A Method for Operation and Management of Electric Vehicle Charging Stations

Abstract

This invention discloses an operation and management method for electric vehicle charging stations, relating to the field of new energy vehicle charging technology. The method includes the following steps: collecting weather data, the number of charging piles, and the power demand of the charging piles through a data acquisition module. By comprehensively considering various influencing factors, such as weather conditions, traffic flow, and charging station load factors, this invention can accurately predict future charging demand. This helps managers to rationally plan and allocate resources for charging stations, ensuring that peak-hour charging demand is met. Furthermore, by real-time monitoring and analysis of key indicators such as charging station power consumption, equipment status, and charging efficiency, it enables efficient power utilization analysis, helping managers reduce power waste and equipment wear, lower operating costs, and improve the economic benefits of charging stations. Therefore, this method enhances the integrated operation and management effectiveness.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy vehicle charging technology, in particular to an electric vehicle charging station operation management method. BACKGROUND

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

[0003] The existing electric vehicle charging station operation management method may have inaccurate charging demand prediction when performing operation management. The existing method may be based on historical data and time series analysis for prediction, thereby possibly ignoring the complexity and diversity of electric vehicle user behavior, specifically, the influence of dynamic factors such as weather factors and traffic conditions, and the existing operation management method may lack in-depth analysis of charging resource consumption and efficiency, thereby possibly leading to serious electric energy waste and equipment loss, resulting in high operating costs. The power demand and efficiency loss of the charging pile are not fully considered when the existing method predicts charging resource consumption, leading to unreasonable allocation and utilization of charging resources. SUMMARY

[0004] The present application provides an electric vehicle charging station operation management method, which solves the problems raised in the background.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: an electric vehicle charging station operation management method, comprising the following steps:

[0006] Step I: Collect weather data, charging pile quantity and power demand of charging piles through a data acquisition module;

[0007] Step II: Input the weather data, charging pile quantity and 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 of period t, the available charging pile quantity and the power demand of the i-th charging pile;

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

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

[0010] Optionally, the analysis calculation module comprises a charging station demand management submodule, a charging station resource management submodule and a charging station efficiency management submodule.

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

[0012]

[0013] Wherein:

[0014] DOC t charging demand amount of time period t, DOC A t weather data of time period t, ranging from 0 to 1, DOC B t traffic flow condition value of time period t, DOC D t charging station load factor of time period t, ranging from 0 to 1, DA

[0015] DA x DOC A t + DB x DOC B t common influence of weather and traffic on charging demand;

[0016] The processing process of the charging station demand management submodule is as follows: inputting the weather data DOC A t of time period t into the charging station demand management submodule, and outputting the charging demand amount DOC t of time period t by the charging station demand management submodule.

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

[0018]

[0019] Wherein:

[0020] CPL t charging resource consumption amount of time period t, CPL A t charging power demand amount of each vehicle, DOC F i power demand of the i th charging pile, CPL B i maximum load capacity of the i th charging pile, CPL C i efficiency loss of the i th charging pile, CPL D

[0021] representing that, in time period t, due to the charging demand DOC tand the number of charging piles CPLA, the total power demand required, the power demand required by each 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 demand and maximum load capacity of all charging piles, and the actual power output after considering the charging pile efficiency loss;

[0023] refers to the influence of traffic flow, expansion coefficient and weather factors on the overall charging capacity of the charging station;

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

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

[0026]

[0027] Wherein:

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

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

[0030] Optionally, the processing process of the weather data DOCA t of period t in the charging station demand management submodule is as follows;

[0031] Based on the networked weather prediction data, the weather condition of period t is evaluated;

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

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

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

[0035] Optionally, the fluctuation coefficient EDLA t of the charging demand in the charging station efficiency management submodule is calculated as follows.

[0036] EDLA t = ADOC / BDOC;

[0037] Wherein: 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.

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

[0039] Based on the charging demand amount DOC t of the period t, the future charging demand trend and peak period are predicted, and the charging station expansion and charging station optimization strategy is developed;

[0040] Based on the charging resource consumption amount CPL t of the period t, the high-energy-consumption and low-efficiency charging piles or equipment are identified, and the targeted maintenance and replacement plan is developed;

[0041] Based on the charging efficiency EDL t of the period t, the utilization rate of the charging pile is evaluated, and the resource scheduling plan is developed.

