A Method for Dynamically Allocating the Power of a Charging Stack Integrated with Demand and Modular Control

Abstract

The present invention discloses a method for dynamically allocating the power of a charging pile integrated with demand and modular control, which relates to the technical field of charging scheduling. The method includes: establishing a charging vehicle identification model and a vehicle charging prediction model; establishing a charging allocation model; obtaining the real-time vehicle set that is charging at the current moment at the charging module to obtain a vehicle replenishment set; calculating the charging congestion index at the charging module to obtain a charging module sequence; calculating the redundant power of the charging pile, obtaining the charging power to be adjusted for the vehicles in the real-time vehicle set, obtaining the power to be allocated to the charging module, and the charging pile allocates the power of the charging module according to the value of the power to be allocated. By establishing a charging vehicle identification model, a vehicle charging prediction model, a charging allocation model and calculating the charging congestion index at the charging module, power is preferentially tilted towards more important vehicles, thereby limiting the scope of the negative impact caused by charging waiting.

Description

Technical Field

[0001] The present invention relates to the technical field of charging scheduling, and specifically relates to a method for dynamically allocating the power of a charging pile integrated with demand and modular control. Background Art

[0002] A charging pile is a large-scale power supply cluster specifically designed to meet large-scale charging demands, such as large parking lots or enterprises. The charging pile can convert alternating current into direct current and is equipped with multiple charging modules to provide services to multiple electric vehicles simultaneously.

[0003] Existing charging piles do not distinguish the attributes of vehicles. When obvious waiting occurs, they cannot preferentially meet the charging demands of more important vehicles, such as buses, which is likely to exacerbate the negative impact of charging waiting. Summary of the Invention

[0004] To solve the above technical problems, a method for dynamically allocating the power of a charging pile integrated with demand and modular control is provided, and this technical solution solves the problems raised in the above background art.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A method for dynamically allocating the power of a charging pile integrated with demand and modular control, comprising:

[0007] Obtain at least one charging module integrated in the charging pile, and obtain the upper limit of the adjustable power of the charging pile;

[0008] Establish a charging vehicle identification model and a vehicle charging prediction model;

[0009] Based on the charging vehicle identification model, establish a charging allocation model;

[0010] Obtain the set of real-time vehicles being charged at the current moment at the charging module, and based on the vehicle charging prediction model, predict the subsequent charging vehicles of the charging module to obtain a vehicle replenishment set;

[0011] Based on the vehicle replenishment set and the set of real-time vehicles, calculate the charging congestion index at the charging module, sort the charging modules in descending order according to the charging congestion index to obtain a charging module sequence;

[0012] Calculate the redundant power of the charging pile, and based on the charging vehicle identification model, the charging allocation model and the charging module sequence, obtain the charging power to be adjusted for the vehicles in the set of real-time vehicles. The charging pile obtains the power to be allocated to the charging module according to the charging power to be adjusted, and the charging pile allocates the power of the charging module according to the value of the power to be allocated.

[0013] Preferably, the establishment of the charging vehicle identification model includes the following steps:

[0014] Obtain at least one sample vehicle for charging, classify the sample vehicles according to usage attributes to obtain sample public vehicles and sample private vehicles, and the usage attributes are public attributes and private attributes;

[0015] Obtain the usage purposes of the sample public vehicles, obtain the scope of impact of the suspension of the sample public vehicles, and obtain the suspension costs of the services involved in the usage purposes of the sample public vehicles;

[0016] Use the urgency comprehensive formula to calculate the urgency coefficient of the usage purpose;

[0017] Take the value range of the urgency coefficient as the urgency range;

[0018] Evenly divide the urgency range into at least one identification interval, and assign grades to the identification intervals in ascending order according to the midpoints of the identification intervals;

[0019] Summarize the sample public vehicles corresponding to the usage purposes whose urgency coefficients belong to the identification interval into a feature set, and the feature set corresponds to the identification interval;

[0020] Extract features from the appearance of the sample public vehicles in the feature set to obtain at least one identification feature, and summarize them into an identification feature set. The identification feature set is paired with the identification interval corresponding to the feature set;

[0021] Extract features from the sample private vehicles and summarize the features to obtain a private feature set;

[0022] Summarize the private feature set, the identification feature set, and the grades of the identification intervals into a charging vehicle identification model;

[0023] The urgency comprehensive formula is as follows: ,

[0024] where A is the urgency coefficient of the usage purpose, a is the scope of impact of the suspension of the sample public vehicle, and b is the suspension cost of the service involved in the usage purpose of the sample public vehicle.

