Charging pile direct current and alternating current intelligent regulation and control system and method

Abstract

The invention discloses a charging pile direct current and alternating current intelligent regulation and control system and method, and relates to the technical field of charging pile control, and the method comprises the steps: calculating the proportion of each charging mode in a charging station to generate an overall information entropy, calculating the proportion of each charging mode under different regulation and control strategies to generate a conditional information entropy, and carrying out the calculation of the overall information entropy; calculating the information gain of each regulation and control strategy according to the conditional information entropy and the overall information entropy, obtaining the abnormal mitigation degree of the charging station under each regulation and control strategy, analyzing the information gain and the abnormal mitigation degree through a joint algorithm, and generating an expression value for each regulation and control strategy, and selecting a regulation and control strategy according to the strategy list to regulate and control the direct current charging pile and the alternating current charging pile in the charging station. The regulation and control system dynamically generates an optimal regulation and control strategy according to the current condition of the charging station, the uncertainty of the power grid load of the charging station is effectively reduced, the regulation and control efficiency of the charging pile is improved, user requirements are met, and the stability of the power grid is protected.

Description

Technical Field

[0001] The present invention relates to the field of charging pile control technology, and in particular to a charging pile direct current and alternating current intelligent control system and method. Background Art

[0002] With the rapid development of the electric vehicle (EV) market, the demand for charging has increased dramatically, which has put forward higher requirements for the intelligence of charging infrastructure. Charging piles are divided into two categories: direct current (DC) and alternating current (AC), which are used for fast charging and slow charging respectively. DC charging piles directly convert the high-voltage AC power of the power grid into DC power to quickly replenish the battery power; AC charging piles provide AC power for electric vehicles, which is converted into DC power for charging through the on-board charger.

[0003] The prior art has the following deficiencies:

[0004] 1. In the existing charging pile regulation, load distribution often relies on fixed power settings or a single regulation rule, lacking a dynamic adjustment mechanism, which may lead to uneven grid load or overload during peak charging periods;

[0005] 2. The traditional charging pile control method responds slowly to the external environment (such as grid fluctuations and car owner needs), cannot effectively adapt to changes, lacks a dynamic control mechanism based on real-time data analysis, and the adaptability of the control strategy is limited, which cannot meet user needs.

[0006] Based on this, the present invention proposes an intelligent control system and method for direct and alternating current of charging piles, which dynamically generates an optimal control strategy according to the current status of the charging station, effectively reduces the uncertainty of the power grid load of the charging station, and improves the control efficiency of the charging piles, thereby meeting user needs and protecting the stability of the power grid. Summary of the invention

[0007] The purpose of the present invention is to provide a charging pile direct and alternating current intelligent control system and method to solve the deficiencies in the background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a method for intelligently controlling direct and alternating current of a charging pile, the method comprising the following steps:

[0009] The control system obtains the historical charging mode of the charging station, calculates the proportion of each charging mode in the charging station, and generates the overall information entropy;

[0010] Perform anomaly analysis on the charging station. When anomalies are found in the charging station, calculate the proportion of each charging mode under different control strategies to generate conditional information entropy. Calculate the information gain of each control strategy based on the conditional information entropy and the overall information entropy.

[0011] The degree of abnormality reduction of the charging station under each control strategy is obtained, and the information gain and the degree of abnormality reduction are analyzed through a joint algorithm to generate a performance value for each control strategy. All control strategies are sorted according to the performance value to generate a strategy list, and the control strategy is selected according to the strategy list to control the DC charging piles and AC charging piles in the charging station.

