Charging pile complete machine charging and power distribution control system
By combining data acquisition, temperature prediction, thermal safety analysis, and urgency analysis, the power allocation of charging piles is dynamically optimized, which solves the shortcomings of existing charging pile systems in thermal management and user demand response, and achieves dual optimization of equipment safety and user satisfaction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 山西尧兴新能源科技有限公司
- Filing Date
- 2026-05-21
- Publication Date
- 2026-07-21
Smart Images
Figure CN122211229B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of charging pile control technology, and particularly relates to the charging and power distribution control system of the entire charging pile. Background Technology
[0002] With the rapid development of the new energy vehicle industry, charging infrastructure is facing unprecedented operational pressure.
[0003] Current charging pile systems face three main technical bottlenecks: First, in terms of thermal management, traditional temperature monitoring relies solely on real-time data and lacks an effective temperature prediction mechanism, making it difficult to promptly anticipate overheating risks in critical components such as power modules and DC contactors. When multiple charging piles operate in a cluster, localized high temperatures can trigger a chain reaction, leading to system-wide thermal runaway risks. Second, regarding user demand response, existing systems employ simple strategies such as first-come, first-served or average allocation, failing to differentiate between high-time-sensitivity demands like ride-hailing and ambulance services and regular demands like private cars, resulting in inefficient utilization of social resources. Third, in terms of system coordination, fixed power allocation models struggle to dynamically balance equipment safety margins with user waiting tolerance, potentially leading to idle charging resources due to excessive conservatism or accelerated equipment aging due to overly aggressive approaches. Especially in high-temperature or continuous operation scenarios during summer, existing systems cannot predict temperature evolution trends through autoregressive models, nor can they establish a multi-dimensional evaluation system by combining users' charging power target value and latency sensitivity characteristics. Furthermore, they lack optimization algorithms that dynamically couple thermal safety penalty index with user urgency penalty index, making it difficult to improve the overall energy efficiency ratio and user satisfaction of charging pile clusters simultaneously. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention provides a charging pile power distribution control system, which solves the aforementioned problems.
[0005] To achieve the above objectives, the present invention provides the following technical solution: a charging pile overall charging and power distribution control system, specifically comprising: The data acquisition unit is used to acquire internal temperature data of the charging pile, the user's target charging capacity, and user charging delay sensitive information. The temperature prediction unit is used to generate predicted internal temperature values of the charging pile based on the internal temperature data of the charging pile using an autoregressive model. The thermal safety analysis unit is used to generate a thermal safety penalty index for the charging pile based on the predicted internal temperature of the charging pile. The urgency analysis unit is used to establish an urgency analysis model based on the user's target charging capacity and the user's charging delay sensitivity information, and generate a user urgency penalty index. The optimal instruction analysis unit is used to establish an optimal power allocation analysis model based on the charging pile thermal safety penalty index and the user urgency penalty index, and generate the optimal final power allocation value. The control unit is used to control the entire charging pile according to the optimal final power allocation value.
[0006] Based on the above technical solutions, the present invention also provides the following optional technical solutions: Further technical solution: The method for generating the predicted internal temperature value of the charging pile specifically includes: Through the formula: ; Generate predicted internal temperature values for charging piles ; In the formula, AR represents the autoregressive model. This represents the internal temperature value of the charging pile at the current moment k. This represents the internal temperature value of the charging pile at time k-1. This represents the internal temperature value of the charging pile at time kn. The "moment" refers to the moment when the charging station starts operating.
[0007] Further technical solution: The specific method for generating the thermal safety penalty index of the charging pile includes: Through the formula: ; Generate a thermal safety penalty index for charging piles ; In the formula, This represents the predicted internal temperature of the charging pile at time k+1. This indicates the internal temperature safety threshold of the charging station.
[0008] Further technical solution: The urgency analysis unit specifically includes: The charging delay analysis module is used to generate the charging extension time based on the user's target charging capacity. The urgency penalty index generation module is used to establish an urgency analysis model based on the charging extension time and user charging delay sensitivity information, and generate a user urgency penalty index.
[0009] Further technical solution: The method for generating the extended charging time specifically includes: Through the formula: ; Generate extended charging time ; In the formula, This indicates the user's target remaining charging capacity. This represents the output power variable of the charging station. This represents the output power of the charging pile at the current moment k.
[0010] A further technical solution: The expression of the urgency analysis model is specifically as follows: ; In the expression, This represents the user's urgency level penalty index. This represents the normalized value of the extended charging time. This represents the flag value of the charging delay sensitive feature information for the i-th user, and m represents the number of user charging delay sensitive feature information. This represents the weighting coefficient of the user's sensitivity to charging delay.
[0011] Further technical solution: The optimal instruction analysis unit specifically includes: The penalty comprehensive value generation module is used to generate a penalty comprehensive value based on the charging pile thermal safety penalty index and the user urgency penalty index. The optimal power allocation value generation module is used to generate an initial optimal power allocation value based on the penalty comprehensive value; The collaborative analysis module is used to establish an optimal power allocation analysis model based on the initial optimal power allocation value and generate the final optimal power allocation value.
[0012] Further technical solution: The method for generating the penalty comprehensive value specifically includes: Through the formula: ; Generate a comprehensive penalty value ; In the formula, This represents the user's urgency level penalty index. This indicates the thermal safety penalty index of the charging station. This represents the weighting coefficient of the user urgency penalty index.
[0013] A further technical solution: The method for generating the initial optimal power allocation value is as follows: ; Generate initial optimal power allocation values ; In the expression, R represents the variable output power of the charging pile, and R represents the set of variable output power of the charging pile. This represents the overall penalty value.
[0014] Further technical solution: The specific expression of the optimal power allocation analysis model is as follows: ; In the expression, This represents the optimal final power allocation value. This represents the initial optimal power allocation value. This represents the collaborative correction factor; The specific methods for obtaining the collaborative correction factor include: Through the formula: ; Generate collaborative correction factor ; In the formula, This represents the initial optimal power allocation value for the j-th charging pile, and N represents the number of charging piles. This represents the output power of the j-th charging pile at the current time k. This represents the remaining available resources of the charging station.
[0015] This invention provides a charging pile power distribution control system, which has the following advantages compared with the prior art: This invention achieves intelligent charging and power distribution management of the entire charging pile by acquiring key parameters through a data acquisition unit, generating internal temperature prediction values through a temperature prediction unit, assessing thermal risks through a thermal safety analysis unit, quantifying user needs through an urgency analysis unit, dynamically optimizing power allocation through an optimal instruction analysis unit, and executing control through a control unit. This effectively balances equipment thermal safety and user needs, thereby improving overall efficiency and user satisfaction. Attached Figure Description
[0016] Figure 1 This is a schematic diagram of the charging pile power distribution control system provided by the present invention.
