by comprehensively considering user charging demand, environment state and facility safety, so that precise adaptation of a charging strategy to user individual demand and real-time health state of a battery can be realized under the premise of ensuring safety of on-site facilities and stability of a power grid.

Description

Technical Field

[0001] This application relates to the field of new energy vehicle charging technology, and in particular to a charging management method, a charging management system, and an electronic device. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the demand for electric vehicle charging is increasing daily, and issues related to safety, efficiency, and battery health management during the charging process are becoming increasingly prominent. Charging management not only affects the user's charging experience but also directly impacts the stability of the power grid and the lifespan of the battery.

[0003] Existing charging management technologies typically focus on regulating a single factor or a few factors. For example, some methods rely primarily on grid load for orderly charging control, smoothing out grid fluctuations through peak shaving and valley filling; other solutions focus on the battery's own condition, adjusting strategies based solely on battery health or real-time temperature to prevent overcharging; and still others attempt to allocate charging resources by incorporating user reservation habits.

[0004] However, the aforementioned existing technologies lack a holistic consideration of multiple factors such as user charging needs, environmental conditions, and facility safety, and are difficult to dynamically adapt charging power and battery status according to real-time operating conditions, making it difficult to balance safety, economy, and battery life guarantee in the charging strategy. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a charging management method, a charging management system, and an electronic device. By comprehensively considering user charging needs, environmental conditions, and facility safety to generate corresponding coefficients, and combining them with real-time operating parameters to determine the degree of charging adaptability, the reference charging current can be dynamically corrected using facility safety coefficients and the degree of charging adaptability. This allows for precise adaptation of the charging strategy to the user's personalized needs and the real-time health status of the battery, while ensuring the safety of on-site facilities and the stability of the power grid. This improves the safety of the charging system and effectively extends battery life while maintaining charging efficiency.

[0006] In a first aspect, the present invention provides a charging management method, comprising: Obtain user charging information for vehicles to be charged, and determine user behavior coefficients based on the user charging information; user behavior coefficients are used to characterize user charging needs.

[0007] Collect the state information of the charging environment and determine the environmental state coefficient based on the state information; the environmental state coefficient is used to characterize the charging environment conditions.

[0008] Obtain safety information about charging facilities and determine the facility safety factor based on the safety information; the facility safety factor is used to characterize the safety status of charging facilities.

[0009] Based on user behavior coefficients, environmental state coefficients, and the real-time operating parameters of the vehicle to be charged, the charging adaptability of the vehicle to be charged under the real-time operating parameters is determined.

[0010] The reference charging current is corrected based on the charging compatibility and facility safety factor to obtain the target charging current for the vehicle to be charged.

[0011] In an optional implementation, the step of obtaining user charging information for the vehicle to be charged and determining user behavior coefficients based on the user charging information includes: Obtain the historical average charging duration, charging frequency, real-time battery level, and battery health status of the vehicle to be charged within a preset statistical period.

[0012] The historical average charging time is normalized, and the processed historical average charging time is converted into a charging time index through a first preset function.

[0013] The charging frequency index is obtained by performing a nonlinear mapping on the charging frequency using a second preset function.

[0014] The real-time battery level and battery health status are processed as percentages to obtain the battery level index and battery health index, respectively.

[0015] The user behavior coefficient is obtained by weighting and summing the charging time index, charging frequency index, power index and battery health index based on preset weighting coefficients.

[0016] In an optional implementation, the step of collecting state information of the charging environment and determining the environmental state coefficient based on the state information includes: Collect the ambient temperature of the current charging station, the real-time available capacity of the regional power grid, and the corresponding real-time electricity price.

[0017] The ambient temperature is converted into an ambient temperature index using the third preset function.

[0018] The ratio of real-time available capacity to the rated capacity of the regional power grid is calculated to obtain the power grid capacity adequacy index.

[0019] Based on the preset peak electricity price and preset valley electricity price, the real-time electricity price is normalized, and the processed real-time electricity price is converted into an electricity price index through the fourth preset function.

[0020] The environmental state coefficient is obtained by multiplying the ambient temperature index, the power grid capacity adequacy index, and the electricity price index.

[0021] In an optional implementation, the step of obtaining safety information about the charging facility and determining the facility's safety factor based on the safety information includes: Obtain the number of people within the preset area of ​​the charging facility and calculate the population density per unit area.

[0022] Substituting the personnel density into the fifth preset function yields the personnel density index.

[0023] Based on the charging station layout and fire protection facility arrangement information, the total area of ​​the charging station and the total fire protection coverage area are determined, and the ratio between the total fire protection coverage area and the total area of ​​the charging station is calculated to obtain the fire protection coverage index.

[0024] Obtain the distance between each charging parking space and the nearest rest area, and substitute the distance into the sixth preset function to obtain the rest area proximity index.

[0025] The facility safety factor is obtained by multiplying the personnel density index, fire protection coverage index, and rest area proximity index.

[0026] In an optional implementation, real-time operating parameters include real-time battery temperature and average charging power.

[0027] The steps for determining the charging compatibility of a vehicle under real-time operating parameters, based on user behavior coefficients, environmental state coefficients, and the real-time operating parameters of the vehicle to be charged, include: The real-time battery temperature is obtained, the temperature deviation between the real-time battery temperature and the preset optimal battery operating temperature is calculated, and the ratio of the temperature deviation to the preset temperature deviation is squared to obtain the temperature deviation term.

[0028] The system obtains the average charging power, the vehicle's rated power, and the charging equipment's rated power. Based on the minimum of the user behavior coefficient, the environmental state coefficient, the vehicle's rated power, and the charging equipment's rated power, it determines the maximum allowable charging power for the vehicle to be charged.

[0029] Calculate the charging power deviation between the average charging power and the maximum allowable charging power, and square the ratio of the charging power deviation to the preset charging power deviation to obtain the power deviation term.

[0030] The charging compatibility is obtained by calculating the sum of the temperature deviation and power deviation terms using the seventh preset function.

[0031] In an optional implementation, the reference charging current is determined in the following manner: Obtain the current battery voltage of the vehicle to be charged.

[0032] The reference charging current is obtained by calculating the ratio of the maximum allowable charging power to the current battery voltage.

[0033] In an optional implementation, the step of correcting the reference charging current based on the charging compatibility and facility safety factor to obtain the target charging current for the vehicle to be charged includes: Multiply the facility safety factor by the reference charging current to obtain the corrected current after facility safety correction.