[0025] Preferably, the establishment of the vehicle charging prediction model includes the following steps:

[0026] Based on historical data, obtain the maximum distance traveled by the vehicle for charging as the characteristic distance;

[0027] Obtain the position coordinates of the charging module, and based on the position coordinates, form a charging identification area, and the charging identification area is an area with the position coordinates as the center and the characteristic distance as the radius;

[0028] Obtain the charging habits of vehicle users, where the charging habits are the range of remaining vehicle battery levels when the vehicle is charging and the full battery level of the vehicle, and obtain the charging speed of the vehicle;

[0029] Evenly divide the time of a day into at least one time sampling point;

[0030] Obtain at least one moving route of the vehicle, where the moving route is the driving path of the vehicle within a day;

[0031] At the time sampling point, when the vehicle is not within the charging recognition area, the charging probability of the vehicle is 0. When the vehicle is within the charging recognition area, obtain the distance the vehicle moves in the moving route, and based on the reference power consumption of the vehicle, obtain the power consumption of the vehicle at the time sampling point, where the reference power consumption is the power consumption of the vehicle per kilometer of driving;

[0032] Subtract the power consumption of the vehicle at the time sampling point from the full battery level of the vehicle to obtain the characteristic remaining battery level, and pair the characteristic remaining battery level with the time sampling point;

[0033] Count the number of characteristic remaining battery levels paired with the time sampling point that belong to the range of vehicle remaining battery levels as the number of characteristics;

[0034] Divide the number of characteristics by the number of characteristic remaining battery levels paired with the time sampling point to obtain the charging probability of the vehicle;

[0035] Pair and fit the time sampling point with the charging probability of the vehicle to obtain a probability fitting function, and the probability fitting function corresponds to the vehicle and the charging module.

[0036] Preferably, based on the charging vehicle recognition model, establishing a charging allocation model includes the following steps:

[0037] Classify the vehicles according to their usage attributes to obtain public vehicles and private vehicles, where the usage attributes are public attributes and private attributes;

[0038] Take the number of sample private vehicles as the target allocation coefficient for private vehicles, and take the total number of sample private vehicles and sample public vehicles as the preliminary allocation coefficient for public vehicles;

[0039] Extract features from the appearance of public vehicles to obtain at least one actual feature, and assign the level of the recognition interval corresponding to the recognition feature set with the largest number of actual features to the public vehicles;

[0040] Multiply the preliminary allocation coefficient of public vehicles by the level of public vehicles to obtain the target allocation coefficient of public vehicles;

[0041] When the vehicle is a public vehicle, the target allocation coefficient of the public vehicle is used as the target allocation coefficient of the vehicle. When the vehicle is a private vehicle, the target allocation coefficient of the private vehicle is used as the target allocation coefficient of the vehicle.

[0042] Preferably, predicting the subsequent charging vehicles of the charging module to obtain the vehicle supplement set includes the following steps:

[0043] Substitute the current moment into at least one probability fitting function corresponding to the charging module to obtain at least one suspected probability;

[0044] Summarize the vehicles corresponding to the probability fitting functions with suspected probabilities greater than 0 to obtain the vehicle supplement set.

[0045] Preferably, calculating the charging congestion index at the charging module based on the vehicle supplement set and the real-time vehicle set includes the following steps:

[0046] Calculate the first charging time of the vehicles in the real-time vehicle set. The first charging time of the vehicle is equal to the difference between the full charge amount of the vehicle and the real-time charge amount of the vehicle divided by the charging speed of the vehicle;

[0047] Multiply and superimpose the first charging time of the vehicles in the real-time vehicle set by the target allocation coefficient of the vehicle to obtain the first congestion index;

[0048] Use the time synthesis formula to calculate the second charging time of the vehicles in the vehicle supplement set;

[0049] Multiply, in sequence, the second charging time of the vehicles in the vehicle supplement set by the corresponding suspected probability and the target allocation coefficient of the vehicle, and then superimpose them to obtain the second congestion index;

[0050] Add the first congestion index and the second congestion index to obtain the charging congestion index at the charging module;

[0051] The time synthesis formula is as follows: ,

[0052] where B is the second charging time, c is the full charge amount of the vehicle, e and f are the endpoint values of the remaining charge amount range of the vehicle, and g is the charging speed of the vehicle.