[0017] Figure 2 This is a schematic diagram of the urgency analysis unit provided by the present invention.
[0018] Figure 3 A schematic diagram of the structure of the optimal instruction analysis unit provided by the present invention.
[0019] Figure 4 This is a flowchart illustrating the charging and power distribution control method for the entire charging pile provided by the present invention. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0021] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.
[0022] Please see Figure 1 The charging pile power distribution control system provided in one embodiment of the present invention specifically includes: Data acquisition unit 10 is used to acquire internal temperature data of the charging pile, the user's target charging capacity, and user charging delay sensitive characteristic information; Temperature prediction unit 20 is used to generate predicted internal temperature values of the charging pile based on the internal temperature data of the charging pile using an autoregressive model. The thermal safety analysis unit 30 is used to generate a thermal safety penalty index for the charging pile based on the predicted internal temperature value of the charging pile. The urgency analysis unit 40 is used to establish an urgency analysis model based on the user's target charging capacity and the user's charging delay sensitivity information, and generate a user urgency penalty index. The optimal instruction analysis unit 50 is used to establish an optimal power allocation analysis model based on the charging pile thermal safety penalty index and the user urgency penalty index, and generate the optimal final power allocation value. The control unit 60 is used to control the entire charging pile according to the optimal final power allocation value; Data acquisition unit 10 is responsible for acquiring basic information required for operation from the internal and external environment of the charging pile, including the internal temperature data of the charging pile, the user's target charging capacity, and the user's charging delay sensitive characteristics. Among them, the user charging delay sensitivity information describes the user's acceptance or sensitivity to the extension of charging time, such as whether the user is in a hurry to leave, the purpose of the vehicle (private car or commercial vehicle), etc., to assess the user's urgency to charge. Autoregressive models are a statistical method that uses past values of time series data to predict future values. They are suitable for predicting time-dependent data, such as the internal temperature of charging piles. Specifically, the data acquisition unit is responsible for acquiring the various basic data required for the operation of the charging pile. For example, the internal temperature data of the charging pile can be monitored and uploaded in real time through the built-in temperature sensor; The user's target charging level can be input by the user through the charging station's human-machine interface at the start of charging; sensitive information about user charging delay can be obtained through user registration information, vehicle type recognition, or the charging mode selected by the user before charging (such as "fast charging" or "economy charging"). This data provides the foundation for subsequent intelligent analysis and decision-making. Furthermore, the system includes a thermal safety analysis unit, which generates a thermal safety penalty index for the charging pile based on the predicted internal temperature value. In one implementation, the thermal safety analysis unit can set a fixed temperature safety threshold. When the predicted temperature exceeds the threshold, a fixed high penalty index is generated; when the predicted temperature is below the threshold, a zero penalty index is generated.
[0023] The system also includes an urgency analysis unit, which establishes an urgency analysis model based on the user's target charging capacity and user charging delay sensitivity information, generating a user urgency penalty index. In one implementation, the urgency analysis unit can directly assign a preset urgency penalty index based on the user's vehicle type (e.g., commercial vehicles are considered high urgency, and private cars are considered medium urgency), without considering the specific target charging capacity. For example, all vehicles identified as taxis have their urgency penalty index set to a high value.
[0024] Furthermore, the system is equipped with an optimal instruction analysis unit. This unit is used to establish an optimal power allocation analysis model based on the aforementioned charging pile thermal safety penalty index and user urgency penalty index, generating the optimal final power allocation value. In one implementation, the optimal instruction analysis unit can use simple priority rules for decision-making. For example, when the thermal safety penalty index is non-zero, power is reduced first to ensure safety, regardless of user urgency; when the thermal safety penalty index is zero, the power is increased based on the user urgency penalty index. This approach ensures a basic safety baseline.
[0025] Finally, the system includes a control unit, which controls the entire charging pile based on the aforementioned optimal final power allocation value. In one implementation, the control unit can directly convert the optimal final power allocation value into a corresponding current or voltage command and send it to the charging pile's power module, which then adjusts the power output.
[0026] The technical solution of this application effectively overcomes the limitations of existing technologies by introducing the coordinated operation of a data acquisition unit, a temperature prediction unit, a thermal safety analysis unit, an urgency analysis unit, an optimal command analysis unit, and a control unit. Specifically: First, the data acquisition unit can comprehensively acquire the internal temperature data of the charging pile, the user's target charging capacity, and the user's charging delay sensitivity information, providing a multi-dimensional and refined data foundation for subsequent intelligent decision-making, rather than relying solely on a single or limited parameter.
[0027] Secondly, the temperature prediction unit generates internal temperature prediction values based on an autoregressive model, enabling the system to anticipate potential thermal risks and thus achieve proactive prevention rather than reactive response. This offers significant advantages in predictability and safety compared to existing technologies that typically only implement power reduction or shutdown measures after the temperature reaches a threshold.
[0028] Furthermore, the thermal safety analysis unit and the urgency analysis unit quantify the thermal safety status of the charging pile and the user's charging urgency into penalty indices, respectively, achieving a unified quantitative assessment of complex multi-objective problems. This quantitative processing method allows demands from different dimensions to be compared and weighed within the same framework, avoiding suboptimal decisions that may result from simple priority rules.
[0029] Finally, the optimal command analysis unit establishes an optimal power allocation analysis model, comprehensively balancing the thermal safety penalty index and the user urgency penalty index to generate the optimal final power allocation value. This makes the power output of the charging pile no longer fixed or simply responding to a single factor, but can be dynamically and intelligently adjusted according to the real-time temperature status of the charging pile and the actual needs of the user.
[0030] In summary, this system avoids the risk of overheating while also addressing users' urgent needs, achieving a dual optimization of equipment safety and user satisfaction. This refined dynamic power allocation capability is difficult to achieve with existing technologies, significantly improving the operating efficiency, safety, and user service quality of charging stations.
[0031] In some of the solutions described above in this application, a predicted value of the internal temperature of the charging pile is proposed to support thermal safety analysis. However, in this process, the prediction method may not be accurate or efficient enough, and may not be able to reliably predict future temperature changes, thereby affecting the accuracy of thermal safety assessment.
[0032] In response, this invention further proposes a method for generating the predicted internal temperature value of the charging pile, specifically including: Through the formula: ; Generate predicted internal temperature values for charging piles ; In the formula, AR represents the autoregressive model. This represents the internal temperature value of the charging pile at the current moment k. This represents the internal temperature value of the charging pile at time k-1. This represents the internal temperature value of the charging pile at time kn. The "moment" refers to the moment when the charging station starts operating.