[0034] Determine if the charging compatibility is below the preset compatibility threshold.

[0035] When the charging compatibility is lower than the preset compatibility threshold, the feedback adjustment coefficient is calculated by the eighth preset function based on the difference between the charging compatibility and the preset compatibility threshold.

[0036] The target charging current is obtained by multiplying the corrected current by the feedback adjustment coefficient.

[0037] In a second aspect, the present invention provides a charging management system, comprising: The user behavior analysis module is used to obtain user charging information for vehicles to be charged and to determine user behavior coefficients based on the user charging information; the user behavior coefficients are used to characterize user charging needs.

[0038] The environmental condition assessment module is used to collect the condition information of the charging environment and determine the environmental condition coefficient based on the condition information; the environmental condition coefficient is used to characterize the charging environment conditions.

[0039] The facility safety monitoring module is used to acquire safety information of charging facilities and determine the facility safety factor based on the safety information; the facility safety factor is used to characterize the safety status of charging facilities.

[0040] The compatibility calculation module is used to determine the charging compatibility of the vehicle to be charged under real-time operating parameters based on user behavior coefficients, environmental state coefficients, and the real-time operating parameters of the vehicle to be charged.

[0041] The current optimization module is used to correct the reference charging current based on the charging compatibility and facility safety factor to obtain the target charging current for the vehicle to be charged.

[0042] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores computer-executable instructions that can run on the processor, and the processor executes the computer-executable instructions to implement the charging management method of any of the foregoing embodiments.

[0043] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the charging management method as described in any of the foregoing embodiments.

[0044] This application provides a charging management method, a charging management system, and an electronic device. By comprehensively collecting user charging information of the vehicle to be charged, status information of the charging environment, and safety information of the charging facilities, corresponding characterization coefficients are generated. Combined with real-time vehicle operating parameters, the charging adaptation degree reflecting the current operating condition is determined. The facility safety coefficient can be used to correct the reference charging current, and feedback adjustment can be implemented according to the charging adaptation degree to obtain the target charging current. Thus, while meeting the personalized charging needs of users, the regional power grid load balance and the physical safety status of the charging site are taken into account. This effectively avoids the battery operating at high power under suboptimal conditions, improves the safety redundancy of the charging system, and effectively extends the battery life.

[0045] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the application.

[0046] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0048] Figure 1 A flowchart of a charging management method provided in an embodiment of this application; Figure 2 A flowchart illustrating the user behavior coefficient calculation method provided in this application embodiment; Figure 3 A flowchart illustrating the environmental state coefficient calculation method provided in this application embodiment; Figure 4 A flowchart illustrating the facility safety factor calculation method provided in this application embodiment; Figure 5 A flowchart of the charging compatibility determination method provided in the embodiments of this application; Figure 6 A flowchart of the reference charging current determination method provided in the embodiments of this application; Figure 7 A flowchart of the target charging current determination method provided in the embodiments of this application; Figure 8 This is a schematic diagram of a charging management system provided in an embodiment of this application.

[0049] Icons: 1-User behavior analysis module; 2-Environmental status assessment module; 3-Facility safety monitoring module; 4-Adaptability calculation module; 5-Current optimization module. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] To help those skilled in the art better understand this application, a brief introduction to its application scenarios and design concepts is provided.

[0052] Currently, although new energy vehicle charging technology has matured, there are still significant limitations in practical management strategies. Existing charging management technologies mostly focus on controlling a single or a few factors. For example, some solutions primarily rely on grid load for orderly charging to smooth out fluctuations, or only on basic overcharge and over-discharge protection based on real-time battery temperature and health. However, these traditional methods lack comprehensive consideration of multiple dimensions, including user behavior, dynamic environmental conditions, and on-site safety of charging facilities. Especially in terms of multi-factor coupling, existing technologies have failed to establish a dynamic adaptation relationship between real-time battery temperature, charging power, and complex environmental conditions, and often neglect key safety factors such as personnel density and fire protection facility coverage at charging stations. This lack of dimension makes it difficult for existing strategies to achieve a balance between safety, economy, and battery life protection when facing complex and ever-changing charging scenarios, thus failing to achieve globally optimal management.

[0053] Based on this, this application proposes a charging management method, a charging management system, and an electronic device. This application constructs a multi-dimensional collaborative optimization model, which not only quantifies users' charging urgency and historical habits but also incorporates environmental conditions such as grid capacity and real-time electricity prices. This application constructs a facility safety model, transforming physical safety factors such as personnel density, fire safety coverage area, and rest area distance within the charging station into quantified safety coefficients, which directly participate in current control. This automatically reduces the charging current when personnel density is high or safety configuration is insufficient, significantly improving the system's safety redundancy. This application can dynamically adjust the power limit based on real-time battery thermal state, effectively preventing the battery from operating under suboptimal conditions while ensuring charging efficiency, thereby extending battery life.

[0054] To facilitate understanding of this embodiment, the embodiments of this application will be described in detail below.

[0055] This application provides a charging management method, referring to... Figure 1 Charging management methods include: Step S101: Obtain user charging information for the vehicle to be charged, and determine user behavior coefficients based on the user charging information; user behavior coefficients are used to characterize user charging needs.

[0056] Here, user charging information may include, but is not limited to, historical charging data of the vehicle to be charged within a preset statistical period (such as average charging time and charging frequency), real-time status data of the vehicle (such as real-time battery level and battery health), and user-inputted preferences.

[0057] In one embodiment, user charging information can be obtained through the communication interface between the vehicle and the charging pile via a vehicle-to-everything (V2X) platform or the charging pile. A multi-dimensional weighted model can be used to calculate the user behavior coefficient. Specifically, the historical average charging time can be normalized based on preset maximum and minimum charging times, and converted into a charging time index using an exponential decay function to reflect the time sensitivity of users accustomed to fast charging. Charging frequency can be handled using a nonlinear mapping function; the higher the frequency, the greater the corresponding index weight. Real-time battery level and battery health are directly quantified as percentages; lower battery levels generally indicate a more urgent charging need. Finally, each of the above indices is multiplied by its corresponding preset weight coefficient and summed to obtain the user behavior coefficient. The user behavior coefficient typically ranges from 0 to 1, with higher values ​​indicating a stronger charging demand from the user.