[0053] Preferably, calculating the redundant power of the charging pile includes the following steps:

[0054] Superimpose the real-time charging powers of at least one charging module in the charging pile to obtain the total charging power;

[0055] Subtract the total charging power from the upper limit of the adjustable power to obtain the redundant power of the charging pile.

[0056] Preferably, obtaining the charging power to be adjusted for the vehicles in the real-time vehicle set based on the charging vehicle recognition model, the charging allocation model, and the charging module sequence includes the following steps:

[0057] Superimpose the charging congestion index at the charging module to obtain the overall charging congestion index;

[0058] Superimpose the target allocation coefficients of the vehicles in the real-time vehicle set corresponding to the charging module to obtain the overall target allocation coefficient;

[0059] When the redundant power is greater than 0, divide the charging congestion index at the charging module by the overall charging congestion index and multiply by the redundant power to obtain the power to be supplemented;

[0060] Divide the power to be supplemented by the overall target allocation coefficient and multiply by the target allocation coefficients of the vehicles in the real-time vehicle set to obtain the power to be increased;

[0061] Add the power to be increased to the real-time charging power of the corresponding vehicles in the real-time vehicle set to obtain the charging power to be adjusted for the vehicles in the real-time vehicle set;

[0062] When the redundant power is equal to 0, divide the charging congestion index at the charging module by the overall charging congestion index and multiply by the upper limit of the adjustable power to obtain the primary power;

[0063] Divide the primary power by the overall target allocation coefficient and multiply by the target allocation coefficients of the vehicles in the real-time vehicle set to obtain the charging power to be adjusted;

[0064] Calculate the charging power to be adjusted for the vehicles in the real-time vehicle set corresponding to the charging module according to the sorting of the charging module sequence.

[0065] Preferably, the charging stack obtaining the allocated power of the charging module according to the charging power to be adjusted includes the following steps:

[0066] Superimpose the charging power to be adjusted for the vehicles in the real-time vehicle set corresponding to the charging module to obtain the allocated power of the charging module.

[0067] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0068] By establishing a charging vehicle recognition model, establishing a vehicle charging prediction model, establishing a charging allocation model, obtaining a vehicle replenishment set, and calculating the charging congestion index at the charging module, the vehicles are classified and identified. When performing charging adjustment, the subsequent charging situation is estimated, and based on the prediction results, the power is tilted preferentially to more important vehicles, thereby ensuring that when the charging power is globally or locally insufficient, the impact on public vehicles is small, and thus limiting the scope of the negative impact caused by charging waiting. Description of the Drawings

[0069] Figure 1 It is a schematic flow chart of the charging pile power dynamic allocation method for the requirements and modular control integration of the present invention;

[0070] Figure 2 It is a schematic flow chart of establishing a charging vehicle identification model of the present invention;

[0071] Figure 3 It is a schematic flow chart of establishing a vehicle charging prediction model of the present invention;

[0072] Figure 4 It is a schematic flow chart of establishing a charging allocation model based on the charging vehicle identification model of the present invention;

[0073] Figure 5 It is a schematic flow chart of predicting the subsequent charging vehicles of the charging module of the present invention to obtain a vehicle supplement set;

[0074] Figure 6 It is a schematic flow chart of calculating the charging congestion index at the charging module based on the vehicle supplement set and the real-time vehicle set of the present invention;

[0075] Figure 7 It is a schematic flow chart of calculating the redundant power of the charging pile of the present invention;

[0076] Figure 8 It is a schematic flow chart of obtaining the charging power to be adjusted for the vehicles in the real-time vehicle set based on the charging vehicle identification model, the charging allocation model and the charging module sequence of the present invention. Detailed implementation manners

[0077] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0078] Referring to Figure 1 As shown, a charging pile power dynamic allocation method for requirements and modular control integration includes:

[0079] Obtain at least one charging module integrated in the charging pile, and obtain the adjustable power upper limit of the charging pile;

[0080] Establish a charging vehicle identification model and establish a vehicle charging prediction model;

[0081] Based on the charging vehicle identification model, establish a charging allocation model;

[0082] Obtain the real-time vehicle set that is being charged at the current moment at the charging module. Based on the vehicle charging prediction model, predict the subsequent charging vehicles of the charging module to obtain the vehicle supplement set;

[0083] Based on the vehicle supplement set and the real-time vehicle set, calculate the charging congestion index at the charging module, and sort the charging modules from largest to smallest according to the charging congestion index to obtain the charging module sequence;

[0084] Calculate the redundant power of the charging pile. Based on the charging vehicle identification model, the charging distribution model, and the charging module sequence, obtain the charging power to be adjusted for the vehicles in the real-time vehicle set. The charging pile obtains the power to be allocated to the charging module according to the charging power to be adjusted, and the charging pile allocates the power of the charging module according to the value of the power to be allocated.

