Charging control method and system for charging pile

Abstract

The present invention discloses a charging control method and system for a charging pile, which belongs to the field of vehicle charging technology. The method comprises the following steps: obtaining charging data of a vehicle connected to the charging pile and a preferential electricity price time range in an area where the charging pile is located, generating a charging strategy feature vector, and formulating a charging mode for the current charging pile; if the charging mode is formulated as a slow charging mode, obtaining driving data of the vehicle connected to the charging pile, generating a vehicle travel event cycle map, and generating a predicted vehicle usage time point; obtaining the current remaining charge amount of the vehicle battery and the predicted power consumption rate corresponding to the predicted vehicle usage time point; establishing a vehicle charging analysis model, and generating a predicted vehicle charge amount; generating a charging power setting value for the charging pile according to the predicted vehicle charge amount and the predicted vehicle usage time point; and controlling the charging power of the charging pile according to the charging power setting value of the charging pile. The present invention can reduce the user's charging cost while ensuring the user's vehicle use needs and improving user satisfaction.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle charging, and in particular to a charging control method and system for a charging pile. Background Art

[0002] Against the backdrop of the rapid development of electric vehicles, charging piles, as key facilities for energy replenishment of electric vehicles, have become the focus of industry attention due to their intelligence and efficiency.

[0003] Most traditional charging piles adopt a fixed charging mode, that is, after the user initiates a charging request, the charging pile starts charging at a preset power until the battery is fully charged or the user manually stops it.

[0004] However, while traditional charging models are simple and straightforward, they fail to consider time-of-use electricity pricing strategies and user habits, resulting in high charging costs or inappropriate power consumption planning. For example, users may charge during off-peak hours, resulting in excessively high charging costs, or delay charging to wait for cheaper electricity, ultimately failing to maintain sufficient power for travel. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides a charging control method and system for a charging pile, which solves the above problems.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A charging control method for a charging pile comprises the following steps:

[0007] Obtain charging data for vehicles connected to the charging pile and the preferential electricity price time range in the area where the charging pile is located to generate a charging strategy feature vector; the charging data includes the start time of charging and the estimated charging duration;

[0008] According to the charging strategy feature vector, the charging mode of the current charging pile is formulated; the charging mode includes user-set mode and slow charging mode;

[0009] If the charging mode is set to slow charging mode, the driving data of the vehicle connected to the charging pile is obtained to generate a vehicle travel event cycle map; the driving data includes the time of vehicle use and the mileage corresponding to the time of vehicle use;

[0010] Generate the predicted vehicle usage time point based on the vehicle travel event cycle map and the charging start time point;

[0011] Obtain the current remaining charge of the vehicle battery and the estimated power consumption rate corresponding to the vehicle's predicted usage time point, establish a vehicle charging analysis model, and generate the vehicle's estimated charge;

[0012] The vehicle charging analysis model is expressed as follows:

[0013] ;

[0014] In the expression, Indicates the estimated charge amount of the vehicle, L time It represents the average mileage corresponding to the time in the vehicle travel event cycle map, P avg It represents the estimated power consumption rate corresponding to the predicted time point of vehicle usage, T represents the weather temperature factor, Q0 represents the current remaining charge of the vehicle; the estimated power consumption rate P corresponding to the predicted time point of vehicle usage avg Refers to the average of the power consumption rates of all vehicles at the corresponding time points in the predicted usage time during the analysis period; the power consumption rate refers to the rate at which the vehicle's charge is consumed;

[0015] Generate a charging power setting value for the charging pile based on the estimated charge amount of the vehicle and the predicted time point of vehicle use;

[0016] Control the charging power of the charging pile according to the charging power setting value of the charging pile.

[0017] On the basis of the above technical solutions, the present invention also provides the following optional technical solutions:

[0018] A further technical solution is to obtain the charging data of the vehicle connected to the charging pile and the preferential electricity price time range in the area where the charging pile is located to generate a charging strategy feature vector, which specifically includes the following steps:

[0019] Obtain the charging start time, electricity price discount period, and estimated charging time for the vehicle connected to the charging pile; the estimated charging time refers to the time required to fully charge the vehicle's battery.

[0020] Generate a charging start time score based on the charging start time of the vehicle connected to the charging pile and the electricity price preferential time range;

[0021] Generate a vehicle charging time score based on the estimated charging time of the vehicle connected to the charging pile;

[0022] A charging strategy feature vector is generated based on the charging start time score and the vehicle charging time score.

[0023] In a further technical solution, the method for generating the charging start time score is as follows:

[0024] Compare the charging start time of the vehicle connected to the charging pile with the electricity price preferential time range. If the charging start time of the vehicle connected to the charging pile is not within the electricity price preferential time range, mark the charging start time as an out-of-peak time point;

[0025] When the charging start time of the vehicle connected to the charging pile is outside the valley time, the charging strategy needs to be adjusted;

[0026] Perform difference processing on the time points outside the valley and the proximal end points of the electricity price preferential time range in the time series to generate the out-of-valley time escape amount;

[0027] The amount of out-of-valley time escape is compared with the total number of time points in a single cycle sequence to generate a charging start time score;

[0028] The vehicle charging time score is generated in the following manner:

[0029] Generate a time range for charging the vehicle connected to the charging pile based on the estimated charging time of the vehicle connected to the charging pile;

[0030] Generate a time range overlap value based on the charging time range of the vehicle connected to the charging pile and the electricity price preferential time range;

