Mobile energy storage charging system and control method thereof

Abstract

The invention discloses a mobile energy storage charging system and a control method thereof, and relates to the technical field of automobile charging, and the method comprises the steps: collecting a pull-in time period corresponding to a historical charging record, and determining a peak time period according to the historical charging record condition of the pull-in time period; calculating the waiting duration ratio of the historical charging record, determining the waiting level of the historical charging record, calculating the waiting score of the peak period, and determining the feature level corresponding to the peak period; screening a characteristic time period, calculating an average operation score of the characteristic time period, and drawing an operation curve graph of the mobile charging system; and drawing a real-time operation curve graph of the mobile charging system, calculating a real-time optimization period of the mobile charging system by combining the real-time operation curve graph with the operation curve graph, and judging an 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 popularity of electric vehicles has continued to rise. However, the resulting surge in demand for public charging facilities has made the contradiction between facility supply and demand increasingly prominent. These contradictions are mainly manifested in: uneven distribution of public charging facilities, unreasonable layout, difficulty in infrastructure construction, and high subsequent maintenance costs; In order to alleviate the above contradictions, mobile charging system as a new technology has gradually attracted attention. With built-in lithium iron phosphate battery pack, the system has the advantage of flexible deployment and can be applied to scenes with greater infrastructure difficulties and areas with significant tidal phenomena. The mobile charging system moves the charging equipment to the energy replenishment area for charging, and then transfers it to the use area to provide services, thus realizing the flexible configuration of charging resources. However, in actual applications, traditional mobile charging systems may experience fluctuations in system operating efficiency. 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 the system is blindly optimized 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 improve the intelligence level of the mobile charging system, thereby better improving the overall operating efficiency of the system. Summary of the invention

[0003] 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.

[0004] 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: Step S100: Whenever a user's vehicle is identified to enter 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 time period according to the number of historical charging records during the entry time period; Step S200: Calculate the waiting time ratio of the historical charging records, determine the waiting level of the 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 characteristic level corresponding to the peak period; Step S300: screening the characteristic time period, calculating the average operation score of the characteristic time period, and drawing an operation curve diagram of the mobile charging system; 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.

[0005] Furthermore, step S100 includes: 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 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; By counting the frequency of different entry periods, the peak period of charging demand can be accurately identified, providing scientific data support for the mobile charging system; Data analysis based on historical charging records makes the decision-making process more objective and accurate, reduces the uncertainty caused by subjective judgment, and improves the intelligent management level of charging stations.

[0006] Further, step S200 includes: Step S201: The time from when the user's vehicle is parked in the parking space to when charging starts is set as the waiting time. In a set of historical charging records during a peak period, a waiting time C and a charging time D of a certain historical charging record are obtained, and 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 certain waiting level, and count the number of historical charging records of a certain waiting level during a certain peak period; Step S203: assign a value to each waiting level, and calculate the waiting score during the peak period 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 ath waiting level, It is represented 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; The longer the waiting time, the larger the waiting time ratio. Therefore, the waiting time ratio can be used to determine whether the historical charging record is abnormal. By aggregating and ranking the historical records of different waiting time ratios, the severity of different peak hours can be clearly identified.

[0007] Furthermore, step S300 includes: Step S301: Selecting several consecutive days as a training cycle, obtaining an optimization cycle of the mobile charging system, setting several sampling time points within the optimization cycle, setting two adjacent sampling time points as a sampling time period, summarizing the waiting time ratio of charging records within a certain sampling time period, obtaining an average waiting time ratio of the sampling time period, setting an average waiting time ratio threshold, comparing 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, setting the sampling time period as a characteristic time period; Step S302: In a certain characteristic time period, the operating parameters of the charging system in a certain peak period are obtained, and the operating score of the characteristic time period is calculated 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 ith operating parameter in the nth 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 an average operation score of a certain characteristic time period, and draw an operation curve diagram of the mobile charging system, wherein the operation curve diagram takes the average operation score of the characteristic time period as the ordinate and the characteristic time period as the abscissa; Determine the characteristic time period, because by setting a threshold, we can filter out the time period with high efficiency and avoid the influence of irrelevant time periods. The time period below the threshold means that the system has high charging efficiency and the equipment load is appropriate, so it is marked as the characteristic time period, which can provide data support for subsequent optimization measures; The operating parameters of charging equipment include equipment utilization, load balance, charging completion rate, etc. By calculating the operating score of the characteristic time period, considering multiple operating parameters of the charging equipment and the impact of peak hours, the overall performance of the system can be more comprehensively evaluated. The scoring takes into account multi-dimensional factors and can more objectively reflect the operating quality and load conditions of the system; By displaying the average operation score of the characteristic time period in the form of a curve graph, the operation change trend of each time period can be intuitively seen. Such visualization results can help operators quickly identify the system optimization effect, evaluate the load capacity of different time periods, and then make adjustments.

