Mobile energy storage charging system and control method thereof

Abstract

The application discloses a mobile energy storage charging system and a control method thereof, and relates to the technical field of automobile charging. The application collects an entry time period corresponding to historical charging records, determines a peak time period according to the historical charging record condition of the entry time period, calculates a waiting time length ratio of the historical charging records, determines a waiting level of the historical charging records, calculates a waiting score of the peak time period, and determines a characteristic level corresponding to the peak time period. The application screens a characteristic time period, calculates an average operation score of the characteristic time period, draws an operation curve diagram of the mobile charging system, draws a real-time operation curve diagram of the mobile charging system, combines the real-time operation curve diagram with the operation curve diagram, calculates a real-time optimization period of the mobile charging system, and judges the optimization state of the mobile charging system according to the real-time optimization period, so that the overall operation efficiency of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile charging, and in particular to a mobile energy storage charging system and a control method thereof. Background Art

[0002] In recent years, the domestic new energy industry has developed rapidly, and the penetration rate of electric vehicles has continued to rise. However, the resulting surge in demand for public charging facilities has led to an increasingly prominent contradiction between the supply and demand of facilities. These contradictions are mainly manifested in: uneven distribution and unreasonable layout of public charging facilities, difficult infrastructure construction, and high subsequent maintenance costs.

[0003] To alleviate these challenges, mobile charging systems are gaining attention as a new technology. These systems, with their built-in lithium iron phosphate battery packs, offer flexible deployment and can be applied in scenarios where infrastructure construction is challenging, as well as in areas with significant tidal activity. Mobile charging systems move charging devices to a charging area for charging, then transfer them to the service area, enabling flexible allocation of charging resources.

[0004] However, traditional mobile charging systems may experience fluctuations in system operating efficiency in actual applications. Therefore, optimizing the mobile charging system becomes the key to improving its operating efficiency. Especially when the system operating efficiency decreases, how to reasonably determine the optimization needs is the core challenge to ensure the overall performance and efficiency of the system. If blind optimization is performed immediately when the operating efficiency decreases, it may have an adverse effect on the overall performance of the system. Therefore, it is necessary to establish a scientific and reasonable optimization judgment mechanism to enhance the intelligence level of the mobile charging system, thereby better improving the overall operating efficiency of the system. Summary of the Invention

[0005] The object of the present invention is to provide a mobile energy storage charging system and a control method thereof to solve the problems raised in the prior art.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a mobile energy storage charging control method, the method comprising:

[0007] Step S100: Whenever a user's vehicle is identified as entering a charging site and the user submits a charging request through the mobile charging system, the system generates a charging record; collects the entry time period corresponding to the historical charging record, and determines the peak time period based on the number of historical charging records during the entry time period;

[0008] Step S200: Calculating the waiting time ratio of historical charging records, determining the waiting level of the historical charging records, calculating the waiting score of the peak period based on the number of historical charging records corresponding to different waiting levels during the peak period, and determining the characteristic level corresponding to the peak period;

[0009] Step S300: screening the feature time period, calculating the average operation score of the feature time period, and drawing the operation curve of the mobile charging system;

[0010] Step S400: drawing the real-time operation curve of the mobile charging system according to the real-time operation score of the sampling time period, combining with the operation curve, calculating the real-time optimization period of the mobile charging system, and judging the optimization state of the mobile charging system according to the real-time optimization period.

[0011] Further, step S100 comprises:

[0012] Step S101: dividing a day into a plurality of time periods, obtaining the time range corresponding to each time period, obtaining the historical charging record of the mobile charging system, collecting the time point at which the user's vehicle enters the charging site in a certain historical charging record, and determining the entry time period corresponding to the historical charging record;

[0013] Step S102: counting the number of historical charging records corresponding to a certain entry time period as A, obtaining the total number of historical charging records as B, and calculating the occurrence frequency of the entry time period as A / B;

[0014] Step S103: setting an occurrence frequency threshold, comparing the occurrence frequency of a certain entry time period with the occurrence frequency threshold, and setting the entry time period as a peak time period if the occurrence frequency exceeds the occurrence frequency threshold;

[0015] By counting the occurrence frequency of different entry time periods, the peak time period of charging demand can be accurately identified, and scientific data support can be provided for the mobile charging system;

[0016] Relying on historical charging records for data analysis makes the decision-making process more objective and accurate, reduces the uncertainty caused by subjective judgment, and improves the intelligent management level of the charging station.