[0031] Performing subtraction processing on the time range overlap value and the time range overlap threshold to generate a time range overlap difference value;

[0032] The time range overlap difference is compared with the time range overlap threshold to generate a vehicle charging time score;

[0033] The time range overlap value is generated as follows:

[0034] Compare each time point in the time range required for charging the vehicle connected to the charging pile with all time points in the time range of the electricity price discount;

[0035] If a time point in the charging time range of the vehicle connected to the charging pile is the same as a time point in the electricity price preferential time range, the time point is marked as an overlapping time point;

[0036] Obtain the total number of all overlapping time points, and compare the total number of all overlapping time points with the total number of time points in the time range required for charging the vehicle connected to the charging pile to generate a time range overlap value;

[0037] The charging strategy feature vector is generated in the following manner:

[0038] By formula:

[0039] ;

[0040] Generate charging strategy feature vector f;

[0041] In the formula, C start It indicates the starting charging time score, C durationIt represents the vehicle charging time score, a1 and a2 are weight coefficients, and a1+a2=1.

[0042] A further technical solution is to formulate the charging mode of the current charging pile according to the charging strategy characteristic vector, specifically:

[0043] comparing the charging strategy feature vector with a charging strategy feature vector threshold;

[0044] If the charging strategy feature vector is greater than the charging strategy feature vector threshold, it means that the larger the charging strategy feature vector is, the higher the tendency is for the current charging to be set to slow charging mode, and the charging mode of the current charging pile is set to slow charging mode;

[0045] If the charging strategy feature vector is less than or equal to the charging strategy feature vector threshold, it means that the smaller the charging strategy feature vector is, the lower the tendency for this charging to be set to slow charging mode is, and the charging mode of the current charging pile is set to the user-set mode.

[0046] A further technical solution, if the charging mode is set to slow charging mode, obtaining the driving data of the vehicle connected to the charging pile and generating a vehicle travel event cycle map, specifically includes the following steps:

[0047] If the charging mode is set to slow charging mode, obtain the charging start time of the vehicle connected to the charging pile, the electricity price discount time range, and the estimated charging time of the vehicle connected to the charging pile;

[0048] Generate a usage frequency distribution diagram of the vehicle usage time according to the vehicle usage time;

[0049] Generate a mileage distribution map corresponding to the vehicle usage time based on the mileage corresponding to the vehicle usage time;

[0050] The frequency distribution graph of vehicle usage time and the mileage distribution graph corresponding to the vehicle usage time are merged to generate a vehicle travel event cycle graph.

[0051] According to a further technical solution, the method for generating the usage frequency distribution diagram of the vehicle usage time is as follows:

[0052] Set an analysis period, divide the analysis period into several analysis sub-periods evenly according to a single time series cycle, obtain the vehicle usage time within the analysis sub-period, and process the vehicle usage time to obtain the vehicle usage time;

[0053] Obtain the number of vehicle usage moments corresponding to each moment in a single timing cycle;

[0054] The number of vehicle usage moments corresponding to each moment in the time series cycle is compared with the total number of vehicle usage moments to generate the vehicle usage frequency at a single moment in the time series cycle;

[0055] Generate a frequency distribution diagram of vehicle usage moments based on the vehicle usage frequencies at individual moments in all sequential cycles during the analysis period;

[0056] The mileage distribution map corresponding to the vehicle usage time is generated in the following manner:

[0057] Obtain the mileage of the vehicle at each moment in a single time series cycle, and mark it as the mileage corresponding to the moment;

[0058] All the moments in the molecular cycle corresponding to the mileage are averaged to generate the moments corresponding to the average mileage;

[0059] According to the average mileage corresponding to all times, a mileage distribution map corresponding to the vehicle usage time is generated.

[0060] In a further technical solution, the method for generating the predicted vehicle usage time point is as follows:

[0061] Comparing the vehicle usage frequency at a single moment in a time series cycle in the vehicle travel event cycle graph with a frequency threshold;

[0062] If the vehicle usage frequency at a single moment in a time series cycle in the vehicle travel event cycle graph is less than the frequency threshold, it means that the lower the vehicle usage frequency at a single moment in the time series cycle in the vehicle travel event cycle graph, the lower the possibility of the vehicle being used at that moment, and the moment is marked as a moment with low vehicle travel possibility;

[0063] If the vehicle usage frequency at a single moment in a time series cycle in the vehicle travel event cycle graph is greater than or equal to the frequency threshold, it means that the greater the vehicle usage frequency at a single moment in a time series cycle in the vehicle travel event cycle graph, the greater the possibility that the vehicle is used at that moment, and the moment is marked as a moment with high vehicle travel possibility;

[0064] Sort all moments with high vehicle travel probability in chronological order to generate a collection of moments with high vehicle travel probability;

[0065] Taking time sequence as the direction and the start time of charging as the starting point, the first moment with high vehicle travel probability in the collection of moments with high vehicle travel probability is obtained, which is the predicted vehicle usage time point.

[0066] A further technical solution is that the weather temperature factor is obtained in the following manner:

[0067] Get the current weather temperature and compare it with the minimum value of the battery's standard operating temperature range;

[0068] If the current weather temperature is lower than the minimum value of the battery's standard operating temperature range, the current weather temperature is marked as abnormal weather temperature;

[0069] The abnormal weather temperature is subtracted from the minimum value of the battery's standard operating temperature range to generate a temperature difference value;

[0070] The temperature difference is compared with the minimum value of the battery's standard operating temperature range to generate a weather temperature factor.

