Electric vehicle charging station operation management method

Abstract

The invention discloses an electric vehicle charging station operation management method, and relates to the technical field of new energy vehicle charging, and the method comprises the following steps: collecting weather data, the number of charging piles, and the power demand condition of the charging piles through a data collection module; by comprehensively considering various influence factors, such as weather conditions, traffic flow and charging station load factors, accurate prediction of future charging demands can be realized, reasonable planning and resource allocation of the charging stations by managers are facilitated, the charging demands in peak periods are ensured to be met, and the charging efficiency is improved. By monitoring and analyzing key indexes such as electric energy consumption, equipment state and charging efficiency of the charging station in real time, efficient utilization and analysis of electric energy can be achieved, management personnel can be helped to reduce electric energy waste and equipment loss, operation cost is reduced, economic benefits of the charging station are improved, and therefore the method can improve the integrated operation management effect.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicle charging, and specifically to an operation management method for an electric vehicle charging station. Background Art

[0002] An electric vehicle charging station is a site for charging electric vehicles. With the rapid development of the electric vehicle industry, as an important facility for electric vehicle energy replenishment, the operation management of electric vehicle charging stations has become increasingly important.

[0003] When the existing operation management methods for electric vehicle charging stations are used for operation management, inaccurate prediction of charging demand may occur. The existing methods may mostly be based on historical data and time series analysis for prediction, thus possibly ignoring the complexity and diversity of electric vehicle user behavior. Specifically, the influence of dynamic factors such as weather conditions and traffic situations may be ignored. Moreover, the existing operation management methods may lack in-depth analysis of charging resource consumption and efficiency, which may lead to serious waste of electric energy and equipment loss, resulting in the unfavorable situation of high operation costs. When predicting the consumption of charging resources by the existing methods, factors such as the power demand and efficiency loss of charging piles may not be fully considered, leading to unreasonable allocation and utilization of charging resources. Summary of the Invention

[0004] The purpose of the present invention is to provide an operation management method for an electric vehicle charging station, which solves the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An operation management method for an electric vehicle charging station, including the following steps:

[0006] Step Ⅰ: Collect weather data, the number of charging piles, and the power demand of charging piles through a data collection module;

[0007] Step Ⅱ: Input the weather data, the number of charging piles, and the power demand of charging piles into a processing module. The processing module performs data cleaning on the collected data and unifies the format of the input data, and outputs the weather data at time period t, the number of available charging piles, and the power demand of the i-th charging pile;

[0008] Step Ⅲ: Input the weather data at time period t, the number of available charging piles, and the power demand of the i-th charging pile into an analysis and calculation module. The management and analysis module outputs the charging demand at time period t, the charging resource consumption at time period t, and the charging efficiency at time period t;

[0009] Step Ⅳ: Input the charging demand at time period t, the charging resource consumption at time period t, and the charging efficiency at time period t into a management module. The management module formulates and implements management measures based on the input data.

[0010] Optionally, the analysis and calculation module includes: a charging station demand management sub-module, a charging station resource management sub-module, and a charging station efficiency management sub-module.

[0011] Optionally, the calculation formula of the charging station demand management sub-module is as follows:

[0012]

[0013] Where:

[0014] DOC t Refers to the charging demand in time period t, DOCA t Refers to the weather data in time period t, ranging from 0 to 1, DOCB t Refers to the traffic flow condition value in time period t, DOCD t Refers to the charging station load factor in time period t, ranging from 0 to 1, DA refers to the weather data weight coefficient, DB refers to the traffic flow weight coefficient, and DD refers to the charging load weight coefficient;

[0015] DA×DOCA t +DB×DOCB t Refers to the combined impact of weather and traffic on charging demand;

[0016] The processing process of the charging station demand management sub-module is as follows: Input the weather data DOCA in time period t t into the charging station demand management sub-module, and the charging station demand management sub-module outputs the charging demand DOC in time period t t .

