Power conversion cabinet charging control method and system based on user power utilization data analysis

Abstract

The invention discloses a power conversion cabinet charging control method and system based on user power utilization data analysis, and relates to the technical field of power conversion cabinet management and control. Historical charging records of each cabinet body are collected, and a historical single-cabinet battery charging expression model group is constructed to reflect a battery charging state and a replacement mode; a battery replacement record is analyzed, replacement frequencies of different time sections are determined, and a representative charging mode is extracted through equivalence classification and averaging calculation; adjusting and optimizing a charging strategy to obtain an optimal reference single-cabinet battery charging expression model group; charging and replacement parameters of the electric cabinet are collected in real time and matched with the optimal model set, and real-time charging regulation and control are achieved; by deeply mining historical data, combining real-time information and dynamically adjusting a charging strategy, the battery service efficiency is effectively improved, the service life of the battery is prolonged, the operation and maintenance cost is reduced, and powerful support is provided for intelligent management of a large-scale electric cabinet.

Description

Technical Field

[0001] The present invention relates to the field of power swap cabinet management and control technology, and in particular to a power swap cabinet charging control method and system based on user power consumption data analysis. Background Art

[0002] With the popularization of electric vehicles and the development of energy Internet technology, user-side electricity consumption data has shown an exponential growth. These data not only reflect the user's electricity consumption behavior, but also contain rich commercial value. However, traditional power data value mining is mainly concentrated in the power grid and the power supply end, and the huge and complex electricity consumption data on the user side has not been fully utilized. Especially in the field of electric vehicle battery replacement, traditional battery replacement cabinet charging control methods are often based on fixed charging strategies and simple power detection, and cannot be dynamically adjusted according to the user's actual power demand and battery status, resulting in low charging efficiency, poor user experience, and greater battery damage. Summary of the invention

[0003] The object of the present invention is to provide a charging control method and system capable of optimizing the charging strategy of a charging cabinet.

[0004] The present invention discloses a charging control method for a power exchange cabinet based on user power consumption data analysis, comprising:

[0005] Obtain the historical single-cabinet battery charging records in each cabinet of the power cabinet, and record the combination of the historical single-cabinet battery charging records as a historical single-cabinet charging record group;

[0006] A reference time loop is constructed, and based on each historical single-rail charging record in the historical single-rail charging record group, the battery power, charging power, and battery replacement record identifier are configured for different time nodes on the reference time loop, and a historical single-cabinet battery charging expression model is obtained, and the combination of the historical single-cabinet battery charging expression models is recorded as a historical single-cabinet battery charging expression model group, wherein the historical single-cabinet battery charging expression model group is used to express the overall historical charging status of the power cabinet;

[0007] Analyze the battery replacement record identifiers in the historical single-cabinet battery charging expression model group, determine the identifier numbers of battery replacement record identifiers corresponding to different time periods, and record the combination of the identifier numbers corresponding to different time periods every day as an identifier number group;

[0008] Classifying several identification quantity groups by their equality to obtain several identification quantity category sets, and averaging the identification quantity groups in the identification quantity category sets to obtain an average identification quantity group;

[0009] Based on the average identification number group, adjust the historical single-cabinet battery charging expression model group to obtain the initial reference single-cabinet battery charging expression model group, and make several charging strategy adjustments to the initial reference single-cabinet battery charging expression model group to obtain several adjusted reference single-cabinet battery charging expression model groups. Perform a comprehensive application effect evaluation on each adjusted reference single-cabinet battery charging expression model group, and select the optimal reference single-cabinet battery charging expression model group from the adjusted reference single-cabinet battery charging expression model groups based on the evaluation results;

[0010] Collect several real-time single-cabinet battery charging parameters and real-time battery replacement parameters of the power cabinet in real time, and select the most suitable optimal reference single-cabinet battery charging expression model group based on the real-time single-cabinet battery charging parameters and the real-time battery replacement parameters, and perform charging control on the power cabinet with reference to the optimal reference single-cabinet battery charging expression model group.

[0011] In some embodiments disclosed in the present invention, the method for determining the number of battery replacement record identifiers corresponding to different time periods includes:

[0012] Establishing a battery replacement record identification mapping diagram, wherein the horizontal axis of the battery replacement record identification mapping diagram is a time axis;

[0013] Analyze the battery replacement record identifiers in the historical single-cabinet battery charging expression model group, determine the battery replacement record identifiers that appear at different time nodes, and map them on the battery replacement record identifier mapping diagram in the form of replacement record identifier mapping points;

[0014] The clustering features of the record identification mapping points in the battery replacement record identification mapping diagram are used to segment the time axis based on the clustering features to obtain a number of time segments, and the number of identifications of the replacement record identification mapping points in the time segments is counted.

[0015] In some embodiments disclosed in the present invention, the method of segmenting the time axis based on the aggregation feature includes:

[0016] The time axis is evenly split to obtain a number of unit time segments, and the number of replacement record identification mapping points corresponding to each unit time segment on the battery replacement record identification mapping diagram is analyzed;

[0017] The vertical axis of the battery replacement record identification mapping diagram is set to have a replacement record identification mapping point quantity axis, and the middle point of the unit time segment is used as the horizontal axis positioning point, and the vertical axis mapping points of the replacement record identification mapping point quantity are marked in the vertical axis direction, and the vertical axis mapping points are smoothly connected to obtain a replacement record identification mapping point quantity curve;

[0018] Analyze the replacement record identification mapping point quantity curve, determine the curve segments whose upward trend meets the preset standard and whose downward trend meets the preset standard, record them as the curve segments that need attention, and determine the number of unit time segments corresponding to the curve segments that need attention to be combined based on the intersection area between the curve segments that need attention and the horizontal axis;

[0019] The expression for determining the number of combinations of the corresponding unit time segments of the curve segments requiring attention is:

[0020]

[0021] Where D is the number of combinations per time segment, R is the combination number conversion adjustment coefficient, d is the preset standard combination number, S is the intersection area between the curve segment to be concerned and the horizontal axis, S 预设 is the preset cross-area, L is the cross-area influence adjustment coefficient, and b is the cross-area influence adjustment constant.