[0058] In other embodiments, user behavior coefficients can also be determined using lookup tables or machine learning models. For example, a user profile database can be pre-built, mapping different combinations of duration, frequency, and battery power to three levels of demand: high, medium, and low, each corresponding to a different coefficient value. Alternatively, a neural network model can be trained, taking the user's historical behavioral characteristics and current battery power as input, and directly outputting the predicted user behavior coefficient. Furthermore, user charging information can also include the user's calendar schedule information (such as the time interval until the next trip) or the distance to the navigation destination. The system dynamically adjusts the coefficients accordingly; for example, when it detects that the user's next trip is very close, the user behavior coefficient is directly set to the maximum value.

[0059] Step S102: Collect the state information of the charging environment and determine the environmental state coefficient based on the state information; the environmental state coefficient is used to characterize the charging environment conditions.

[0060] Here, the status information of the charging environment typically includes natural environmental parameters (such as ambient temperature and humidity), power grid operating parameters (such as the real-time available capacity of the regional power grid and voltage stability), and economic parameters (such as the real-time electricity price for the current period).

[0061] Specifically, for ambient temperature, the actual temperature can be mapped to an ambient temperature index using a Sigmoid function or a Gaussian function. Within the battery's optimal operating temperature range, the ambient temperature index approaches 1, while it decreases in excessively high or low temperatures. For grid capacity, the ratio of real-time available capacity to rated capacity is calculated to reflect the grid's load margin. For real-time electricity prices, normalization and inverse mapping are performed based on peak-valley price ranges (the higher the price, the lower the index).

[0062] The above exponents are combined through multiplication to obtain the environmental state coefficient. If any one of the environmental factors (such as grid overload or extreme high temperature) fails to meet the conditions, the overall environmental state coefficient will decrease significantly, thereby limiting the charging power.

[0063] In other embodiments, environmental state information may also include wind speed, rainfall, or the real-time output of the photovoltaic power generation system. Besides the product model, fuzzy logic control methods can also be used to fuzzify temperature, grid load, and electricity price into linguistic variables such as suitable, general, and unsuitable, and derive environmental state coefficients through fuzzy inference rules. Alternatively, the weakest link principle (minimum value logic) can be adopted, where the environmental state coefficient directly depends on the lowest-scoring parameter among the aforementioned parameters to ensure the most stringent environmental constraints.

[0064] Step S103: Obtain safety information of the charging facility and determine the facility safety factor based on the safety information; the facility safety factor is used to characterize the safety status of the charging facility.

[0065] Here, the safety information of charging facilities is used to characterize the physical safety status of the charging station and its surroundings, including but not limited to the distribution of people in the preset area, the configuration and status of fire-fighting facilities, and emergency evacuation conditions.

[0066] Specifically, video surveillance equipment installed at charging stations, combined with image recognition algorithms, can be used to count the number of people per unit area in real time, calculate the personnel density, and convert it into a personnel density index using a negative exponential function (the fewer people, the higher the index). Simultaneously, by combining this with a digital floor plan of the charging station, the percentage of area covered by fire-fighting facilities can be calculated to obtain a fire coverage index. Furthermore, the distance from the current parking space to the nearest rest area or safety exit can be obtained through Bluetooth beacons or indoor positioning technology, and converted into a rest area proximity index. Finally, multiplying these three indices yields a facility safety coefficient, reflecting the current safety assurance capability of the scenario.

[0067] In other embodiments, safety information may also include the health status of the equipment itself, such as the wear level of the charging gun head, insulation test data of the charging cable, and the real-time status of the smoke alarm. If an abnormality is detected in the smoke alarm or a decrease in the cable insulation resistance, the facility safety factor can be directly set to zero or a minimum value. Alternatively, a risk matrix assessment method can be used to calculate a comprehensive risk value based on the severity and probability of occurrence of different risk factors, and then normalize it into a facility safety factor (1 minus the risk value).

[0068] Step S104: Based on user behavior coefficients, environmental state coefficients, and the real-time operating parameters of the vehicle to be charged, determine the charging compatibility of the vehicle to be charged under the real-time operating parameters.

[0069] Here, the real-time operating parameters mainly include the battery's real-time temperature, current voltage, current, and average charging power.

[0070] In one embodiment, charging compatibility refers to the compatibility between battery temperature and charging power. Based on user behavior coefficients and environmental state coefficients, combined with the rated power of the vehicle and charging station, the maximum allowable power is dynamically calculated. The deviation between the real-time battery temperature and the optimal operating temperature, as well as the deviation between the actual average charging power and the maximum allowable power, are calculated. A Gaussian exponential function is used to evaluate the sum of squares of these two deviations. The result is a value between 0 and 1; the closer the value is to 1, the more suitable the current charging power is at the current temperature, and the battery is in its comfort zone. The lower the value, the more likely the temperature is too high, the power is too high, or both.

[0071] In other embodiments, determining the charging suitability can also be achieved through a lookup table method or an electrochemical mechanism model. For example, a health charging profile based on temperature and rate can be pre-calibrated, and real-time parameters can be substituted into the profile to look up the corresponding health score as the suitability. Alternatively, a simplified battery electrochemical model (such as a single-particle model) can be used to estimate the lithium plating risk potential inside the battery in real time, and the lithium plating risk level under the current operating conditions can be inversely mapped to the charging suitability.

[0072] Step S105: Correct the reference charging current based on the charging compatibility and facility safety factor to obtain the target charging current for the vehicle to be charged.

[0073] Here, the reference charging current is usually a theoretical reference value calculated based on the maximum allowable power and battery voltage.

[0074] In one embodiment, the correction method can be divided into two layers: the first layer is a hard safety constraint, which directly multiplies the reference charging current by the facility safety factor. This means that no matter how good the battery condition is, if there are dense crowds or poor fire safety conditions on site, the output current will be forcibly reduced proportionally to reduce physical risks. The second layer is a soft adjustment based on operating conditions, which involves feedback adjustment based on the degree of charging compatibility. When the degree of charging compatibility is detected to be lower than a preset threshold, a feedback adjustment coefficient based on an exponential function is introduced to further suppress the current. The result of the two layers of correction is the final target charging current sent to the charging pile controller.

[0075] In other embodiments, the correction process can also employ PID control or model predictive control. For example, the charging fit degree can be used as a feedback variable, and a preset ideal fit degree (e.g., 0.95) can be used as a setpoint. The PID controller can then calculate the adjustment amount of the current. Alternatively, the facility safety factor can be used as a constraint condition for the model predictive control controller, and the shortest charging time and the minimum battery life degradation can be used as the optimization objective function to solve for the optimal target charging current at the next moment in real time. Furthermore, the object of correction can be not only current, but also charging power or charging voltage, depending on the control mode of the charging pile.