[0017] Optionally, the calculation formula of the charging station resource management sub-module is as follows:

[0018]

[0019] Where:

[0020] CPL t Refers to the charging resource consumption in time period t, CPLA refers to the number of available charging piles, DOCS t Refers to the charging power demand per vehicle, DOCF i Refers to the power demand of the i-th charging pile, CPLB i Refers to the maximum load capacity of the i-th charging pile, CPLC i Refers to the efficiency loss of the i-th charging pile, CPLD refers to the charging station expansion coefficient;

[0021] Refers to indicating that during time period t, due to the charging demand DOC tAffected jointly by the number of charging piles CPLA, the total power demand required, the charging power demand per vehicle, DOCS t is used to calculate how much charging power is needed to meet the current vehicle charging demand;

[0022] Refers to the sum of the power demands and maximum load capacities of all charging piles, as well as the actual power output after considering the efficiency losses of the charging piles;

[0023] Refers to the impact of traffic flow, expansion factor, and weather factors on the overall charging capacity of the charging station;

[0024] The processing process of the charging station resource management sub-module is as follows: The available number of charging piles CPLA, the power demand DOCF of the i-th charging pile i , the charging demand DOC at time period t t , the weather data DOCA at time period t t and the traffic flow situation value DOCB at time period t t are input into the charging station resource management sub-module, and the charging station resource management sub-module outputs the charging resource consumption CPL at time period t t .

[0025] Optionally, the calculation formula of the charging station efficiency management sub-module is as follows:

[0026]

[0027] Where:

[0028] EDL t Refers to the charging efficiency at time period t, EDLA t Refers to the fluctuation coefficient of the charging demand, ranging from 0 to 1, indicating the impact of the charging demand fluctuation on the charging efficiency, EDLB t Refers to the resource scheduling optimization coefficient;

[0029] The processing process of the charging station efficiency management sub-module is as follows: The charging demand DOC at time period t t and the charging resource consumption CPL at time period t t are input into the charging station efficiency management sub-module, and the charging station efficiency management sub-module outputs the charging efficiency EDL at time period t t .

[0030] Optionally, the processing process of the weather data DOCA at time period t in the charging station demand management sub-module is as follows; t Evaluate the weather conditions at time period t based on the networked weather prediction data;

[0031] Evaluate the weather conditions at time period t based on the networked weather prediction data;

[0032] If time period t is sunny, then DOCA t The output is 1;

[0033] If time period t is cloudy, then DOCA t The output is 0.7;

[0034] If time period t is bad weather, then DOCA t The output is 0.3.

[0035] Optionally, the fluctuation coefficient EDLA of the charging demand in the charging station efficiency management sub-module t The calculation process is as follows;

[0036] EDLA t = ADOC / BDOC;

[0037] Where: ADOC refers to the standard deviation of the charging demand in the past time period, and BDOC refers to the average value of the charging demand in the past time period.

[0038] Optionally, the formulation and implementation of the management measures in step IV are specifically as follows:

[0039] Based on the charging demand DOC of time period t t Predict the future charging demand trend and peak period, and formulate a charging station expansion and charging station optimization strategy;

[0040] Based on the charging resource consumption CPL of time period t t Identify high-energy-consuming and low-efficiency charging piles or equipment, and formulate targeted maintenance and replacement plans;

[0041] Based on the charging efficiency EDL of time period t t Evaluate the utilization rate of the charging piles, and formulate a resource scheduling plan.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] First, the present invention outputs the charging demand DOC of time period t through the charging station demand management sub-module t , this sub-module can predict the charging demand in the future period based on multi-dimensional information such as historical charging data, the number of electric vehicles, user charging habits, and time factors. The prediction can help the charging station formulate more reasonable operation strategies, such as price strategies and service time adjustments, etc., to optimize the user experience and operation efficiency. The calculation of the charging demand in time period t comprehensively considers various influencing factors, improving the accuracy and reliability of the prediction. Managers can plan the charging resources in advance through the calculation of the charging demand in time period t, such as increasing the number of charging piles and optimizing the layout, to meet the charging demand during peak periods, and can correspondingly formulate differentiated price strategies and service times, thereby balancing the supply and demand relationship and improving the operation efficiency.

[0044] II. The charging resource consumption at time period t is output by the charging station resource management sub-module of the present invention. This sub-module can analyze the resource consumption during the charging process, including electric energy, equipment loss, and maintenance cost, etc., and optimize it in combination with the charging efficiency. Through this sub-module, the manager can clearly understand the actual operation efficiency of the charging station, identify the charging links with high energy consumption and low efficiency, and then take corresponding improvement measures. The calculation of the charging resource consumption at time period t can deeply analyze the resource consumption during the charging process and give optimization suggestions, which is convenient for the charging station to reduce the operation cost, improve the energy efficiency ratio of the equipment, and achieve sustainable development. This sub-module considers multiple aspects such as equipment loss and maintenance cost, and can provide a comprehensive resource consumption analysis for the charging station.