[0012] In a preferred embodiment, calculating the proportion of each charging mode in the charging station and generating the overall information entropy includes the following steps:

[0013] Obtain the number of fast charging mode, medium charging mode and slow charging mode in the history of the charging station for a period of time, sum the number of fast charging mode, medium charging mode and slow charging mode to obtain the total number of charging modes, obtain the proportion of fast charging mode by dividing the number of fast charging mode by the total number of charging modes, obtain the proportion of medium charging mode by dividing the number of medium charging mode by the total number of charging modes, and obtain the proportion of slow charging mode by dividing the number of slow charging mode by the total number of charging modes;

[0014] The overall information entropy is generated based on the proportion of fast charging mode, the proportion of medium charging mode, and the proportion of slow charging mode. The expression is: Where H(Z) is the overall information entropy, n is the number of charging modes, P i Indicates the proportion of the i-th charging mode.

[0015] In a preferred embodiment, performing abnormal analysis on a charging station includes the following steps:

[0016] Obtain the grid load, energy consumption increase deviation and power factor of the charging station, and calculate the grid load, energy consumption increase deviation and power factor to obtain the abnormal coefficient. The expression is:

[0017] In the formula, dzy s is the abnormal coefficient, gls is the power factor, dwz is the grid load, bhp is the energy consumption increase deviation, α, β, γ are the adjustment coefficients of power factor, grid load, and energy consumption increase deviation, and α, β, γ are all greater than 0;

[0018] The abnormality coefficient is compared with the preset abnormality threshold. If the abnormality coefficient is less than or equal to the abnormality threshold, it is analyzed that there is no abnormality in the charging station. If the abnormality coefficient is greater than the abnormality threshold, it is analyzed that there is an abnormality in the charging station.

[0019] In a preferred embodiment, the information gain of each control strategy is calculated based on the conditional information entropy and the overall information entropy, including the following steps:

[0020] After obtaining the conditional information entropy and the overall information entropy, the information gain of each control strategy is calculated, and the expression is: H(K│Z) i =H(Z)-H(K) i , where H(K│Z) i is the information gain of the ith control strategy, H(Z) is the overall information entropy, and H(K)i is the conditional information entropy of the ith control strategy.

[0021] In a preferred embodiment, the degree of abnormality reduction of the charging station under each control strategy is obtained, and the information gain and the degree of abnormality reduction are analyzed by a joint algorithm to generate a performance value for each control strategy, including the following steps:

[0022] Obtaining an initial abnormality coefficient of the charging station, which is an abnormality coefficient when analyzing the abnormality of the charging station, obtaining a current abnormality coefficient of the charging station after adopting the current control strategy, and subtracting the current abnormality coefficient from the initial abnormality coefficient to obtain an abnormality reduction value;

[0023] After analyzing the information gain and the degree of abnormality reduction through a joint algorithm, a performance value is generated for each control strategy, expressed as: bxz i =0.6*ycj i +0.4*H(K│Z) i , where bxz i is the performance value of the ith control strategy, ycj i is the abnormality reduction value of the i-th control strategy, H(K│Z) i is the information gain of the i-th control strategy;

[0024] All control strategies are sorted from large to small according to their performance values ​​to generate a strategy list, and the control strategy ranked first in the strategy list is selected to control the DC charging piles and AC charging piles in the charging station.

[0025] In a preferred embodiment, the control system obtains the historical charging mode of the charging station, including the following steps:

[0026] When there is a vehicle charging at the charging pile, the initial charging speed is first obtained according to the current charging stage, and the warning factor is obtained during the charging process, and the warning factor is compared with the preset first warning threshold and second warning threshold. The first warning threshold is used to determine whether there is an abnormality in the current charging process, and the second warning threshold is used to determine the severity of the abnormality in the current charging process, and the first warning threshold is less than the second warning threshold;

[0027] If the warning factor is less than or equal to the first warning threshold, it is determined that there is no abnormality in the current charging process, and the charging pile charges the vehicle at the initial charging speed;