[0076] In one embodiment, reference is made to Figure 2 Step S101 includes the following steps S201-S205.

[0077] Step S201: Obtain the historical average charging time, charging frequency, real-time battery level, and battery health status of the vehicle to be charged within a preset statistical period.

[0078] Here, Battery Health Status (SOH) refers to the percentage of the battery's current health state relative to its initial state. This can be calculated using capacity degradation data collected by the battery management system, and its function is to reflect the physical limits of the battery's ability to withstand charging loads. Real-time Battery Charge Status (SOC) refers to the percentage of remaining charge relative to full charge, which can be converted from battery voltage sampling data. Its function is to characterize the urgency of the user's current charging needs. Historical average charging time and charging frequency reflect the user's long-term usage habits.

[0079] Step S202: Normalize the historical average charging time and convert the processed historical average charging time into a charging time index using a first preset function.

[0080] Here, to quantify the impact of users' long-term charging habits on the current strategy, the historical average charging time is imported into the following formula to obtain the charging time index: .in, This indicates the charging time index. This indicates the historical average charging time. Indicates the minimum charging time. This indicates the maximum charging time.

[0081] The first preset function is an exponential decay function. First, the difference between the user's historical average charging time and the preset extreme value range (maximum and minimum charging time) is processed to normalize the data. Then, the exponential decay function is used to highlight the corrective effect of abnormal charging behavior on the current strategy. For example, when a user's historical average charging time is short, the calculated exponential value is large, indicating that the user may be accustomed to rapid charging.

[0082] Step S203: The charging frequency is nonlinearly mapped using a second preset function to obtain the charging frequency index.

[0083] Here, to mitigate the risk of numerical overflow in high-frequency charging scenarios, the charging frequency is imported into the following formula to obtain the charging frequency index: .in, This indicates the charging frequency index. Indicates the charging frequency.

[0084] The second preset function is an asymptotic saturation function. The asymptotic saturation function performs a non-linear transformation on the number of charging cycles, causing the exponent to rise rapidly with increasing frequency when the charging frequency is low, and to level off when the charging frequency is high.

[0085] Step S204: Perform percentage processing on the real-time battery power and battery health status to obtain the power index and battery health index.

[0086] Here, the real-time battery level and battery health status are processed as percentages to directly obtain the battery index. and battery health index These two indices are directly correlated with the battery's physical state through percentage processing, ensuring that the assessment results reflect actual operating conditions.

[0087] Step S205: Based on preset weighting coefficients, the charging time index, charging frequency index, power index, and battery health index are weighted and summed to obtain the user behavior coefficient.

[0088] Here, the current battery level index, current battery health index, current charging time index, and current charging frequency index are imported into the user behavior model to output the current user behavior coefficient. The user behavior model is represented as follows: .in, Represents the user behavior coefficient. This indicates the power consumption index. Indicates the battery health index. This indicates the charging time index. This indicates the charging frequency index. , , , This indicates the preset weighting coefficient.

[0089] The weighting coefficients are adjustment parameters for each component index in the linear combination model, and satisfy the condition that the sum of all weighting coefficients is 1 (i.e., ...). The specific values ​​of the weighting coefficients can be dynamically determined using preset empirical values, the analytic hierarchy process (AHP), or the entropy weighting method. Their purpose is to adjust the priority of different behavioral characteristics according to the operational scenario. The calculated user behavior coefficients... Furthermore, the larger the user behavior coefficient, the greater the user's charging demand, which drives the charging system to prioritize the allocation of charging resources.

[0090] By using a nonlinear transformation function to process data of different dimensions, evaluation bias caused by differences in parameter scales is avoided. At the same time, by dynamically adjusting the contribution of each factor through weight coefficients, the adaptability of the charging strategy to different user groups is enhanced.

[0091] In one embodiment, reference is made to Figure 3 Step S102 includes the following steps S301-S305.

[0092] Step S301: Collect the ambient temperature of the current charging station, the real-time available capacity of the regional power grid, and the corresponding real-time electricity price.

[0093] Here, environmental monitoring equipment continuously collects ambient temperature data around the charging station. Real-time available capacity data of the regional power grid is obtained at a preset frequency (e.g., every 5 minutes). Real-time electricity prices are updated in real time based on the real-time electricity price data released by the power grid.

[0094] Step S302: Convert the ambient temperature into an ambient temperature index using a third preset function.

[0095] Here, to reflect the nonlinear effect of temperature on charging efficiency, a third preset function is used to map the ambient temperature to a standardized ambient temperature index. The ambient temperature index refers to the standardized index mapped from the temperature parameter using a nonlinear function, specifically the Sigmoid function. The ambient temperature is imported into the following formula to obtain the ambient temperature index: .in, Indicates the ambient temperature index. Indicates ambient temperature. Indicates the reference ambient temperature. This represents the temperature sensitivity coefficient.

[0096] slope parameter It can be set to a range of 0.1-0.3 to match different climate zones. The above formula allows the physical temperature value to be converted into an environmental temperature index that reflects the suitability of the environment.

[0097] Step S303: Calculate the ratio of real-time available capacity to the rated capacity of the regional power grid to obtain the power grid capacity adequacy index.

[0098] Here, the grid capacity adequacy index refers to the ratio of the grid's real-time power supply capacity to its design capacity. It is used to quantify the grid's load margin to mitigate overload risks. Specifically, it is obtained by calculating the ratio of the regional grid's real-time available capacity to its rated regional grid capacity. The grid capacity adequacy index fluctuates between 0 and 1. The closer the value is to 1, the lighter the grid load and the more abundant the capacity; the lower the value, the heavier the grid load.

[0099] Step S304: Based on the preset peak electricity price and preset valley electricity price, normalize the real-time electricity price, and convert the processed real-time electricity price into an electricity price index through the fourth preset function.

[0100] Here, the electricity price index refers to mapping electricity price fluctuations to standardized parameters through an exponential function, serving to balance user economics with grid regulation needs. First, the real-time electricity price is normalized relative to the range comprised of preset peak and valley prices. Then, a fourth preset function is used for conversion. This fourth preset function can be an exponential decay function. The electricity price index is obtained by importing the real-time electricity price into the following formula: .in, This represents the electricity price index. Indicates real-time electricity price. This indicates the preset off-peak electricity price. This indicates the preset peak electricity price.