[0085] The coverage range of the charging pile is large, and the charging modules of the charging pile may be far apart from each other. Therefore, it is not convenient for vehicles to transfer between different charging modules. Therefore, it is necessary to adjust the power of the charging pile to relieve the charging waiting time. However, when the power is insufficient, it is difficult to completely solve the congestion situation no matter how it is adjusted. Therefore, in this case, priority is given to ensuring the charging of public vehicles. Therefore, different power distributions are carried out according to the different attributes of the vehicles to avoid the resulting public impact as much as possible. For private vehicles, when they encounter very urgent situations, they can take a taxi instead, but it is difficult to quickly find an alternative solution for public vehicles.

[0086] Refer to Figure 2 As shown, the steps to establish the charging vehicle identification model are as follows:

[0087] Obtain at least one sample vehicle for charging, classify the sample vehicles according to their usage attributes to obtain sample public vehicles and sample private vehicles, and the usage attributes are public attributes and private attributes;

[0088] Obtain the usage purpose of the sample public vehicle, obtain the scope of influence of the sample public vehicle when it is out of service, and obtain the out-of-service cost of the business involved in the usage purpose of the sample public vehicle;

[0089] Use the urgency comprehensive formula to calculate the urgency coefficient of the usage purpose;

[0090] Take the value range of the urgency coefficient as the urgency range;

[0091] Evenly divide the urgency range into at least one identification interval, and assign grades to the identification intervals from smallest to largest according to the midpoints of the identification intervals;

[0092] Summarize the sample public vehicles corresponding to the usage purposes whose urgency coefficients belong to the identification interval into a feature set, and the feature set corresponds to the identification interval;

[0093] Extract the appearance features of the sample public vehicles in the feature set to obtain at least one recognition feature, and summarize them into a recognition feature set. The recognition feature set is paired with the corresponding recognition interval of the feature set;

[0094] Extract the features of the sample private vehicles and summarize the features to obtain a private feature set;

[0095] Summarize the private feature set, the recognition feature set, and the levels of the recognition intervals into a charging vehicle recognition model;

[0096] The urgency comprehensive formula is as follows: ,

[0097] Among them, A is the urgency coefficient of the usage purpose, a is the deactivation influence range of the sample public vehicles, and b is the deactivation cost of the business involved in the usage purpose of the sample public vehicles.

[0098] The purpose of establishing the charging vehicle recognition model is to identify public vehicles and private vehicles, so as to perform charging distribution to different degrees. At the same time, the public vehicles are also graded, so that different charging schemes are used for charging public vehicles of different levels. Thus, when the power is insufficient, more important vehicles can be guaranteed to be charged first.

[0099] Refer to Figure 3 As shown, establishing a vehicle charging prediction model includes the following steps:

[0100] Based on historical data, obtain the maximum distance traveled by the vehicle for charging as the feature distance;

[0101] Obtain the position coordinates of the charging module. Based on the position coordinates, form a charging recognition area, and the charging recognition area is an area with the position coordinates as the center and the feature distance as the radius;

[0102] Obtain the charging habits of the vehicle users. The charging habits are the remaining power range of the vehicle when charging and the full charge power of the vehicle, and obtain the charging speed of the vehicle;

[0103] Evenly divide a day into at least one time sampling point;

[0104] Obtain at least one movement route of the vehicle, and the movement route is the driving path of the vehicle within a day;

[0105] At the time sampling point, when the vehicle is not within the charging recognition area, the charging probability of the vehicle is 0. When the vehicle is within the charging recognition area, obtain the distance traveled by the vehicle in the movement route, and obtain the power consumption of the vehicle at the time sampling point according to the reference power consumption of the vehicle. The reference power consumption is the power consumption of the vehicle per kilometer traveled;

[0106] Subtract the power consumption of the vehicle at the time sampling point from the full charge power of the vehicle to obtain the characteristic remaining power, and pair the characteristic remaining power with the time sampling point;

[0107] Count the number of characteristic remaining powers paired with the time sampling point that fall within the range of the vehicle's remaining power as the number of characteristics;

[0108] Divide the number of characteristics by the number of characteristic remaining powers paired with the time sampling point to obtain the charging probability of the vehicle;

[0109] Pair the time sampling point with the charging probability of the vehicle and fit them to obtain a probability fitting function, which corresponds to the vehicle and the charging module.