[0017] Further, step S200 comprises:

[0018] Step S201: setting the time length from when the user's vehicle is parked in the parking space to when the charging starts as the waiting time length, obtaining the waiting time length C and the charging time length D of a certain historical charging record in the historical charging record set of a certain peak time period, and calculating the waiting time length ratio of the historical charging record as (C+D) / D;

[0019] Step S202: summarizing the waiting time length ratios of all historical charging records, dividing the waiting time length ratios into a plurality of waiting grades, obtaining the waiting time length ratio range corresponding to a certain waiting grade, and counting the number of historical charging records of a certain waiting grade within a certain peak time period;

[0020] Step S203: Assign each waiting level, calculate the waiting score of the peak period according to the following formula:

[0021] ;

[0022] Wherein, E represents the waiting score of the peak period, F a represents the number of historical charging records of the a-th waiting level, represents the weight value of the a-th waiting level, and b is the total number of waiting levels;

[0023] Step S204: Summarize all the waiting scores of the peak period, divide the waiting scores into several characteristic levels, and assign each characteristic level to obtain the waiting score range corresponding to each characteristic level, and determine the characteristic level corresponding to each peak period;

[0024] If the waiting time is longer, the value of the waiting time ratio will also be larger, so the waiting time ratio can be used to judge whether the historical charging record is abnormal;

[0025] By summarizing and classifying the historical records of different waiting time ratios, the severity of different peak periods can be clearly identified.

[0026] Further, step S300 includes:

[0027] Step S301: Select a continuous number of days as a training period, obtain an optimization period of the mobile charging system, set a number of sampling time points in the optimization period, set two adjacent sampling time points as a sampling time period, summarize the waiting time ratio of the charging record in a certain sampling time period, obtain the average waiting time ratio of the sampling time period, set an average waiting time ratio threshold, compare the average waiting time ratio of a certain sampling time period with the average waiting time ratio threshold, if it is lower than the average waiting time ratio threshold, set the sampling time period as a characteristic time period;

[0028] Step S302: In a certain characteristic time period, obtain the running parameters of the charging system in a certain peak period, and calculate the running score of the characteristic time period according to the following formula:

[0029] ;

[0030] Wherein, Y represents the running score of the characteristic time period, K ni represents the value of the i-th running parameter in the n-th peak period, represents the weight value of the i-th running parameter, and r represents the total number of running parameters, H n represents the weight value of the characteristic level corresponding to the n-th peak period, and m represents the total number of peak periods;

[0031] Step S303: aggregate the operation scores corresponding to a certain feature time period in each optimization period to obtain an average operation score of the certain feature time period, and draw an operation curve of the mobile charging system, wherein the average operation score of the feature time period is taken as the ordinate, and the feature time period is taken as the abscissa;

[0032] The feature time period is determined because a threshold is set to screen out the time period with high operation efficiency, thereby avoiding the influence of irrelevant time periods. The time period below the threshold means that the charging efficiency of the system is high and the equipment load is appropriate, and thus is marked as the feature time period, which can provide data support for subsequent optimization measures.

[0033] The operation parameters of the charging equipment include equipment utilization rate, load balancing degree, charging completion rate, etc. By calculating the operation score of the feature time period and considering the influence of the peak period and multiple operation parameters of the charging equipment, the overall performance of the system can be more comprehensively evaluated. The score considers multiple dimensions, which can more objectively reflect the operation quality and load condition of the system.

[0034] By displaying the average operation score of the feature time period in the form of a curve, the operation trend of each time period can be intuitively seen. Such a visual result can help the operator quickly identify the optimization effect of the system, evaluate the load capacity of different time periods, and then make adjustments.

[0035] Further, step S400 includes:

[0036] Step S401: obtain a real-time operation score in a certain sampling time period, draw a real-time operation curve of the mobile charging system, and compare and analyze it with the operation curve of the mobile charging system. When the real-time operation curve is higher than the operation curve in a certain sampling time period, the sampling time period is marked as an efficient time period. When the real-time operation curve is lower than the operation curve in a certain sampling time period, the sampling time period is marked as an inefficient time period.

[0037] Step S402: aggregate the efficient time period and the inefficient time period respectively. In a certain time period set, the real-time operation score corresponding to a certain sampling time period in the real-time operation curve is collected as Y1, the operation score corresponding to a certain sampling time period in the operation curve is collected as Y2, and the change value of the certain sampling time period is calculated as ;

[0038] Step S403: calculate the real-time optimization period of the mobile charging system according to the following formula:

[0039] ;

[0040] Wherein, T represents the real-time optimization period of the mobile charging system, t represents the optimization period of the mobile charging system, L e represents the change value of the e-th sampling period in the efficient period, P1 represents the weight value of the efficient period, f represents the total number of sampling periods in the efficient period, L h represents the change value of the h-th sampling period in the inefficient period, P2 represents the weight value of the inefficient period, j represents the total number of sampling periods in the inefficient period;

[0041] Step S404: Compare the real-time optimization period with the time interval of the next sampling period, and if it is less than the time interval, prompt the staff to optimize the mobile charging system;

[0042] The real-time running score reflects the current running state of the charging system in a certain period of time. By drawing the real-time running curve, the running performance of the system can be dynamically tracked, and abnormal or fluctuation conditions can be found in time. The running curve represents the average running level of the system in the past optimization period, while the real-time running curve reflects the current system state. By comparison, the running condition of the current system can be judged. By marking the efficient and inefficient periods, targeted feedback can be provided for the optimization of the system, facilitating subsequent analysis and adjustment.