[0071] A further technical solution is to generate a charging power setting value for the charging pile based on the estimated charge amount of the vehicle and the predicted time point of use of the vehicle, specifically:

[0072] By formula:

[0073] ;

[0074] Generate charging pile charging power setting value P sct ;

[0075] In the formula, It represents the estimated charge amount of the vehicle, t use It represents the predicted time point of vehicle usage, t start Indicates the time when charging starts.

[0076] The charging control system of the charging pile includes:

[0077] A charging strategy feature analysis unit is used to obtain charging data of vehicles connected to the charging pile and the preferential electricity price time range in the area where the charging pile is located, and generate a charging strategy feature vector; wherein the charging data includes the start time of charging and the estimated charging time;

[0078] The charging mode formulation module is used to formulate the charging mode of the current charging pile according to the charging strategy feature vector;

[0079] A vehicle travel time cycle map construction unit, which is used to obtain driving data of vehicles connected to the charging piles and generate a vehicle travel event cycle map if the charging mode is set to slow charging mode; the driving data includes the time of vehicle use and the mileage corresponding to the time of vehicle use;

[0080] A vehicle usage prediction module is used to generate a predicted vehicle usage time point based on the vehicle travel event cycle map and the charging start time point;

[0081] The vehicle charging analysis module is used to obtain the current remaining charge of the vehicle battery and the estimated power consumption rate corresponding to the vehicle's predicted usage time point, establish a vehicle charging analysis model, and generate the estimated charge amount of the vehicle;

[0082] The charging power analysis module of the charging pile is used to generate the charging power setting value of the charging pile according to the expected charge amount of the vehicle and the predicted usage time of the vehicle;

[0083] The charging power control module of the charging pile is used to control the charging power of the charging pile according to the charging power setting value of the charging pile;

[0084] The charging strategy feature analysis unit specifically includes:

[0085] The data acquisition module is used to obtain the charging start time of the vehicle connected to the charging pile, the preferential electricity price time range, and the estimated charging time of the vehicle connected to the charging pile; the estimated charging time refers to the time required for the vehicle battery to be fully charged;

[0086] The charging start time analysis module is used to generate a charging start time score based on the charging start time of the vehicle connected to the charging pile and the electricity price preferential time range;

[0087] The vehicle charging time analysis module is used to generate a vehicle charging time score based on the estimated charging time of the vehicle connected to the charging pile;

[0088] A charging strategy feature vector generation module is used to generate a charging strategy feature vector based on the charging start time score and the vehicle charging time score;

[0089] The vehicle travel time cycle graph construction unit specifically includes:

[0090] The vehicle usage frequency analysis module is used to obtain the vehicle usage time and generate a usage frequency distribution diagram of the vehicle usage time;

[0091] The vehicle mileage analysis module is used to obtain the mileage corresponding to the time the vehicle is used and generate a mileage distribution map corresponding to the time the vehicle is used;

[0092] The vehicle travel event cycle map generation module is used to merge the usage frequency distribution map of the vehicle usage time and the mileage distribution map corresponding to the vehicle usage time to generate a vehicle travel event cycle map.

[0093] The present invention provides a charging control method and system for a charging pile, which has the following advantages compared with the prior art:

[0094] The present invention can not only adjust the charging mode of the charging pile when the vehicle is charging during the low electricity price period by judging the relationship between the charging time and the preferential electricity price time range, thereby reducing the user's charging cost, but also dynamically adjust the charging power of the charging pile based on the user's vehicle's frequent use time and corresponding mileage. Under the premise of ensuring the demand for the vehicle, the low-price period is given priority for efficient charging, thereby reducing the user's charging cost while improving the user's satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 This is a flowchart of the charging control method for the charging pile provided by the present invention.

[0096] Figure 2 This is a flowchart of step S1 provided by the present invention.

[0097] Figure 3 This is a flow chart of step S3 provided by the present invention.

[0098] Figure 4 This is a module block diagram of the charging control system of the charging pile provided by the present invention.

[0099] Figure 5 This is a module block diagram of the charging strategy feature analysis unit provided by the present invention.

[0100] Figure 6 This is a module block diagram of the vehicle travel time cycle graph construction unit provided by the present invention. DETAILED DESCRIPTION

[0101] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0102] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0103] like Figure 1 As shown, the present invention provides a charging control method for a charging pile, comprising the following steps:

[0104] S1: Obtain charging data of vehicles connected to the charging pile and the preferential electricity price time range in the area where the charging pile is located, and generate a charging strategy feature vector; the charging data includes the starting time of charging and the estimated charging time;

[0105] S2: Based on the charging strategy feature vector, the charging mode of the current charging pile is formulated; the charging mode includes the user-set mode and the slow charging mode;

[0106] S3: If the charging mode is set to slow charging mode, obtain the driving data of the vehicle connected to the charging pile and generate a vehicle travel event cycle map; the driving data includes the time of vehicle use and the mileage corresponding to the time of vehicle use;

[0107] S4: Generate a predicted vehicle usage time point based on the vehicle travel event cycle map and the charging start time point;

[0108] S5: Obtain the current remaining charge of the vehicle battery and the estimated power consumption rate corresponding to the vehicle's predicted usage time point, establish a vehicle charging analysis model, and generate the estimated charge amount of the vehicle;

[0109] The vehicle charging analysis model is expressed as follows:

[0110] ;

[0111] In the expression, Indicates the estimated charge amount of the vehicle, L time It represents the average mileage corresponding to the time in the vehicle travel event cycle map, P avg It represents the estimated power consumption rate corresponding to the predicted time point of vehicle usage, T represents the weather temperature factor, Q0 represents the current remaining charge of the vehicle; the estimated power consumption rate P corresponding to the predicted time point of vehicle usage avg Refers to the average of the power consumption rates of all vehicles at the corresponding time points in the predicted usage time during the analysis period; the power consumption rate refers to the rate at which the vehicle's charge is consumed;

[0112] It should be noted that the unit of the power consumption rate corresponds to the unit of the average mileage corresponding to the time in the vehicle travel event cycle map. For example, if the unit of the power consumption rate is 20 kWh / 100 km, the unit of the average mileage corresponding to the time in the vehicle travel event cycle map should be nkm, where n is a specific number and km is the unit.

[0113] S6: Generate a charging power setting value for the charging pile based on the estimated charge amount of the vehicle and the predicted time point of vehicle use;

[0114] S7: Control the charging power of the charging pile according to the charging power setting value of the charging pile.

[0115] like Figure 2 As shown, as a preferred embodiment of the present invention, the S1 specifically includes the following steps:

[0116] S10. If the charging mode is set to slow charging mode, obtain the start charging time of the vehicle connected to the charging pile, the preferential electricity price time range, and the estimated charging time of the vehicle connected to the charging pile; the estimated charging time refers to the time required for the vehicle battery to be fully charged;

[0117] It should be noted that the time required to fully charge the vehicle battery is the predicted time required to fully charge the vehicle (from the current remaining charge to the maximum charge of the vehicle) under standard charging conditions at the charging station (no abnormalities at the charging station and the charging power of the charging station is the maximum safe power). This technology is existing and will not be further described here.

[0118] In addition, the time point is numerical data, and its value is obtained by converting time; for example, if the charging time of the vehicle connected to the charging pile starts at 9 am, the value of the time point is 9; at the same time, the time points mentioned in the present invention are all numerical data, and the conversion method is the same, which will not be repeated hereafter;

[0119] S11. Generate a charging start time score based on the charging start time of the vehicle connected to the charging pile and the electricity price preferential time range;

[0120] S12. Generate a vehicle charging time score based on the estimated charging time of the vehicle connected to the charging pile;

[0121] S13. Generate a charging strategy feature vector based on the charging start time score and the vehicle charging duration score.

[0122] As a preferred embodiment of the present invention, the method for generating the charging start time score is specifically as follows:

[0123] Compare the charging start time of the vehicle connected to the charging pile with the electricity price preferential time range. If the charging start time of the vehicle connected to the charging pile is not within the electricity price preferential time range, mark the charging start time as an out-of-peak time point;

[0124] When the charging start time of the vehicle connected to the charging pile is outside the valley time, the charging strategy needs to be adjusted;

[0125] If the charging start time of the vehicle connected to the charging pile is within the preferential electricity price period, the charging start time will be marked as the valley time point;

[0126] When the charging start time of the vehicle connected to the charging pile is within the valley time point, the optimal charging strategy is implemented;

[0127] The optimal charging strategy refers to the optimal charging mode of the charging pile. If the user specifies the charging mode, charging will be carried out according to the charging mode specified by the customer;

[0128] For example, the preferential electricity price period in the area where the charging pile is located is from 10 pm to 6 am the next day. If the user does not specify a charging mode when charging the vehicle within this period, the charging pile will automatically operate the optimal charging mode (the charging mode under normal conditions of the charging pile, which needs to be determined based on the model and other data of the charging pile). If the user specifies a charging mode when charging the vehicle within this period (within the functional range of the charging pile), the charging mode of the charging pile will be based on the user-specified mode.

[0129] Perform difference processing on the time points outside the valley and the proximal end points of the electricity price preferential time range in the time series to generate the out-of-valley time escape amount;

[0130] The proximal end point of the electricity price preferential time range in the time series refers to the starting time point of the electricity price preferential time range in the time series, and the time series refers to the direction of time passage;

[0131] It should be explained that the proximal endpoint of the electricity price discount time range in the time series is a fixed value (for example, starting at 10 pm every night); in real life, if time is measured in hours, a cycle will be formed, and the electricity price discount time range is part of the cycle. The start charging time point of the vehicle connected to the charging pile will repeatedly enter the cycle, and there will be a time difference between this time point and the start time point of the electricity price discount time range in the time series, which is the out-of-valley time escape;

[0132] Regarding the near-end point of the preferential electricity price time range in the time series, the present invention provides an embodiment. If the preferential electricity price time range in the area where the charging pile is located is from 10 pm to 6 am the next day, and the charging time point of the vehicle connected to the charging pile is 9 pm, then the near-end point of the preferential electricity price time range in the time series is the time point represented by 10 pm. If the charging time point of the vehicle connected to the charging pile is 9 am, then the near-end point of the preferential electricity price time range in the time series is also the time point represented by 10 pm.