[0008] Furthermore, step S400 includes: 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 an inefficient time period. Step S402: Summarize the efficient time periods and the inefficient time periods respectively. 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 as ; 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, and L h It is represented as the change value of the hth sampling time period in the inefficient time period, P2 is represented as the weight of the inefficient time period, and j is represented as the total number of sampling time periods in the inefficient time period; Step S404: compare the time interval of the real-time optimization cycle with the next sampling time period, and if it is less than the time interval, prompt the staff to optimize the mobile charging system; The real-time operation score reflects the current operating status of the charging system in a certain period of time. By drawing a real-time operation curve, the system's operating performance can be dynamically tracked to detect abnormalities or fluctuations in a timely manner. The operation curve represents the average operating level of the system in the past optimization cycle, while the real-time operation curve reflects the current system status. By comparison, the current system operating status can be judged. By marking high-efficiency and low-efficiency time periods, targeted feedback can be provided for system optimization, facilitating subsequent analysis and adjustments. The real-time optimization cycle can be dynamically adjusted according to the performance of the high-efficiency and low-efficiency time periods, reflecting the system's current need to speed up or slow down the frequency of adjustment. If the high-efficiency time period contributes more, T increases, indicating that the optimization cycle can be appropriately extended to reduce unnecessary adjustments. If the inefficient time period has a greater impact, T decreases, indicating that faster adjustments are needed to solve the inefficiency problem. The dynamic optimization cycle can improve the agility of system adjustment, avoid excessive or delayed adjustment, and maximize system performance.

[0009] In order to better implement the above method, a mobile energy storage charging system is also proposed, which includes a peak period module, a characteristic level module, an operation curve module and a real-time judgment module; Peak time module: Whenever a user's vehicle is identified to enter a charging site and the user submits 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 time period based on the number of historical charging records during the entry time period; 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 peak hours according to the number of historical charging records corresponding to different waiting levels in peak hours, and determine the feature level corresponding to peak hours; Operation curve chart module: screen the characteristic time period, calculate the average operation score of the characteristic time period, and draw the operation curve chart of the mobile charging system; Real-time judgment module: draws a real-time operation curve diagram 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 diagram, and judges the optimization state of the mobile charging system according to the real-time optimization period.

[0010] Furthermore, the peak time module includes a unit for calculating the frequency of occurrence and a unit for determining the peak time: Calculating the frequency of occurrence 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 when the user's vehicle enters the charging site in a certain 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; Peak period determination unit: 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.

[0011] Furthermore, the feature level module includes a waiting level unit and a determining feature level unit: Waiting level unit: the time from when the user's vehicle is parked in the parking space to when charging starts is set as the waiting time, in a set of historical charging records during a peak period, the waiting time and charging time of a certain historical charging record are obtained, the waiting time ratio of the historical charging record is calculated, the waiting time ratios of all historical charging records are summarized, the waiting time ratios are divided into several waiting levels, and the waiting time ratio range corresponding to a certain waiting level is obtained; Determine the feature level unit: assign a value to each waiting level, calculate the waiting score for the peak period, summarize the waiting scores for all peak 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 period.