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

[0043] Firstly, the present application outputs the charging demand amount DOC t of the period t through the charging station demand management submodule. This submodule 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, etc. The prediction can help develop more reasonable operation strategies for the charging station, such as price strategy and service time adjustment, to optimize user experience and operation efficiency. The calculation of the charging demand amount of the period t considers multiple influencing factors, improving the accuracy and reliability of the prediction. Through the calculation of the charging demand amount of the period t, the management personnel can plan the charging resources in advance, such as increasing the number of charging piles and optimizing the layout, to meet the charging demand in the peak period, and can correspondingly develop differentiated price strategies and service times, thereby balancing the supply and demand relationship and improving the operation efficiency.

[0044] Secondly, the charging resource consumption of the time period t is output by the charging station resource management submodule, the submodule can analyze the resource consumption in the charging process, including electric energy, equipment wear and tear, maintenance cost and the like, and the charging efficiency is optimized, the actual operation efficiency of the charging station can be clearly understood by the manager of the submodule, the charging link with high energy consumption and low efficiency is identified, and then corresponding improvement measures are taken, the calculation of the charging resource consumption of the time period t can deeply analyze the resource consumption in the charging process and give optimization suggestions, so as to facilitate the charging station to reduce operation cost, improve equipment energy efficiency ratio and realize sustainable development, the submodule considers equipment wear and tear, maintenance cost and the like, and can provide comprehensive resource consumption analysis for the charging station.

[0045] Thirdly, the charging efficiency of the time period t is output by the charging station efficiency management submodule, the submodule considers multiple factors such as the arrival time of the electric vehicle, charging demand and load condition, intelligent resource scheduling is carried out correspondingly to ensure that each electric vehicle can obtain the required charging service in the shortest time, which helps to reduce user waiting time and improve customer satisfaction of the charging station, the calculation of the charging efficiency of the time period t can comprehensively consider multiple factors to realize optimized allocation of resources, compared with the traditional charging efficiency resource scheduling method, it is more flexible and efficient, and can better meet the actual demand of the electric vehicle charging station.

[0046] Fourthly, the weather data weight coefficient is iterated based on the charging efficiency of the time period t, the charging station can dynamically adjust the sensitivity of the weather data according to the actual charging efficiency during the iteration process, when the charging efficiency is reduced due to bad weather, the weather data weight coefficient value is increased, so that the operation and management method pays more attention to the change of the weather data and makes a coping strategy in advance, the iteration process can significantly improve the operation efficiency of the charging station, the charging station can more accurately predict the charging demand through iteration, so as to reasonably allocate charging resources, reduce unnecessary energy consumption and improve the energy utilization efficiency of the whole charging station. BRIEF DESCRIPTION OF DRAWINGS

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

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

[0049] Figure 3 The structure schematic diagram of the analysis and calculation module of the electric vehicle charging station operation management method. DETAILED DESCRIPTION

[0050] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0051] Regarding the electric vehicle charging station operation management method, unlike the existing electric vehicle charging station operation management method, the existing management method often ignores the complexity, diversity and dynamics of electric vehicle user behavior when managing charging demand, specifically ignoring the influence of dynamic factors such as weather and traffic flow, and the existing operation management method often lacks in-depth analysis of charging resource consumption and efficiency, resulting in serious waste of electric energy and equipment, high operating costs, for example, the power demand, maximum load capacity and efficiency loss of charging piles are not fully considered, resulting in unreasonable allocation and utilization of charging resources;

[0052] And the modules of the present management method can realize accurate prediction of future charging demand by comprehensively considering various influencing factors such as weather conditions, traffic flow and charging station load factors, which helps management personnel to reasonably plan and allocate resources for charging stations to ensure that peak charging demand is met, and through real-time monitoring and analysis of key indicators such as electric energy consumption, equipment status and charging efficiency of charging stations, efficient use of electric energy can be realized, which helps management personnel to reduce electric energy waste and equipment loss, reduce operating costs and improve economic benefits of charging stations, thereby improving the integrated operation management effect.