[0028] If the warning factor is greater than the first warning threshold, and the warning factor is less than or equal to the second warning threshold, it is determined that there is a slight abnormality in the current charging process, and the initial charging speed is dynamically adjusted according to the warning factor. The adjustment algorithm is: In the formula, sd new is the adjusted charging speed, sd old is the initial charging speed, jsz is the warning factor, that is, the larger the warning factor is, the more it is necessary to reduce the initial charging speed;

[0029] If the warning factor is greater than the second warning threshold, it is determined that there is a serious abnormality in the current charging process, indicating that further charging is not supported, and the charging pile is controlled to stop charging the vehicle, and a warning signal is sent to the administrator;

[0030] After the initial charging speed of the charging pile is adjusted, the adjusted charging speed is compared with the preset first charging speed threshold and the second charging speed threshold, and the first charging speed threshold is less than the second charging speed threshold. If the adjusted charging speed is greater than the second charging speed threshold, the charging pile is judged to be in fast charging mode. If the adjusted charging speed is less than or equal to the second charging speed threshold, and the adjusted charging speed is greater than the first charging speed threshold, the charging pile is judged to be in medium-speed charging mode. If the adjusted charging speed is less than or equal to the first charging speed threshold, the charging pile is in slow charging mode.

[0031] After obtaining the historical charging modes of all charging piles in the charging station, the number of times each charging mode is used is recorded.

[0032] In a preferred embodiment, the processing logic of the warning factor is: real-time monitoring of the voltage fluctuation rate of the charging pile and the real-time temperature and temperature rise rate of the vehicle battery, normalizing the voltage fluctuation rate, real-time temperature and temperature rise rate, mapping the value range of the voltage fluctuation rate, real-time temperature and temperature rise rate to [0,1], and summing the normalized voltage fluctuation rate, real-time temperature and temperature rise rate to obtain the warning factor.

[0033] An intelligent control system for direct and alternating current of charging piles, including a charging station analysis module, a calculation module, and a control module;

[0034] Charging station analysis module: obtain the historical charging mode of the charging station, calculate the proportion of each charging mode in the charging station, and generate the overall information entropy;

[0035] Calculation module: Perform abnormal analysis on the charging station. When the charging station is abnormal, calculate the proportion of each charging mode under different control strategies to generate conditional information entropy. Calculate the information gain of each control strategy based on the conditional information entropy and the overall information entropy.

[0036] Control module: obtain the degree of abnormality reduction of the charging station under each control strategy, analyze the information gain and the degree of abnormality reduction through a joint algorithm, generate a performance value for each control strategy, sort all the control strategies according to the performance value and generate a strategy list, and select the control strategy according to the strategy list to control the DC charging piles and AC charging piles in the charging station.

[0037] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0038] The present invention generates overall information entropy by calculating the proportion of each charging mode in the charging station, performs abnormal analysis on the charging station, and when analyzing the abnormality of the charging station, calculates the proportion of each charging mode under different control strategies and generates conditional information entropy, calculates the information gain of each control strategy based on the conditional information entropy and the overall information entropy, and obtains the degree of abnormality reduction of the charging station under each control strategy, and generates a performance value for each control strategy after analyzing the information gain and the degree of abnormality reduction through a joint algorithm, sorts all the control strategies according to the performance value and generates a strategy list, and selects the control strategy according to the strategy list to control the DC charging pile and the AC charging pile in the charging station. The control system dynamically generates the optimal control strategy according to the current status of the charging station, effectively reduces the uncertainty of the power grid load of the charging station, and improves the control efficiency of the charging pile, which not only meets user needs but also protects the stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0040] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Example 1: Please refer to Figure 1 As shown, the embodiment of the present invention provides a method for intelligently controlling direct and alternating current of a charging pile, and the control method includes the following steps:

[0043] The control system obtains the historical charging mode of the charging station, which includes the fast charging mode, the medium charging mode and the slow charging mode (the charging piles here include AC charging piles and DC charging piles). The proportion of each charging mode in the charging station is calculated to generate the overall information entropy. The overall information entropy reflects the overall uncertainty of the charging station, and the charging station is analyzed for abnormalities. When the charging station is analyzed to have an abnormality, the proportion of each charging mode under different control strategies is calculated to generate the conditional information entropy. The information gain of each control strategy is calculated based on the conditional information entropy and the overall information entropy, and the degree of abnormality reduction of the charging station under each control strategy is obtained. After analyzing the information gain and the degree of abnormality reduction through a joint algorithm, a performance value is generated for each control strategy, and all control strategies are sorted according to the performance value to generate a strategy list. The control strategy is selected according to the strategy list to control the DC charging piles and AC charging piles in the charging station.

[0044] This application generates overall information entropy by calculating the proportion of each charging mode in the charging station, performs abnormal analysis on the charging station, and when analyzing the abnormality of the charging station, calculates the proportion of each charging mode under different control strategies and generates conditional information entropy, calculates the information gain of each control strategy based on the conditional information entropy and the overall information entropy, and obtains the degree of abnormality reduction of the charging station under each control strategy, and generates a performance value for each control strategy after analyzing the information gain and the degree of abnormality reduction through a joint algorithm, sorts all the control strategies according to the performance value and generates a strategy list, and selects the control strategy according to the strategy list to control the DC charging piles and AC charging piles in the charging station. The control system dynamically generates the optimal control strategy according to the current status of the charging station, effectively reduces the uncertainty of the power grid load of the charging station, and improves the control efficiency of the charging piles, which not only meets user needs but also protects the stability of the power grid.

[0045] Embodiment 2: The control system obtains the historical charging mode of the charging station, the charging mode includes the charging pile being in a fast charging mode, the charging pile being in a medium charging mode, and the charging pile being in a slow charging mode (the charging pile here includes an AC charging pile and a DC charging pile), including the following steps:

[0046] Whether it is an AC slow charging station or a DC fast charging station, when charging a vehicle, the corresponding charging stage will be generated according to the remaining power of the vehicle's current battery, including the pre-charging stage, the constant current charging stage and the trickle charging stage;

[0047] At each charging stage, the vehicle will be charged at the initial charging speed (i.e., the preset optimal charging speed) when all monitored variables remain unchanged;

[0048] When there is a vehicle charging at the charging pile, the initial charging speed is first obtained according to the current charging stage, and during the charging process, the voltage fluctuation rate of the charging pile and the real-time temperature and temperature rise rate of the vehicle battery are monitored in real time, and the voltage fluctuation rate, real-time temperature and temperature rise rate are normalized so that the value range of the voltage fluctuation rate, real-time temperature and temperature rise rate is mapped to between [0,1], and the normalized voltage fluctuation rate, real-time temperature and temperature rise rate are summed to obtain the warning factor. The larger the warning factor is, the more serious the abnormality of the current charging process is. The warning factor is compared with the preset first warning threshold and the second warning threshold. The first warning threshold is used to determine whether there is an abnormality in the current charging process, and the second warning threshold is used to determine the severity of the abnormality of the current charging process, and the first warning threshold is less than the second warning threshold;

[0049] If the warning factor is less than or equal to the first warning threshold, it is determined that there is no abnormality in the current charging process, and the charging pile charges the vehicle at the initial charging speed;

[0050] If the warning factor is greater than the first warning threshold, and the warning factor is less than or equal to the second warning threshold, it is determined that there is a slight abnormality in the current charging process, and the initial charging speed is dynamically adjusted according to the warning factor. The adjustment algorithm is: In the formula, sd new is the adjusted charging speed, sd old is the initial charging speed, jsz is the warning factor, that is, the larger the warning factor is, the more it is necessary to reduce the initial charging speed;

[0051] If the warning factor is greater than the second warning threshold, it is determined that there is a serious abnormality in the current charging process, indicating that further charging is not supported. The charging pile is controlled to stop charging the vehicle and a warning signal is sent to the administrator.