[0033] Autoregressive (AR) models are statistical models whose core idea is to predict future values using past values of time series data. This model captures the time dependence of data by establishing a linear relationship between the current value and several lagged values from the past. In practical applications, the parameters of the autoregressive model can be trained and optimized using statistical methods such as least squares and maximum likelihood estimation to ensure that the model can accurately fit historical data and make effective predictions. Furthermore, to adapt to the dynamic changes in the charging pile operating environment, an adaptive autoregressive model based on a sliding window can also be used, adjusting the model parameters in real time according to the latest temperature data, thereby improving the accuracy and robustness of predictions.
[0034] The internal temperature data of a charging pile refers to the temperature measurements obtained through real-time monitoring of its internal key components or their surrounding environment during operation. These key components may include heat sources such as power modules, transformers, capacitors, connectors, and internal cables. Internal temperature data can be acquired by placing various temperature sensors, such as thermistors, thermocouples, or infrared temperature sensors, at these key heat-generating points or heat dissipation channels. These sensors convert temperature signals into electrical signals, which are then read and processed by the data acquisition unit. Furthermore, some advanced power modules may integrate temperature sensors to directly provide the chip's real-time operating temperature, thereby obtaining more accurate internal temperature data.
[0035] This application's solution introduces a temperature prediction method based on an autoregressive model, enabling more accurate and efficient prediction of the charging pile's internal temperature. Specifically, this method utilizes an autoregressive (AR) model to generate a predicted internal temperature value for the charging pile at time k+1, based on the internal temperature values at the current time k, k-1, and kn up to the time the charging pile begins operation. This approach allows the model to fully capture the temporal dependence and historical trends of temperature changes, ensuring the reliability of the prediction results. By considering historical data from the charging pile's start-up time, this solution avoids the limitations of simple predictions based on a single or limited number of data points, significantly improving the robustness and adaptability of the prediction. The generated predicted internal temperature value is then used by the thermal safety analysis unit to generate a thermal safety penalty index for the charging pile, thereby providing an accurate thermal safety assessment basis for the entire charging pile's power distribution control system and ensuring that the system allocates power while guaranteeing equipment thermal safety.
[0036] Through the above technical solution, this application provides a more accurate and efficient method for predicting the internal temperature of charging piles, effectively solving the problems of inaccurate prediction or low efficiency that may exist in traditional prediction methods. Predicting the internal temperature of charging piles based on an autoregressive model can fully utilize historical temperature data to capture the dynamic patterns of temperature changes, thereby generating reliable internal temperature prediction values. This provides a solid data foundation for subsequent thermal safety analysis, enabling the charging pile's overall charging and power distribution control system to more accurately assess the thermal safety status of the equipment, avoiding safety hazards or unnecessary power limitations caused by inaccurate temperature predictions, and thus improving the stability and safety of charging pile operation.
[0037] In some of the solutions mentioned above in this application, a thermal safety penalty index for charging piles is proposed to quantify the thermal safety risk of equipment. However, in this process, there is a lack of an accurate calculation method, which may lead to inaccurate risk assessment and failure to effectively reflect the degree of temperature exceeding the standard, thereby affecting the reliability of power distribution optimization and increasing the risk of equipment overheating.
[0038] In response, this invention further proposes a method for generating the thermal safety penalty index of the charging pile, specifically including: Through the formula: ; Generate a thermal safety penalty index for charging piles ; In the formula, This represents the predicted internal temperature of the charging pile at time k+1. This indicates the internal temperature safety threshold of the charging station.
[0039] This generation method aims to provide a mechanism for quantifying the thermal safety risks of charging piles; its function is to transform the internal temperature state of the charging pile into a calculable and comparable value so that subsequent power allocation decisions can comprehensively consider the thermal safety status of the equipment.
[0040] In the charging and distribution control system of this application, the thermal safety analysis unit generates the thermal safety penalty index of the charging pile by introducing a precise calculation method.
[0041] Specifically, the unit receives the predicted internal temperature of the charging pile at time k+1 from the temperature prediction unit, as well as a preset safe internal temperature threshold for the charging pile. Based on these inputs, the thermal safety analysis unit performs calculations using the formulas in this generation method.
[0042] The formula in the generation method utilizes a max function to ensure that a positive temperature deviation is only generated when the predicted temperature exceeds the safety threshold, thus avoiding unnecessary penalties when the equipment is in a safe operating state. Subsequently, this temperature deviation is normalized by the safety threshold to generate a dimensionless charging pile thermal safety penalty index. This generation method allows the thermal safety penalty index to accurately reflect the degree to which the internal temperature of the charging pile exceeds the safe range, providing a unified-scale risk assessment basis for the optimal command analysis unit. Through this proactive and quantitative thermal safety risk assessment, the system can incorporate potential thermal risks into power allocation considerations before the actual temperature reaches a dangerous level, thus playing a crucial role in ensuring the long-term safe and stable operation of the charging pile. This mechanism works in conjunction with the temperature prediction unit to transform predicted future thermal risks into actionable quantitative indicators, thereby guiding the control unit to adjust power, effectively avoiding the risk of equipment overheating and improving the reliability and safety of the entire system.
[0043] In some of the solutions mentioned above in this application, an urgency analysis unit is proposed to generate a user urgency penalty index. However, in this process, how to accurately quantify the charging delay based on the user's charging power target value and establish an analysis model in combination with the user's charging delay sensitive feature information, so as to avoid the inaccurate urgency assessment leading to the inability of power allocation to effectively distinguish the user's differentiated needs and affect the system balance and efficiency, is a problem that needs to be solved.
[0044] For this, please refer to Figure 2 The present invention further proposes that the urgency analysis unit 40 specifically includes: The charging delay analysis module 41 is used to generate the charging extension time based on the user's target charging capacity. The urgency penalty index generation module 42 is used to establish an urgency analysis model based on the charging extension time and user charging delay sensitivity information, and generate a user urgency penalty index. The urgency analysis unit 40 is designed to assess the user’s sensitivity to charging delay and quantify it into an indicator that can be used for power allocation decisions. The charging delay analysis module calculates the potential extension of charging time under specific power adjustments based on the user's charging needs. Its purpose is to provide an objective time benchmark for subsequent urgency assessment.