[0045] III. The charging efficiency at time period t is output by the charging station efficiency management sub-module of the present invention. This sub-module considers multiple factors such as the arrival time, charging demand, and load condition of the electric vehicle, and conducts intelligent resource scheduling correspondingly to ensure that each electric vehicle can obtain the required charging service in the shortest time, which helps to reduce the user waiting time and improve the customer satisfaction of the charging station. The calculation of the charging efficiency at time period t can comprehensively consider various factors to realize the optimal allocation of resources. Compared with the traditional charging efficiency resource scheduling method, it is more flexible and efficient, and can better meet the actual needs of the electric vehicle charging station.

[0046] IV. The present invention iterates the weight coefficient of weather data based on the charging efficiency at time period t. As the iteration process progresses, the charging station can dynamically adjust the sensitivity of weather data according to the actual charging efficiency. When the charging efficiency decreases due to bad weather, by increasing the value of the weather data weight coefficient, the operation management method pays more attention to the change of weather data and then makes a response strategy in advance. The iteration process will significantly improve the operation efficiency of the charging station. Through iteration, the charging station can more accurately predict the charging demand, thus reasonably allocate the charging resources, which is convenient for reducing unnecessary energy loss and improving the energy utilization efficiency of the whole charging station. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is the step flow chart of the operation management method for this electric vehicle charging station;

[0048] Figure 2 is the overall structure schematic diagram of the operation management method for this electric vehicle charging station;

[0049] Figure 3 is the structure schematic diagram of the analysis and calculation module of the operation management method for this electric vehicle charging station. DETAILED DESCRIPTION OF THE INVENTION

[0050] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] Regarding the operation management method of this electric vehicle charging station, it is different from the existing operation management methods of vehicle charging stations. When the existing management methods manage the charging demand, they often make predictions based on historical data or simple time series analysis, ignoring the complexity, diversity, and dynamics of electric vehicle user behavior. Specifically, they ignore the influence of dynamic factors such as weather and traffic flow. The existing operation management methods often lack in-depth analysis of the consumption and efficiency of charging resources, resulting in serious waste of electric energy, equipment loss, and high operation costs. For example, factors such as the power demand, maximum load capacity, and efficiency loss of charging piles are not fully considered, leading to unreasonable allocation and utilization of charging resources.

[0052] However, the module of this management method can accurately predict the future charging demand by comprehensively considering various influencing factors such as weather conditions, traffic flow, and charging station load factors. Furthermore, it helps the management personnel to reasonably plan and allocate resources for the charging station, ensuring that the charging demand during peak periods is met. And by real-time monitoring and analyzing key indicators such as the electric energy consumption, equipment status, and charging efficiency of the charging station, it can achieve efficient utilization analysis of electric energy, helping the management personnel to reduce electric energy waste and equipment loss, lower operation costs, and improve the economic benefits of the charging station. Thus, this method can improve the integrated operation management effect.

[0053] Embodiment 1: Please refer to Figures 1 to 3 , this embodiment provides an operation management method for an electric vehicle charging station, including the following steps:

[0054] Step I: Collect weather data, the number of charging piles, and the power demand situation of charging piles through a data collection module;

[0055] Step II: Input the weather data, the number of charging piles, and the power demand situation of charging piles into a processing module. The processing module performs data cleaning on the collected data and unifies the format of the input data, and outputs the weather data at time period t, the number of available charging piles, and the power demand of the i-th charging pile;

[0056] Step III: Input the weather data at time period t, the number of available charging piles, and the power demand of the i-th charging pile into an analysis and calculation module. The management analysis module outputs the charging demand at time period t, the charging resource consumption at time period t, and the charging efficiency at time period t;

[0057] Step IV: Input the charging demand, charging resource consumption, and charging efficiency during period t into the management module, and the management module formulates and implements management measures based on the input data;

[0058] The analysis and calculation module includes: a charging station demand management sub-module, a charging station resource management sub-module, and a charging station efficiency management sub-module.

[0059] In this embodiment: This method can help managers predict charging demand and optimize resource scheduling to a certain extent. The charging station can allocate charging resources more reasonably, improve operation efficiency, and by optimizing resource consumption and increasing the energy efficiency ratio of equipment, the charging station can reduce operation costs and achieve sustainable development. Also, by reducing user waiting time and providing personalized charging services, the charging station can improve service quality and enhance customer satisfaction. The combination of multiple modules forms a comprehensive operation management system that can comprehensively consider all aspects of charging station operation management.