[0012] Furthermore, the operation curve diagram module includes a unit for determining a characteristic time period and a unit for drawing an operation curve diagram: Determine a characteristic time period unit: select a number of consecutive days as a training cycle, obtain an optimization cycle of the mobile charging system, set a number of sampling time points within the optimization cycle, set two adjacent sampling time points as a sampling time period, summarize the waiting time ratio of charging records within 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, and if it is lower than the average waiting time ratio threshold, set the sampling time period as a characteristic time period; Operation curve drawing unit: in a certain characteristic time period, obtain the operation parameters of the charging equipment in a certain peak period, calculate the operation score of the characteristic time period, summarize the operation score corresponding to a certain characteristic time period in each optimization cycle, obtain the average operation score of a certain characteristic time period, and draw the operation curve of the mobile charging system. The operation curve uses the average operation score of the characteristic time period as the vertical axis and the characteristic time period as the horizontal axis.

[0013] Furthermore, the real-time judgment module includes a real-time operation curve drawing unit and a judgment optimization unit: A 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 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. Judgment optimization unit: summarize the high-efficiency time periods and the inefficient time periods respectively, collect the real-time operation score corresponding to a sampling time period in the real-time operation curve in a certain time period set, collect the operation score corresponding to a sampling time period in the operation curve, calculate the change value of a certain sampling time period, calculate the real-time optimization cycle of the mobile charging system, compare the time interval between the real-time optimization cycle and the next sampling time period, if it is less than the time interval, prompt the staff to optimize the mobile charging system.

[0014] Compared with the prior art, the present invention has the following beneficial effects: By dynamically drawing the real-time operation curve and comparing it with the historical operation curve, the system operation status can be identified and optimized in real time; By real-time monitoring of the running score of each sampling period, efficient and inefficient time periods can be identified and marked in real time, providing targeted data support for dynamic optimization; By calculating the real-time optimization cycle and adjusting the system operating status according to its changing value, the charging system can respond to demand fluctuations in a timely manner and avoid excessive load or inefficient operation of the charging system. This dynamic optimization can improve the efficiency of the charging system, reduce resource waste, increase the response speed of charging services, ensure that the charging system can operate efficiently, and reduce unnecessary energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of a mobile energy storage charging control method of the present invention; Figure 2 The figure is a structural schematic diagram of a mobile energy storage and charging system of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1 and Figure 2 The present invention provides a technical solution: a mobile energy storage charging control method, the method comprising: Step S100: Whenever a user's vehicle is identified to enter 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 time period according to the number of historical charging records during the entry time period; Wherein, step S100 includes: 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 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.

[0018] Step S200: Calculate the waiting time ratio of the historical charging records, determine the waiting level of the 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 characteristic level corresponding to the peak period; Wherein, step S200 includes: Step S201: The time from when the user's vehicle is parked in the parking space to when charging starts is set as the waiting time. In a set of historical charging records during a peak period, a waiting time C and a charging time D of a certain historical charging record are obtained, and 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 certain waiting level, and count the number of historical charging records of a certain waiting level during a certain peak period; Step S203: assign a value to each waiting level, and calculate the waiting score during the peak period 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 ath waiting level, It is represented 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; For example, in the Ath peak period, the number of historical charging records in the ath waiting level is 2, the number of historical charging records in the bth waiting level is 1, and the number of historical charging records in the cth waiting level is 3. The calculated waiting score for the Ath peak period is 10.

[0019] Step S300: screening the characteristic time period, calculating the average operation score of the characteristic time period, and drawing an operation curve diagram of the mobile charging system; Wherein, step S300 includes: Step S301: Selecting several consecutive days as a training cycle, obtaining an optimization cycle of the mobile charging system, setting several sampling time points within the optimization cycle, setting two adjacent sampling time points as a sampling time period, summarizing the waiting time ratio of charging records within a certain sampling time period, obtaining an average waiting time ratio of the sampling time period, setting an average waiting time ratio threshold, comparing 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, setting the sampling time period as a characteristic time period; Step S302: In a certain characteristic time period, the operating parameters of the charging system in a certain peak period are obtained, and the operating score of the characteristic time period is calculated 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 ith operating parameter in the nth 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 an average operation score of a certain characteristic time period, and draw an operation curve diagram of the mobile charging system, wherein the operation curve diagram takes the average operation score of the characteristic time period as the ordinate and the characteristic time period as the abscissa; For example, in the first characteristic time period, there are peak periods A and B. The first operating parameter in peak period A is 85%, the second operating parameter is 75%, and the third operating parameter is 90%. The first operating parameter in peak period B is 80%, the second operating parameter is 70%, and the third operating parameter is 85%. The calculated operating score of the first characteristic time period is 16.6.