[0053] Embodiment one: please refer to Figures 1 to 3 The present embodiment provides an electric vehicle charging station operation management method, comprising the following steps:

[0054] Step I: Collect weather data, charging pile quantity and power demand of charging piles through a data acquisition module;

[0055] Step II: Input the weather data, charging pile quantity and 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 of 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 of period t, the number of available charging piles and the power demand of the i th charging pile into an analysis and calculation module, and the management analysis module outputs the charging demand of period t, the charging resource consumption of period t and the charging efficiency of period t;

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

[0058] 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.

[0059] In this embodiment: the method can help the management personnel to predict the charging demand and optimize the resource scheduling to a certain extent, the charging station can more reasonably allocate the charging resources, improve the operation efficiency, and through optimizing the resource consumption and improving the equipment energy efficiency ratio, the charging station can reduce the operation cost, realize the sustainable development, and through reducing the user waiting time and providing the individualized charging service, the charging station can improve the service quality, enhance the customer satisfaction, and multiple modules are combined to form a comprehensive operation management system, which can comprehensively consider various aspects of the charging station operation management.

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

[0061]

[0062] Among them:

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

[0064] DOCA t refers to the weather data of period t, and the range is 0 to 1;

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

[0066] Based on the networked weather prediction data, the weather condition of period t is evaluated;

[0067] If period t is sunny, the output of DOCA t is 1;

[0068] If period t is overcast, the output of DOCA t is 0.7;

[0069] If period t is bad weather, the output of DOCA t is 0.3;

[0070] DOCB t refers to the traffic flow condition value of period t;

[0071] DOCB tThe unit is vehicle / hour, which can be obtained through traffic monitoring cameras or traffic management system data, usually through real-time collection of traffic flow counters on the road, and DOCB is used in this sub-module t The meaning of traffic flow is high, which means that more electric vehicles may pass through or stop near the charging station, which will lead to an increase in charging demand. For example, during rush hour, the demand for charging stations usually increases, while during periods of less traffic, charging demand naturally decreases.

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

[0073] DOCD t Refers to the charging station load factor at time period t, ranging from 0 to 1.

[0074] DOCD t Indicates the current load of the charging station, usually estimated by the utilization rate of the charging pile. This data can be collected in real time through the charging pile monitoring system, and DOCD 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 effect of weather and traffic on charging demand.

[0077] The weather data DOCA t at time period t is input into the charging station demand management sub-module, and the charging station demand management sub-module outputs the charging demand DOC t at time period t.

[0078] In this embodiment: This sub-module can predict the charging demand in the future period of time based on historical charging data, the number of electric vehicles, user charging habits, time factors such as weekdays and weekends, seasonal changes, and other multi-dimensional information. This is crucial for charging station operation and management, as it can help managers plan charging resources in advance, such as adjusting the number, layout, and power configuration of charging piles to meet different time periods of charging demand. At the same time, the prediction model can also develop more reasonable operation strategies for charging stations, such as price strategies, service time adjustments, etc., to optimize user experience and operational efficiency. Through the charging demand DOC tThe calculation of the DOC can help the manager accurately predict the charging demand in the future period of time, and provide a scientific decision basis for the charging station. This helps the charging station to plan charging resources in advance, optimize operation strategy, improve service quality and operation efficiency. Traditional charging demand prediction is often based on simple statistical methods or experience judgment, while the DOC t adopts advanced mathematical models and algorithms, and comprehensively considers various influencing factors, thereby improving the accuracy and reliability of the prediction. Through the calculation of the DOC t , the manager 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 periods, and can correspondingly develop differentiated price strategies and service times 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 submodule is as follows:

[0080]

[0081] Among them:

[0082] CPL t refers to the charging resource consumption amount of the period t, CPLA refers to the number of available charging piles, DOCS t refers to the charging power demand amount 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, which is usually determined by the maintenance condition and loss coefficient of the equipment, and the data sources include technical parameters provided by the equipment manufacturer and maintenance records of the charging pile. The efficiency loss can be calculated based on long-term monitoring of the charging equipment, such as monitoring the difference between actual charging energy consumption and nominal energy consumption through metering devices, or according to the standard efficiency parameters provided by the equipment manufacturer. For high-efficiency charging piles, the loss is usually between 10% and 15%, and ordinary equipment may be higher. The loss rate can be calculated by statistical analysis of the actual use records of the equipment. Assuming that through monitoring, it is found that the actual energy consumption of a certain charging pile during each charging process is 1.1 times the nominal value, then the loss rate CPLC i is 0.1;