[0060] Please refer to Figures 1 to 3 , and the processing process of charging station demand management is as follows:

[0061]

[0062] Among them:

[0063] DOC t refers to the charging demand during period t;

[0064] DOCA t refers to the weather data during period t, with a range of 0 to 1;

[0065] The processing process of weather data DOCA t is as follows;

[0066] Evaluate the weather condition during period t based on the networked weather prediction data;

[0067] If period t is sunny, then DOCA t outputs 1;

[0068] If period t is cloudy, then DOCA t outputs 0.7;

[0069] If period t is bad weather, then DOCA t outputs 0.3;

[0070] DOCB t refers to the traffic flow situation value during period t;

[0071] DOCB tThe unit is vehicles per hour, which can be obtained from the data of traffic monitoring cameras or traffic management systems. Usually, it is collected in real time through traffic flow counters on the road. DOCB is used in this sub-module t When it means high traffic flow, it means that more electric vehicles may pass by or stop near the charging station, which will lead to an increase in charging demand. For example, during peak traffic hours, the demand for charging stations usually increases, while during periods of less traffic, the charging demand will naturally decrease;

[0072] It should be normalized to the range of 0 to 1 to ensure the consistency of calculation results

[0073] DOCD t Refers to the charging station load factor at time period t, with a range of 0 to 1;

[0074] DOCD t Represents the current load situation of the charging station, usually estimated by the utilization rate of charging piles. This data can be collected in real time through the charging pile monitoring system. DOCD can be set t = 0.8;

[0075] DA refers to the weather data weight coefficient, DB refers to the traffic flow weight coefficient, and DD refers to the charging load weight coefficient;

[0076] DA×DOCA t +DB×DOCB t Refers to the combined impact of weather and traffic on charging demand;

[0077] Input the weather data DOCA at time period t t Into the charging station demand management sub-module, and the charging station demand management sub-module outputs the charging demand DOC at time period t t 。

[0078] In this embodiment: This sub-module can predict the charging demand in the next period based on multi-dimensional information such as historical charging data, the number of electric vehicles, user charging habits, and time factors (such as weekdays and weekends, seasonal changes, etc.). This is crucial for the operation and management of charging stations because it can help managers plan charging resources in advance, such as adjusting the number, layout, and power configuration of charging piles to meet the charging demands at different time periods. At the same time, the prediction model can also formulate more reasonable operation strategies for charging stations, such as price strategies and service time adjustments, to optimize the user experience and operation efficiency. Through the charging demand DOC at time period t tThe calculation of DOC can facilitate managers to accurately predict the charging demand in a future period, providing a scientific decision-making basis for the charging station. This helps the charging station plan charging resources in advance, optimize operation strategies, and improve service quality and operation efficiency. Traditional charging demand prediction often relies on simple statistical methods or empirical judgments, while DOC t adopts advanced mathematical models and algorithms, comprehensively considering various influencing factors, and improving the accuracy and reliability of prediction. Through the calculation of DOC, managers t can plan charging resources in advance, such as increasing the number of charging piles and optimizing the layout, to meet the charging demand during peak hours, and can correspondingly formulate differentiated price strategies and service hours to balance the supply and demand relationship and improve operation efficiency.

[0079] Please refer to Figures 1 to 3 , the processing process of the charging station resource management sub-module is as follows:

[0080]

[0081] Among them:

[0082] CPL t refers to the charging resource consumption in period t, CPLA refers to the number of available charging piles, DOCS t refers to the charging power demand of each vehicle, DOCF i refers to the power demand of the i-th charging pile, CPLB i refers to the maximum load capacity of the i-th charging pile;

[0083] CPLC i refers to the efficiency loss of the i-th charging pile, ranging from 0 to 1, usually determined by the maintenance condition of the equipment and the loss coefficient. The data sources include the technical parameters of the equipment manufacturer and the maintenance records of the charging pile. It can be based on the long-term monitoring of the energy efficiency loss of the charging equipment, such as monitoring the difference between the actual charging energy consumption and the nominal energy consumption through a metering device, or according to the standard efficiency parameters provided by the equipment manufacturer. For high-efficiency charging piles, the loss is usually between 10% - 15%, and that of ordinary equipment may be higher. The calculation of the loss rate can be statistically analyzed through the actual use records of the equipment. Assuming that through monitoring, it is obtained that during each charging process of a specific charging pile, the actual energy consumption is 1.1 times the nominal value, then the loss rate CPLC i is 0.1;

[0084] CPLD refers to the charging station expansion coefficient;

[0085] The flexibility of the charging station to expand and schedule according to actual needs and operating conditions is usually set based on experience. The CPLD can be set to 1.2, indicating that the charging station can expand resources when demand is high. Specifically, the CPLD can be estimated through the historical efficiency of the manual scheduling and automatic scheduling systems. For example, a CPLD of 1.2 means that the charging station's ability to expand resources at high demand is 1.2 times that of its normal capacity.