[0020] Step S400: drawing a real-time operation curve of the mobile charging system according to the real-time operation score of the sampling time period, and calculating the real-time optimization period of the mobile charging system in combination with the operation curve, and judging the optimization state of the mobile charging system according to the real-time optimization period; Wherein, step S400 includes: 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 an inefficient time period. Step S402: Summarize the efficient time periods and the inefficient time periods respectively. 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 as ; 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, and L h It is represented as the change value of the hth sampling time period in the inefficient time period, P2 is represented as the weight of the inefficient time period, and j is represented as the total number of sampling time periods in the inefficient time period; Step S404: compare the time interval of the real-time optimization cycle with the next sampling time period. If it is less than the time interval, prompt the staff to optimize the mobile charging system.

[0021] In order to better implement the above method, a mobile energy storage charging system is also proposed, which includes a peak period module, a characteristic level module, an operation curve module and a real-time judgment module; Peak time module: Whenever a user's vehicle is identified to enter a charging site and the user submits 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 time period based on the number of historical charging records during the entry time period; The peak time module includes a unit for calculating the frequency of occurrence and a unit for determining the peak time: Calculating the frequency of occurrence 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 when the user's vehicle enters the charging site in a certain 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; Peak period determination unit: 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.

[0022] 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 peak hours according to the number of historical charging records corresponding to different waiting levels in peak hours, and determine the feature level corresponding to peak hours; Among them, the feature level module includes a waiting level unit and a determining feature level unit: Waiting level unit: the time from when the user's vehicle is parked in the parking space to when charging starts is set as the waiting time, in a set of historical charging records during a peak period, the waiting time and charging time of a certain historical charging record are obtained, the waiting time ratio of the historical charging record is calculated, the waiting time ratios of all historical charging records are summarized, the waiting time ratios are divided into several waiting levels, and the waiting time ratio range corresponding to a certain waiting level is obtained; Determine the feature level unit: assign a value to each waiting level, calculate the waiting score for the peak period, summarize the waiting scores for all peak 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 period.

[0023] Operation curve chart module: screen the characteristic time period, calculate the average operation score of the characteristic time period, and draw the operation curve chart of the mobile charging system; The operation curve diagram module includes a unit for determining a characteristic time period and a unit for drawing an operation curve diagram: Determine a characteristic time period unit: select a number of consecutive days as a training cycle, obtain an optimization cycle of the mobile charging system, set a number of sampling time points within the optimization cycle, set two adjacent sampling time points as a sampling time period, summarize the waiting time ratio of charging records within 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, and if it is lower than the average waiting time ratio threshold, set the sampling time period as a characteristic time period; Operation curve drawing unit: in a certain characteristic time period, obtain the operation parameters of the charging equipment in a certain peak period, calculate the operation score of the characteristic time period, summarize the operation score corresponding to a certain characteristic time period in each optimization cycle, obtain the average operation score of a certain characteristic time period, and draw the operation curve of the mobile charging system. The operation curve uses the average operation score of the characteristic time period as the vertical axis and the characteristic time period as the horizontal axis.

[0024] 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 in combination with the operation curve, and judges the optimization state of the mobile charging system according to the real-time optimization period; The real-time judgment module includes a real-time operation curve drawing unit and a judgment optimization unit: A 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 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. Judgment optimization unit: summarize the high-efficiency time periods and the inefficient time periods respectively, collect the real-time operation score corresponding to a sampling time period in the real-time operation curve in a certain time period set, collect the operation score corresponding to a sampling time period in the operation curve, calculate the change value of a certain sampling time period, calculate the real-time optimization cycle of the mobile charging system, compare the time interval between the real-time optimization cycle and the next sampling time period, if it is less than the time interval, prompt the staff to optimize the mobile charging system.