[0110] Since the route of the vehicle is regular but not constant, for the same moment of the day, it may appear in the charging recognition area today but not tomorrow, but the overall possibility of appearance can be estimated. Therefore, the possibility of the vehicle charging at the charging module is described by probability;

[0111] Here, each combination of vehicle and charging module determines a probability fitting function;

[0112] Due to the regularity of the vehicle's operation, the position of the vehicle at each moment can be predicted. The significance of the prediction is to predict the subsequent charging situation of the charging module. When calculating its congestion, not only the currently charging vehicles are considered, but also the vehicles in the subsequent period are considered. Thus, the allocated power is obtained based on the combination of the two. Taking charging modules E and F as an example, if there are 5 currently charging vehicles at E and 20 predicted charging vehicles in the future, and 8 currently charging vehicles at F and 2 predicted charging vehicles in the future, then the power allocated to E must be greater. Otherwise, serious congestion will occur in the future. Therefore, by making charging predictions, adjustments can be made in advance.

[0113] Refer to Figure 4 As shown, based on the charging vehicle recognition model, establishing a charging allocation model includes the following steps:

[0114] Classify the vehicles according to their usage attributes to obtain public vehicles and private vehicles, and the usage attributes are public attributes and private attributes;

[0115] Take the number of sample private vehicles as the target allocation coefficient for private vehicles, and take the total number of sample private vehicles and sample public vehicles as the preliminary allocation coefficient for public vehicles;

[0116] Extract features from the appearance of public vehicles to obtain at least one actual feature, and assign the level of the recognition interval corresponding to the recognition feature set with the largest number of actual features to the public vehicles;

[0117] Multiply the preliminary allocation coefficient of the public vehicle by the level of the public vehicle to obtain the target allocation coefficient of the public vehicle;

[0118] When the vehicle is a public vehicle, the target allocation coefficient of the public vehicle is used as the target allocation coefficient of the vehicle. When the vehicle is a private vehicle, the target allocation coefficient of the private vehicle is used as the target allocation coefficient of the vehicle.

[0119] The target allocation coefficient of the vehicle determines the power value obtained by the vehicle. The larger the power value, the faster the charging speed. Therefore, the target allocation coefficient of the vehicle must be proportional to the importance of the vehicle. In the above setting process, the target allocation coefficient of the private vehicle is less than that of the public vehicle, and the target allocation coefficient of the public vehicle with a lower level is less than that of the public vehicle with a higher level. Therefore, it meets the adjustment requirements.

[0120] Refer to Figure 5 As shown, predicting the subsequent charging vehicles of the charging module to obtain the vehicle supplement set includes the following steps:

[0121] Substitute the current time into at least one probability fitting function corresponding to the charging module to obtain at least one suspected probability;

[0122] Summarize the vehicles corresponding to the probability fitting functions with suspected probabilities greater than 0 to obtain the vehicle supplement set.

[0123] The vehicle supplement set is the vehicles that may be charged at the charging module subsequently.

[0124] Refer to Figure 6 As shown, calculating the charging congestion index at the charging module based on the vehicle supplement set and the real-time vehicle set includes the following steps:

[0125] Calculate the first charging time of the vehicles in the real-time vehicle set. The first charging time of the vehicle is equal to the difference between the full charge power of the vehicle and the real-time power of the vehicle divided by the charging speed of the vehicle;

[0126] Multiply and superimpose the first charging time of the vehicles in the real-time vehicle set by the target allocation coefficient of the vehicle to obtain the first congestion index;

[0127] Use the time synthesis formula to calculate the second charging time of the vehicles in the vehicle supplement set;

[0128] Multiply and superimpose the second charging time of the vehicles in the vehicle supplement set by the corresponding suspected probability and the target allocation coefficient of the vehicle in sequence to obtain the second congestion index;

[0129] Add the first congestion index and the second congestion index to obtain the charging congestion index at the charging module;

[0130] The time synthesis formula is as follows: ,

[0131] where B is the second charging time, c is the full charge of the vehicle, e and f are the endpoint values of the remaining power range of the vehicle, and g is the charging speed of the vehicle.