[0086] Refers to the total power demand required during time period t due to the charging demand DOC t and the number of charging piles CPLA, which together affect the charging power demand per vehicle, DOCS t and is used to calculate how much charging power is needed to meet the current vehicle charging demand.

[0087] Refers to the sum of the power demands and maximum load capacities of all charging piles, as well as the actual power output considering the efficiency losses of the charging piles.

[0088] Refers to the impact of traffic flow, expansion factor, and weather factors on the overall charging capacity of the charging station.

[0089] Input the available number of charging piles CPLA, the power demand DOCF of the i-th charging pile i , the charging demand DOC during time period t t , the weather data DOCA during time period t t and the traffic flow situation value DOCB during time period t t into the charging station resource management sub-module, and the charging station resource management sub-module outputs the charging resource consumption CPL during time period t t .

[0090] In this embodiment: This sub-module aims to analyze the resource consumption during the charging process, including electrical energy, equipment losses, maintenance costs, etc., and optimize it in combination with the charging efficiency. Through this sub-module, the manager can clearly understand the actual operating efficiency of the charging station, identify high-energy-consuming and low-efficiency charging links, and then take corresponding improvement measures. For example, optimize the power distribution of the charging piles, improve the energy efficiency ratio of the charging equipment, reduce unnecessary equipment losses, etc., so as to reduce the operating cost and improve the overall operating efficiency. CPL t can deeply analyze the resource consumption during the charging process and give optimization suggestions, which helps the charging station reduce the operating cost, improve the energy efficiency ratio of the equipment, and achieve sustainable development. This sub-module considers multiple aspects such as equipment losses and maintenance costs, provides a comprehensive resource consumption analysis for the charging station. At the same time, it can also give specific optimization measures based on the analysis results, with strong practicality and operability. Based on CPL tThe calculation can help managers identify high - energy - consuming and low - efficiency charging piles, conduct targeted maintenance or replacement, reduce operating costs, and can optimize the power distribution and charging strategies of charging piles, improve the power usage efficiency, and reduce energy waste.

[0091] Please refer to Figures 1 to 3 , and the processing procedure of the charging station efficiency management sub - module is as follows:

[0092]

[0093] Among them:

[0094] EDL t refers to the charging efficiency at time period t;

[0095] EDLA t refers to the fluctuation coefficient of charging demand, with a range from 0 to 1, indicating the impact of charging demand fluctuations on charging efficiency. This value reflects the degree of charging demand fluctuations. The fluctuation coefficient is estimated through the standard deviation of charging demand. By collecting charging demand data for the past few months, the fluctuation coefficient can be obtained by calculating the standard deviation of the demand data. The fluctuation coefficient reflects the impact of demand changes on charging efficiency. For example, when the demand fluctuates greatly, more power dispatching or higher equipment efficiency may be required;

[0096] The fluctuation coefficient of charging demand, EDLA t The calculation process is as follows;

[0097] EDLA t = ADOC / BDOC;

[0098] Among them: ADOC refers to the standard deviation of charging demand in the past time period, and BDOC refers to the average value of charging demand in the past time period;

[0099] EDLB t refers to the resource scheduling optimization coefficient, indicating the efficiency of charging pile resource scheduling. It is usually set according to the operating efficiency of the charging station and is obtained by analyzing the resource scheduling records of the charging station during historical peak periods. For example, if the charging station has a high scheduling ability, EDLB t can be set to 0.05;

[0100] The processing procedure of the charging station efficiency management sub - module is as follows: Input the charging demand DOC t at time period t and the charging resource consumption CPL t at time period t into the charging station efficiency management sub - module, and the charging station efficiency management sub - module outputs the charging efficiency EDL t .