[0101] The closer the real-time electricity price is to the preset off-peak electricity price, the larger the index value, indicating better economic conditions during this period, and the system tends to encourage charging. Conversely, the closer the electricity price is to the preset peak electricity price, the lower the index, and the system will suppress charging power.

[0102] Step S305: Calculate the product of the ambient temperature index, the power grid capacity adequacy index, and the electricity price index to obtain the environmental state coefficient.

[0103] Here, the current ambient temperature index, current power grid capacity adequacy index, and current electricity price index are imported into the charging environment state model, and the current environment state coefficient is obtained through product calculation. The charging environment state model is represented as follows: .in, Represents the environmental state coefficient. Indicates the ambient temperature index. Indicators representing the adequacy of power grid capacity This represents the electricity price index.

[0104] The calculated environmental state coefficient ranges from [0,1], with a larger value indicating a better charging environment. When the ambient temperature is close to the device's optimal operating temperature, the power grid capacity is sufficient, and the electricity price is at a preset off-peak level, the environmental state coefficient approaches 1, at which point the system determines it to be the optimal charging environment.

[0105] By employing an environmental state coefficient determination method, a multi-dimensional dynamic assessment of the charging environment state is achieved, resolving the misjudgment problem caused by single-parameter evaluation. For example, during high-temperature periods in summer, even with sufficient grid capacity and low electricity prices, abnormally high ambient temperatures will still lower the overall environmental state coefficient, automatically triggering a charging power limiting mechanism to avoid safety hazards caused by the combined effects of high-temperature environments and charging heat. Simultaneously, by guiding users to charge during the most economically efficient periods through real-time electricity price indices, the system will appropriately reduce charging power to maintain grid stability even when grid capacity is low and temperature conditions are suitable.

[0106] In one embodiment, reference is made to Figure 4 Step S103 includes the following steps S401-S405.

[0107] Step S401: Obtain the number of people in the preset area of ​​the charging facility and calculate the personnel density per unit area.

[0108] Here, a video surveillance system deployed within the charging station can acquire real-time footage and use crowd density estimation algorithms (such as deep learning-based image recognition algorithms) to identify the number of people in the footage. Combined with the actual physical area of ​​the preset zone, the number of people per square meter, i.e., the crowd density, can be calculated. .

[0109] Step S402: Substitute the personnel density into the fifth preset function to obtain the personnel density index.

[0110] Here, the personnel density index is a quantitative indicator that uses an exponential function to non-linearly transform the number of people per unit area within a charging station. The personnel density is then incorporated into the following formula: .in, Indicates the population density index. Indicates population density.

[0111] The fifth preset function can be a negative exponential function.

[0112] As personnel density increases, the index value decreases exponentially. For example, when there are few people in the charging station, Approaching 0, A value approaching 1 indicates a good safety condition. However, when population density exceeds a certain critical value... It rapidly approaches zero.

[0113] Step S403: Based on the charging station layout and fire protection facility arrangement information, determine the total area of ​​the charging station and the total fire protection coverage area, and calculate the ratio between the total fire protection coverage area and the total area of ​​the charging station to obtain the fire protection coverage index.

[0114] Here, the fire protection coverage index is used to quantify the rationality of fire protection resource allocation. Specifically, geographic information systems or digital building modeling technology can be used to map the effective protection radius of fire protection equipment (such as fire extinguishers, fire hydrants, and automatic sprinkler systems) onto the charging station's floor plan, and calculate the intersection area of ​​these protection ranges with the total area of ​​the charging station, i.e., the total fire protection coverage area. The ratio of this coverage area to the total area of ​​the charging station is then calculated to obtain the fire protection coverage index. A higher fire safety coverage index value indicates a more comprehensive fire safety guarantee.

[0115] Step S404: Obtain the distance between each charging parking space and the nearest rest area, and substitute the distance into the sixth preset function to obtain the rest area proximity index.

[0116] Here, the rest area proximity index is an indicator reflecting emergency evacuation capability. The actual straight-line distance between a currently operating charging station and the nearest rest area or safety exit can be measured using Bluetooth beacons deployed within the charging station, a UWB indoor positioning system (Ultra-Wideband Indoor Positioning System), or preset ranging data. Subsequently, the distance is converted into a rest area proximity index using a sixth preset function, with the specific formula as follows: .in, This indicates the proximity index of the rest area. This indicates the distance between the charging station and the nearest rest area or safety exit.

[0117] The closer the distance, the higher the proximity index of the rest area. The proximity index represents the user's ability to quickly evacuate to a safe area in an emergency. As the distance increases (for every additional 1 meter), the proximity index of the rest area decreases exponentially, indicating an increased evacuation risk.

[0118] Step S405: Calculate the product of the personnel density index, fire coverage index, and rest area proximity index to obtain the facility safety factor.

[0119] Here, the personnel density index, fire protection coverage index, and rest area proximity index are imported into the facility safety model, and the current facility safety coefficient is output through multiplication. The facility safety model is represented as follows: .in, This indicates the safety factor of the facility.

[0120] The calculated facility safety factor ranges from [0,1], with a higher factor indicating a better level of safety and service at the charging station. The advantage of using multiplication is that it creates a strong coupling relationship between safety elements: if any of the above parameters approaches zero (e.g., insufficient fire protection coverage, extremely high personnel density, or extreme distance from evacuation points), the overall safety factor drops sharply. This ensures that even under favorable conditions, the system will significantly reduce charging power or current if a significant safety weakness exists, thereby maximizing on-site safety.

[0121] In one embodiment, real-time operating parameters include real-time battery temperature and average charging power. (See reference...) Figure 5 Step S104 includes the following steps S501-S504.

[0122] Step S501: Obtain the real-time battery temperature, calculate the temperature deviation between the real-time battery temperature and the preset optimal battery operating temperature, and square the ratio of the temperature deviation to the preset temperature deviation to obtain the temperature deviation term.

[0123] Here, real-time battery temperature refers to the current battery temperature value collected by a temperature sensor, which can be implemented using a thermocouple or infrared temperature measurement device, with the optimal battery operating temperature preset. This refers to the median of the ideal operating temperature range set by the battery manufacturer. Specific values ​​can be obtained from the battery specifications or experimental calibration and are used to establish a temperature adaptation benchmark. Preset temperature deviation value. It refers to the standard deviation of the temperature fluctuation range that the battery can withstand. The specific value is set according to the battery type and safety test data, and is used to quantify the degree of impact of temperature deviation on compatibility.