[0133] In addition, when performing difference processing, the time points converted from the off-peak time point and the proximal endpoint of the electricity price preferential time range in the time series must be of the same standard; for example, if the charging time point of the vehicle connected to the charging pile is 10 pm on the same day, and the proximal endpoint of the electricity price preferential time range in the time series is 1 am the next day, then when converting 1 am the next day into a time point, the current day must be used as the standard, that is, the time point is 25 (a day is 24 hours, and the total time points of the day must be added to the next day, that is, 25);

[0134] The amount of out-of-valley time escape is compared with the total number of time points in a single cycle sequence to generate a charging start time score;

[0135] It should be explained that in practical applications, a single cyclic sequence generally refers to one day, and the total number of time points in a single cyclic sequence refers to the sum of the number of time points contained in the single cyclic sequence; for example, if the time points are divided by hours, the total number of time points in a single cyclic sequence is 24.

[0136] As a preferred embodiment of the present invention, the vehicle charging time score is generated in the following manner:

[0137] Generate a time range for charging the vehicle connected to the charging pile based on the estimated charging time of the vehicle connected to the charging pile;

[0138] Generate a time range overlap value based on the charging time range of the vehicle connected to the charging pile and the electricity price preferential time range;

[0139] Performing subtraction processing on the time range overlap value and the time range overlap threshold to generate a time range overlap difference value;

[0140] The time range overlap difference is compared with the time range overlap threshold to generate a vehicle charging time score;

[0141] The time range overlap value is generated as follows:

[0142] Compare each time point in the time range required for charging the vehicle connected to the charging pile with all time points in the time range of the electricity price discount;

[0143] If a time point in the charging time range of the vehicle connected to the charging pile is the same as a time point in the electricity price preferential time range, the time point is marked as an overlapping time point;

[0144] The total number of all overlapping time points is obtained, and the total number of overlapping time points is compared with the total number of time points in the charging time range required for the vehicle connected to the charging pile to generate a time range overlap value.

[0145] As a preferred embodiment of the present invention, the charging strategy feature vector is generated in the following manner:

[0146] By formula:

[0147] ;

[0148] Generate charging strategy feature vector f;

[0149] In the formula, C start It indicates the starting charging time score, C duration It represents the vehicle charging time score, a1 and a2 are weight coefficients, and a1+a2=1.

[0150] As a preferred embodiment of the present invention, the S2 specifically includes:

[0151] comparing the charging strategy feature vector with a charging strategy feature vector threshold;

[0152] If the charging strategy feature vector is greater than the charging strategy feature vector threshold, it means that the larger the charging strategy feature vector is, the higher the tendency is for the current charging to be set to slow charging mode, and the charging mode of the current charging pile is set to slow charging mode;

[0153] If the charging strategy feature vector is less than or equal to the charging strategy feature vector threshold, it means that the smaller the charging strategy feature vector is, the lower the tendency for this charging to be set to slow charging mode is, and the charging mode of the current charging pile is set to the user-set mode.

[0154] like Figure 3 As shown, as a preferred embodiment of the present invention, the S3 specifically includes the following steps:

[0155] S30 obtains the vehicle usage time and generates a frequency distribution diagram of the vehicle usage time;

[0156] S31 obtains the mileage corresponding to the time of vehicle use and generates a mileage distribution map corresponding to the time of vehicle use;

[0157] S32. Combine the usage frequency distribution graph of the vehicle usage time and the mileage distribution graph corresponding to the vehicle usage time to generate a vehicle travel event cycle graph.

[0158] As a preferred embodiment of the present invention, the method for generating the usage frequency distribution diagram of the vehicle usage time is specifically as follows:

[0159] Set an analysis period, divide the analysis period into several analysis sub-periods evenly according to a single time series cycle, obtain the vehicle usage time within the analysis sub-period, and process the vehicle usage time to obtain the vehicle usage time;

[0160] It should be explained that data processing refers to the data simplification of the vehicle usage time within the analysis sub-period, taking the time series cycle as a single cycle and removing the number of cycles in the vehicle usage time; for example, if the analysis period is set to one month, and the month is divided into 30 days, and the vehicle usage time is 9:00 on the first day, 14:00 on the second day, 20:00 on the third day, and 6:00 on the fourth day, after removing the number of cycles in the vehicle usage time, the vehicle usage time is 9:00, 4:00, 20:00, 6:00, etc.;

[0161] Obtain the number of vehicle usage moments corresponding to each moment in a single timing cycle;

[0162] The number of vehicle usage moments corresponding to each moment in the time series cycle is compared with the total number of vehicle usage moments to generate the vehicle usage frequency at a single moment in the time series cycle;

[0163] A frequency distribution diagram of vehicle usage moments is generated based on the vehicle usage frequencies at single moments in all sequential cycle periods in the analysis period.

[0164] As a preferred embodiment of the present invention, the mileage distribution map corresponding to the vehicle usage time is generated in the following manner:

[0165] Obtain the mileage of the vehicle at each moment in a single time series cycle, and mark it as the mileage corresponding to the moment;

[0166] All the moments in the molecular cycle corresponding to the mileage are averaged to generate the moments corresponding to the average mileage;

[0167] According to the average mileage corresponding to all times, a mileage distribution map corresponding to the vehicle usage time is generated.