[0052] After the initial charging speed of the charging pile is adjusted, the adjusted charging speed is compared with the preset first charging speed threshold and the second charging speed threshold, and the first charging speed threshold is less than the second charging speed threshold. If the adjusted charging speed is greater than the second charging speed threshold, the charging pile is judged to be in fast charging mode. If the adjusted charging speed is less than or equal to the second charging speed threshold, and the adjusted charging speed is greater than the first charging speed threshold, the charging pile is judged to be in medium-speed charging mode. If the adjusted charging speed is less than or equal to the first charging speed threshold, the charging pile is in slow charging mode.

[0053] In this application, the method for determining the charging mode of an AC charging pile and a DC charging pile is the same as above and will not be described in detail here.

[0054] After obtaining the historical charging modes of all charging piles in the charging station, the number of times each charging mode is used is recorded.

[0055] Taking AC charging pile and DC charging pile as examples, the initial charging speed of each charging stage is as follows:

[0056] 1) AC charging pile

[0057] AC charging piles are usually low power (for example, 3.3kW to 22kW). The following are common initial charging speed configurations:

[0058] 1.1) Pre-charge stage

[0059] Initial charging speed: usually 0.1C (C is the multiple of the battery's rated capacity), that is, 10% of the battery capacity is the initial current. Power range: 0.3kW-1kW. Purpose: To avoid using excessive current when the battery is low, and gradually increase the battery voltage.

[0060] 1.2) Constant current charging stage

[0061] Initial charging speed: about 0.2C to 0.3C (20%-30% of battery capacity as constant charging current). Power range: 1kW-3kW (usually the upper limit of AC charging pile power). Purpose: Fast charging at constant current to increase battery power to a safe range.

[0062] 1.3) Trickle charging stage

[0063] Initial charging speed: usually 0.05C (5% battery capacity rate) or lower. Power range: about 0.1kW-0.3kW. Purpose: to slow down the charging speed, reduce the current impact after the battery is fully charged, and protect the battery life.

[0064] 2) DC charging pile

[0065] The power of DC charging piles is higher (usually 30kW to 350kW or even higher), so the charging speed is also faster. The following are common initial charging speed configurations:

[0066] 2.1) Pre-charge stage

[0067] Initial charging speed: about 0.1C. Power range: 3kW-10kW. Purpose: To avoid using high current when the battery voltage is low, to steadily increase the battery voltage, and to ensure that the battery is in a state where it can be charged safely and quickly.

[0068] 2.2) Constant current charging stage

[0069] Initial charging speed: usually 0.5C to 1C (50%-100% rate of battery capacity), which can be dynamically adjusted to adapt to the battery status in actual applications. Power range: 30kW-150kW. Purpose: Fast charging at high power, this stage usually occupies most of the charging time.

[0070] 2.3) Trickle charging stage

[0071] Initial charging speed: about 0.05C to 0.1C. Power range: 5kW-10kW. Purpose: To ensure that the current slows down at the end of charging to prevent overcharging, and to replenish the last part of the power to fully charge the battery.

[0072] After calculating the proportion of each charging mode in the charging station, the overall information entropy is generated. The overall information entropy reflects the overall uncertainty of the charging station, including the following steps:

[0073] The charging modes include a fast charging mode for the charging pile, a medium charging mode for the charging pile, and a slow charging mode for the charging pile;

[0074] Obtain the number of fast charging mode, medium charging mode and slow charging mode in the history of the charging station for a period of time (which can be one week or one month), sum the number of fast charging mode, the number of medium charging mode and the number of slow charging mode to obtain the total number of charging modes, obtain the proportion of fast charging mode by dividing the number of fast charging mode by the total number of charging modes, obtain the proportion of medium charging mode by dividing the number of medium charging mode by the total number of charging modes, and obtain the proportion of slow charging mode by dividing the number of slow charging mode by the total number of charging modes;

[0075] The overall information entropy is generated based on the proportion of fast charging mode, the proportion of medium charging mode, and the proportion of slow charging mode. The expression is: Where H(Z) is the overall information entropy, n is the number of charging modes, P i It represents the proportion of the i-th charging mode. The larger the value of the overall information entropy is, the greater the overall uncertainty of the charging station is.