[0022] In some embodiments disclosed in the present invention, the method of dividing the time axis into sections further includes:

[0023] The curve segments not requiring attention on the replacement record identification mapping point quantity curve are determined, and the unit time segments within the curve segments not requiring attention are combined in sequence based on the preset standard combination quantity.

[0024] In some embodiments disclosed in the present invention, a method for classifying a plurality of identification quantity groups by equality includes:

[0025] Analyze the identification quantity groups, determine the length of the corresponding time segment division and the sorting method, form a time segment sequence, and use the time segment interval to which each time segment in the time segment sequence belongs as the first classification condition to perform the first equivalent classification on several identification quantity groups;

[0026] Based on the identification quantity interval to which the identification quantity corresponding to each time segment in the identification quantity group belongs, the identification quantity groups that are equally classified for the first time are equally classified for a second time.

[0027] In some embodiments disclosed in the present invention, the method for adjusting the historical single-cabinet battery charging expression model group includes:

[0028] Based on the average identification quantity group, configure the average identification quantity group for different time segments on the reference time cycle line corresponding to the historical single cabinet battery charging expression model group, and determine the battery power change curve of each time segment, and determine the sudden drop point of the battery power change curve and the battery power at the sudden drop point corresponding to the sudden drop point;

[0029] Determine whether the difference between the average number of identifications in each time segment and the sudden drop point is within a preset difference interval. If it is determined to be within the preset difference interval, determine whether the battery power at each sudden drop point is greater than or equal to a preset value. If not, increase the charging power of the first preset number of cabinets, and increase the charging power at a time node of a preset time length in the forward direction. If the difference between the average number of identifications in each time segment and the sudden drop point is not within the preset difference interval, determine whether the average number of identifications is greater than the sudden drop point or less than the sudden drop point. If it is determined to be greater than the sudden drop point, increase the charging power of the second preset number of cabinets, and increase the charging power at a time node of a preset time length in the forward direction. If it is determined to be less than the sudden drop point, reduce the charging power of the third preset number of cabinets, and reduce the charging power at a time node of a preset time length in the forward direction.

[0030] After power adjustment of the historical single-cabinet battery charging expression model group, the average number of identifiers in the average identifier number group is used as the battery replacement demand, and the historical cabinet charging expression model group is configured to determine the adjusted sag points of different reference time cycle lines and the battery power at the adjusted sag points, and the adjusted reference single-cabinet battery charging expression model group is obtained.

[0031] In an embodiment disclosed in the present invention, before increasing or decreasing the charging power of a cabinet, determining a cabinet for adjusting the charging power, and determining the method for adjusting the charging power include:

[0032] Methods for determining the cabinet for charging power boost include:

[0033] The time node corresponding to the sudden drop point in each time segment is determined, and the average value of the time node is analyzed and recorded as the average sudden drop time point. The battery power of each cabinet corresponding to the average sudden drop time point is determined. If the battery power is in the first preset power interval, the corresponding cabinet is identified as a cabinet for charging power boost;

[0034] Methods for determining cabinets for charging power reduction include:

[0035] The time node corresponding to the sudden drop point in each time segment is determined, and the average value of the time node is analyzed and recorded as the average sudden drop time point. The battery power of each cabinet corresponding to the average sudden drop time point is determined. If the battery power is in the second preset power interval, the corresponding cabinet is identified as a cabinet for charging power boost.

[0036] In an embodiment disclosed in the present invention, a method for evaluating the comprehensive application effect of the adjusted reference single-cabinet battery charging expression model group includes:

[0037] Analyze the reference time cycle line corresponding to each adjusted reference single-cabinet battery charging expression model in the adjusted reference single-cabinet battery charging expression model group to determine the adjusted sag points of the battery power change curve in different time sections and the adjusted sag point battery power;

[0038] Compare the number of sudden drop points and the average number of identifications of the adjusted sudden drop points corresponding to different time segments to obtain a first effect comparison result, compare the average sudden drop point battery power and the standard battery power corresponding to different time segments to obtain a second effect comparison result, and determine a comprehensive application effect evaluation based on the first effect comparison result and the second effect comparison result;

[0039] Among them, the expression for calculating the comprehensive application effect evaluation is:

[0040]

[0041] Among them, G is the comprehensive application effect evaluation, δ(Δx i ∈x yushe ) is the first effect comparison function corresponding to the i-th time segment, Δx i is the first difference between the number of sudden drop points and the average number of marks in the i-th time segment, x yushe is a preset first difference interval. If the first difference is within the first difference interval, then δ(Δx i ∈x yushe ) outputs 1, otherwise outputs 0, β(Δh i ∈h yushe ) is the second effect comparison function corresponding to the i-th time segment, Δh i is the second difference between the average battery capacity at the sudden drop point and the standard battery capacity in the i-th time segment, h yushe is a preset second difference interval. If the second difference is within the second difference interval, then β(Δh i ∈h yushe ) outputs 1, otherwise outputs 0, K i is the weight coefficient corresponding to the i-th time segment, n is the total number of time segments, g man Charge capacity for low power charging, g zong is the total charge capacity, J is the charging characteristic impact adjustment coefficient, C is the charging characteristic impact adjustment constant, and F is the charging power effect conversion coefficient.

[0042] In some embodiments disclosed in the present invention, the method for selecting the most suitable optimal reference single cabinet battery charging expression model includes:

[0043] The real-time single-cabinet battery charging parameters and the real-time battery replacement parameters are substituted into different optimal reference single-cabinet battery charging expression model groups, and the first difference of the single-cabinet charging parameters and the second difference of the battery replacement parameters are analyzed in real time. If several consecutive first differences and second differences are less than or equal to the preset value, the corresponding optimal reference single-cabinet battery charging expression model is determined to be the most consistent.