[0101] In this embodiment: This sub-module mainly focuses on how to achieve reasonable resource scheduling while ensuring charging efficiency. It takes into account multiple factors such as the arrival time of electric vehicles, charging demands, the available status of charging piles, and grid load conditions, and conducts resource scheduling through intelligent algorithms to ensure that each electric vehicle can obtain the required charging service in the shortest possible time. This helps to reduce the waiting time of users and improve the customer satisfaction of the charging station. At the same time, through reasonable resource scheduling, it can also avoid potential safety hazards and equipment damage caused by excessive grid load, EDL t It can achieve reasonable resource scheduling to ensure that each electric vehicle can obtain the required charging service in the shortest possible time, which helps to improve the customer satisfaction and service quality of the charging station, EDL t It adopts intelligent algorithms for resource scheduling, can comprehensively consider various factors, and achieve optimal allocation of resources. Compared with traditional resource scheduling methods, it is more flexible and efficient, and can better meet the actual needs of electric vehicle charging stations, EDL t The calculation can facilitate the intelligent scheduling of resources by management personnel, ensure that each electric vehicle can obtain the required charging service in the shortest possible time, help reduce the waiting time of users, improve the customer satisfaction and service quality of the charging station, and balance the grid load to ensure the safety and stability of the charging process.

[0102] It should be noted that the charging efficiency EDL at time period t t is further operated to affect the weather data weight coefficient DA in the charging station demand management sub-module, so as to optimize the charging demand DOC at time period t t , the charging resource consumption CPL at time period t t and the charging station efficiency management sub-module EDL t continuously. The specific processing process is as follows:

[0103] First: DA new = DA old + α×(EDL t -EDL st );

[0104] Second: Set the iteration termination conditions:

[0105] Termination condition one: The number of iterations is 100 times;

[0106] Termination condition two: |EDL t，new -EDL t，old | < 0.001;

[0107] Among them:

[0108] DA new refers to the weather data weight coefficient after iteration, DAold represents the weight coefficient of weather data before iteration, α represents the learning rate, which controls the step size of iteration, and EDL st represents the target charging efficiency, and EDL t，new represents the charging efficiency at time period t after iteration, and EDL t，old represents the charging efficiency at time period t before iteration.

[0109] In this embodiment: As the iteration process progresses, the charging station can dynamically adjust its sensitivity to weather data according to the actual charging efficiency. When the charging efficiency decreases due to bad weather, by increasing the value of DA, the operation management method pays more attention to the changes in weather data, so as to make corresponding strategies in advance, such as increasing the maintenance frequency of charging piles or adjusting the charging price to guide users. The iteration process will significantly improve the operation efficiency of the charging station. Since DA can be dynamically adjusted according to the charging efficiency, the charging station can allocate resources more effectively, reduce charging delays and energy waste caused by weather changes. Through iteration, the charging station can more accurately predict the charging demand, so as to reasonably allocate charging resources, which helps to reduce unnecessary energy consumption and improve the energy utilization efficiency of the entire charging station. The iterated DA will directly affect the accuracy of the charging station demand management sub-module. With the dynamic adjustment of DA, the impact of weather factors on the charging demand can be better considered, thus improving the prediction accuracy, which will help the charging station make more reasonable operation decisions. And because the prediction of charging demand is more accurate, the charging station can arrange the use of charging piles more effectively, reduce resource waste and improve the charging efficiency. The iterated charging efficiency EDL t will be used as a feedback signal to continuously affect the adjustment of DA, which will form a closed-loop control system to continuously optimize the operation efficiency of the charging station. At the same time, because EDL t reflects the actual operation status of the charging station, the iteration process will help to discover and solve the bottleneck problems in operation. EDL t As a direct reflection of the charging efficiency, it is an important basis for adjusting DA. By continuously monitoring the changes of EDL t the charging station can timely discover the problems in operation and optimize the charging strategy by adjusting DA. The adjustment of DA will directly affect the charging station demand management sub-module, and then affect the charging station resource management sub-module and the charging station efficiency management sub-module. And the increase or decrease of the charging efficiency will become a new basis for adjusting DA, forming a virtuous cycle of mutual dependence and mutual promotion. EDL t The common goal of EDL and DA is to improve the operation efficiency and energy utilization efficiency of the charging station. Through the iteration process, they will jointly promote the charging station to develop in a more efficient and intelligent direction.

[0110] In the specific implementation process, a system for the operation and management of electric vehicle charging stations is constructed using multiple sub-modules in this method. By inputting the weather data DOCA at time period t t into the charging station demand management sub-module, the charging station demand management sub-module outputs the charging demand DOC at time period t t . This sub-module can predict the charging demand within a certain period in the future based on multi-dimensional information such as historical charging data, the number of electric vehicles, user charging habits, and time factors. The prediction can also formulate more reasonable operation strategies for the charging station, such as price strategies and service time adjustments, etc., to optimize the user experience and operation efficiency. DOC t Taking into account various influencing factors improves the accuracy and reliability of the prediction. Managers can plan the charging resources in advance by calculating DOC t , such as increasing the number of charging piles and optimizing the layout, to meet the charging demand during peak hours, and can correspondingly formulate differential price strategies and service times to balance the supply and demand relationship and improve the operation efficiency.