[0132] The calculation of the congestion coefficient should reflect the importance of the vehicle, that is, in the case of the same charging waiting time, the congestion coefficient of a more important vehicle is larger. Therefore, when calculating the charging congestion index, it is necessary to weight the charging time, and the weight is the target allocation coefficient of the vehicle;

[0133] The calculation of the first congestion index and the second congestion index is different. Because when calculating the first congestion index, the real-time power of the vehicles in the real-time vehicle set is known. Therefore, the charging time can be directly calculated. However, the real-time power of the vehicles in the vehicle replenishment set is unknown. Therefore, only the charging time can be estimated according to the charging habits of the vehicle users, and the average value of the calculation results of the endpoints of the remaining power range of the vehicle is used for approximate estimation.

[0134] Refer to Figure 7 As shown, the steps for calculating the redundant power of the charging pile include the following:

[0135] Superimpose the real-time charging powers of at least one charging module in the charging pile to obtain the total charging power;

[0136] Subtract the total charging power from the upper limit of the adjustable power to obtain the redundant power of the charging pile.

[0137] Refer to Figure 8 As shown, the steps for obtaining the charging power to be adjusted for the vehicles in the real-time vehicle set based on the charging vehicle recognition model, the charging distribution model, and the charging module sequence include the following:

[0138] Superimpose the charging congestion indices at the charging modules to obtain the overall charging congestion index;

[0139] Superimpose the target allocation coefficients of the vehicles in the real-time vehicle set corresponding to the charging modules to obtain the overall target allocation coefficient;

[0140] When the redundant power is greater than 0, divide the charging congestion index at the charging module by the overall charging congestion index and then multiply by the redundant power to obtain the power to be supplemented;

[0141] Divide the power to be supplemented by the overall target allocation coefficient and then multiply by the target allocation coefficient of the vehicles in the real-time vehicle set to obtain the power to be increased;

[0142] The power to be increased is added to the real-time charging power of the corresponding vehicle in the real-time vehicle set to obtain the charging power to be adjusted for the vehicle in the real-time vehicle set;

[0143] When the redundant power is equal to 0, divide the charging congestion index at the charging module by the overall charging congestion index and then multiply by the upper limit of the adjustable power to obtain the primary power;

[0144] Divide the primary power by the overall target allocation coefficient and then multiply by the target allocation coefficient of the vehicle in the real-time vehicle set to obtain the charging power to be adjusted;

[0145] According to the sorting of the charging module sequence, calculate the charging power to be adjusted for the vehicle in the real-time vehicle set corresponding to the charging module.

[0146] According to the different redundant powers, different situations are adjusted. When the redundant power is greater than 0, the redundant power is re-supplemented and distributed according to the charging congestion index at the charging module and the target allocation coefficient of the vehicle. However, when the redundant power is 0, it means that there is no spare charging power. Therefore, only the charging power can be re-distributed according to the charging congestion index at the charging module and the target allocation coefficient of the vehicle. According to this distribution method, more important vehicles are allocated more charging power.

[0147] The charging pile obtains the power to be allocated to the charging module according to the charging power to be adjusted, including the following steps:

[0148] Superimpose the charging power to be adjusted for the vehicle in the real-time vehicle set corresponding to the charging module to obtain the power to be allocated to the charging module.

[0149] Furthermore, this solution also proposes a storage medium, on which a computer-readable program is stored. When the computer-readable program is called, it executes the above-mentioned charging pile power dynamic allocation method for requirements and modular control integration.

[0150] It can be understood that the storage medium can be a magnetic medium, such as a floppy disk, a hard disk, a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid-state drive (SSD).

[0151] In summary, the advantages of the present invention are as follows: By establishing a charging vehicle identification model, a vehicle charging prediction model, a charging distribution model, obtaining a vehicle supplement set, and calculating the charging congestion index at the charging module, the vehicles are classified and identified. When performing charging adjustment, the subsequent charging situation is estimated, and based on the prediction results, the power is tilted preferentially to more important vehicles, so as to ensure that when the charging power is globally insufficient or locally insufficient, the impact on public vehicles is small, thereby limiting the scope of the negative impact caused by charging waiting.