[0044] In the embodiment disclosed in the present invention, a battery swap cabinet charging control system based on user power consumption data analysis is also disclosed, including:

[0045] The first module is used to obtain the historical single-cabinet battery charging records in each cabinet of the power cabinet, and record the combination of the historical single-cabinet battery charging records as a historical single-cabinet charging record group;

[0046] The second module is used to construct a reference time loop line, and based on each historical single-rail charging record in the historical single-rail charging record group, configure the battery power, charging power and battery replacement record identifier for different time nodes on the reference time loop line, obtain the historical single-cabinet battery charging expression model, and record the combination of the historical single-cabinet battery charging expression model as a historical single-cabinet battery charging expression model group, wherein the historical single-cabinet battery charging expression model group is used to express the overall historical charging status of the power cabinet;

[0047] The third module is used to analyze the battery replacement record identifiers in the historical single-cabinet battery charging expression model group, determine the number of battery replacement record identifiers corresponding to different time periods, and record the combination of the number of identifiers corresponding to different time periods every day as an identifier number group;

[0048] The fourth module is used to classify the plurality of identification quantity groups by their equivalence to obtain a plurality of identification quantity category sets, and to average the identification quantity groups in the identification quantity category sets to obtain an average identification quantity group;

[0049] The fifth module is used to adjust the historical single-cabinet battery charging expression model group based on the average identification number group to obtain the initial reference single-cabinet battery charging expression model group, and make several charging strategy adjustments to the initial reference single-cabinet battery charging expression model group to obtain several adjusted reference single-cabinet battery charging expression model groups, conduct a comprehensive application effect evaluation on each adjusted reference single-cabinet battery charging expression model group, and select the optimal reference single-cabinet battery charging expression model group from the adjusted reference single-cabinet battery charging expression model group based on the evaluation results;

[0050] The sixth module is used to collect several real-time single-cabinet battery charging parameters and real-time battery replacement parameters of the power cabinet in real time, and select the most suitable optimal reference single-cabinet battery charging expression model group based on the real-time single-cabinet battery charging parameters and the real-time battery replacement parameters, and perform charging regulation on the power cabinet with reference to the optimal reference single-cabinet battery charging expression model group.

[0051] The present invention discloses a charging control method and system for a battery swap cabinet based on analysis of user electricity consumption data, which relates to the technical field of battery swap cabinet management and control, collects historical charging records of each cabinet, constructs a historical single-cabinet battery charging expression model group to reflect the battery charging status and replacement mode; analyzes battery replacement records to determine the replacement frequency in different time segments, and extracts representative charging modes through equivalence classification and averaging calculation; adjusts and optimizes the charging strategy to obtain the optimal reference single-cabinet battery charging expression model group; collects the charging and replacement parameters of the power cabinet in real time, matches them with the optimal model group, and realizes real-time charging regulation; the present invention deeply mines historical data, combines real-time information, and dynamically adjusts the charging strategy, which effectively improves the battery utilization efficiency, extends the battery life, and reduces the operation and maintenance cost, providing strong support for the intelligent management of large-scale power cabinets.

[0052] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a method step diagram of a battery swap cabinet charging control method based on user power consumption data analysis disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0055] The following will be combined with the accompanying drawings and specific embodiments to clearly and completely describe the technical solution of the present invention. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art in this field can make some non-essential improvements and adjustments based on the content of the present invention described below. In the present invention, unless otherwise clearly specified and limited, the technical terms used in the present invention should be the common meanings understood by the technical personnel described in the present invention.

[0056] Example:

[0057] The object of the present invention is to provide a charging control method and system capable of optimizing the charging strategy of a charging cabinet.

[0058] The present invention discloses a charging control method for a battery swap cabinet based on user power consumption data analysis. Figure 1 ,include:

[0059] Step S100: acquiring the historical single-cabinet battery charging records in each cabinet of the power cabinet, and recording the combination of the historical single-cabinet battery charging records as a historical single-cabinet charging record group.

[0060] Data collection is the basic step of the entire power cabinet battery charging management solution. It comprehensively collects the historical charging records of the batteries in each cabinet through the built-in system records of the power cabinet or external sensors. These records cover key information such as the battery charging start time, end time, charging amount, charging power, and possible abnormal status. The accuracy, completeness and time span of the data are crucial for the subsequent construction of charging models and analysis of charging behaviors. They provide solid data support for in-depth understanding of battery charging characteristics and optimization of charging strategies.

[0061] Step S200, construct a reference time loop line, and based on each historical single-rail charging record in the historical single-rail charging record group, configure the battery power, charging power and battery replacement record identifier for different time nodes on the reference time loop line to obtain a historical single-cabinet battery charging expression model, and record the combination of the historical single-cabinet battery charging expression models as a historical single-cabinet battery charging expression model group, wherein the historical single-cabinet battery charging expression model group is used to express the overall historical charging status of the power cabinet.

[0062] Constructing a historical single-cabinet battery charging expression model is the process of converting complex charging records into an analyzable model. This step maps the charging records to a reference time cycle line in chronological order based on the collected historical charging data to form time series data. In the model, different time nodes are assigned key identifiers such as battery power, charging power, and battery replacement records, which together constitute a comprehensive description of the battery charging status. By constructing such a model, the changing trend of battery charging behavior over time can be intuitively observed and analyzed, providing a strong model foundation for subsequent strategy optimization.

[0063] Step S300, analyzing the battery replacement record identifiers in the historical single-cabinet battery charging expression model group, determining the identifier quantities of the battery replacement record identifiers corresponding to different time periods, and recording the combination of the identifier quantities corresponding to different time periods each day as an identifier quantity group.