[0043] The real-time optimization period can be dynamically adjusted according to the performance of the efficient and inefficient periods, reflecting the current need to speed up or slow down the adjustment of the system. If the contribution of the efficient period is greater, T increases, indicating that the optimization period can be appropriately extended to reduce unnecessary adjustments. If the influence of the inefficient period is greater, T decreases, indicating that faster adjustment is needed to solve the inefficiency problem. The dynamic optimization period can improve the agility of system adjustment, avoid excessive adjustment or delayed adjustment, and maximize system efficiency.

[0044] In order to better realize the above method, a mobile energy storage charging system is also proposed, which comprises a peak period module, a feature level module, a running curve module and a real-time judgment module.

[0045] Peak period module: whenever a user's vehicle enters a charging site and the user issues a charging application through the mobile charging system, the system will generate a charging record; collect the entry period corresponding to the historical charging record, and determine the peak period according to the number of historical charging records of the entry period;

[0046] Feature level module: calculate the waiting time ratio of historical charging records, determine the waiting level of historical charging records, calculate the waiting score of the peak period according to the number of historical charging records corresponding to different waiting levels in the peak period, and determine the feature level corresponding to the peak period;

[0047] The operation curve drawing module: screening a characteristic time period, calculating the average operation score of the characteristic time period, and drawing the operation curve of the mobile charging system;

[0048] The real-time judgment module: according to the real-time operation score of the sampling time period, drawing the real-time operation curve of the mobile charging system, combining with the operation curve, calculating the real-time optimization period of the mobile charging system, and judging the optimization state of the mobile charging system according to the real-time optimization period.

[0049] Further, the peak period module includes a calculation frequency unit and a determination peak period unit:

[0050] The calculation frequency unit: dividing a day into several time periods, obtaining the time range corresponding to each time period, obtaining the historical charging records of the mobile charging system, collecting the time point of the user vehicle entering the charging site in a historical charging record, determining the entry time period corresponding to the historical charging record, counting the number of historical charging records corresponding to a certain entry time period, obtaining the total number of historical charging records, and calculating the occurrence frequency of the entry time period;

[0051] The determination peak period unit: setting an occurrence frequency threshold, comparing the occurrence frequency of a certain entry time period with the occurrence frequency threshold, and if it exceeds the occurrence frequency threshold, setting the entry time period as a peak period.

[0052] Further, the characteristic level module includes a waiting level unit and a determination characteristic level unit:

[0053] The waiting level unit: setting the time length from the start of parking in the parking space to the start of charging as the waiting time length, obtaining the waiting time length and charging time length of a historical charging record in a set of historical charging records in a certain peak period, calculating the waiting time length ratio of the historical charging record, summarizing the waiting time length ratios of all historical charging records, dividing the waiting time length ratio into several waiting levels, and obtaining the waiting time length ratio range corresponding to a certain waiting level.

[0054] The determination characteristic level unit: assigning each waiting level, calculating the waiting score of the peak period, summarizing the waiting scores of all peak periods, dividing the waiting score into several characteristic levels, assigning each characteristic level, obtaining the waiting score range corresponding to a certain characteristic level, and determining the characteristic level corresponding to each peak period.

[0055] Further, the operation curve drawing module includes a determination characteristic time period unit and a drawing operation curve unit:

[0056] The determining characteristic time period unit selects a plurality of consecutive days as a training period, acquires an optimization period of the mobile charging system, sets a plurality of sampling time points in the optimization period, sets two adjacent sampling time points as a sampling time period, aggregates a waiting time ratio of a charging record in a certain sampling time period, acquires an average waiting time ratio of the sampling time period, sets an average waiting time ratio threshold, compares the average waiting time ratio of the certain sampling time period with the average waiting time ratio threshold, and if the average waiting time ratio is lower than the average waiting time ratio threshold, sets the sampling time period as a characteristic time period.

[0057] The drawing operation curve unit acquires an operation parameter of a charging device in a certain peak period in a certain characteristic time period, calculates an operation score of the characteristic time period, aggregates the operation score corresponding to the characteristic time period in each optimization period, acquires an average operation score of the characteristic time period, and draws an operation curve of the mobile charging system, wherein the average operation score of the characteristic time period is used as a vertical coordinate, and the characteristic time period is used as a horizontal coordinate.