[0076] Performing abnormal analysis on the charging station includes the following steps:

[0077] Obtain the grid load, energy consumption increase deviation and power factor of the charging station, and calculate the grid load, energy consumption increase deviation and power factor to obtain the abnormal coefficient. The expression is:

[0078] In the formula, dzy s is the abnormal coefficient, gls is the power factor, dwz is the grid load, bhp is the energy consumption increase deviation, α, β, γ are the adjustment coefficients of power factor, grid load, and energy consumption increase deviation, and α, β, γ are all greater than 0;

[0079] The abnormality coefficient is compared with the preset abnormality threshold. If the abnormality coefficient is less than or equal to the abnormality threshold, it is analyzed that there is no abnormality in the charging station. If the abnormality coefficient is greater than the abnormality threshold, it is analyzed that there is an abnormality in the charging station.

[0080] When analyzing the abnormality of the charging station, the proportion of each charging mode under different control strategies is calculated to generate conditional information entropy, including the following steps:

[0081] In this application, the control strategies include limiting fast charging power, extending slow charging time, dynamic load distribution, and energy storage system support;

[0082] Under the current control strategy, obtain the number of each charging mode, including the number of fast charging mode, the number of medium-speed charging mode and the number of slow charging mode, sum the number of fast charging mode, the number of medium-speed charging mode and the number of slow charging mode to obtain the total number of charging modes, obtain the proportion of fast charging mode by dividing the number of fast charging mode by the total number of charging modes, obtain the proportion of medium-speed charging mode by dividing the number of medium-speed charging mode by the total number of charging modes, and obtain the proportion of slow charging mode by dividing the number of slow charging mode by the total number of charging modes;

[0083] The conditional information entropy of the control strategy is generated according to the proportion of fast charging mode, medium charging mode and slow charging mode, and the expression is: In the formula, H(K) is the conditional information entropy, n is the number of charging modes, K i It represents the proportion of the i-th charging mode under the current control mode. The larger the value of conditional information entropy is, the greater the uncertainty of the current control strategy is.

[0084] The information gain of each control strategy is calculated based on the conditional information entropy and the overall information entropy, including the following steps:

[0085] After obtaining the conditional information entropy and the overall information entropy, the information gain of each control strategy is calculated, and the expression is: H(K│Z) i =H(Z)-H(K) i , where H(K│Z) i is the information gain of the ith control strategy, H(Z) is the overall information entropy, and H(K)i is the conditional information entropy of the ith control strategy;

[0086] Greater information gain means that the strategy significantly optimizes the operating status of the charging station during the adjustment process, reduces load fluctuations and resource waste, and significantly improves the overall performance of the charging station.

[0087] A smaller information gain indicates that the strategy has limited optimization effect on the system and may only make slight adjustments to some charging demands, failing to effectively alleviate the grid load or the pressure on charging stations.

[0088] The degree of abnormality reduction of the charging station under each control strategy is obtained, and the information gain and the degree of abnormality reduction are analyzed by a joint algorithm to generate a performance value for each control strategy, including the following steps:

[0089] Obtaining the initial abnormality coefficient of the charging station, which is the abnormality coefficient when analyzing the abnormality of the charging station, obtaining the current abnormality coefficient of the charging station after adopting the current control strategy, and subtracting the current abnormality coefficient from the initial abnormality coefficient to obtain the abnormality reduction value. The larger the abnormality reduction value, the greater the degree of abnormality reduction of the charging station after adopting the current control strategy;

[0090] After analyzing the information gain and the degree of abnormality reduction through a joint algorithm, a performance value is generated for each control strategy, expressed as: bxz i =0.6*ycj i +0.4*H(K│Z) i , where bxz i is the performance value of the ith control strategy, ycj i is the abnormality reduction value of the i-th control strategy, H(K│Z) i is the information gain of the i-th control strategy.