[0064] Analyzing battery replacement records is an important part of evaluating battery life, predicting maintenance needs, and optimizing charging strategies. This step conducts in-depth analysis of battery replacement records in the historical single-cabinet battery charging expression model to count the frequency and pattern of battery replacements in different time periods. By identifying the peak and trough periods of battery replacement, as well as possible replacement reasons, it can provide key reference information for subsequent charging strategy adjustments, helping to extend the overall battery life and reduce operation and maintenance costs.

[0065] In some embodiments disclosed in the present invention, the method for determining the number of battery replacement record identifiers corresponding to different time periods includes:

[0066] Step S301, establishing a battery replacement record identification map, wherein the horizontal axis of the battery replacement record identification map is the time axis;

[0067] Step S302, analyzing the battery replacement record identifiers in the historical single-cabinet battery charging expression model group, determining the battery replacement record identifiers appearing at different time nodes, and mapping them on a battery replacement record identifier mapping map in the form of replacement record identifier mapping points;

[0068] Step S303, clustering features of the record identification mapping points in the battery replacement record identification mapping map, and based on the clustering features, segmenting the time axis to obtain a number of time segments, and counting the number of identifications of the replacement record identification mapping points in the time segments.

[0069] In some embodiments disclosed in the present invention, the method of segmenting the time axis based on the aggregation feature includes:

[0070] Step S3031, splitting the time axis by averaging to obtain a number of unit time segments, and analyzing the number of replacement record identification mapping points corresponding to each unit time segment on the battery replacement record identification mapping diagram.

[0071] Step S3032, set the replacement record identification mapping point quantity axis for the vertical axis direction of the battery replacement record identification mapping diagram, and use the middle point of the unit time segment as the horizontal axis positioning point, mark the vertical axis mapping points of the replacement record identification mapping point quantity in the vertical axis direction, and smoothly connect the vertical axis mapping points to obtain the replacement record identification mapping point quantity curve.

[0072] Step S3033, analyze the replacement record identification mapping point quantity curve, determine the curve segments whose upward trend meets the preset standard and whose downward trend meets the preset standard, record them as the curve segments that need attention, and based on the intersection area between the curve segments that need attention and the horizontal axis, determine the number of unit time segments corresponding to the curve segments that need attention to be combined.

[0073] The expression for determining the number of combinations of the corresponding unit time segments of the curve segments requiring attention is:

[0074]

[0075] Where D is the number of combinations per time segment, R is the combination number conversion adjustment coefficient, d is the preset standard combination number, S is the intersection area between the curve segment to be concerned and the horizontal axis, S预设 is the preset cross-area, L is the cross-area influence adjustment coefficient, and b is the cross-area influence adjustment constant.

[0076] In some embodiments disclosed in the present invention, the method of dividing the time axis into sections further includes:

[0077] Step S3034: determine the curve segments that do not need attention on the replacement record identification mapping point quantity curve, and combine the unit time segments in the curve segments that do not need attention in sequence based on the preset standard combination quantity.

[0078] Step S400, classifying a plurality of identification quantity groups by equality to obtain a plurality of identification quantity category sets, and averaging the identification quantity groups in the identification quantity category sets to obtain an average identification quantity group.

[0079] Equivalence classification and averaging calculation are steps to further process and analyze battery replacement records. This step first uses a clustering algorithm to classify similar battery replacement records together to form different category sets. Then, an averaging calculation is performed within each category set to obtain an average identification number group to represent the typical battery replacement pattern of that category. This step aims to reduce data fluctuations, extract more representative battery replacement rules, and provide a more accurate and stable basis for the subsequent optimization of charging strategies.

[0080] In some embodiments disclosed in the present invention, a method for classifying a plurality of identification quantity groups by equality includes:

[0081] Step S401, analyze the identification quantity groups, determine the length of the corresponding time segment division and the sorting method, form a time segment sequence, and use the time segment interval to which each time segment in the time segment sequence belongs as the first classification condition to perform the first equivalent classification on several identification quantity groups.

[0082] The core purpose of step S401 is to perform a preliminary classification of the identification quantity groups through the time segment sequence. This step first requires a detailed analysis of the collected identification quantity groups, clarifying the time segment corresponding to each identification quantity group, and determining the length of the time segment division according to actual needs or data characteristics. Subsequently, these identification quantity groups are sorted in chronological order to form an ordered time segment sequence. On this basis, the time segment interval to which each time segment in the time segment sequence belongs is used as the first classification condition, and all identification quantity groups are first classified as equivalent. This classification process is essentially a division of data in the time dimension, which helps to group together identification quantity groups with similar time characteristics, laying the foundation for subsequent in-depth analysis.

[0083] Step S402 , based on the identification quantity interval to which the identification quantity corresponding to each time segment in the identification quantity group belongs, the identification quantity groups that are first equally classified are secondarily classified.

[0084] After completing the first equivalent classification based on time segments, step S402 further introduces the number of identifiers as a classification condition to perform a more detailed classification of the number of identifiers groups. This step first requires setting a reasonable number of identifiers interval based on the distribution of the number of identifiers. These intervals can be continuous or discrete, depending on the characteristics of the data and the needs of the analysis. Then, based on the results of the first classification, the number of identifiers groups in each time segment are classified for the second equivalent classification according to the number of identifiers intervals to which their corresponding number of identifiers belongs. This classification process not only takes into account the time factor, but also incorporates the consideration of the quantity dimension, making the classification results more comprehensive and accurate. Through this step, we can group together the number of identifiers groups with similar time characteristics and quantity characteristics, providing more powerful support for subsequent data analysis and processing.

[0085] Step S500: Based on the average identification quantity group, adjust the historical single-cabinet battery charging expression model group to obtain an initial reference single-cabinet battery charging expression model group, and perform several charging strategy adjustments on the initial reference single-cabinet battery charging expression model group to obtain several adjusted reference single-cabinet battery charging expression model groups, perform a comprehensive application effect evaluation on each adjusted reference single-cabinet battery charging expression model group, and select the optimal reference single-cabinet battery charging expression model group from the adjusted reference single-cabinet battery charging expression model groups based on the evaluation results.