[0168] As a preferred embodiment of the present invention, the method for generating the predicted vehicle usage time point is specifically as follows:

[0169] Comparing the vehicle usage frequency at a single moment in a time series cycle in the vehicle travel event cycle graph with a frequency threshold;

[0170] If the vehicle usage frequency at a single moment in a time series cycle in the vehicle travel event cycle graph is less than a frequency threshold, it means that the lower the vehicle usage frequency at a single moment in a time series cycle in the vehicle travel event cycle graph, the lower the possibility of the vehicle being used at that moment, and the moment is marked as a moment with low vehicle travel probability;

[0171] If the vehicle usage frequency at a single moment in a time series cycle in the vehicle travel event cycle graph is greater than or equal to the frequency threshold, it means that the greater the vehicle usage frequency at a single moment in a time series cycle in the vehicle travel event cycle graph, the greater the possibility that the vehicle is used at that moment, and the moment is marked as a moment with high vehicle travel possibility;

[0172] Sort all moments with high vehicle travel probability in chronological order to generate a collection of moments with high vehicle travel probability;

[0173] Taking time sequence as the direction and the start time of charging as the starting point, the first moment with high vehicle travel probability in the collection of moments with high vehicle travel probability is obtained, which is the predicted vehicle usage time point.

[0174] As a preferred embodiment of the present invention, the weather temperature factor is obtained in the following manner:

[0175] Get the current weather temperature and compare it with the minimum value of the battery's standard operating temperature range;

[0176] If the current weather temperature is lower than the minimum value of the battery's standard operating temperature range, the current weather temperature is marked as abnormal weather temperature;

[0177] The abnormal weather temperature is subtracted from the minimum value of the battery's standard operating temperature range to generate a temperature difference value;

[0178] Ratio the temperature difference to the minimum value of the battery's standard operating temperature range to generate a weather temperature factor;

[0179] It should be explained that when a vehicle battery is used at low temperatures, the upper limit of its battery charge will be reduced; in addition, in a low-temperature environment, some of the charge in the vehicle battery is heated (to enhance its chemical reaction activity) to ensure its normal operation.

[0180] As a preferred embodiment of the present invention, the S6 specifically includes:

[0181] By formula:

[0182] ;

[0183] Generate charging pile charging power setting value P sct ;

[0184] In the formula, It represents the estimated charge amount of the vehicle, t use It represents the predicted time point of vehicle usage, t start Indicates the time when charging starts.

[0185] like Figure 4 As shown, the present invention also provides a charging control system for a charging pile, the system comprising:

[0186] A charging strategy feature analysis unit is used to obtain charging data of vehicles connected to the charging pile and the preferential electricity price time range in the area where the charging pile is located, and generate a charging strategy feature vector; wherein the charging data includes the start time of charging and the estimated charging time;

[0187] The charging mode formulation module is used to formulate the charging mode of the current charging pile according to the charging strategy feature vector;

[0188] A vehicle travel time cycle map construction unit, which is used to obtain driving data of vehicles connected to the charging piles and generate a vehicle travel event cycle map if the charging mode is set to slow charging mode; the driving data includes the time of vehicle use and the mileage corresponding to the time of vehicle use;

[0189] A vehicle usage prediction module is used to generate a predicted vehicle usage time point based on the vehicle travel event cycle map and the charging start time point;

[0190] The vehicle charging analysis module is used to obtain the current remaining charge of the vehicle battery and the estimated power consumption rate corresponding to the vehicle's predicted usage time point, establish a vehicle charging analysis model, and generate the estimated charge amount of the vehicle;

[0191] The charging power analysis module of the charging pile is used to generate the charging power setting value of the charging pile according to the expected charge amount of the vehicle and the predicted usage time of the vehicle;

[0192] The charging pile charging power control module is used to control the charging power of the charging pile according to the charging power setting value of the charging pile.

[0193] like Figure 5 As shown, as a preferred embodiment of the present invention, the charging strategy feature analysis unit specifically includes:

[0194] The data acquisition module is used to obtain the charging start time of the vehicle connected to the charging pile, the preferential electricity price time range, and the estimated charging time of the vehicle connected to the charging pile; the estimated charging time refers to the time required for the vehicle battery to be fully charged;

[0195] The charging start time analysis module is used to generate a charging start time score based on the charging start time of the vehicle connected to the charging pile and the electricity price preferential time range;

[0196] The vehicle charging time analysis module is used to generate a vehicle charging time score based on the estimated charging time of the vehicle connected to the charging pile;

[0197] The charging strategy feature vector generation module is used to generate a charging strategy feature vector based on the charging start time score and the vehicle charging time score.

[0198] like Figure 6 As shown in FIG. 1 , as a preferred embodiment of the present invention, the vehicle travel time cycle graph construction unit specifically includes:

[0199] The vehicle usage frequency analysis module is used to obtain the vehicle usage time and generate a usage frequency distribution diagram of the vehicle usage time;

[0200] The vehicle mileage analysis module is used to obtain the mileage corresponding to the time the vehicle is used and generate a mileage distribution map corresponding to the time the vehicle is used;

[0201] The vehicle travel event cycle map generation module is used to merge the usage frequency distribution map of the vehicle usage time and the mileage distribution map corresponding to the vehicle usage time to generate a vehicle travel event cycle map.