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

[0085] The flexibility of charging stations to expand and schedule based on actual needs and operational conditions is usually based on experience. A CPLD of 1.2 can be set, which means that the charging station can expand its resources when demand is high. The specific CPLD can be estimated by the historical efficiency of manual and automatic scheduling systems. For example, a CPLD of 1.2 indicates that the charging station can expand its resources when demand is high.

[0086] The term refers to the amount of charge (DOC) generated during time period t due to charging demand. t The total power requirement, the charging power requirement per vehicle, and the charging power demand per DOCS are all influenced by the number of charging stations and the CPLA. t It is used to calculate how much charging power is needed to meet the current charging needs of a vehicle.

[0087] It refers to the sum of the power requirements and maximum load capacity of all charging piles, as well as the actual power output after taking into account the efficiency loss of the charging piles.

[0088] This refers to the impact of traffic flow, expansion coefficient, and weather factors on the overall charging capacity of charging stations.

[0089] The number of available charging stations (CPLA) and the power requirement (DOCF) of the i-th charging station are specified. i Charging demand (DOC) during time period t t Weather data for time period t (DOCA) t Traffic flow data for time period t (DOCB) t The input is sent to the charging station resource management submodule, which then outputs the charging resource consumption CPL for time period t. t .

[0090] In this embodiment, this submodule aims to analyze resource consumption during the charging process, including electrical energy, equipment wear and tear, and maintenance costs, and optimize charging efficiency. Through this submodule, managers 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, such as optimizing the power allocation of charging piles, improving the energy efficiency ratio of charging equipment, and reducing unnecessary equipment wear and tear, thereby reducing operating costs and improving overall operating efficiency. t This module can deeply analyze resource consumption during the charging process and provide optimization suggestions. This helps charging stations reduce operating costs, improve equipment energy efficiency, and achieve sustainable development. It considers multiple aspects such as equipment wear and tear and maintenance costs, providing a comprehensive resource consumption analysis for charging stations. Furthermore, it can provide specific optimization measures based on the analysis results, making it highly practical and operable. (Based on CPL...) tThe calculations can help managers identify high-energy-consuming and inefficient charging piles, carry out targeted maintenance or replacement, reduce operating costs, and optimize the power distribution and charging strategies of charging piles to improve energy use efficiency and reduce energy waste.

[0091] Please see Figures 1 to 3 The processing procedure of the charging station efficiency management submodule is as follows:

[0092]

[0093] in:

[0094] EDL t The charging efficiency refers to the time period t.

[0095] EDLA t The fluctuation coefficient, ranging from 0 to 1, refers to the impact of fluctuations in charging demand on charging efficiency. This value reflects the degree of fluctuation in charging demand and is estimated by the standard deviation of charging demand. By collecting charging demand data from 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 changes in demand on charging efficiency. For example, when demand fluctuates greatly, more power dispatching or higher equipment efficiency may be required.

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

[0097] EDLA t =ADOC / BDOC;

[0098] Where: 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;

[0099] EDLB t The EDLB (Efficiency of Resource Scheduling) is a resource scheduling optimization coefficient that represents the efficiency of charging pile resource scheduling. It is typically set based on the charging station's operational efficiency and is derived by analyzing the station's resource scheduling records during historical peak periods. For example, if the charging station has a high scheduling capacity, the EDLB will be higher. t It can be set to 0.05;

[0100] The charging station efficiency management submodule processes the following: It calculates the charging demand (DOC) for time period t. t Charging resource consumption CPL during time period t t The input is sent to the charging station efficiency management submodule, which then outputs the charging efficiency EDL for time period t. t .