[0045] The responsibility of the urgency penalty index generation module is to comprehensively consider the charging delay duration and the user's sensitivity to charging timeouts to generate a quantified user urgency penalty index. This index reflects the degree of dissatisfaction or loss experienced by the user due to charging delays. This module can be implemented in several ways: First, by using a machine learning model to learn the complex relationship between features such as charging delay duration, user type, and vehicle usage and user urgency, through training on historical user behavior data and feedback, thereby predicting and generating the penalty index. Second, by employing a rule engine based on expert experience, which maps charging delay duration and user sensitivity features (such as vehicle type and charging scenario) to different penalty index levels according to a pre-defined rule set.
[0046] This application's solution achieves precise quantification of user charging urgency by refining the specific structure of the urgency analysis unit. Specifically, the charging delay analysis module first objectively calculates the potential charging delay under power adjustment based on the user's target charging capacity. This step ensures that the delay calculation is directly related to the user's actual charging needs, avoiding the deficiency that target values alone cannot reflect the impact of time increments, and providing a solid time basis for urgency assessment. Based on this, the urgency penalty index generation module further combines the charging delay duration and user charging delay sensitivity information to establish an urgency analysis model and generate a user urgency penalty index. This approach, combining objective delay data with user subjective sensitivity characteristics (such as vehicle type, charging scenario, etc.), allows the urgency analysis model to dynamically adapt to the sensitivity differences of different users, solving the problem that a single indicator cannot comprehensively capture urgency. In this way, the urgency analysis unit can output a more accurate and differentiated user urgency penalty index, thereby providing a more refined decision-making basis for the optimal instruction analysis unit, enabling it to more effectively balance the thermal safety of the charging pile and the differentiated needs of users during power allocation.
[0047] Through the above technical solution, this application can achieve accurate quantification and differentiated assessment of users' charging urgency. This enables the charging pile's overall charging and distribution control system to more accurately identify and respond to the actual needs and latency sensitivity of different users when allocating power, thereby avoiding the problem of inaccurate urgency assessment leading to ineffective power allocation to differentiate user needs. Ultimately, this helps improve the system's balance and operational efficiency, ensuring the thermal safety of the charging pile while maximizing user satisfaction.
[0048] Traditional charging pile control systems may use a coarse estimation method when assessing the impact of power adjustments on user charging time. This results in insufficiently accurate or dynamic calculation of charging extension time, failing to accurately reflect the impact of power changes on charging time, and consequently leading to inaccurate urgency assessment.
[0049] In response, the present invention further proposes a method for generating the extended charging time, specifically including: Through the formula: ; Generate extended charging time ; In the formula, This indicates the user's target remaining charging capacity. This represents the output power variable of the charging station. This represents the output power of the charging pile at the current moment k; The extended charging time refers to the change in the time required for a user to complete a predetermined charging target when the output power of the charging station changes, relative to the current power level. Its purpose is to quantify the impact of power adjustments on the user's charging experience, providing crucial input for subsequent assessments of user urgency. This time can be expressed in units such as minutes or hours, reflecting the increase or decrease in user waiting time. Specifically, this technical solution quantifies the impact of power adjustment on charging time by comparing the time difference required to complete the remaining charge at different power levels. The introduction of this formula transforms the calculation of charging time extension from a simple estimation into a precise derivation based on actual physical quantities, thereby improving the accuracy of subsequent urgency analysis.
[0050] in, This represents the difference between the amount of electricity the user expects to charge and the amount already charged, i.e., the amount of electricity the user still needs to charge. This value is dynamically changing and can be obtained in real time through the metering module inside the charging station, or it can be calculated by the user setting a target amount of electricity at the start of charging, combined with the current charging progress.
[0051] This refers to the new output power value that a charging station might be adjusted to during power allocation optimization. This is a variable to be optimized, representing the power that the system might allocate to the charging station after considering thermal safety and user urgency. This power variable can take values between the charging station's maximum and minimum output power. P(k) represents the actual power output of the charging pile at the current time k. This value can be obtained in real time through the charging pile's power sensor. It is the benchmark power for calculating the extended charging time, reflecting the time required for the user to complete charging before power adjustment.
[0052] This solution dynamically calculates the charging extension time by introducing a precise mathematical formula. Specifically, the formula utilizes three key parameters: the user's remaining target charging capacity, the charging pile's output power variable, and the charging pile's current output power at time k. When the system needs to assess the impact of adjusting the charging pile's output power on the user's charging time, it first obtains the user's current remaining target charging capacity and the charging pile's current output power at time k. Subsequently, the system considers a potential new charging pile output power variable; this yields the time required to complete the remaining charging under two different power levels. The difference between these two times is the charging extension time. This calculation method allows the charging extension time to reflect the charging time extension in real time and accurately when the charging pile's output power is adjusted from P(k) to... This involves tracking the specific changes in the time required for a user to complete charging. This precise quantification is crucial for subsequent user urgency analysis. In this way, the system can more accurately assess a user's sensitivity and urgency to changes in charging time, thus providing a more reliable basis for the optimal instruction analysis unit. This allows for a more refined balance of different users' charging needs while ensuring the thermal safety of the charging station, ultimately achieving the optimal power allocation value.
[0053] Through the above technical solution, this application can accurately calculate the charging extension time, solving the problem of inaccurate time assessment under dynamic power changes in traditional methods. Specifically, by introducing the user's remaining target charging capacity, the charging pile's output power variable, and the charging pile's output power at the current moment k, and using a specific formula for calculation, the charging extension time can accurately reflect the impact of power adjustment on the user's charging completion time in real time. This precise quantification provides a more reliable and accurate input for the urgency analysis unit, making the generation of the user urgency penalty index more scientific and reasonable. When establishing the optimal power allocation analysis model together with the charging pile thermal safety penalty index, due to the improved accuracy of user urgency assessment, the system can more effectively balance equipment thermal safety and differentiated user needs, avoiding unreasonable power allocation caused by inaccurate charging time assessment. Ultimately, this helps to achieve the optimal final power allocation value, ensuring that the charging pile operates safely while significantly improving user satisfaction and overall operational efficiency.
[0054] In traditional charging pile power distribution control systems, the urgency analysis unit is used to establish an urgency analysis model and generate a user urgency penalty index based on the charging delay duration and the user's sensitivity to charging delay. However, in this process, there is a lack of a specific mathematical expression to quantify the calculation of this index, which leads to a subjective and inaccurate evaluation process. It cannot effectively capture the multi-dimensional characteristics of user urgency, thus affecting the accuracy and efficiency of subsequent optimal power allocation.