[0086] Model adjustment and optimization is the process of improving and perfecting the historical single-cabinet battery charging expression model based on the analysis results of the previous steps. This step is to find the optimal charging strategy combination by trying different charging strategies (such as adjusting charging power, charging time, charging frequency, etc.) and evaluating the comprehensive application effect of each strategy (such as charging efficiency, battery life, operation and maintenance costs, etc.). The optimization process may involve a variety of methods such as experimental verification and simulation to ensure that the selected strategy can not only meet the user's power demand, but also maximize the battery life and reduce the operation and maintenance costs.

[0087] In some embodiments disclosed in the present invention, the method for adjusting the historical single-cabinet battery charging expression model group includes:

[0088] Step S501, based on the average identification quantity group, configure the average identification quantity group for different time segments on the reference time cycle line corresponding to the historical single cabinet battery charging expression model group, and determine the battery power change curve of each time segment, and determine the sudden drop point of the battery power change curve and the sudden drop point battery power corresponding to the sudden drop point.

[0089] This step aims to configure an average identification number for each time segment in the model group through the analysis of historical single-cabinet battery charging data. This number represents the average performance of the battery charging status in the time segment. First, based on the existing average identification number group, these data are mapped to the reference time cycle line corresponding to the historical single-cabinet battery charging expression model group to ensure that each time segment has a corresponding average identification number. Then, based on these data and the charging characteristics of the battery, the battery power change curve for each time segment is determined. In this process, special attention is paid to the points where the battery power drops suddenly (sudden drop points) and the battery power values ​​corresponding to these points (sudden drop point battery power), because these sudden drop points may reflect problems or bottlenecks in the battery charging process.

[0090] Step S502, determine whether the difference between the average number of identifiers in each time segment and the sudden drop point is within a preset difference range. If it is determined to be within the preset difference range, determine whether the battery power at each sudden drop point is greater than or equal to the preset value. If not, increase the charging power of the first preset number of cabinets, and increase the charging power at the time node of the preset time length forward. If the difference between the average number of identifiers in each time segment and the sudden drop point is not within the preset difference range, determine whether the average number of identifiers is greater than the sudden drop point or less than the sudden drop point. If it is determined to be greater than the sudden drop point, increase the charging power of the second preset number of cabinets, and increase the charging power at the time node of the preset time length forward. If it is determined to be less than the sudden drop point, reduce the charging power of the third preset number of cabinets, and reduce the charging power at the time node of the preset time length forward.

[0091] The core of this step is to decide whether it is necessary to adjust the charging power of the cabinet based on the difference between the average number of identifications and the sag point, as well as the battery power at the sag point. First, determine whether the difference between the average number of identifications and the sag point in each time segment falls within the preset difference range. If it is within this range, and the battery power at the sag point is lower than the preset value, it means that the charging effect of the battery in this time segment is not good, and the charging power may need to be increased. According to the preset rules, the charging power of the first preset number of cabinets is increased, and this adjustment is implemented at the time node of the preset time length forward. If the difference is not within the preset range, it is necessary to further determine whether the average number of identifications is greater than or less than the sag point, and decide whether to increase or decrease the charging power of the cabinet, as well as the specific number and time node of the adjustment.

[0092] In an embodiment disclosed in the present invention, before increasing or decreasing the charging power of a cabinet, determining a cabinet for adjusting the charging power, and determining the method for adjusting the charging power include:

[0093] Methods for determining the cabinet for charging power boost include:

[0094] Step S5021, determine the time node corresponding to the sudden drop point in each time segment, and analyze the average value of the time node, which is recorded as the average sudden drop time point, and determine the battery power of each cabinet corresponding to the average sudden drop time point. If the battery power is in the first preset power interval, the corresponding cabinet is identified as a cabinet for charging power boost.

[0095] Methods for determining cabinets for charging power reduction include:

[0096] Step S5021, determine the time node corresponding to the sudden drop point in each time segment, and analyze the average value of the time node, which is recorded as the average sudden drop time point, and determine the battery power of each cabinet corresponding to the average sudden drop time point. If the battery power is in the second preset power interval, the corresponding cabinet is identified as a cabinet for charging power boost.

[0097] Step S503, after power adjustment of the historical single-cabinet battery charging expression model group, the average number of identifiers in the average number of identifiers group is used as the battery replacement demand, the historical cabinet charging expression model group is configured, and the adjusted sag points of different reference time cycle lines and the battery power at the adjusted sag points are determined to obtain the adjusted reference single-cabinet battery charging expression model group.

[0098] After completing the adjustment of the charging power, this step aims to update the historical single-cabinet battery charging expression model group to reflect the actual situation after the adjustment. First, the average number of identifications in the average identification number group is used as a benchmark and regarded as a demand indicator for battery replacement. Then, based on this demand and the charging characteristics of the battery, the historical cabinet charging expression model group is reconfigured to determine the adjusted sag points under different reference time cycle lines and the battery power corresponding to these points. Finally, the adjusted reference single-cabinet battery charging expression model group is obtained. This model group is closer to the battery performance in the actual charging process, and can provide a more accurate basis for subsequent battery management and charging strategy formulation.

[0099] In an embodiment disclosed in the present invention, a method for evaluating the comprehensive application effect of the adjusted reference single-cabinet battery charging expression model group includes:

[0100] Step S504, analyze the reference time cycle line corresponding to each adjusted reference single cabinet battery charging expression model in the adjusted reference single cabinet battery charging expression model group, and determine the adjusted sag point of the battery power change curve in different time segments and the adjusted sag point battery power.

[0101] Step S505, compare the number of adjusted sag points and the average number of identifications corresponding to different time segments to obtain a first effect comparison result, compare the average sag point battery power corresponding to different time segments and the standard battery power to obtain a second effect comparison result, and determine a comprehensive application effect evaluation based on the first effect comparison result and the second effect comparison result.