[0101] In this embodiment, this submodule primarily focuses on how to achieve rational resource allocation while ensuring charging efficiency. It considers multiple factors such as the arrival time of electric vehicles, charging demand, the availability of charging stations, and the grid load. Through intelligent algorithms, it schedules resources to ensure that each electric vehicle can obtain the necessary charging service in the shortest possible time. This helps reduce user waiting time and improves customer satisfaction at charging stations. Simultaneously, rational resource allocation can also avoid safety hazards and equipment damage caused by excessive grid load. t This enables the rational allocation of resources, ensuring that every electric vehicle can obtain the necessary charging service in the shortest possible time. This helps improve customer satisfaction and service quality at charging stations. (EDL) t It employs intelligent algorithms for resource scheduling, comprehensively considering multiple factors to achieve optimal resource allocation. Compared to traditional resource scheduling methods, it is more flexible and efficient, better meeting the actual needs of electric vehicle charging stations. t The calculations facilitate intelligent resource scheduling by managers, ensuring that each electric vehicle can obtain the necessary charging service in the shortest possible time. This also helps reduce user waiting time, improve customer satisfaction and service quality at charging stations, balance grid load, and ensure the safety and stability of the charging process.

[0102] It is worth noting that the charging efficiency EDL for time period t... t Further calculations influence the weather data weighting coefficient DA in the charging station demand management submodule, to affect the charging demand DOC for time period t. t Charging resource consumption CPL during time period t t and the charging station efficiency management submodule EDL t Continuous optimization is underway, and the specific processing steps are as follows:

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

[0104] Secondly: Set the iteration termination condition:

[0105] Termination condition 1: The number of iterations is 100;

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

[0107] in:

[0108] DA new Refers to the weighting coefficients of the weather data after iteration, DAold The weight coefficients of the weather data before iteration, α refers to the learning rate, and controls the step size and EDL of the iteration. st Refers to target charging efficiency, EDL t，new Refers to the charging efficiency and EDL of time period t after iteration. t，old This refers to the charging efficiency during the time period t before the iteration.

[0109] In this embodiment: As the iteration process progresses, the charging station can dynamically adjust its sensitivity to weather data based on actual charging efficiency. When severe weather leads to a decrease in charging efficiency, the DA value is increased, making the operation and management methods pay closer attention to changes in weather data, thereby enabling proactive response strategies, such as increasing the maintenance frequency of charging piles or adjusting charging prices to guide users. The iteration process will significantly improve the operational efficiency of the charging station. Because DA can be dynamically adjusted according to charging efficiency, the charging station can allocate resources more effectively, reducing charging delays and energy waste caused by weather changes. Through iteration, the charging station can more accurately predict charging demand, thereby rationally allocating charging resources. This helps reduce unnecessary energy consumption and improve the overall energy utilization efficiency of the charging station. The iterated DA will directly affect the accuracy of the charging station's demand management submodule. With the dynamic adjustment of DA, the impact of weather factors on charging demand can be better considered, thereby improving the accuracy of predictions. This will help the charging station make more reasonable operational decisions. Furthermore, because the prediction of charging demand is more accurate, the charging station can more effectively arrange the use of charging piles, reduce resource waste, and improve charging efficiency. The iterated charging efficiency EDL... t This will serve as a feedback signal, continuously influencing the adjustment of DA (Data Demand), thus forming a closed-loop control system that continuously optimizes the operational efficiency of charging stations. Simultaneously, due to EDL (Electronic Data Demand), t Reflecting the actual operating status of charging stations, the iterative process helps to identify and resolve operational bottlenecks. EDL t As a direct reflection of charging efficiency, EDL is an important basis for adjusting DA (Charge Averaging) and EDA. This is achieved through continuous monitoring of EDL. t Changes in charging station efficiency allow for timely detection of operational issues and optimization of charging strategies through adjustments to Demand Management (DA). These DA adjustments directly impact the charging station's Demand Management submodule, which in turn affects the Resource Management and Efficiency Management submodules. Conversely, improvements or decreases in charging efficiency become new criteria for adjusting DA, creating a virtuous cycle of interdependence and mutual reinforcement. t The shared goal with DA is to improve the operational and energy efficiency of charging stations. Through iterative processes, they will jointly drive the development of charging stations towards greater efficiency and intelligence.