[0124] Step S502: Obtain the average charging power, vehicle rated power, and charging equipment rated power, and determine the maximum allowable charging power of the vehicle to be charged based on the minimum value among the user behavior coefficient, environmental state coefficient, vehicle rated power, and charging equipment rated power.

[0125] Here, the minimum rated power of the vehicle and charging station. This refers to the maximum power limit that the charging equipment is compatible with the vehicle battery. Specifically, it is obtained by reading the parameters on the charging pile nameplate and the data from the vehicle battery management system, and is used to determine the physical upper limit of power adjustment.

[0126] Maximum allowable power The calculation formula is: .in, Represents the user behavior coefficient. This represents the charging environment state coefficient.

[0127] The maximum allowable power is not a fixed value, but is dynamically generated by multiplying the rated power by the user behavior coefficient and the environmental state coefficient. This ensures that the charging power does not exceed the physical limit of the equipment, and can adjust the power threshold in real time according to the urgency of the user's charging needs (reflected by the user behavior coefficient) and the grid load and environmental conditions (reflected by the charging environment state coefficient). For example, the power limit can be increased when the user urgently needs to charge, or the power threshold can be decreased when the grid capacity is tight.

[0128] Step S503: Calculate the charging power deviation value between the average charging power and the maximum allowable charging power, and square the ratio of the charging power deviation value to the preset charging power deviation value to obtain the power deviation term.

[0129] Here, the average charging power This refers to the average power within the most recent set time window during the charging process. It can be calculated using a moving average algorithm and is used to reflect the actual charging load status. Preset charging power deviation value. It refers to the standard deviation of the allowable range of power fluctuations, which is specifically set according to the performance parameters of the charging pile and is used to evaluate the degree of matching between the actual power and the theoretical maximum value.

[0130] Step S504: The sum of the temperature deviation term and the power deviation term is calculated using the seventh preset function to obtain the charging compatibility level.

[0131] Here, the seventh preset function can be a Gaussian exponential function.

[0132] A temperature-power adaptation model is constructed, and the degree of charging adaptation is calculated using a Gaussian exponential function. The temperature-power adaptation model is expressed as: .in, Indicates the current charging compatibility level. Indicates real-time battery temperature. Indicates the optimal operating temperature of the battery. This indicates the maximum permissible power.

[0133] The charging compatibility is calculated by adding the squared deviation of the real-time battery temperature from the optimal operating temperature (i.e., the temperature deviation term) and the squared deviation of the actual charging power from the dynamic maximum allowable power (i.e., the power deviation term), and taking the negative exponent. . Normalized to between 0 and 1, a larger value indicates a better health match for the battery when the current average power is applied at the current battery temperature.

[0134] When the temperature or power exceeds the set threshold range, the value of the squared term increases, causing the result of the exponential function to decrease significantly.

[0135] The above-described method for calculating charging compatibility solves the problem of insufficient dynamic adaptation between temperature and power, enabling dynamic adjustment of evaluation results based on real-time battery temperature, user charging needs, and grid conditions. For example, when the battery temperature is close to its optimal operating range and the grid capacity is sufficient, charging power can be increased to shorten charging time; when the temperature deviates from the threshold or environmental conditions worsen, the compatibility decreases, providing a quantitative basis for subsequent automatic power reduction, extending battery life, and ensuring equipment safety.

[0136] In one embodiment, reference is made to Figure 6 The reference charging current in step S105 is determined by the following steps S601-S602.

[0137] Step S601: Obtain the current battery voltage of the vehicle to be charged.

[0138] Here, battery voltage refers to the real-time total voltage of the power battery pack, which can be read in real time through the communication protocol between the charging pile and the electric vehicle battery management system, or it can be directly measured by the voltage acquisition module at the charging pile.

[0139] Step S602: Calculate the ratio between the maximum allowable charging power and the current battery voltage to obtain the reference charging current.

[0140] Here, the maximum allowable charging power is compared with the current battery voltage to obtain the reference charging current. The specific calculation follows the power formula. In other words, the reference charging current equals the maximum permissible charging power divided by the current battery voltage. The maximum permissible charging power is a dynamic power ceiling determined based on a combination of factors including the vehicle's rated power, the charging equipment's rated power, user behavior coefficients, and environmental condition coefficients.

[0141] The reference charging current calculated using the above method meets the physical rated limits of the vehicle and the charging pile, and corresponds to the current value of the power boundary after adjustment based on user habits and environmental conditions. This reference charging current serves as the input basis for the subsequent current optimization model, providing an adjustable benchmark for the final current cut based on facility safety status and real-time battery thermal state.

[0142] In one embodiment, reference is made to Figure 7 Step S105 includes the following steps S701-S704.

[0143] Step S701: Multiply the facility safety factor by the reference charging current to obtain the corrected current after facility safety correction.

[0144] Here, the facility safety factor is a quantitative indicator that integrates personnel density, fire protection coverage, and evacuation conditions. By directly multiplying the facility safety factor by the baseline charging current, the magnitude of the correction current is positively correlated with the safety level of the charging station. For example, when fire protection coverage increases or personnel density decreases, the facility safety factor increases, and the permissible correction current increases accordingly. Conversely, if the on-site personnel are too dense, the safety factor decreases, and the correction current will be forcibly reduced.

[0145] Step S702: Determine whether the charging compatibility level is lower than the preset compatibility threshold.

[0146] Here, the charging compatibility level reflects the matching between the current charging power and the real-time battery temperature. Preset compatibility threshold. This refers to the critical value of the matching degree between battery temperature and charging power, used to determine whether current limiting protection needs to be triggered. If the real-time calculated charging compatibility degree is higher than or equal to the preset compatibility threshold, it indicates that the current operating condition is within the battery's comfortable range. If it is lower than the preset compatibility threshold, it indicates that the battery may face the risk of overheating or overload, and current reduction is required.

[0147] Step S703: When the charging compatibility level is lower than the preset compatibility threshold, the feedback adjustment coefficient is calculated by the eighth preset function based on the compatibility level difference between the charging compatibility level and the preset compatibility threshold.

[0148] Here, the eighth preset function can be an exponential decay function.

[0149] When a decrease in charging compatibility is detected and falls below a preset compatibility threshold, a negative feedback mechanism between charging compatibility and current is established using an exponential decay function. Specifically, the difference between the preset compatibility threshold and the current charging compatibility is calculated, and combined with the compatibility sensitivity coefficient, a feedback adjustment coefficient is generated. The compatibility sensitivity coefficient is less than or equal to 1, and the larger the difference, the smaller the feedback adjustment coefficient.