[0102] Among them, the expression for calculating the comprehensive application effect evaluation is:

[0103]

[0104] Among them, G is the comprehensive application effect evaluation, δ(Δx i ∈x yushe ) is the first effect comparison function corresponding to the i-th time segment, Δx i is the first difference between the number of sudden drop points and the average number of marks in the i-th time segment, x yushe is a preset first difference interval. If the first difference is within the first difference interval, then δ(Δx i ∈x yushe ) outputs 1, otherwise outputs 0, β(Δh i ∈h yushe ) is the second effect comparison function corresponding to the i-th time segment, Δh i is the second difference between the average battery capacity at the sudden drop point and the standard battery capacity in the i-th time segment, h yushe is a preset second difference interval. If the second difference is within the second difference interval, then β(Δh i ∈h yushe ) outputs 1, otherwise outputs 0, K i is the weight coefficient corresponding to the i-th time segment, n is the total number of time segments, g man Charge capacity for low power charging, g zong is the total charge capacity, J is the charging characteristic impact adjustment coefficient, C is the charging characteristic impact adjustment constant, and F is the charging power effect conversion coefficient.

[0105] Step S600, real-time collection of several real-time single-cabinet battery charging parameters and real-time battery replacement parameters of the power cabinet, and based on the real-time single-cabinet battery charging parameters and real-time battery replacement parameters, the most consistent optimal reference single-cabinet battery charging expression model group is selected, and charging of the power cabinet is regulated with reference to the optimal reference single-cabinet battery charging expression model group.

[0106] Real-time charging regulation is the last step to achieve intelligent charging management of power cabinet batteries. This step is based on the real-time collected power cabinet charging parameters and battery replacement parameters, and selects the optimal reference single cabinet battery charging expression model group that best matches the current state through a similarity matching algorithm. Then, the power cabinet is charged and regulated in real time according to the selected model group, including adjusting the charging power, controlling the charging time, etc. The goal of real-time charging regulation is to ensure the efficiency, safety and reliability of the charging process, and to achieve intelligent management of power cabinet batteries by dynamically adjusting the charging strategy to adapt to the changing power demand and battery status.

[0107] In some embodiments disclosed in the present invention, the method for selecting the most suitable optimal reference single cabinet battery charging expression model includes:

[0108] Step S601, substitute the real-time single-cabinet battery charging parameters and the real-time battery replacement parameters into different optimal reference single-cabinet battery charging expression model groups, and analyze the first difference of the single-cabinet charging parameters and the second difference of the battery replacement parameters in real time. If several consecutive first differences and second differences are less than or equal to the preset value, the corresponding optimal reference single-cabinet battery charging expression model is determined to be the most consistent.

[0109] In the embodiment disclosed in the present invention, a battery swap cabinet charging control system based on user power consumption data analysis is also disclosed, including:

[0110] The first module is used to obtain the historical single-cabinet battery charging records in each cabinet of the power cabinet, and record the combination of the historical single-cabinet battery charging records as a historical single-cabinet charging record group;

[0111] The second module is used to construct a reference time loop line, and based on each historical single-rail charging record in the historical single-rail charging record group, configure the battery power, charging power and battery replacement record identifier for different time nodes on the reference time loop line, obtain the historical single-cabinet battery charging expression model, and record the combination of the historical single-cabinet battery charging expression model as a historical single-cabinet battery charging expression model group, wherein the historical single-cabinet battery charging expression model group is used to express the overall historical charging status of the power cabinet;

[0112] The third module is used to analyze the battery replacement record identifiers in the historical single-cabinet battery charging expression model group, determine the number of battery replacement record identifiers corresponding to different time periods, and record the combination of the number of identifiers corresponding to different time periods every day as an identifier number group;

[0113] The fourth module is used to classify the plurality of identification quantity groups by their equivalence to obtain a plurality of identification quantity category sets, and to average the identification quantity groups in the identification quantity category sets to obtain an average identification quantity group;

[0114] The fifth module is used to adjust the historical single-cabinet battery charging expression model group based on the average identification number group to obtain the initial reference single-cabinet battery charging expression model group, and make several charging strategy adjustments to the initial reference single-cabinet battery charging expression model group to obtain several adjusted reference single-cabinet battery charging expression model groups, conduct a comprehensive application effect evaluation on each adjusted reference single-cabinet battery charging expression model group, and select the optimal reference single-cabinet battery charging expression model group from the adjusted reference single-cabinet battery charging expression model group based on the evaluation results;

[0115] The sixth module is used to collect several real-time single-cabinet battery charging parameters and real-time battery replacement parameters of the power cabinet in real time, and select the most suitable optimal reference single-cabinet battery charging expression model group based on the real-time single-cabinet battery charging parameters and the real-time battery replacement parameters, and perform charging regulation on the power cabinet with reference to the optimal reference single-cabinet battery charging expression model group.

[0116] The present invention discloses a charging control method and system for a battery swap cabinet based on analysis of user electricity consumption data, which relates to the technical field of battery swap cabinet management and control, collects historical charging records of each cabinet, constructs a historical single-cabinet battery charging expression model group to reflect the battery charging status and replacement mode; analyzes battery replacement records to determine the replacement frequency in different time segments, and extracts representative charging modes through equivalence classification and averaging calculation; adjusts and optimizes the charging strategy to obtain the optimal reference single-cabinet battery charging expression model group; collects the charging and replacement parameters of the power cabinet in real time, matches them with the optimal model group, and realizes real-time charging regulation; the present invention deeply mines historical data, combines real-time information, and dynamically adjusts the charging strategy, which effectively improves the battery utilization efficiency, extends the battery life, and reduces the operation and maintenance cost, providing strong support for the intelligent management of large-scale power cabinets.