[0058] Further, the real-time judgment module comprises a drawing real-time operation curve unit and a judgment optimization unit.

[0059] The drawing real-time operation curve unit acquires a real-time operation score in a certain sampling time period, draws a real-time operation curve of the mobile charging system, and compares and analyzes the real-time operation curve with the operation curve of the mobile charging system, wherein when the real-time operation curve is higher than the operation curve in a certain sampling time period, the sampling time period is marked as an efficient time period, and when the real-time operation curve is lower than the operation curve in a certain sampling time period, the sampling time period is marked as an inefficient time period.

[0060] The judgment optimization unit aggregates the efficient time period and the inefficient time period respectively, acquires a real-time operation score corresponding to a certain sampling time period in a real-time operation curve in a certain time period set, acquires an operation score corresponding to a certain sampling time period in an operation curve, calculates a change value of the certain sampling time period, calculates a real-time optimization period of the mobile charging system, compares the real-time optimization period with a time interval of a next sampling time period, and if the real-time optimization period is smaller than the time interval, prompts a staff to optimize the mobile charging system.

[0061] Compared with the prior art, the present application has the following beneficial effects:

[0062] By dynamically drawing a real-time operation curve and a historical operation curve for comparison and analysis, the state of system operation can be identified and optimized in real time;

[0063] By monitoring the operation score of each sampling time period in real time, the efficient and inefficient time periods are identified and marked in real time, targeted data support is provided, and dynamic optimization is performed.

[0064] By calculating the real-time optimization period and adjusting the system operation state according to the change value, the charging system can respond to demand fluctuations in time, avoid excessive load or inefficient operation of the charging system, and improve the efficiency of the charging system, reduce resource waste, improve the response speed of the charging service, ensure that the charging system can operate efficiently, and reduce unnecessary energy waste. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 A flowchart of a mobile energy storage charging control method of the present application is shown in the figure.

[0066] Figure 2 A structural diagram of a mobile energy storage charging system of the present application is shown in the figure. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0068] Please refer to Figure 1 and Figure 2 The present application provides a technical solution: a mobile energy storage charging control method, the method comprising:

[0069] Step S100: whenever a user's vehicle enters a charging site and the user issues a charging application through the mobile charging system, the system will generate a charging record; collect the entry time period corresponding to the historical charging record, determine the peak period according to the number of historical charging records in the entry time period;

[0070] Wherein, step S100 comprises:

[0071] Step S101: divide a day into several time periods, obtain the time range corresponding to each time period, obtain the historical charging record of the mobile charging system, collect the time point at which the user's vehicle enters the charging site in a certain historical charging record, and determine the entry time period corresponding to the historical charging record;

[0072] Step S102: count the number of historical charging records corresponding to a certain entry time period as A, obtain the total number of historical charging records as B, and calculate the occurrence frequency of the entry time period as A / B;

[0073] Step S103: set an occurrence frequency threshold, compare the occurrence frequency of a certain inbound time period with the occurrence frequency threshold, and if the occurrence frequency exceeds the occurrence frequency threshold, set the inbound time period as a peak time period.

[0074] Step S200: calculate the waiting duration ratio of the historical charging record, determine the waiting level of the historical charging record, calculate the waiting score of the peak time period according to the number of historical charging records corresponding to different waiting levels in the peak time period, and determine the feature level corresponding to the peak time period;

[0075] Among them, step S200 includes:

[0076] Step S201: set the duration from when the user's vehicle is parked in a parking space to when charging begins as the waiting duration, obtain the waiting duration C and the charging duration D of a certain historical charging record in the historical charging record set of a certain peak time period, and calculate the waiting duration ratio of the historical charging record as (C+D) / D;

[0077] Step S202: aggregate the waiting duration ratios of all historical charging records, divide the waiting duration ratios into several waiting levels, obtain the waiting duration ratio range corresponding to a certain waiting level, and count the number of historical charging records of a certain waiting level within a certain peak time period;

[0078] Step S203: assign a value to each waiting level, and calculate the waiting score of the peak time period according to the following formula:

[0079] ;

[0080] Among them, E represents the waiting score of the peak time period, F a represents the number of historical charging records of the a-th waiting level, represents the weight of the a-th waiting level, and b is the total number of waiting levels;

[0081] Step S204: aggregate the waiting scores of all peak time periods, divide the waiting scores into several feature levels, assign a value to each feature level, obtain the waiting score range corresponding to a certain feature level, and determine the feature level corresponding to each peak time period;

[0082] For example, in the A-th peak time period, the number of historical charging records in the a-th waiting level is 2, the number of historical charging records in the b-th waiting level is 1, and the number of historical charging records in the c-th waiting level is 3. The waiting score of the A-th peak time period is calculated to be 10.