[0055] In response, this invention further proposes the following expression for the urgency analysis model: ; In the expression, This represents the user's urgency level penalty index. This represents the normalized value of the extended charging time. This represents the flag value of the charging delay sensitive feature information for the i-th user, and m represents the number of user charging delay sensitive feature information. This represents the weighting coefficient of the user's sensitivity to charging delay. The User Urgency Penalty Index is an indicator used to quantify users' sensitivity to charging delays. The index value directly reflects the user's dissatisfaction or urgency caused by extended charging time due to adjustments in charging power. Its purpose is to provide an objective and comparable basis for the charging pile's overall power distribution control system, enabling comprehensive consideration of users' actual needs when making power allocation decisions. The generation of the User Urgency Penalty Index allows the system to shift from qualitative judgment to quantitative analysis, thereby achieving more refined management.
[0056] The normalized value of the charging extension time refers to the result of converting the charging extension time calculated by the charging delay analysis module to a standardized numerical range. The purpose of this normalization is to eliminate the influence of differences in different units or dimensions of charging time, ensuring that their weights and contributions in the urgency analysis model are fair and comparable.
[0057] The flag value of the i-th user's charging delay sensitivity feature information is a numerical value used to indicate whether a user has a specific charging delay sensitivity feature or its state. These features may include, but are not limited to, vehicle type (e.g., commercial vehicles are generally more sensitive to charging time than private cars), charging scenario (e.g., users in highway service areas may be more urgent than users in office parks), user's historical charging behavior data, and the current remaining battery percentage of the vehicle, etc. A label value refers to a binary variable (e.g., 1 indicates the presence of the feature, 0 indicates its absence), or it can be a numerical value representing the strength or category of the feature. The weighting coefficients of user charging delay sensitivity features are numerical values used to measure the relative importance of different user charging delay sensitivity features in determining the user's urgency penalty index. Different features may have different degrees of impact on user urgency. For example, for users of commercial vehicles, extended charging time may directly lead to economic losses, so the weighting coefficient of their vehicle type feature may be higher. These weighting coefficients can be set through expert experience, or trained and optimized through historical data analysis and machine learning algorithms (such as regression analysis, decision trees, etc.) to more accurately reflect the actual situation.
[0058] The solution proposed in this application introduces the aforementioned mathematical expression, enabling the urgency analysis unit to accurately quantify and assess user urgency. Specifically, firstly, the charging delay analysis module generates a charging extension duration based on the user's target charging capacity. Subsequently, this charging extension duration is normalized to generate a normalized value. Simultaneously, the system acquires various user charging delay-sensitive features and assigns corresponding flag values and weighting coefficients to each feature. Finally, by multiplying the normalized charging extension duration by the weighted user charging delay-sensitive features, the user urgency penalty index is calculated. This calculation method organically combines the core factor of charging extension duration with the user's multi-dimensional sensitive features, forming a comprehensive urgency quantification index. The generation of this quantification index provides the optimal instruction analysis unit with accurate user urgency data, enabling it to more accurately balance the thermal safety of the charging pile and the user's charging needs when establishing the optimal power allocation analysis model, thereby achieving better power allocation decisions.
[0059] Through the above technical solution, this application solves the problem of subjective and inaccurate user urgency assessment in traditional methods. The expression normalizes the charging extension time, ensuring the standardization and comparability of the duration data in the calculation. Simultaneously, by introducing multi-dimensional user charging delay-sensitive feature information and their weighting coefficients, a comprehensive and objective quantification of user urgency is achieved. This quantitative assessment method not only improves the accuracy and precision of urgency analysis but also provides reliable input data for subsequent optimal power allocation analysis models. This enables the charging pile's overall charging and power distribution control system to more effectively balance the thermal safety of the charging pile with the differentiated charging needs of users, thereby optimizing the charging experience and improving overall operational efficiency.
[0060] In some of the solutions mentioned above in this application, an optimal instruction analysis unit is proposed to establish an optimal power allocation analysis model based on the charging pile thermal safety penalty index and the user urgency penalty index to generate the optimal final power allocation value. However, in this process, there are shortcomings in how to efficiently integrate the two penalty indices and optimize the power allocation, and how to coordinate the power allocation of multiple charging piles to improve decision accuracy and system adaptability.
[0061] For this, please refer to Figure 3 The present invention further proposes that the optimal instruction analysis unit 50 specifically includes: The penalty comprehensive value generation module 51 is used to generate a penalty comprehensive value based on the charging pile thermal safety penalty index and the user urgency penalty index. The optimal power allocation value generation module 52 is used to generate an initial optimal power allocation value based on the penalty comprehensive value; The collaborative analysis module 53 is used to establish an optimal power allocation analysis model based on the initial optimal power allocation value and generate the final optimal power allocation value. The penalty comprehensive value generation module aims to effectively integrate the charging pile thermal safety penalty index from the thermal safety analysis unit and the user urgency penalty index from the urgency analysis unit to form a unified quantitative indicator that comprehensively reflects the equipment safety risks and the urgency of user needs under the current charging status. Its concept lies in merging two originally independent dimensions of information to provide a comprehensive evaluation basis for subsequent power optimization decisions.
[0062] The function of the optimal power allocation value generation module is to find a power setting that minimizes the penalty comprehensive value within the acceptable output power range of the charging pile, based on the penalty comprehensive value, thereby generating an initial optimal power allocation value. Its core role is to initially determine an ideal power output that achieves a balance between equipment safety and user needs based on the comprehensive evaluation results.
[0063] The purpose of the collaborative analysis module is to further refine and optimize the initial optimal power allocation value generated by the optimal power allocation value generation module, in order to adapt to more complex operating environments, especially when multiple charging piles are working collaboratively or when the overall resources of the charging station are limited. By establishing an optimal power allocation analysis model, this module can generate the final optimal power allocation value, ensuring that the power allocation decision of a single charging pile is consistent with the global optimization goal of the entire charging station.
[0064] The charging pile power distribution control system of this application, after the data acquisition unit acquires the internal temperature data of the charging pile, the user's target charging capacity, and the user's charging delay sensitivity information, generates a charging pile thermal safety penalty index and a user urgency penalty index, respectively, through a temperature prediction unit and an urgency analysis unit. Based on this, the optimal instruction analysis unit efficiently processes and optimizes these penalty indices through its internal penalty comprehensive value generation module, optimal power allocation value generation module, and collaborative analysis module. Specifically, the penalty comprehensive value generation module first receives the charging pile thermal safety penalty index and the user urgency penalty index and merges them into a unified penalty comprehensive value. This process integrates two originally independent considerations—equipment safety risk and user demand urgency—into a single quantitative indicator, providing a comprehensive evaluation basis for subsequent power optimization. Subsequently, the optimal power allocation value generation module uses this penalty comprehensive value as input to perform optimization calculations within the variable range of the charging pile's output power, aiming to find an initial optimal power allocation value that minimizes the penalty comprehensive value. This step ensures that the power allocation decision achieves an initial balance and optimization while taking into account both equipment safety and user needs. Finally, the collaborative analysis module receives the initial optimal power allocation value and establishes an optimal power allocation analysis model based on it. This model further considers the overall resource constraints of the charging station and the collaborative needs among multiple charging piles. For example, by introducing a collaborative correction factor, the initial optimal power allocation value is adjusted to generate the final optimal power allocation value. This collaborative correction process ensures that the power allocation decision of a single charging pile is aligned with the global optimization objective of the entire charging station, avoiding the overall efficiency decline or resource conflicts that may result from local optima, thereby improving the accuracy of power allocation and the overall adaptability of the system. Through the collaborative work of the above modules, this system can dynamically and precisely balance the thermal safety of charging piles with the differentiated needs of users, and effectively manage the power allocation of multiple charging piles, ensuring that charging piles operate safely while maximizing user satisfaction and operational efficiency.