[0111] By inputting the available number of charging piles CPLA, the power demand DOCF of the i-th charging pile i , the charging demand DOC at time period t t , the weather data DOCA at time period t t and the traffic flow situation value DOCB at time period t t into the charging station resource management sub-module, the charging station resource management sub-module outputs the charging resource consumption CPL at time period t t . This sub-module can analyze the resource consumption during the charging process, including electric energy, equipment loss, and maintenance costs, etc., and optimize it in combination with the charging efficiency. Through this sub-module, managers can clearly understand the actual operation efficiency of the charging station, identify the charging links with high energy consumption and low efficiency, and then take corresponding improvement measures. CPL t can deeply analyze the resource consumption during the charging process and give optimization suggestions, which helps the charging station reduce the operation cost, improve the equipment energy efficiency ratio, and achieve sustainable development. This sub-module considers multiple aspects such as equipment loss and maintenance costs, and provides a comprehensive resource consumption analysis for the charging station;

[0112] By inputting the charging demand DOC at time period t t and the charging resource consumption CPL at time period t t into the charging station efficiency management sub-module, the charging station efficiency management sub-module outputs the charging efficiency EDL at time period t t . This sub-module considers multiple factors such as the arrival time, charging demand, and load situation of electric vehicles, and conducts intelligent resource scheduling correspondingly to ensure that each electric vehicle can obtain the required charging service in the shortest time. This helps reduce the user waiting time and improve the customer satisfaction of the charging station. EDLt The use of intelligent algorithms for resource scheduling can comprehensively consider multiple factors to achieve optimal resource allocation. Compared with traditional resource scheduling methods, it is more flexible and efficient and can better meet the actual needs of electric vehicle charging stations;

[0113] By taking the charging efficiency EDL of the time period t t Further calculations are performed to affect the weather data weight coefficient DA in the charging station demand management submodule. As the iteration process proceeds, the charging station can dynamically adjust its sensitivity to weather data according to the actual charging efficiency. When the charging efficiency decreases due to bad weather, the DA value is increased to make the operation management method pay more attention to the changes in weather data, and then make response strategies in advance. The iteration process will significantly improve the operation efficiency of the charging station. Through iteration, the charging station can more accurately predict the charging demand and reasonably allocate charging resources, which helps to reduce unnecessary energy loss and improve the energy utilization efficiency of the entire charging station. Through the iteration process, this method, based on the cooperation of multiple modules, jointly promotes the development of charging stations in a more efficient and intelligent direction.

[0114] This allows the various sub-modules to cooperate with each other in calculations, and to perform overall cycles and iterations, so that the overall system has the effect of automatic optimization and updating, and thus better adaptability.

[0115] Example 2: Please refer to Figure 1 , Figure 2 and Figure 3 The formulation and implementation of management measures in step IV are as follows:

[0116] Charging demand DOC based on time period t t Predict future charging demand trends and peak periods, and develop strategies for charging station expansion and optimization;

[0117] Charging resource consumption CPL based on time period t t Identify charging piles or equipment with high energy consumption and low efficiency, and develop targeted maintenance and replacement plans;

[0118] Charging efficiency EDL based on time period t t Evaluate the utilization of charging piles and develop resource scheduling plans.

[0119] In this embodiment: based on the charging demand DOC of time period t t Adjust charging prices and service time to balance supply and demand and improve operational efficiency, based on the charging resource consumption CPL in time period t t Optimize charging strategies, such as adjusting charging power and charging time to reduce energy waste and equipment loss, based on the charging efficiency EDL of time period t tImplement intelligent scheduling and real-time monitoring to improve charging efficiency and customer satisfaction.

[0120] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for operating and managing an electric vehicle charging station, characterized in that: The following steps are involved: Step I: Collect weather data, number of charging piles and power demand of charging piles through the data acquisition module; Step II: Input the weather data, the number of charging piles and the power demand of the charging piles into the processing module, which cleans the collected data and unifies the format of the input data, and outputs the weather data of time period t, the number of available charging piles and the power demand of the i-th charging pile; Step III: Input the weather data of time period t, the number of available charging piles and the power demand of the i-th charging pile into the analysis and calculation module, and the management and analysis module outputs the charging demand of time period t, the charging resource consumption of time period t and the charging efficiency of time period t; Step IV: Input the charging demand during time period t, the charging resource consumption during time period t, and the charging efficiency during time period t into the management module, and the management module formulates and implements management measures based on the input data.