[0083] Step S300: screen the feature time period, calculate the average operation score of the feature time period, and draw an operation curve of the mobile charging system;

[0084] wherein, the step S300 comprises:

[0085] Step S301: select a plurality of consecutive days as a training period, obtain an optimization period of the mobile charging system, set a plurality of sampling time points in the optimization period, set two adjacent sampling time points as a sampling time period, aggregate the waiting time ratio of the charging record in a certain sampling time period to obtain the average waiting time ratio of the sampling time period, set an average waiting time ratio threshold, compare the average waiting time ratio of a certain sampling time period with the average waiting time ratio threshold, if lower than the average waiting time ratio threshold, set the sampling time period as a feature time period;

[0086] Step S302: in a certain feature time period, obtain the operation parameters of the charging system in a certain peak period, and calculate the operation score of the feature time period according to the following formula:

[0087] ;

[0088] wherein, Y represents the operation score of the feature time period, K ni represents the value of the i-th operation parameter in the n-th peak period, represents the weight of the i-th operation parameter, r represents the total number of operation parameters, H n represents the weight of the feature level corresponding to the n-th peak period, m represents the total number of peak periods;

[0089] Step S303: aggregate the operation score corresponding to a certain feature time period in each optimization period to obtain the average operation score of a certain feature time period, and draw an operation curve of the mobile charging system, wherein the operation curve takes the average operation score of the feature time period as the vertical coordinate and takes the feature time period as the horizontal coordinate;

[0090] For example, in the first feature time period, there are the A-th peak period and the B-th peak period, the 1st operation parameter in the A-th peak period is 85%, the 2nd operation parameter is 75%, and the 3rd operation parameter is 90%, the 1st operation parameter in the B-th peak period is 80%, the 2nd operation parameter is 70%, and the 3rd operation parameter is 85%, and the operation score of the first feature time period is calculated to be 16.6.

[0091] Step S400: draw a real-time operation curve of the mobile charging system according to the real-time operation score of the sampling time period, and calculate the real-time optimization period of the mobile charging system in combination with the operation curve, and judge the optimization state of the mobile charging system according to the real-time optimization period;

[0092] wherein, the step S400 comprises:

[0093] Step S401: Obtain the real-time operation score in a certain sampling time period, draw the real-time operation curve of the mobile charging system, and compare and analyze with the operation curve of the mobile charging system. When the real-time operation curve is higher than the operation curve in a certain sampling time period, the sampling time period is marked as an efficient time period. When the real-time operation curve is lower than the operation curve in a certain sampling time period, the sampling time period is marked as an inefficient time period.

[0094] Step S402: Collect the efficient time period and the inefficient time period respectively. In a certain time period set, the real-time operation score corresponding to a certain sampling time period in the real-time operation curve is Y1, the operation score corresponding to a certain sampling time period in the operation curve is Y2, and the change value of a certain sampling time period is calculated.

[0095] Step S403: Calculate the real-time optimization period of the mobile charging system according to the following formula:

[0096]

[0097] Wherein, T represents the real-time optimization period of the mobile charging system, t represents the optimization period of the mobile charging system, L e represents the change value of the e-th sampling time period in the efficient time period, P1 represents the weight value of the efficient time period, f represents the total number of sampling time periods in the efficient time period, L h represents the change value of the h-th sampling time period in the inefficient time period, P2 represents the weight value of the inefficient time period, and j represents the total number of sampling time periods in the inefficient time period.

[0098] Step S404: Compare the real-time optimization period with the time interval of the next sampling time period. If it is less than the time interval, prompt the staff to optimize the mobile charging system.

[0099] In order to better realize the above method, a mobile energy storage charging system is also proposed, which comprises a peak period module, a feature level module, an operation curve module and a real-time judgment module.

[0100] Peak period module: whenever a user's vehicle enters a charging site and the user issues a charging application through the mobile charging system, the system will generate a charging record; collect the entry time period corresponding to the historical charging record, and determine the peak period according to the number of historical charging records of the entry time period.

[0101] Wherein, the peak period module comprises a calculation frequency unit and a peak period determination unit:

[0102] ​​The occurrence frequency unit divides a day into time periods, obtains a time range corresponding to each time period, obtains historical charging records of the mobile charging system, collects a time point at which a user's vehicle enters a charging site in a historical charging record, determines an entry time period corresponding to the historical charging record, counts a number of historical charging records corresponding to a certain entry time period, obtains a total number of historical charging records, and calculates an occurrence frequency of the entry time period.

[0103] The peak time period determination unit sets an occurrence frequency threshold, compares the occurrence frequency of a certain entry time period with the occurrence frequency threshold, and sets the entry time period as a peak time period if the occurrence frequency exceeds the occurrence frequency threshold.