[0065] Through the above technical solutions, this application effectively addresses the challenges in power distribution control of charging piles, specifically how to efficiently integrate equipment thermal safety and user urgency, and how to optimize power allocation based on these factors. It also addresses resource management and power coordination issues when multiple charging piles are working collaboratively. The penalty comprehensive value generation module quantifies heterogeneous penalty indicators, avoiding decision conflicts and providing a clear target for subsequent optimization. The optimal power allocation value generation module can quickly and accurately find the initial optimal power allocation scheme that balances safety and demand based on this comprehensive indicator. Furthermore, the collaborative analysis module refines the initial allocation value by introducing global resources and multi-pile collaborative considerations, ensuring that the power allocation decision of a single charging pile is coordinated with the overall operation strategy of the charging station. This effectively avoids overall inefficiency or resource overload caused by local optima, significantly improving the accuracy of power allocation, system adaptability, and overall operational stability. This enables the charging pile system to better meet users' diverse charging needs while ensuring long-term safe and stable operation of the equipment, thereby improving user satisfaction and operational efficiency.
[0066] Traditional charging pile power distribution control systems often struggle to effectively balance the relationship between users' charging urgency and the charging pile's thermal safety when optimizing power allocation. In actual operation, the lack of a clear quantitative method to comprehensively consider these two factors can lead to suboptimal power allocation strategies, failing to dynamically adjust weights based on actual scenarios, thus affecting the system's coordination between ensuring thermal safety and meeting user needs.
[0067] In response, this invention further proposes a method for generating the comprehensive penalty value, specifically including: Through the formula: ; Generate a comprehensive penalty value ; In the formula, This represents the user's urgency level penalty index. This indicates the thermal safety penalty index of the charging station. This represents the weighting coefficient of the user urgency penalty index; Among them, the user urgency penalty index is a quantitative indicator that measures the dissatisfaction or loss caused by the user due to the charging delay. The higher the value, the higher the user's sensitivity to the charging delay or the more urgent the current charging need. The thermal safety penalty index of charging piles is a quantitative indicator that assesses the impact of the internal temperature state of charging piles on equipment safety. The higher the value, the greater the thermal risk faced by the charging pile. The weighting coefficient is a value between 0 and 1, used to adjust the relative importance of the user urgency penalty index and the charging pile thermal safety penalty index in the overall penalty value. This weighting coefficient can be dynamically adjusted or preset according to factors such as the actual operating environment, operational strategies, or user types. This application's solution effectively addresses the problem of quantifying and balancing user urgency and thermal safety indices during power allocation optimization by introducing a weighted formula to generate a comprehensive penalty value. Specifically, the solution first obtains a user urgency penalty index from the urgency analysis unit and a charging pile thermal safety penalty index from the thermal safety analysis unit. These two indices quantify the user's sensitivity to charging delays and the thermal risks faced by the charging pile, respectively. Subsequently, the two indicators are merged into a single comprehensive penalty value using a formula. The weighting coefficient plays a crucial role in this process, allowing the system to flexibly adjust the priority between user urgency and thermal safety based on preset strategies or real-time conditions. When the weighting coefficient is high, user urgency dominates the comprehensive penalty value, prompting the system to prioritize meeting the user's fast charging needs during power allocation; conversely, when the weighting coefficient is low, thermal safety factors are given higher weight, guiding the system to prioritize the charging pile's heat dissipation and safe operation. This weighted combination approach transforms the original multi-objective optimization problem into a single-objective optimization problem, providing a clear and quantifiable optimization objective for the subsequent optimal power allocation analysis model. This enables the system to efficiently find the power allocation strategy that achieves the best balance between user satisfaction and equipment safety within a unified framework.
[0068] Through the above technical solution, this application provides a flexible and quantifiable method for comprehensively balancing the relationship between user charging urgency and charging pile thermal safety. This solution can dynamically adjust the priority of user demand and equipment safety in power allocation decisions based on actual operating conditions and operational strategies, thereby avoiding the power allocation suboptimal problems that may result from traditional fixed strategies. This not only helps extend the service life of charging piles and reduce safety risks, but also effectively improves the user charging experience, enabling the charging pile's overall power distribution control system to achieve more intelligent and efficient operation and management in complex and ever-changing application scenarios.
[0069] In some of the solutions mentioned above in this application, a method for generating the initial optimal power allocation value is proposed to optimize power allocation. However, in this process, there is a lack of specific mathematical optimization methods, which may lead to low computational efficiency or failure to accurately find the power value that minimizes the penalty comprehensive value, thereby affecting the dynamic balance between the thermal safety of the charging pile and the urgent needs of users.
[0070] In response, this invention further proposes a specific method for generating the initial optimal power allocation value as follows: Through the formula: ; Generate initial optimal power allocation values ; In the expression, R represents the variable output power of the charging pile, and R represents the set of variable output power of the charging pile. This represents the overall penalty value; This formula defines how to generate the initial optimal power allocation value. Its core lies in using mathematical optimization to find a charging pile output power variable within the set of variable charging pile output power R that minimizes the overall penalty value. This method ensures that the selected power value achieves an optimal balance between the charging pile's thermal safety and the user's urgency to charge.
[0071] The output power of the charging pile is an independent variable in the optimization process, representing the power that the charging pile may output at a certain moment. This variable can be continuous, for example, taking any value between 0 and the maximum rated power; or it can be discrete, such as the several fixed power levels supported by the charging pile. The range of values for this variable is limited by various factors such as the hardware design of the charging pile, the power grid supply capacity, and the charging protocol. The set R of variable output power of the charging pile defines the effective range or set of values for the output power variable of the charging pile. This set limits the search space of the optimization algorithm, ensuring that the obtained initial optimal power allocation value is the power value that the charging pile can actually achieve; The comprehensive penalty value is the objective function in the optimization process. It quantifies the overall "penalty" between the thermal safety risk of the charging pile and the user's urgency to charge under a specific charging pile output power variable. This value is a weighted combination of the charging pile thermal safety penalty index and the user urgency penalty index. By minimizing this comprehensive penalty value, the system can find a power allocation scheme that achieves the optimal balance between thermal safety and user satisfaction.