[0202] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A charging control method for a charging pile, characterized in that: The following steps are involved: Obtain charging data for vehicles connected to the charging pile and the preferential electricity price time range in the area where the charging pile is located to generate a charging strategy feature vector; the charging data includes the start time of charging and the estimated charging duration; According to the charging strategy feature vector, the charging mode of the current charging pile is formulated; the charging mode includes user-set mode and slow charging mode; If the charging mode is set to slow charging mode, the driving data of the vehicle connected to the charging pile is obtained to generate a vehicle travel event cycle map; the driving data includes the time of vehicle use and the mileage corresponding to the time of vehicle use; Generate the predicted vehicle usage time point based on the vehicle travel event cycle map and the charging start time point; Obtain the current remaining charge of the vehicle battery and the estimated power consumption rate corresponding to the vehicle's predicted usage time point, establish a vehicle charging analysis model, and generate the vehicle's estimated charge; The vehicle charging analysis model is expressed as follows: ； In the expression, Indicates the estimated charge amount of the vehicle, L time It represents the average mileage corresponding to the time in the vehicle travel event cycle map, P avg It represents the estimated power consumption rate corresponding to the predicted time point of vehicle usage, T represents the weather temperature factor, Q0 represents the current remaining charge of the vehicle; the estimated power consumption rate P corresponding to the predicted time point of vehicle usage avg Refers to the average of the power consumption rates of all vehicles at the corresponding time points in the predicted usage time during the analysis period; the power consumption rate refers to the rate at which the vehicle's charge is consumed; Generate a charging power setting value for the charging pile based on the estimated charge amount of the vehicle and the predicted time point of vehicle use; Control the charging power of the charging pile according to the charging power setting value of the charging pile.

2. The charging control method of the charging pile according to claim 1, characterized in that: The step of obtaining charging data of vehicles connected to the charging pile and a preferential electricity price time range in the area where the charging pile is located, and generating a charging strategy feature vector specifically includes the following steps: Obtain the charging start time, electricity price discount period, and estimated charging time for the vehicle connected to the charging pile; the estimated charging time refers to the time required to fully charge the vehicle's battery. Generate a charging start time score based on the charging start time of the vehicle connected to the charging pile and the electricity price preferential time range; Generate a vehicle charging time score based on the estimated charging time of the vehicle connected to the charging pile; A charging strategy feature vector is generated based on the charging start time score and the vehicle charging time score.

3. The charging control method of the charging pile according to claim 2, characterized in that: The method for generating the charging start time score is specifically as follows: Compare the charging start time of the vehicle connected to the charging pile with the electricity price preferential time range. If the charging start time of the vehicle connected to the charging pile is not within the electricity price preferential time range, mark the charging start time as an out-of-peak time point; When the charging start time of the vehicle connected to the charging pile is outside the valley time, the charging strategy needs to be adjusted; Perform difference processing on the time points outside the valley and the proximal end points of the electricity price preferential time range in the time series to generate the out-of-valley time escape amount; The amount of out-of-valley time escape is compared with the total number of time points in a single cycle sequence to generate a charging start time score; The vehicle charging time score is generated in the following manner: Generate a time range for charging the vehicle connected to the charging pile based on the estimated charging time of the vehicle connected to the charging pile; Generate a time range overlap value based on the charging time range of the vehicle connected to the charging pile and the electricity price preferential time range; Performing subtraction processing on the time range overlap value and the time range overlap threshold to generate a time range overlap difference value; The time range overlap difference is compared with the time range overlap threshold to generate a vehicle charging time score; The time range overlap value is generated as follows: Compare each time point in the time range required for charging the vehicle connected to the charging pile with all time points in the time range of the electricity price discount; If a time point in the charging time range of the vehicle connected to the charging pile is the same as a time point in the electricity price preferential time range, the time point is marked as an overlapping time point; Obtain the total number of all overlapping time points, and compare the total number of all overlapping time points with the total number of time points in the time range required for charging the vehicle connected to the charging pile to generate a time range overlap value; The charging strategy feature vector is generated in the following manner: By formula: ； Generate charging strategy feature vector f; In the formula, C start It indicates the starting charging time score, C duration It represents the vehicle charging time score, a1 and a2 are weight coefficients, and a1+a2=1.

4. The charging control method of the charging pile according to claim 2, characterized in that: The charging mode of the current charging pile is formulated according to the charging strategy feature vector, specifically: comparing the charging strategy feature vector with a charging strategy feature vector threshold; If the charging strategy feature vector is greater than the charging strategy feature vector threshold, it means that the larger the charging strategy feature vector is, the higher the tendency is for the current charging to be set to slow charging mode, and the charging mode of the current charging pile is set to slow charging mode; If the charging strategy feature vector is less than or equal to the charging strategy feature vector threshold, it means that the smaller the charging strategy feature vector is, the lower the tendency for this charging to be set to slow charging mode is, and the charging mode of the current charging pile is set to the user-set mode.

5. The charging control method of the charging pile according to claim 1, characterized in that: If the charging mode is set to slow charging mode, the driving data of the vehicle connected to the charging pile is obtained to generate a vehicle travel event cycle map, which specifically includes the following steps: If the charging mode is set to slow charging mode, obtain the charging start time of the vehicle connected to the charging pile, the electricity price discount time range, and the estimated charging time of the vehicle connected to the charging pile; Generate a usage frequency distribution diagram of the vehicle usage time according to the vehicle usage time; Generate a mileage distribution map corresponding to the vehicle usage time based on the mileage corresponding to the vehicle usage time; The frequency distribution graph of vehicle usage time and the mileage distribution graph corresponding to the vehicle usage time are merged to generate a vehicle travel event cycle graph.