[0150] Step S704: Multiply the correction current by the feedback adjustment coefficient to obtain the target charging current.

[0151] Here, the target charging current The current optimization model is calculated and expressed as follows: .in, Indicates the reference charging current. Indicates the facility's safety factor. This represents the fitness sensitivity coefficient. This indicates the preset adaptation threshold.

[0152] Among them, the exponent term This is the feedback adjustment coefficient. The compatibility sensitivity coefficient refers to the adjustment parameter that controls the impact of charging compatibility deviation on current. It can be determined through empirical calibration or historical data analysis and is used to control the rate of current decay (i.e., the slope of the decay curve). When this coefficient increases, the same compatibility deviation will result in more significant current decay. For example, a higher sensitivity coefficient can be set in high-temperature environments to enhance thermal protection sensitivity.

[0153] By employing a target charging current calculation method, dynamic closed-loop control of the target current can be achieved. On one hand, by multiplying it by a facility safety factor, the target current increases as the charging station's safety level improves. On the other hand, a negative feedback loop is established using an exponential function. When the battery temperature exceeds its optimal operating range, leading to a decrease in compatibility, the current automatically decreases to avoid overheating risks. This exponential function-based adjustment method achieves a smooth transition in current adjustment, avoiding equipment shocks caused by traditional step-like adjustments. Simultaneously, it ensures a warning-like current decay when the charging compatibility approaches a threshold, thereby effectively extending battery life while eliminating safety hazards.

[0154] Based on the above embodiments, this application provides a charging management system, referring to... Figure 8 The charging management system provided in this application includes: User behavior analysis module 1 is used to obtain user charging information of vehicles to be charged and determine user behavior coefficients based on the user charging information; user behavior coefficients are used to characterize user charging needs.

[0155] The environmental status assessment module 2 is used to collect the status information of the charging environment and determine the environmental status coefficient based on the status information; the environmental status coefficient is used to characterize the charging environment conditions.

[0156] Facility safety monitoring module 3 is used to acquire safety information of charging facilities and determine the facility safety coefficient based on the safety information; the facility safety coefficient is used to characterize the safety status of charging facilities.

[0157] The adaptability calculation module 4 is used to determine the charging adaptability of the vehicle to be charged under the real-time operating parameters based on user behavior coefficients, environmental state coefficients, and the real-time operating parameters of the vehicle to be charged.

[0158] The current optimization module 5 is used to correct the reference charging current based on the charging compatibility and facility safety factor to obtain the target charging current for the vehicle to be charged.

[0159] In an optional implementation, the user behavior analysis module 1 is further configured to: Obtain the historical average charging duration, charging frequency, real-time battery level, and battery health status of the vehicle to be charged within a preset statistical period.

[0160] The historical average charging time is normalized, and the processed historical average charging time is converted into a charging time index through a first preset function.

[0161] The charging frequency index is obtained by performing a nonlinear mapping on the charging frequency using a second preset function.

[0162] The real-time battery level and battery health status are processed as percentages to obtain the battery level index and battery health index, respectively.

[0163] The user behavior coefficient is obtained by weighting and summing the charging time index, charging frequency index, power index and battery health index based on preset weighting coefficients.

[0164] In an optional implementation, the environmental status assessment module 2 is further configured to: Collect the ambient temperature of the current charging station, the real-time available capacity of the regional power grid, and the corresponding real-time electricity price.

[0165] The ambient temperature is converted into an ambient temperature index using the third preset function.

[0166] The ratio of real-time available capacity to the rated capacity of the regional power grid is calculated to obtain the power grid capacity adequacy index.

[0167] Based on the preset peak electricity price and preset valley electricity price, the real-time electricity price is normalized, and the processed real-time electricity price is converted into an electricity price index through the fourth preset function.

[0168] The environmental state coefficient is obtained by multiplying the ambient temperature index, the power grid capacity adequacy index, and the electricity price index.

[0169] In an optional implementation, the facility safety monitoring module 3 is further configured to: Obtain the number of people within the preset area of ​​the charging facility and calculate the population density per unit area.

[0170] Substituting the personnel density into the fifth preset function yields the personnel density index.

[0171] Based on the charging station layout and fire protection facility arrangement information, the total area of ​​the charging station and the total fire protection coverage area are determined, and the ratio between the total fire protection coverage area and the total area of ​​the charging station is calculated to obtain the fire protection coverage index.

[0172] Obtain the distance between each charging parking space and the nearest rest area, and substitute the distance into the sixth preset function to obtain the rest area proximity index.

[0173] The facility safety factor is obtained by multiplying the personnel density index, fire protection coverage index, and rest area proximity index.

[0174] In an optional implementation, real-time operating parameters include real-time battery temperature and average charging power. The adaptability calculation module 4 is also used for: The real-time battery temperature is obtained, the temperature deviation between the real-time battery temperature and the preset optimal battery operating temperature is calculated, and the ratio of the temperature deviation to the preset temperature deviation is squared to obtain the temperature deviation term.

[0175] The system obtains the average charging power, the vehicle's rated power, and the charging equipment's rated power. Based on the minimum of the user behavior coefficient, the environmental state coefficient, the vehicle's rated power, and the charging equipment's rated power, it determines the maximum allowable charging power for the vehicle to be charged.

[0176] Calculate the charging power deviation between the average charging power and the maximum allowable charging power, and square the ratio of the charging power deviation to the preset charging power deviation to obtain the power deviation term.

[0177] The charging compatibility is obtained by calculating the sum of the temperature deviation and power deviation terms using the seventh preset function.

[0178] In an optional implementation, the current optimization module 5 is further configured to: Obtain the current battery voltage of the vehicle to be charged.

[0179] The reference charging current is obtained by calculating the ratio of the maximum allowable charging power to the current battery voltage.

[0180] In an optional implementation, the current optimization module 5 is further configured to: Multiply the facility safety factor by the reference charging current to obtain the corrected current after facility safety correction.

[0181] Determine if the charging compatibility is below the preset compatibility threshold.

[0182] When the charging compatibility is lower than the preset compatibility threshold, the feedback adjustment coefficient is calculated by the eighth preset function based on the difference between the charging compatibility and the preset compatibility threshold.

[0183] The target charging current is obtained by multiplying the corrected current by the feedback adjustment coefficient.

[0184] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the charging management method provided in the above embodiments.

[0185] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the charging management method described above.