2. The electric vehicle charging station operation and management method according to claim 1, characterized in that: The analysis and calculation module includes: a charging station demand management submodule, a charging station resource management submodule and a charging station efficiency management submodule.

3. The electric vehicle charging station operation and management method according to claim 2, characterized in that: The calculation formula of the charging station demand management submodule is as follows: in: DOC t Refers to the charging demand in time period t, DOCA t Refers to the weather data of time period t, ranging from 0 to 1, DOCB t Refers to the traffic flow value at time period t, DOCD t Refers to the charging station load factor at time period t, ranging from 0 to 1, DA refers to the weather data weight coefficient, DB refers to the traffic flow weight coefficient, and DD refers to the charging load weight coefficient; DA×DOCA t +DB×DOCB t Refers to the combined effects of weather and traffic on charging demand; The processing process of the charging station demand management submodule is as follows: the weather data DOCA of time period t is converted into t Input to the charging station demand management submodule, the charging station demand management submodule outputs the charging demand DOC for time period t t .

4. The electric vehicle charging station operation and management method according to claim 3, characterized in that: The calculation formula of the charging station resource management submodule is as follows: in: CPL t Refers to the charging resource consumption in time period t, CPLA refers to the number of available charging piles, DOCS t Refers to the charging power demand of each vehicle, DOCF i Refers to the power demand of the i-th charging pile, CPLB i Refers to the maximum load capacity of the i-th charging pile, CPLC i refers to the efficiency loss of the i-th charging pile, and CPLD refers to the expansion coefficient of the charging station; Refers to the charging demand DOC in the period t. t The total power required and the charging power requirement of each vehicle, DOCS, are jointly affected by the number of charging piles CPLA. t It is used to calculate how much charging power is needed to meet the current vehicle charging needs; Refers to the sum of the power requirements and maximum load capacity of all charging piles, as well as the actual power output after considering the efficiency loss of the charging piles; Refers to the impact of traffic flow, expansion factor and weather factors on the overall charging capacity of the charging station; The processing process of the charging station resource management submodule is as follows: the number of available charging piles CPLA, the power demand DOCF of the i-th charging pile i , charging demand DOC in period t t , weather data DOCA for period t t and the traffic flow value DOCB at time period t t Input to the charging station resource management submodule, the charging station resource management submodule outputs the charging resource consumption CPL in time period t t .

5. The electric vehicle charging station operation and management method according to claim 4, characterized in that: The calculation formula of the charging station efficiency management submodule is as follows: in: EDL t Refers to the charging efficiency of time period t, EDLA t Refers to the fluctuation coefficient of charging demand, ranging from 0 to 1, indicating the impact of charging demand fluctuation on charging efficiency, EDLB t Refers to the resource scheduling optimization coefficient; The processing process of the charging station efficiency management submodule is as follows: the charging demand DOC in time period t is t and the charging resource consumption CPL in period t t Input to the charging station efficiency management submodule, the charging station efficiency management submodule outputs the charging efficiency EDL of time period t t .

6. The electric vehicle charging station operation and management method according to claim 3, characterized in that: The weather data DOCA for time period t in the charging station demand management submodule t The processing process is as follows; Evaluate the weather conditions in time period t based on online weather forecast data; If time period t is sunny, then DOCA t The output is 1; If time period t is cloudy, then DOCA t The output is 0.7; If the weather during period t is bad, then DOCA t The output is 0.

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7. The electric vehicle charging station operation and management method according to claim 5, characterized in that: The fluctuation coefficient EDLA of the charging demand in the charging station efficiency management submodule t The calculation process is as follows; EDLA t =ADOC / BDOC; Among them: ADOC refers to the standard deviation of charging demand in the past period, and BDOC refers to the average value of charging demand in the past period.

8. The electric vehicle charging station operation and management method according to claim 1, characterized in that: The formulation and implementation of the management measures in step IV are specifically as follows: Charging demand DOC based on time period t t Predict future charging demand trends and peak periods, and develop strategies for charging station expansion and optimization; Charging resource consumption CPL based on time period t t Identify charging piles or equipment with high energy consumption and low efficiency, and develop targeted maintenance and replacement plans; Charging efficiency EDL based on time period t t Evaluate the utilization of charging piles and develop resource scheduling plans.

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