6. The charging control method of the charging pile according to claim 5, characterized in that: The method for generating the usage frequency distribution diagram of the vehicle usage time is specifically as follows: Set an analysis period, divide the analysis period into several analysis sub-periods evenly according to a single time series cycle, obtain the vehicle usage time within the analysis sub-period, and process the vehicle usage time to obtain the vehicle usage time; Obtain the number of vehicle usage moments corresponding to each moment in a single timing cycle; The number of vehicle usage moments corresponding to each moment in the time series cycle is compared with the total number of vehicle usage moments to generate the vehicle usage frequency at a single moment in the time series cycle; Generate a frequency distribution diagram of vehicle usage moments based on the vehicle usage frequencies at individual moments in all sequential cycles during the analysis period; The mileage distribution map corresponding to the vehicle usage time is generated in the following manner: Obtain the mileage of the vehicle at each moment in a single time series cycle, and mark it as the mileage corresponding to the moment; All the moments in the molecular cycle corresponding to the mileage are averaged to generate the moments corresponding to the average mileage; According to the average mileage corresponding to all times, a mileage distribution map corresponding to the vehicle usage time is generated.

7. The charging control method of the charging pile according to claim 1, characterized in that: The method for generating the predicted vehicle usage time point is specifically as follows: Comparing the vehicle usage frequency at a single moment in a time series cycle in the vehicle travel event cycle graph with a frequency threshold; If the vehicle usage frequency at a single moment in a time series cycle in the vehicle travel event cycle graph is less than the frequency threshold, it means that the lower the vehicle usage frequency at a single moment in the time series cycle in the vehicle travel event cycle graph, the lower the possibility of the vehicle being used at that moment, and the moment is marked as a moment with low vehicle travel possibility; If the vehicle usage frequency at a single moment in a time series cycle in the vehicle travel event cycle graph is greater than or equal to the frequency threshold, it means that the greater the vehicle usage frequency at a single moment in a time series cycle in the vehicle travel event cycle graph, the greater the possibility that the vehicle is used at that moment, and the moment is marked as a moment with high vehicle travel possibility; Sort all moments with high vehicle travel probability in chronological order to generate a collection of moments with high vehicle travel probability; Taking time sequence as the direction and the start time of charging as the starting point, the first moment with high vehicle travel probability in the collection of moments with high vehicle travel probability is obtained, which is the predicted vehicle usage time point.

8. The charging control method of the charging pile according to claim 1, characterized in that: The weather temperature factor is obtained in the following manner: Get the current weather temperature and compare it with the minimum value of the battery's standard operating temperature range; If the current weather temperature is lower than the minimum value of the battery's standard operating temperature range, the current weather temperature is marked as abnormal weather temperature; The abnormal weather temperature is subtracted from the minimum value of the battery's standard operating temperature range to generate a temperature difference value; The temperature difference is compared with the minimum value of the battery's standard operating temperature range to generate a weather temperature factor.

9. The charging control method of the charging pile according to claim 1, characterized in that: The charging power setting value of the charging pile is generated according to the estimated charge amount of the vehicle and the predicted use time of the vehicle, specifically: By formula: ； Generate charging pile charging power setting value P sct ; In the formula, It represents the estimated charge amount of the vehicle, t use It represents the predicted time point of vehicle usage, t start Indicates the time when charging starts.

10. The charging control system of the charging pile is characterized by: The system is used to execute the method according to any one of claims 1 to 9, and the system comprises: A charging strategy feature analysis unit is used to obtain charging data of vehicles connected to the charging pile and the preferential electricity price time range in the area where the charging pile is located, and generate a charging strategy feature vector; wherein the charging data includes the start time of charging and the estimated charging time; The charging mode formulation module is used to formulate the charging mode of the current charging pile according to the charging strategy feature vector; A vehicle travel time cycle map construction unit, which is used to obtain driving data of vehicles connected to the charging piles and generate a vehicle travel event cycle map if the charging mode is set to slow charging mode; the driving data includes the time of vehicle use and the mileage corresponding to the time of vehicle use; A vehicle usage prediction module is used to generate a predicted vehicle usage time point based on the vehicle travel event cycle map and the charging start time point; The vehicle charging analysis module is used to obtain the current remaining charge of the vehicle battery and the estimated power consumption rate corresponding to the vehicle's predicted usage time point, establish a vehicle charging analysis model, and generate the estimated charge amount of the vehicle; The charging power analysis module of the charging pile is used to generate the charging power setting value of the charging pile according to the expected charge amount of the vehicle and the predicted usage time of the vehicle; The charging power control module of the charging pile is used to control the charging power of the charging pile according to the charging power setting value of the charging pile; The charging strategy feature analysis unit specifically includes: The data acquisition module is used to obtain the charging start time of the vehicle connected to the charging pile, the preferential electricity price time range, and the estimated charging time of the vehicle connected to the charging pile; the estimated charging time refers to the time required for the vehicle battery to be fully charged; The charging start time analysis module is used to generate a charging start time score based on the charging start time of the vehicle connected to the charging pile and the electricity price preferential time range; The vehicle charging time analysis module is used to generate a vehicle charging time score based on the estimated charging time of the vehicle connected to the charging pile; A charging strategy feature vector generation module is used to generate a charging strategy feature vector based on the charging start time score and the vehicle charging time score; The vehicle travel time cycle graph construction unit specifically includes: The vehicle usage frequency analysis module is used to obtain the vehicle usage time and generate a usage frequency distribution diagram of the vehicle usage time; The vehicle mileage analysis module is used to obtain the mileage corresponding to the time the vehicle is used and generate a mileage distribution map corresponding to the time the vehicle is used; The vehicle travel event cycle map generation module is used to merge the usage frequency distribution map of the vehicle usage time and the mileage distribution map corresponding to the vehicle usage time to generate a vehicle travel event cycle map.

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