[0104] The feature level module calculates a waiting time ratio of historical charging records, determines a waiting level of the historical charging records, calculates a waiting score of a peak time period according to a number of historical charging records corresponding to different waiting levels in the peak time period, and determines a feature level corresponding to the peak time period.

[0105] The feature level module includes a waiting level unit and a feature level determination unit.

[0106] The waiting level unit sets a time length from when a user's vehicle is parked in a parking space to when charging starts as a waiting time, obtains a waiting time and a charging time of a historical charging record in a set of historical charging records in a certain peak time period, calculates a waiting time ratio of the historical charging record, aggregates waiting time ratios of all historical charging records, divides the waiting time ratios into a plurality of waiting levels, and obtains a waiting time ratio range corresponding to a certain waiting level.

[0107] The feature level determination unit values each waiting level, calculates a waiting score of a peak time period, aggregates waiting scores of all peak time periods, divides the waiting scores into a plurality of feature levels, values each feature level, obtains a waiting score range corresponding to a certain feature level, and determines a feature level corresponding to each peak time period.

[0108] The running curve module filters feature time periods, calculates an average running score of the feature time periods, and draws a running curve of the mobile charging system.

[0109] The running curve module includes a feature time period determination unit and a running curve drawing unit.

[0110] The determining characteristic time period unit selects a plurality of consecutive days as a training period, acquires an optimization period of the mobile charging system, sets a plurality of sampling time points in the optimization period, sets two adjacent sampling time points as a sampling time period, aggregates the waiting time ratio of the charging record in a certain sampling time period, obtains the average waiting time ratio of the sampling time period, sets an average waiting time ratio threshold, compares the average waiting time ratio of a certain sampling time period with the average waiting time ratio threshold, and if it is lower than the average waiting time ratio threshold, the sampling time period is set as a characteristic time period;

[0111] The drawing operation curve unit acquires the operation parameter of the charging device in a certain peak period in a certain characteristic time period, calculates the operation score of the characteristic time period, aggregates the operation score corresponding to the characteristic time period in each optimization period, obtains the average operation score of the characteristic time period, and draws the operation curve of the mobile charging system, which takes the average operation score of the characteristic time period as the vertical coordinate and takes the characteristic time period as the horizontal coordinate.

[0112] The real-time judgment module draws the real-time operation curve of the mobile charging system according to the real-time operation score of the sampling time period, and calculates the real-time optimization period of the mobile charging system in combination with the operation curve, and judges the optimization state of the mobile charging system according to the real-time optimization period;

[0113] The real-time judgment module includes a drawing real-time operation curve unit and a judgment optimization unit.

[0114] The drawing real-time operation curve unit acquires the real-time operation score in a certain sampling time period, draws the real-time operation curve of the mobile charging system, and compares and analyzes the real-time operation curve of the mobile charging system, when the real-time operation curve in a certain sampling time period is higher than the operation curve, the sampling time period is marked as an efficient time period, and when the real-time operation curve in a certain sampling time period is lower than the operation curve, the sampling time period is marked as an inefficient time period.

[0115] The judgment optimization unit aggregates the efficient time period and the inefficient time period respectively, collects the real-time operation score corresponding to a certain sampling time period in the real-time operation curve and the operation score corresponding to a certain sampling time period in the operation curve in a certain time period set, calculates the change value of a certain sampling time period, calculates the real-time optimization period of the mobile charging system, compares the real-time optimization period with the time interval of the next sampling time period, and if it is smaller than the time interval, prompts the staff to optimize the mobile charging system.

[0116] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A mobile energy storage charging control method, characterized in that: Methods include: Step S100: Whenever a user's vehicle is identified as entering a charging site and the user submits a charging request through the mobile charging system, the system generates a charging record; collects the entry time period corresponding to the historical charging record, and determines the peak time period based on the number of historical charging records during the entry time period; Step S200: Calculating the waiting time ratio of historical charging records, determining the waiting level of the historical charging records, calculating the waiting score of the peak period based on the number of historical charging records corresponding to different waiting levels during the peak period, and determining the characteristic level corresponding to the peak period; Step S300: screening characteristic time periods, calculating the average operation score of the characteristic time periods, and drawing an operation curve graph of the mobile charging system; Step S400: Based on the real-time operation score of the sampling time period, a real-time operation curve of the mobile charging system is drawn, and the real-time optimization period of the mobile charging system is calculated in combination with the operation curve, and the optimization state of the mobile charging system is determined based on the real-time optimization period.