[0152] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic allocation of charging stack power integrating demand and modular control, characterized in that: include: Obtain at least one charging module integrated in the charging stack, and obtain an upper limit of the adjustable power of the charging stack; Establish a charging vehicle identification model and a vehicle charging prediction model; Based on the charging vehicle identification model, a charging allocation model is established; Obtain the real-time vehicle set currently being charged at the charging module, and predict the subsequent charging vehicles of the charging module based on the vehicle charging prediction model to obtain a vehicle supplement set; Based on the vehicle supplement set and the real-time vehicle set, the charging congestion index at the charging module is calculated, and the charging modules are sorted from large to small according to the charging congestion index to obtain a charging module sequence; The redundant power of the charging stack is calculated, and based on the charging vehicle identification model, the charging allocation model and the charging module sequence, the charging power to be adjusted of the vehicles in the real-time vehicle set is obtained. The charging stack obtains the power to be allocated of the charging module according to the charging power to be adjusted, and the charging stack allocates the power of the charging module according to the value of the power to be allocated; The establishment of the charging vehicle identification model comprises the following steps: Acquire at least one sample vehicle for charging, classify the sample vehicles according to usage attributes, and obtain sample public vehicles and sample private vehicles, where the usage attributes are public attributes and private attributes; Obtain the usage purposes of the sample public vehicles, obtain the impact scope of the suspension of the sample public vehicles, and obtain the suspension costs of the businesses involved in the usage purposes of the sample public vehicles; Use the urgency comprehensive formula to calculate the urgency factor of the use; The value range of the urgency coefficient is taken as the urgency range; Evenly divide the urgency range into at least one identification interval, and assign grades to the identification intervals from small to large according to the midpoints of the identification intervals; The sample public vehicles corresponding to the usages with urgency coefficients in the identification interval are summarized into a feature set, and the feature set corresponds to the identification interval; Extracting features from the appearance of the sample public vehicles in the feature set to obtain at least one identification feature, summarizing the features into an identification feature set, and pairing the identification feature set with the identification interval corresponding to the feature set; Extract and summarize the features of the sample private vehicles to obtain a private feature set; Aggregating the private feature set, the identification feature set, and the levels of the identification interval into a charging vehicle identification model; The urgent comprehensive formula is as follows: , Among them, A is the urgency coefficient of the use purpose, a is the impact scope of the suspension of the sample public vehicle, and b is the suspension cost of the business involved in the use of the sample public vehicle.

2. According to claim 1, a method for dynamic allocation of charging stack power integrating demand and modular control, characterized in that: The establishment of the vehicle charging prediction model comprises the following steps: Based on historical data, the maximum distance traveled by the vehicle for charging is obtained as a characteristic distance; Obtain the position coordinates of the charging module, and based on the position coordinates, form a charging identification area, where the charging identification area is an area with the position coordinates as the center and the characteristic distance as the radius; Obtaining the charging habits of the vehicle user, the charging habits being the remaining power range of the vehicle when the vehicle is being charged and the full power of the vehicle, and obtaining the charging speed of the vehicle; Divide a day evenly into at least one time sampling point; Obtain at least one movement route of the vehicle, where the movement route is a travel path of the vehicle within a day; At the time sampling point, when the vehicle is not in the charging identification area, the charging probability of the vehicle is 0. When the vehicle is in the charging identification area, the distance the vehicle moves in the moving route is obtained, and the power consumption of the vehicle at the time sampling point is obtained according to the vehicle's benchmark power consumption. The benchmark power consumption is the power consumption of the vehicle traveling one kilometer. The full power of the vehicle minus the power consumption of the vehicle at the time sampling point is used to obtain the characteristic remaining power, and the characteristic remaining power is paired with the time sampling point; Count the number of characteristic remaining power matched with the time sampling point within the range of the vehicle's remaining power as the number of characteristics; The number of features is divided by the number of remaining power features paired with the time sampling point to obtain the charging probability of the vehicle; The time sampling points are paired and fitted with the charging probability of the vehicle to obtain a probability fitting function, which corresponds to the vehicle and the charging module.