[0025] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered 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 to enter 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 time period according to the number of historical charging records during the entry time period; Step S200: Calculate the waiting time ratio of the historical charging records, determine the waiting level of the 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 characteristic level corresponding to the peak period; Step S300: screening the characteristic time period, calculating the average operation score of the characteristic time period, and drawing an operation curve diagram of the mobile charging system; 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.

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 the historical charging record 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 starts is set as the waiting time. In a set of historical charging records during a peak period, a waiting time C and a charging time D of a certain historical charging record are obtained, and 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 certain waiting level, and count the number of historical charging records of a certain waiting level during a certain peak period; Step S203: assign a value to each waiting level, and calculate the waiting score during the peak period 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 ath waiting level, It is represented 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, obtaining an optimization cycle of the mobile charging system, setting several sampling time points within the optimization cycle, setting two adjacent sampling time points as a sampling time period, summarizing the waiting time ratio of charging records within a certain sampling time period, obtaining an average waiting time ratio of the sampling time period, setting an average waiting time ratio threshold, comparing 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, setting the sampling time period as a characteristic time period; Step S302: In a certain characteristic time period, the operating parameters of the charging system in a certain peak period are obtained, and the operating score of the characteristic time period is calculated 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 ith operating parameter in the nth 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, obtain the average operation score of a certain 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 an inefficient time period. Step S402: Summarize the efficient time periods and the inefficient time periods respectively. 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 as ; 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, and L h It is represented as the change value of the hth sampling time period in the inefficient time period, P2 is represented as the weight of the inefficient time period, and j is represented as the total number of sampling time periods in the inefficient time period; Step S404: compare the time interval of the real-time optimization cycle with the next sampling time period. If it 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 as described in 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 time module: Whenever a user's vehicle is identified to enter 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 time period according to the number of historical charging records during the entry time period; The characteristic level module: calculates the waiting time ratio of the 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 select a characteristic time period, calculate an average operation score for the characteristic time period, and draw an operation curve diagram of the mobile charging system; The real-time judgment module draws a real-time operation curve diagram 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 in combination with the operation curve diagram, and judges the optimization state of the mobile charging system according to the real-time optimization period.

7. A mobile energy storage charging system according to claim 6, characterized in that: The peak time module includes a unit for calculating the frequency of occurrence and a unit for determining the peak time: The frequency calculation unit: divides a day into several time periods, obtains the time range corresponding to each time period, obtains the historical charging record of the mobile charging system, collects the time point when the user's vehicle enters the charging site in a certain 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 charging system according to claim 6, characterized in that: The feature level module includes a waiting level unit and a determining feature level unit: The waiting level unit: sets the time from when the user's vehicle is parked in the parking space to when charging starts as the waiting time, obtains the waiting time and charging time of a certain 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 certain waiting level; The characteristic level determination unit: assigns a value to each waiting level, calculates the waiting score of the peak time period, summarizes the waiting scores of all peak time 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 time period.

9. A mobile energy storage 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 several consecutive days as a training cycle, obtains an optimization cycle of the mobile charging system, sets several sampling time points within the optimization cycle, sets two adjacent sampling time points as a sampling time period, summarizes the waiting time ratio 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 a certain sampling time period with the average waiting time ratio threshold, and if it is lower than the average waiting time ratio threshold, sets the sampling time period as a characteristic time period; The operation curve drawing unit: in a certain characteristic time period, obtains the operation parameters of the charging equipment in a certain peak 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 a certain characteristic time period, and draws the operation curve of the mobile charging system, wherein the operation curve 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 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 an inefficient 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 a certain sampling time period, calculates the real-time optimization period of the mobile charging system, compares the time interval between the real-time optimization period and 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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