2. A mobile energy storage charging control method according to claim 1, characterized in that: The step S100 includes the following steps: Step S101: Divide a day into several time periods, obtain the time range corresponding to each time period, obtain historical charging records of the mobile charging system, collect the time point when the user's vehicle enters the charging site in a historical charging record, and determine the entry time period corresponding to the historical charging record; Step S102: Count the number of historical charging records corresponding to a certain station entry period as A, obtain the total number of historical charging records as B, and calculate the occurrence frequency of the station entry period as A / B; Step S103: setting an occurrence frequency threshold, comparing the occurrence frequency of a certain station entry period with the occurrence frequency threshold, and if the occurrence frequency threshold is exceeded, setting the station entry period as a peak period.

3. A mobile energy storage charging control method according to claim 2, characterized in that: The step S200 includes the following steps: Step S201: The time from when the user's vehicle is parked in the parking space to when charging begins is set as the waiting time. From a set of historical charging records during a peak period, a waiting time C and a charging time D are obtained for a historical charging record. The waiting time ratio of the historical charging record is calculated as (C + D) / D. Step S202: Summarize the waiting time ratios of all historical charging records, divide the waiting time ratios into several waiting levels, obtain the waiting time ratio range corresponding to a waiting level, and count the number of historical charging records of a waiting level during a peak period; Step S203: Assign a value to each waiting level and calculate the waiting score during peak hours according to the following formula: ； Among them, E represents the waiting score during peak hours, F a It is represented by the number of historical charging records of the a-th waiting level, It is expressed as the weight of the ath waiting level, and b is the total number of waiting levels; Step S204: Summarize the waiting scores of all peak time periods, divide the waiting scores into several characteristic levels, assign a value to each characteristic level, obtain the waiting score range corresponding to a certain characteristic level, and determine the characteristic level corresponding to each peak time period.

4. A mobile energy storage charging control method according to claim 3, characterized in that: The step S300 includes the following steps: Step S301: Selecting several consecutive days as a training cycle to obtain an optimization cycle for the mobile charging system. Within the optimization cycle, setting several sampling time points, setting two adjacent sampling time points as a sampling time period, summarizing the waiting time ratios of charging records within a certain sampling time period to obtain an average waiting time ratio for the sampling time period, setting an average waiting time ratio threshold, and comparing the average waiting time ratio of the certain sampling time period with the average waiting time ratio threshold. If the average waiting time ratio is lower than the average waiting time ratio threshold, setting the sampling time period as a characteristic time period. Step S302: Within a characteristic time period, obtain the operating parameters of the charging system during a peak period, and calculate the operating score of the characteristic time period according to the following formula: ； Among them, Y represents the running score of the characteristic time period, K ni It is expressed as the value of the i-th operating parameter in the n-th peak period, is the weight of the i-th operating parameter, r is the total number of operating parameters, H n It is represented as the weight of the feature level corresponding to the nth peak period, and m is represented as the total number of peak periods; Step S303: Summarize the operation scores corresponding to a certain characteristic time period in each optimization cycle to obtain the average operation score of the characteristic time period, and draw an operation curve chart of the mobile charging system, wherein the operation curve chart uses the average operation score of the characteristic time period as the vertical axis and the characteristic time period as the horizontal axis.

5. A mobile energy storage charging control method according to claim 4, characterized in that: The step S400 includes the following steps: Step S401: obtaining a real-time operation score in a certain sampling time period, drawing a real-time operation curve of the mobile charging system, and comparing and analyzing it with the operation curve of the mobile charging system. When the real-time operation curve is higher than the operation curve in a certain sampling time period, the sampling time period is marked as a high-efficiency time period; when the real-time operation curve is lower than the operation curve in a certain sampling time period, the sampling time period is marked as a low-efficiency time period; Step S402: Summarize the efficient time periods and the inefficient time periods separately. In a certain time period set, collect the real-time operation score corresponding to a certain sampling time period in the real-time operation curve as Y1, collect the operation score corresponding to a certain sampling time period in the operation curve as Y2, and calculate the change value of a certain sampling time period. ; Step S403: Calculate the real-time optimization period of the mobile charging system according to the following formula: ； Where T represents the real-time optimization period of the mobile charging system, t represents the optimization period of the mobile charging system, and L e It is represented as the change value of the e-th sampling time period in the efficient time period, P1 is represented as the weight of the efficient time period, f is represented as the total number of sampling time periods in the efficient time period, L h It is represented as the change value of the hth sampling period in the inefficient period, P2 is represented as the weight of the inefficient period, and j is represented as the total number of sampling periods in the inefficient period; Step S404: Compare the real-time optimization period with the time interval of the next sampling period. If the time interval is less than the time interval, prompt the staff to optimize the mobile charging system.