3. The method for dynamic allocation of charging stack power integrating demand and modular control according to claim 2 is characterized in that: The establishment of a charging allocation model based on the charging vehicle identification model comprises the following steps: Classify vehicles according to usage attributes to obtain public vehicles and private vehicles, and the usage attributes are public attributes and private attributes; The number of sample private vehicles is used as the target allocation coefficient for private vehicles, and the total number of sample private vehicles and sample public vehicles is used as the preliminary allocation coefficient for public vehicles; Extracting features from the appearance of the public vehicle to obtain at least one actual feature, and assigning a level of the identification interval corresponding to the identification feature set containing the largest number of actual features to the public vehicle; Multiplying the preliminary allocation coefficient of the public vehicle by the grade of the public vehicle to obtain the target allocation coefficient of the public vehicle; When the vehicle is a public vehicle, the target allocation coefficient of the public vehicle is used as the target allocation coefficient of the vehicle; when the vehicle is a private vehicle, the target allocation coefficient of the private vehicle is used as the target allocation coefficient of the vehicle.

4. The method for dynamic allocation of charging stack power integrating demand and modular control according to claim 3 is characterized in that: The predicting of the subsequent charging vehicles of the charging module to obtain the vehicle supplement set includes the following steps: Substituting the current moment into at least one probability fitting function corresponding to the charging module to obtain at least one suspected probability; The vehicles corresponding to the probability fitting functions corresponding to the suspected probabilities greater than 0 are summarized to obtain a vehicle supplement set.

5. The method for dynamic allocation of charging stack power integrating demand and modular control according to claim 4 is characterized in that: The calculation of the charging congestion index at the charging module based on the vehicle supplement set and the real-time vehicle set includes the following steps: Calculating a first charging time of a vehicle in the real-time vehicle set, where the first charging time of the vehicle is equal to a difference between a full charge of the vehicle and a real-time charge of the vehicle divided by a charging speed of the vehicle; The first charging time of the vehicles in the real-time vehicle set is multiplied and superimposed by the target allocation coefficient of the vehicles to obtain a first congestion index; Calculating the second charging time of the vehicles in the vehicle replenishment set using the time comprehensive formula; The second charging time of the vehicle in the vehicle supplement set is multiplied by the corresponding suspected probability and the target allocation coefficient of the vehicle in sequence and then superimposed to obtain a second congestion index; The first congestion index is added to the second congestion index to obtain a charging congestion index at the charging module; The time synthesis formula is as follows: , Wherein, B is the second charging time, c is the full charge of the vehicle, e and f are the endpoint values ​​of the remaining power range of the vehicle respectively, and g is the charging speed of the vehicle.

6. The method for dynamic allocation of charging stack power integrating demand and modular control according to claim 5, characterized in that: The calculation of the redundant power of the charging stack comprises the following steps: Adding the real-time charging power of at least one charging module in the charging stack to obtain the total charging power; The redundant power of the charging stack is obtained by subtracting the total charging power from the adjustable power upper limit.

7. The method for dynamic allocation of charging stack power integrating demand and modular control according to claim 6 is characterized in that: The method of obtaining the charging power to be adjusted of the vehicles in the real-time vehicle set based on the charging vehicle identification model, the charging allocation model and the charging module sequence comprises the following steps: The charging congestion index at the charging module is superimposed to obtain the overall charging congestion index; The target allocation coefficients of the vehicles in the real-time vehicle set corresponding to the charging module are superimposed to obtain the overall target allocation coefficient; When the redundant power is greater than 0, the charging congestion index at the charging module is divided by the overall charging congestion index and then multiplied by the redundant power to obtain the power to be supplemented; The power to be supplemented is divided by the overall target allocation coefficient and then multiplied by the target allocation coefficient of the vehicles in the real-time vehicle set to obtain the power to be increased; The power to be increased is added to the real-time charging power of the corresponding vehicle in the real-time vehicle set to obtain the charging power to be adjusted of the vehicle in the real-time vehicle set; When the redundant power is equal to 0, the charging congestion index at the charging module is divided by the overall charging congestion index and then multiplied by the upper limit of the adjustable power to obtain the primary power; The primary power is divided by the overall target allocation coefficient and then multiplied by the target allocation coefficient of the vehicles in the real-time vehicle set to obtain the charging power to be adjusted; According to the order of the charging module sequence, the charging power to be adjusted of the vehicles in the real-time vehicle set corresponding to the charging module is calculated.

8. The method for dynamic allocation of charging stack power integrating demand and modular control according to claim 7, characterized in that: The charging stack obtains the power to be allocated to the charging module according to the charging power to be adjusted, comprising the following steps: The charging powers to be adjusted of the vehicles in the real-time vehicle set corresponding to the charging module are superimposed to obtain the power to be allocated to the charging module.

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