[0186] The computer program product provided in this application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0187] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0188] Furthermore, in the description of the embodiments of this application, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0189] If the aforementioned functions are implemented as software functional units 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 this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0190] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0191] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in this application, or make equivalent substitutions for some of the technical features. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the protection scope of this application.

Claims

1. A charging management method, characterized in that, include: Obtain user charging information for vehicles to be charged, and determine user behavior coefficients based on the user charging information; The user behavior coefficient is used to characterize the user's charging needs. Collect charging environment status information and determine environmental status coefficients based on the status information; the environmental status coefficients are used to characterize charging environment conditions. Obtain the number of people within the preset area of ​​the charging facility and calculate the population density per unit area; Substituting the personnel density into the fifth preset function yields the personnel density index; Based on the charging station layout and fire protection facility arrangement information, the total area of ​​the charging station and the total fire protection coverage area are determined, and the ratio between the total fire protection coverage area and the total area of ​​the charging station is calculated to obtain the fire protection coverage index. Obtain the distance between each charging parking space and the nearest rest area, and substitute the distance into the sixth preset function to obtain the rest area proximity index; The facility safety factor is obtained by multiplying the personnel density index, the fire protection coverage index, and the rest area proximity index; the facility safety factor is used to characterize the safety status of the charging facility. Based on the user behavior coefficient, the environmental state coefficient, and the real-time operating parameters of the vehicle to be charged, the charging adaptability of the vehicle to be charged under the real-time operating parameters is determined. The reference charging current is corrected based on the charging compatibility and the facility safety factor to obtain the target charging current for the vehicle to be charged.

2. The charging management method according to claim 1, characterized in that, The step of obtaining user charging information for vehicles to be charged and determining user behavior coefficients based on the user charging information includes: The historical average charging duration, charging frequency, real-time battery level, and battery health status of the vehicle to be charged are obtained within a preset statistical period. The historical average charging time is normalized, and the processed historical average charging time is converted into a charging time index through a first preset function. The charging frequency is nonlinearly mapped using a second preset function to obtain the charging frequency index; The real-time battery power and the battery health status are processed as percentages to obtain the power index and the battery health index, respectively. The user behavior coefficient is obtained by weighting and summing the charging time index, the charging frequency index, the power index, and the battery health index based on preset weighting coefficients.

3. The charging management method according to claim 1, characterized in that, The step of collecting the state information of the charging environment and determining the environmental state coefficient based on the state information includes: Collect the ambient temperature of the current charging station, the real-time available capacity of the regional power grid, and the corresponding real-time electricity price; The ambient temperature is converted into an ambient temperature index using a third preset function. The ratio of the real-time available capacity to the rated capacity of the regional power grid is calculated to obtain the power grid capacity adequacy index. Based on the preset peak electricity price and the preset valley electricity price, the real-time electricity price is normalized, and the processed real-time electricity price is converted into an electricity price index through the fourth preset function; The environmental state coefficient is obtained by multiplying the ambient temperature index, the power grid capacity adequacy index, and the electricity price index.

4. The charging management method according to claim 1, characterized in that, The real-time operating parameters include real-time battery temperature and average charging power; The step of determining the charging adaptability of the vehicle to be charged under the real-time operating parameters based on the user behavior coefficient, the environmental state coefficient, and the real-time operating parameters of the vehicle to be charged includes: The real-time battery temperature is obtained, the temperature deviation between the real-time battery temperature and the preset optimal battery operating temperature is calculated, and the ratio of the temperature deviation to the preset temperature deviation is squared to obtain the temperature deviation term. The average charging power, vehicle rated power, and charging equipment rated power are obtained, and the maximum allowable charging power of the vehicle to be charged is determined based on the minimum value among the user behavior coefficient, the environmental state coefficient, the vehicle rated power, and the charging equipment rated power. Calculate the charging power deviation between the average charging power and the maximum allowable charging power, and square the ratio of the charging power deviation to the preset charging power deviation to obtain the power deviation term; The charging compatibility is obtained by calculating the sum of the temperature deviation term and the power deviation term using the seventh preset function.

5. The charging management method according to claim 4, characterized in that, The reference charging current is determined in the following manner: Obtain the current battery voltage of the vehicle to be charged; The reference charging current is obtained by calculating the ratio between the maximum allowable charging power and the current battery voltage.

6. The charging management method according to claim 1, characterized in that, The step of correcting the reference charging current based on the charging compatibility and the facility safety factor to obtain the target charging current for the vehicle to be charged includes: Multiply the facility safety factor by the reference charging current to obtain the corrected current after facility safety correction; Determine whether the charging compatibility level is lower than a preset compatibility threshold; When the charging compatibility is lower than the preset compatibility threshold, the feedback adjustment coefficient is calculated by the eighth preset function based on the compatibility difference between the charging compatibility and the preset compatibility threshold. The target charging current is obtained by multiplying the corrected current by the feedback adjustment coefficient.

7. A charging management system, characterized in that, The system for performing the charging management method as described in any one of claims 1-6 includes: The user behavior analysis module is used to acquire user charging information of vehicles to be charged, and to determine user behavior coefficients based on the user charging information; the user behavior coefficients are used to characterize user charging needs. An environmental condition assessment module is used to collect state information of the charging environment and determine environmental condition coefficients based on the state information; the environmental condition coefficients are used to characterize the charging environment conditions. The facility safety monitoring module is used to acquire the number of people in the preset area of ​​the charging facility and calculate the personnel density per unit area; substitute the personnel density into a fifth preset function to obtain a personnel density index; determine the total area of ​​the charging station and the total fire protection coverage area based on the charging station's layout and fire protection facility arrangement information, and calculate the ratio between the total fire protection coverage area and the total area of ​​the charging station to obtain a fire protection coverage index; acquire the distance between each charging parking space and the nearest rest area, and substitute the distance into a sixth preset function to obtain a rest area proximity index; calculate the product of the personnel density index, the fire protection coverage index, and the rest area proximity index to obtain a facility safety coefficient; the facility safety coefficient is used to characterize the safety status of the charging facility. The adaptability calculation module is used to determine the charging adaptability of the vehicle to be charged under the real-time operating parameters based on the user behavior coefficient, the environmental state coefficient, and the real-time operating parameters of the vehicle to be charged. The current optimization module is used to correct the reference charging current based on the charging compatibility and the facility safety factor to obtain the target charging current for the vehicle to be charged.

8. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores computer-executable instructions that can run on the processor, and the processor executes the computer-executable instructions to implement the charging management method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the charging management method as described in any one of claims 1-6.

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