[0117] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A charging control method for a battery swap cabinet based on user power consumption data analysis, characterized in that: include: Obtain the historical single-cabinet battery charging records in each cabinet of the power cabinet, and record the combination of the historical single-cabinet battery charging records as a historical single-cabinet charging record group; A reference time loop is constructed, and based on each historical single-rail charging record in the historical single-rail charging record group, the battery power, charging power, and battery replacement record identifier are configured for different time nodes on the reference time loop, and a historical single-cabinet battery charging expression model is obtained, and the combination of the historical single-cabinet battery charging expression models is recorded as a historical single-cabinet battery charging expression model group, wherein the historical single-cabinet battery charging expression model group is used to express the overall historical charging status of the power cabinet; Analyze the battery replacement record identifiers in the historical single-cabinet battery charging expression model group, determine the identifier numbers of battery replacement record identifiers corresponding to different time periods, and record the combination of the identifier numbers corresponding to different time periods every day as an identifier number group; Classifying several identification quantity groups by their equality to obtain several identification quantity category sets, and averaging the identification quantity groups in the identification quantity category sets to obtain an average identification quantity group; Based on the average identification number group, adjust the historical single-cabinet battery charging expression model group to obtain the initial reference single-cabinet battery charging expression model group, and make several charging strategy adjustments to the initial reference single-cabinet battery charging expression model group to obtain several adjusted reference single-cabinet battery charging expression model groups. Perform a comprehensive application effect evaluation on each adjusted reference single-cabinet battery charging expression model group, and select the optimal reference single-cabinet battery charging expression model group from the adjusted reference single-cabinet battery charging expression model groups based on the evaluation results; Collect several real-time single-cabinet battery charging parameters and real-time battery replacement parameters of the power cabinet in real time, and select the most suitable optimal reference single-cabinet battery charging expression model group based on the real-time single-cabinet battery charging parameters and the real-time battery replacement parameters, and perform charging control on the power cabinet with reference to the optimal reference single-cabinet battery charging expression model group.

2. The method for controlling charging of a battery swap cabinet based on user power consumption data analysis according to claim 1 is characterized in that: The method for determining the number of battery replacement record identifiers corresponding to different time periods includes: Establishing a battery replacement record identification mapping diagram, wherein the horizontal axis of the battery replacement record identification mapping diagram is a time axis; Analyze the battery replacement record identifiers in the historical single-cabinet battery charging expression model group, determine the battery replacement record identifiers that appear at different time nodes, and map them on the battery replacement record identifier mapping diagram in the form of replacement record identifier mapping points; The clustering features of the record identification mapping points in the battery replacement record identification mapping diagram are used to segment the time axis based on the clustering features to obtain a number of time segments, and the number of identifications of the replacement record identification mapping points in the time segments is counted.

3. The method for controlling charging of a battery swap cabinet based on user power consumption data analysis according to claim 2 is characterized in that: Methods for segmenting the timeline based on clustering features include: The time axis is evenly split to obtain a number of unit time segments, and the number of replacement record identification mapping points corresponding to each unit time segment on the battery replacement record identification mapping diagram is analyzed; The vertical axis of the battery replacement record identification mapping diagram is set to have a replacement record identification mapping point quantity axis, and the middle point of the unit time segment is used as the horizontal axis positioning point, and the vertical axis mapping points of the replacement record identification mapping point quantity are marked in the vertical axis direction, and the vertical axis mapping points are smoothly connected to obtain a replacement record identification mapping point quantity curve; Analyze the replacement record identification mapping point quantity curve, determine the curve segments whose upward trend meets the preset standard and whose downward trend meets the preset standard, record them as the curve segments that need attention, and determine the number of unit time segments corresponding to the curve segments that need attention to be combined based on the intersection area between the curve segments that need attention and the horizontal axis; The expression for determining the number of combinations of the corresponding unit time segments of the curve segments requiring attention is: Where D is the number of combinations per time segment, R is the combination number conversion adjustment coefficient, d is the preset standard combination number, S is the intersection area between the curve segment to be concerned and the horizontal axis, S 预设 is the preset cross-area, L is the cross-area influence adjustment coefficient, and b is the cross-area influence adjustment constant.

4. The method for controlling charging of a power exchange cabinet based on user power consumption data analysis according to claim 3 is characterized in that: The method of segmenting the time axis also includes: The curve segments not requiring attention on the replacement record identification mapping point quantity curve are determined, and the unit time segments within the curve segments not requiring attention are combined in sequence based on the preset standard combination quantity.

5. The method for controlling charging of a battery swap cabinet based on user power consumption data analysis according to claim 1 is characterized in that: Methods for classifying several groups of identification quantities as equivalent include: Analyze the identification quantity groups, determine the length of the corresponding time segment division and the sorting method, form a time segment sequence, and use the time segment interval to which each time segment in the time segment sequence belongs as the first classification condition to perform the first equivalent classification on several identification quantity groups; Based on the identification quantity interval to which the identification quantity corresponding to each time segment in the identification quantity group belongs, the identification quantity groups that are equally classified for the first time are equally classified for a second time.

6. The method for controlling charging of a battery swap cabinet based on user power consumption data analysis according to claim 1 is characterized in that: Methods for adjusting the historical single-cabinet battery charging expression model group include: Based on the average identification quantity group, configure the average identification quantity group for different time segments on the reference time cycle line corresponding to the historical single cabinet battery charging expression model group, and determine the battery power change curve of each time segment, and determine the sudden drop point of the battery power change curve and the battery power at the sudden drop point corresponding to the sudden drop point; Determine whether the difference between the average number of identifications in each time segment and the sudden drop point is within a preset difference interval. If it is determined to be within the preset difference interval, determine whether the battery power at each sudden drop point is greater than or equal to a preset value. If not, increase the charging power of the first preset number of cabinets, and increase the charging power at a time node of a preset time length forward. If the difference between the average number of identifications in each time segment and the sudden drop point is not within the preset difference interval, determine whether the average number of identifications is greater than the sudden drop point or less than the sudden drop point. If it is determined to be greater than the sudden drop point, increase the charging power of the second preset number of cabinets, and increase the charging power at a time node of a preset time length forward. If it is determined to be less than the sudden drop point, reduce the charging power of the third preset number of cabinets, and reduce the charging power at a time node of a preset time length forward. After power adjustment of the historical single-cabinet battery charging expression model group, the average number of identifiers in the average identifier number group is used as the battery replacement demand, and the historical cabinet charging expression model group is configured to determine the adjusted sag points of different reference time cycle lines and the battery power at the adjusted sag points, and the adjusted reference single-cabinet battery charging expression model group is obtained.