[0072] In this scheme, the optimal instruction analysis unit first generates a comprehensive penalty value based on the charging pile thermal safety penalty index and the user urgency penalty index. Then, the optimal power allocation value generation module receives this comprehensive penalty value and uses it as the objective function. This module performs a minimization optimization process, searching for a charging pile output power variable within the set R of variable charging pile output power to minimize the comprehensive penalty value. The output of this minimization process is the initial optimal power allocation value.
[0073] This solution integrates the thermal safety considerations of charging piles and user charging needs into a unified penalty value. By mathematically minimizing this penalty value, the system can automatically and accurately find an optimal power output point. This not only avoids suboptimal solutions resulting from empirical judgments or simple rules that may exist in traditional methods, but also dynamically adapts to changes in the internal temperature of the charging pile and changes in the urgency of user needs, adjusting power allocation in real time to ensure the best balance between safety and user satisfaction under any operating condition. This mathematical optimization-based method makes power allocation decisions more scientific, efficient, and accurate, providing high-quality initial power allocation suggestions for subsequent overall collaborative analysis of the charging station.
[0074] Through the above technical solution, this application effectively addresses the problems of low computational efficiency and inability to accurately find the optimal power value in traditional charging pile power allocation due to the lack of specific mathematical optimization methods. This solution introduces a mathematical optimization model based on minimizing the penalty comprehensive value, achieving precise and dynamic adjustment of the charging pile's output power. This enables the system to find the optimal balance between charging pile thermal safety and user charging urgency in real time and efficiently, avoiding the risk of accelerated aging of equipment due to overheating or causing safety accidents. It also significantly improves the user charging experience, especially under high load or high temperature environments, enabling more intelligent power allocation to ensure long-term stable operation of the charging pile and increased user satisfaction.
[0075] In some of the solutions mentioned above in this application, a collaborative analysis module is proposed to generate the optimal final power allocation value based on the initial optimal power allocation value. However, in this process, if the initial allocation value is calculated based only on the optimization of a single charging pile without considering the resource constraints of the entire charging station and the collaborative needs of multiple charging piles, the total allocated power may exceed the remaining available resources, causing system overload, resource conflicts or unstable operation.
[0076] In response, this invention further proposes the following expression for the optimal power allocation analysis model: ; In the expression, This represents the optimal final power allocation value. This represents the initial optimal power allocation value. This represents the collaborative correction factor; The specific methods for obtaining the collaborative correction factor include: Through the formula: ; Generate collaborative correction factor ; In the formula, This represents the initial optimal power allocation value for the j-th charging pile, and N represents the number of charging piles. This represents the output power of the j-th charging pile at the current time k. This represents the remaining available resources of the charging station. The optimal power allocation value is the power command ultimately determined and issued to the charging pile by the charging pile's overall power distribution control system. It represents the power that a single charging pile should output at the next moment, after comprehensively considering the charging pile's thermal safety, the user's charging urgency, and the resource constraints of the entire charging station. This value is a direct result of the system's optimization decision and is used to guide the control unit in actually adjusting the power of the charging pile. The initial optimal power allocation value is a preliminary optimized power value calculated by the optimal power allocation value generation module for a single charging pile, without considering the overall resource constraints of the entire charging station. It is typically based on local optimization using the charging pile's own thermal safety penalty index and user urgency penalty index, aiming to maximize user satisfaction and ensure equipment safety. This value forms the basis for collaborative correction, providing a starting point for subsequent global resource coordination. The collaborative correction factor is a key adjustment parameter used to globally correct the initial optimal power allocation value. Its function is to ensure that the total power allocation of all charging piles does not exceed the available resources of the charging station. This factor is generated by quantifying the gap between the overall power demand of the charging station and the available resources. When the total demand exceeds the available resources, it generates a positive value, thereby proportionally reducing the initial power allocation of each charging pile to avoid system overload. The collaborative correction factor can be a dynamically calculated value or a parameter adjusted based on preset strategies or real-time monitoring data. The initial optimal power allocation value for the j-th charging pile refers to the preliminary power allocation recommendation value calculated by each charging pile (numbered j) within the charging station based on its own local optimization logic (such as thermal safety and user urgency) before global resource coordination. These values are the input for calculating the collaborative correction factor, reflecting the independent demand of each charging pile. The number of charging piles N refers to the total number of all charging piles participating in power allocation within the charging station. This parameter is used to summarize the initial power demand of all charging piles for comparison with the overall available resources of the charging station. The output power of the j-th charging pile at current time k refers to the actual power output of the j-th charging pile in the charging station at current time k. This value is used to calculate the power demand increment of each charging pile, thereby more accurately assessing the overall power demand change of the charging station; The remaining available resources of a charging station refer to the total remaining power capacity currently available for allocation to all charging piles. This value represents the upper limit of the charging station's power supply capacity, and the calculation of the coordination correction factor aims to ensure that the final total allocated power of all charging piles does not exceed this limit. This resource can be determined by factors such as the charging station's grid connection capacity, energy storage system status, or load management strategies.
[0077] To address system overload and resource conflicts that may result from local optimization based solely on individual charging piles when generating the optimal final power allocation value, the charging pile power distribution control system of this application introduces a global collaborative correction mechanism. Specifically, the system first generates an initial optimal power allocation value for each charging pile, obtained through local optimization based on the charging pile's own thermal safety penalty index and user urgency penalty index. Subsequently, to ensure that the power allocation of the entire charging station remains within total resource constraints, the system calculates a collaborative correction factor. This factor is calculated by first summing the initial optimal power allocation values of all charging piles and subtracting their current output power at time k to obtain the total power demand increment for all charging piles. If this total demand increment exceeds the remaining available resources of the charging station, a positive collaborative correction factor is calculated, quantifying the proportion of the excess. Finally, the optimal final power allocation value is obtained by multiplying the initial optimal power allocation value by 1 - the collaborative correction factor. This mechanism enables the system to move from local optimization (thermal safety and user urgency of individual charging piles) to global optimization (resource balance of the entire charging station). When the overall power demand of a charging station exceeds its remaining available resources, the collaborative correction factor dynamically adjusts the initial optimal power allocation value of each charging pile proportionally, thereby avoiding overall resource overruns caused by the accumulation of local optimization decisions. This not only ensures the stable operation of the charging station's power supply system and prevents overload risks, but also enables more rational power allocation when resources are limited. It ensures that the charging pile's overall power distribution control system meets user needs and equipment safety while achieving dynamic optimization under multi-charging pile collaborative operation. In this way, the system can effectively balance individual needs with overall resources, improving the overall operational efficiency and reliability of the charging station.