6. A mobile energy storage charging system, used to implement a mobile energy storage charging control method according to any one of claims 1 to 5, characterized in that: The system includes a peak period module, a characteristic level module, an operation curve diagram module and a real-time judgment module; The peak period module: Whenever a user's vehicle enters a charging site and the user submits a charging request through the mobile charging system, the system will generate a charging record; collect the entry time corresponding to the historical charging record, and determine the peak period based on the number of historical charging records during the entry time; The characteristic level module calculates the waiting time ratio of historical charging records, determines the waiting level of the historical charging records, calculates the waiting score of the peak period according to the number of historical charging records corresponding to different waiting levels in the peak period, and determines the characteristic level corresponding to the peak period; The operation curve diagram module is used to screen the characteristic time period, calculate the average operation score of the characteristic time period, and draw the operation curve diagram of the mobile charging system; The real-time judgment module draws a real-time operation curve of the mobile charging system according to the real-time operation score of the sampling time period, calculates the real-time optimization period of the mobile charging system by combining the operation curve, and judges the optimization state of the mobile charging system according to the real-time optimization period.

7. A mobile energy storage and charging system according to claim 6, characterized in that: The peak period module includes a unit for calculating the frequency of occurrence and a unit for determining the peak period: The occurrence frequency calculation unit divides a day into several time periods, obtains the time range corresponding to each time period, obtains historical charging records of the mobile charging system, collects the time point when the user's vehicle enters the charging site in a historical charging record, determines the entry time period corresponding to the historical charging record, counts the number of historical charging records corresponding to a certain entry time period, obtains the total number of historical charging records, and calculates the occurrence frequency of the entry time period; The peak time period determination unit sets an occurrence frequency threshold, compares the occurrence frequency of a certain station entry period with the occurrence frequency threshold, and sets the station entry period as a peak time period if the occurrence frequency threshold is exceeded.

8. A mobile energy storage and charging system according to claim 6, characterized in that: The feature level module includes a waiting level unit and a feature level determination unit: The waiting level unit: defines the time from when the user's vehicle is parked in the parking space to when charging begins as the waiting time, obtains the waiting time and charging time of a historical charging record in a set of historical charging records during a peak period, calculates the waiting time ratio of the historical charging record, summarizes the waiting time ratios of all historical charging records, divides the waiting time ratios into several waiting levels, and obtains the waiting time ratio range corresponding to a waiting level; The characteristic level determination unit assigns a value to each waiting level, calculates the waiting score of the peak period, summarizes the waiting scores of all peak periods, divides the waiting scores into several characteristic levels, assigns a value to each characteristic level, obtains the waiting score range corresponding to a certain characteristic level, and determines the characteristic level corresponding to each peak period.

9. A mobile energy storage and charging system according to claim 6, characterized in that: The operation curve diagram module includes a characteristic time period determination unit and an operation curve diagram drawing unit: The characteristic time period determination unit selects a number of consecutive days as a training cycle, obtains an optimization cycle of the mobile charging system, sets a number of sampling time points within the optimization cycle, sets two adjacent sampling time points as a sampling time period, summarizes the waiting time ratios of charging records within a certain sampling time period, obtains an average waiting time ratio of the sampling time period, sets an average waiting time ratio threshold, compares the average waiting time ratio of the certain sampling time period with the average waiting time ratio threshold, and sets the sampling time period as a characteristic time period if the average waiting time ratio is lower than the average waiting time ratio threshold; The operation curve drawing unit: obtains the operating parameters of the charging equipment in a peak period within a certain characteristic time period, calculates the operation score of the characteristic time period, summarizes the operation score corresponding to a certain characteristic time period in each optimization cycle, obtains the average operation score of the certain characteristic time period, and draws an operation curve graph of the mobile charging system, wherein the operation curve graph uses the average operation score of the characteristic time period as the vertical axis and the characteristic time period as the horizontal axis.

10. A mobile energy storage and charging system according to claim 6, characterized in that: The real-time judgment module includes a real-time operation curve drawing unit and a judgment optimization unit: The real-time operation curve drawing unit obtains the real-time operation score in a certain sampling time period, draws the real-time operation curve of the mobile charging system, and compares and analyzes it with the operation curve of the mobile charging system. When the real-time operation curve is higher than the operation curve in a certain sampling time period, the sampling time period is marked as a high-efficiency time period; when the real-time operation curve is lower than the operation curve in a certain sampling time period, the sampling time period is marked as a low-efficiency time period; The judgment optimization unit summarizes the high-efficiency time periods and the low-efficiency time periods respectively, collects the real-time operation score corresponding to a sampling time period in the real-time operation curve diagram in a certain time period set, collects the operation score corresponding to a sampling time period in the operation curve diagram, calculates the change value of the certain sampling time period, calculates the real-time optimization period of the mobile charging system, compares the real-time optimization period with the time interval of the next sampling time period, and if it is less than the time interval, prompts the staff to optimize the mobile charging system.

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