[0110] In the specific implementation process, various sub-modules of this method are used to construct an electric vehicle charging station operation and management system, which is based on the weather data DOCA for time period t. t The input is sent to the charging station demand management submodule, which then outputs the charging demand (DOC) for time period t. t This submodule can predict charging demand over a future period based on historical charging data, the number of electric vehicles, user charging habits, and time factors. The prediction can also help charging stations develop more reasonable operational strategies, such as pricing strategies and service hour adjustments, to optimize user experience and operational efficiency. (DOC) t Taking into account multiple influencing factors improves the accuracy and reliability of forecasts. Managers, through analysis of DOC... t The calculation can plan charging resources in advance, such as increasing the number of charging piles and optimizing their layout, to meet charging demand during peak hours. It can also formulate differentiated pricing strategies and service hours to balance supply and demand and improve operational efficiency.

[0111] By comparing the number of available charging stations (CPLA) and the power requirement (DOCF) of the i-th charging station... i Charging demand (DOC) during time period t t Weather data for time period t (DOCA) t Traffic flow data for time period t (DOCB) t The input is sent to the charging station resource management submodule, which then outputs the charging resource consumption CPL for time period t. t This submodule analyzes resource consumption during the charging process, including electrical energy, equipment wear and tear, and maintenance costs, and optimizes charging efficiency. Through this submodule, managers 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. (CPL) t It can deeply analyze the resource consumption during the charging process and provide optimization suggestions, which helps charging stations reduce operating costs, improve equipment energy efficiency and achieve sustainable development. This submodule considers multiple aspects such as equipment loss and maintenance costs, and provides charging stations with comprehensive resource consumption analysis.

[0112] By measuring the charging demand DOC during time period t t Charging resource consumption CPL during time period t t The input is sent to the charging station efficiency management submodule, which then outputs the charging efficiency EDL for time period t. t This submodule considers multiple factors such as the arrival time of electric vehicles, charging demand, and load conditions to intelligently schedule resources, ensuring that each electric vehicle can obtain the necessary charging service in the shortest possible time. This helps reduce user waiting time and improve customer satisfaction at charging stations.t Using 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 measuring the charging efficiency EDL during time period t t Further calculations influence the weather data weighting coefficient DA in the charging station demand management submodule. As the iteration process progresses, the charging station can dynamically adjust its sensitivity to weather data based on actual charging efficiency. When severe weather leads to a decrease in charging efficiency, the DA value is increased, making the operation and management method pay more attention to changes in weather data and thus take countermeasures in advance. The iteration process will significantly improve the operational efficiency of the charging station. Through iteration, the charging station can more accurately predict charging demand and thus rationally allocate charging resources, which helps to reduce unnecessary energy consumption and improve the overall energy utilization efficiency of the charging station. Through the iteration process, this method, based on the cooperation of multiple modules, jointly promotes the development of charging stations towards a more efficient and intelligent direction.

[0114] This allows various sub-modules to cooperate and calculate in pairs, and also enables overall looping and iteration, giving the overall system an automated optimization and update effect, thus improving its adaptability.

[0115] Example 2: Please refer to Figure 1 , Figure 2 and Figure 3 The specific steps for formulating and implementing management measures in step IV are as follows:

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

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

[0118] EDL based on time period t t Assess the utilization rate of charging stations and develop a resource scheduling plan.

[0119] In this embodiment: the charging demand (DOC) based on time period t t Adjusting charging prices and service hours to balance supply and demand and improve operational efficiency, based on charging resource consumption CPL during time period t. t Optimize charging strategies, such as adjusting charging power and charging time, to reduce energy waste and equipment wear, and achieve charging efficiency (EDL) based on time period t. tTo achieve intelligent scheduling and real-time monitoring, thereby improving charging efficiency and customer satisfaction.