[0078] Through the above technical solution, this application effectively solves the problems of system overload and resource conflicts that may be caused by local optimization based solely on a single charging pile when generating the optimal final power allocation value. By introducing a collaborative correction factor, the system can dynamically evaluate the relationship between the power demand of the entire charging station and the remaining available resources. When the sum of the initial optimal power allocation values of all charging piles exceeds the remaining available resources of the charging station, the collaborative correction factor will generate a positive value and proportionally reduce the initial optimal power allocation value of each charging pile. This ensures that the total power finally allocated to all charging piles will not exceed the upper limit of the charging station's power supply capacity, thereby avoiding safety hazards such as system overload, line tripping, or equipment damage. In addition, this solution further improves the intelligence and robustness of the system based on the charging pile's overall charging and distribution control system and the optimal command analysis unit. It enables the system to not only take into account the thermal safety and user urgency of a single charging pile, but also to achieve global resource optimization management in multi-charging pile collaborative working scenarios. This collaborative correction mechanism enables charging stations to operate more efficiently and stably, especially during peak charging periods or when resources are scarce. It ensures the continuous and reliable operation of critical infrastructure while maximizing the balance between user needs and system safety, thereby improving overall operational efficiency and user satisfaction.
[0079] Please see Figure 4 In another embodiment, the present invention also proposes a charging pile whole-machine charging and power distribution control method, which is applied to the above-mentioned charging pile whole-machine charging and power distribution control system, and specifically includes the following steps: Step S10: Obtain the internal temperature data of the charging pile, the user's target charging capacity, and the user's charging delay sensitivity information; Step S20: Based on the autoregressive model, generate the predicted internal temperature value of the charging pile according to the internal temperature data of the charging pile; Step S30: Generate the thermal safety penalty index of the charging pile based on the predicted internal temperature value of the charging pile; Step S40: Establish an urgency analysis model based on the user's target charging capacity and the user's charging delay sensitivity information, and generate a user urgency penalty index; Step S50: Establish an optimal power allocation analysis model based on the charging pile thermal safety penalty index and the user urgency penalty index, and generate the optimal final power allocation value; Step S60: Control the entire charging pile according to the optimal final power allocation value.
[0080] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A charging pile overall charging and power distribution control system, characterized in that, The system specifically includes: The data acquisition unit is used to acquire internal temperature data of the charging pile, the user's target charging capacity, and user charging delay sensitive characteristic information. The temperature prediction unit is used to generate predicted internal temperature values of the charging pile based on the internal temperature data of the charging pile using an autoregressive model. The thermal safety analysis unit is used to generate a thermal safety penalty index for the charging pile based on the predicted internal temperature of the charging pile. The urgency analysis unit is used to establish an urgency analysis model based on the user's target charging capacity and the user's charging delay sensitivity information, and generate a user urgency penalty index. The optimal instruction analysis unit is used to establish an optimal power allocation analysis model based on the charging pile thermal safety penalty index and the user urgency penalty index, and generate the optimal final power allocation value. The control unit is used to control the entire charging pile according to the optimal final power allocation value; The urgency analysis unit specifically includes: The charging delay analysis module is used to generate the charging extension time based on the user's target charging capacity. The urgency penalty index generation module is used to establish an urgency analysis model based on the charging extension time and user charging delay sensitivity information, and generate a user urgency penalty index. The specific methods for generating the extended charging time include: Through the formula: ； Generate extended charging time ; In the formula, This indicates the user's target remaining charging capacity. This represents the output power variable of the charging station. This represents the output power of the charging pile at the current moment k; The specific expression of the urgency analysis model is as follows: ； In the expression, This represents the user's urgency level penalty index. This represents the normalized value of the extended charging time. This represents the flag value of the charging delay sensitive feature information for the i-th user, and m represents the number of user charging delay sensitive feature information. This represents the weighting coefficient of the user's sensitivity to charging delay.
2. The charging pile power distribution control system according to claim 1, characterized in that, The specific methods for generating the predicted internal temperature value of the charging pile include: Through the formula: ； Generate predicted internal temperature values for charging piles ; In the formula, AR represents the autoregressive model. This represents the internal temperature value of the charging pile at the current moment k. This represents the internal temperature value of the charging pile at time k-1. This represents the internal temperature value of the charging pile at time k-n, where k-n refers to the moment when the charging pile starts working.
3. The charging pile power distribution control system according to claim 1, characterized in that, The specific methods for generating the charging pile thermal safety penalty index include: Through the formula: ； Generate a thermal safety penalty index for charging piles ; In the formula, This represents the predicted internal temperature of the charging pile at time k+1. This indicates the internal temperature safety threshold of the charging station.
4. The charging pile power distribution control system according to claim 1, characterized in that, The optimal instruction analysis unit specifically includes: The penalty comprehensive value generation module is used to generate a penalty comprehensive value based on the charging pile thermal safety penalty index and the user urgency penalty index. The optimal power allocation value generation module is used to generate an initial optimal power allocation value based on the penalty comprehensive value; The collaborative analysis module is used to establish an optimal power allocation analysis model based on the initial optimal power allocation value and generate the final optimal power allocation value.
5. The charging pile power distribution control system according to claim 4, characterized in that, The specific methods for generating the comprehensive penalty value include: Through the formula: ； Generate a comprehensive penalty value ; In the formula, This represents the user's urgency level penalty index. This represents the thermal safety penalty index of the charging pile, and β represents the weighting coefficient of the user urgency penalty index.
6. The charging pile power distribution control system according to claim 4, characterized in that, The method for generating the initial optimal power allocation value is as follows: ； Generate initial optimal power allocation values ; In the expression, R represents the variable output power of the charging pile, and R represents the set of variable output power of the charging pile. This represents the overall penalty value.
7. The charging pile power distribution control system according to claim 4, characterized in that, The specific expression of the optimal power allocation analysis model is as follows: ； In the expression, This represents the optimal final power allocation value. This represents the initial optimal power allocation value, and φ represents the cooperative correction factor; The specific methods for obtaining the collaborative correction factor include: Through the formula: ； Generate a collaborative correction factor φ; In the formula, This represents the initial optimal power allocation value for the j-th charging pile, and N represents the number of charging piles. This represents the output power of the j-th charging pile at the current time k. This represents the remaining available resources of the charging station.