7. The method for controlling charging of a battery swap cabinet based on user power consumption data analysis according to claim 6 is characterized in that: Before increasing or decreasing the charging power of the cabinet, the cabinet for adjusting the charging power is determined, and the method for determining the charging power adjustment includes: Methods for determining the cabinet for charging power boost include: The time node corresponding to the sudden drop point in each time segment is determined, and the average value of the time node is analyzed and recorded as the average sudden drop time point. The battery power of each cabinet corresponding to the average sudden drop time point is determined. If the battery power is in the first preset power interval, the corresponding cabinet is identified as a cabinet for charging power boost; Methods for determining cabinets for charging power reduction include: The time node corresponding to the sudden drop point in each time segment is determined, and the average value of the time node is analyzed and recorded as the average sudden drop time point. The battery power of each cabinet corresponding to the average sudden drop time point is determined. If the battery power is in the second preset power interval, the corresponding cabinet is identified as a cabinet for charging power boost.

8. The method for controlling charging of a battery swap cabinet based on user power consumption data analysis according to claim 1 is characterized in that: The method for evaluating the comprehensive application effect of the adjusted reference single-cabinet battery charging expression model group includes: Analyze the reference time cycle line corresponding to each adjusted reference single-cabinet battery charging expression model in the adjusted reference single-cabinet battery charging expression model group to determine the adjusted sag points of the battery power change curve in different time sections and the adjusted sag point battery power; Compare the number of sudden drop points and the average number of identifications of the adjusted sudden drop points corresponding to different time segments to obtain a first effect comparison result, compare the average sudden drop point battery power and the standard battery power corresponding to different time segments to obtain a second effect comparison result, and determine a comprehensive application effect evaluation based on the first effect comparison result and the second effect comparison result; Among them, the expression for calculating the comprehensive application effect evaluation is: Among them, G is the comprehensive application effect evaluation, δ(Δx i ∈x yushe ) is the first effect comparison function corresponding to the i-th time segment, Δx i is the first difference between the number of sudden drop points and the average number of marks in the i-th time segment, x yushe is a preset first difference interval. If the first difference is within the first difference interval, then δ(Δx i ∈x yushe ) outputs 1, otherwise outputs 0, β(Δh i ∈h yushe ) is the second effect comparison function corresponding to the i-th time segment, Δh i is the second difference between the average battery capacity at the sudden drop point and the standard battery capacity in the i-th time segment, h yushe is a preset second difference interval. If the second difference is within the second difference interval, then β(Δh i ∈h yushe ) outputs 1, otherwise outputs 0, K i is the weight coefficient corresponding to the i-th time segment, n is the total number of time segments, g man Charge capacity for low power charging, g zong is the total charge capacity, J is the charging characteristic impact adjustment coefficient, C is the charging characteristic impact adjustment constant, and F is the charging power effect conversion coefficient.

9. The method for controlling charging of a battery swap cabinet based on user power consumption data analysis according to claim 1, characterized in that: Methods for selecting the most appropriate optimal reference single cabinet battery charging expression model include: The real-time single-cabinet battery charging parameters and the real-time battery replacement parameters are substituted into different optimal reference single-cabinet battery charging expression model groups, and the first difference of the single-cabinet charging parameters and the second difference of the battery replacement parameters are analyzed in real time. If several consecutive first differences and second differences are less than or equal to the preset value, the corresponding optimal reference single-cabinet battery charging expression model is determined to be the most consistent.

10. The battery swap cabinet charging control system based on user power consumption data analysis is characterized by: include: The first module is used to obtain the historical single-cabinet battery charging records in each cabinet of the power cabinet, and record the combination of the historical single-cabinet battery charging records as a historical single-cabinet charging record group; The second module is used to construct a reference time loop line, and based on each historical single-rail charging record in the historical single-rail charging record group, configure the battery power, charging power and battery replacement record identifier for different time nodes on the reference time loop line, obtain the historical single-cabinet battery charging expression model, and record the combination of the historical single-cabinet battery charging expression model as a historical single-cabinet battery charging expression model group, wherein the historical single-cabinet battery charging expression model group is used to express the overall historical charging status of the power cabinet; The third module is used to analyze the battery replacement record identifiers in the historical single-cabinet battery charging expression model group, determine the number of battery replacement record identifiers corresponding to different time periods, and record the combination of the number of identifiers corresponding to different time periods every day as an identifier number group; The fourth module is used to classify the plurality of identification quantity groups by their equivalence to obtain a plurality of identification quantity category sets, and to average the identification quantity groups in the identification quantity category sets to obtain an average identification quantity group; The fifth module is used to adjust the historical single-cabinet battery charging expression model group based on the average identification number group to obtain the initial reference single-cabinet battery charging expression model group, and make several charging strategy adjustments to the initial reference single-cabinet battery charging expression model group to obtain several adjusted reference single-cabinet battery charging expression model groups, conduct a comprehensive application effect evaluation on each adjusted reference single-cabinet battery charging expression model group, and select the optimal reference single-cabinet battery charging expression model group from the adjusted reference single-cabinet battery charging expression model group based on the evaluation results; The sixth module is used to collect several real-time single-cabinet battery charging parameters and real-time battery replacement parameters of the power cabinet in real time, and select the most suitable optimal reference single-cabinet battery charging expression model group based on the real-time single-cabinet battery charging parameters and the real-time battery replacement parameters, and perform charging regulation on the power cabinet with reference to the optimal reference single-cabinet battery charging expression model group.

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