[0120] 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 method of operating and managing an electric vehicle charging station, characterized by: The method comprises the following steps: Step I: collecting weather data, charging pile quantity and charging pile power demand through a data collection module; Step II: inputting the weather data, charging pile quantity and charging pile power demand into a processing module, the processing module performing data cleaning on the collected data and unifying the format of the input data, and outputting weather data of a time period t, available charging pile quantity and power demand of an i-th charging pile; Step III: inputting the weather data of the time period t, available charging pile quantity and power demand of the i-th charging pile into an analysis and calculation module, the analysis and calculation module outputting charging demand of the time period t, charging resource consumption of the time period t and charging efficiency of the time period t; Step IV: inputting the charging demand of the time period t, charging resource consumption of the time period t and charging efficiency of the time period t into a management module, the management module formulating and implementing management measures based on the input data; The analysis and calculation module comprises a charging station demand management submodule, a charging station resource management submodule and a charging station efficiency management submodule; The calculation formula of the charging station demand management submodule is as follows: ； Wherein: DOCt represents charging demand of the time period t, DOCAt represents weather data of the time period t, the range is 0 to 1, DOCBt represents traffic flow condition value of the time period t, DOCDt represents charging station load factor of the time period t, the range is 0 to 1, DA represents weather data weight coefficient, DB represents traffic flow weight coefficient, and DD represents charging load weight coefficient; refers to the combined effect of weather and traffic on the joint impact on charging demand; The processing procedure of the charging station demand management submodule is as follows: input weather data DOCA t of time period t into the charging station demand management submodule, and the charging station demand management submodule outputs charging demand DOC t of time period t; The calculation formula of the charging station resource management submodule is as follows: ； Wherein: CPLt represents charging resource consumption of the time period t, CPLA represents available charging pile quantity, DOCSt represents charging power demand of each vehicle, DOCFi represents power demand of the i-th charging pile, CPLBi represents maximum load capacity of the i-th charging pile, CPLCi represents efficiency loss of the i-th charging pile, and CPLD represents charging station expansion coefficient; denotes the charging demand DOC in time period t t and the number of charging piles CPLA together, the total power demand required, the charging power demand of each vehicle, DOCS t is used to calculate how much charging power is needed to meet the current vehicle charging demand; Ptotal refers to the sum of power demand and maximum load capacity of all charging piles, and the actual power output after considering the charging pile efficiency loss; refer to the influence of traffic flow, expansion coefficient and weather factors on the overall charging capacity of the charging station; The processing procedure of the charging station resource management submodule is as follows: inputting the available charging pile number CPLA, the power demand DOCF of the i th charging pile, the charging demand amount DOC of the time period t, the weather data DOCA of the time period t, and the traffic flow condition value DOCB of the time period t into the charging station resource management submodule, and outputting the charging resource consumption amount CPL of the time period t by the charging station resource management submodule. i t t t t ;​​​​ The calculation formula of the charging station efficiency management submodule is as follows: ； Wherein: EDLt represents charging efficiency of the time period t, EDLAt represents fluctuation coefficient of charging demand, the range is 0 to 1, indicating the influence of charging demand fluctuation on charging efficiency, and EDLBt represents resource scheduling optimization coefficient; The processing process of the charging station efficiency management submodule is as follows: inputting charging demand DOCt of the time period t and charging resource consumption CPLt of the time period t into the charging station efficiency management submodule, and the charging station efficiency management submodule outputting charging efficiency ELDt of the time period t.

2. The electric vehicle supply station operations management method of claim 1, wherein: The processing process of weather data DOCAt of the time period t in the charging station demand management submodule is as follows: Evaluating weather condition of the time period t based on networked weather prediction data; If the time period t is sunny, DOCAt output is 1; If the time period t is overcast, DOCAt output is 0.7; If the time period t is severe weather, DOCAt output is 0.

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3. The electric vehicle supply station operations management method of claim 1, wherein: The calculation process of fluctuation coefficient EDLAt of charging demand in the charging station efficiency management submodule is as follows: EDLAt = ADOC / BDOC; Wherein: ADOC refers to the charging demand standard deviation in the past period, and BDOC refers to the average value of the charging demand in the past period.

4. The electric vehicle supply station operations management method of claim 1, wherein: The formulation and implementation of the management measures in step IV are specifically: Based on the charging demand amount DOCt of the period t, the future charging demand trend and peak period are predicted, and the expansion and optimization strategy of the charging station is formulated; Based on the charging resource consumption amount CPLt of the period t, the charging piles or equipment with high energy consumption and low efficiency are identified, and the targeted maintenance and replacement plan is formulated; Based on the charging efficiency EDLt of the period t, the utilization rate of the charging pile is evaluated, and the resource scheduling plan is formulated.

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