[0091] The larger the performance value, the greater the effect of the control strategy applied in the charging station. All control strategies are sorted from large to small according to the performance value to generate a strategy list. The control strategy ranked first in the strategy list is selected to control the DC charging piles and AC charging piles in the charging station. The charging station status is monitored regularly and the strategy list is dynamically updated, as well as the control strategy is replaced.

[0092] Embodiment 3: The intelligent control system for direct and alternating current of a charging pile described in this embodiment includes a charging station analysis module, a calculation module, and a control module;

[0093] Charging station analysis module: obtain the historical charging mode of the charging station, which includes the fast charging mode, medium charging mode and slow charging mode (the charging piles here include AC charging piles and DC charging piles), calculate the proportion of each charging mode in the charging station, and then generate the overall information entropy. The overall information entropy reflects the overall uncertainty of the charging station, and the overall information entropy is sent to the control module;

[0094] Calculation module: Perform abnormal analysis on the charging station. When an abnormality is found in the charging station, the proportion of each charging mode under different control strategies is calculated to generate conditional information entropy. The information gain of each control strategy is calculated based on the conditional information entropy and the overall information entropy, and the information gain is sent to the control module.

[0095] Control module: obtain the degree of abnormality reduction of the charging station under each control strategy, analyze the information gain and the degree of abnormality reduction through a joint algorithm, generate a performance value for each control strategy, sort all the control strategies according to the performance value and generate a strategy list, and select the control strategy according to the strategy list to control the DC charging piles and AC charging piles in the charging station.

[0096] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0097] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0098] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0099] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0100] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for intelligently controlling direct and alternating current of a charging pile, characterized in that: The control method comprises the following steps: The control system obtains the historical charging mode of the charging station, calculates the proportion of each charging mode in the charging station, and generates the overall information entropy; Perform anomaly analysis on the charging station. When anomalies are found in the charging station, calculate the proportion of each charging mode under different control strategies to generate conditional information entropy. Calculate the information gain of each control strategy based on the conditional information entropy and the overall information entropy. The degree of abnormality reduction of the charging station under each control strategy is obtained, and the information gain and the degree of abnormality reduction are analyzed through a joint algorithm to generate a performance value for each control strategy. All control strategies are sorted according to the performance value to generate a strategy list, and the control strategy is selected according to the strategy list to control the DC charging piles and AC charging piles in the charging station.

2. The method for intelligently controlling direct and alternating current of a charging pile according to claim 1, characterized in that: After calculating the proportion of each charging mode in the charging station, the overall information entropy is generated, including the following steps: Obtain the number of fast charging mode, medium charging mode and slow charging mode in the history of the charging station for a period of time, sum the number of fast charging mode, medium charging mode and slow charging mode to obtain the total number of charging modes, obtain the proportion of fast charging mode by dividing the number of fast charging mode by the total number of charging modes, obtain the proportion of medium charging mode by dividing the number of medium charging mode by the total number of charging modes, and obtain the proportion of slow charging mode by dividing the number of slow charging mode by the total number of charging modes; The overall information entropy is generated based on the proportion of fast charging mode, the proportion of medium charging mode, and the proportion of slow charging mode. The expression is: Where H(Z) is the overall information entropy, n is the number of charging modes, P i Indicates the proportion of the i-th charging mode.

3. A charging pile direct and alternating current intelligent control method according to claim 2, characterized in that: Performing abnormal analysis on the charging station includes the following steps: Obtain the grid load, energy consumption increase deviation and power factor of the charging station, and calculate the grid load, energy consumption increase deviation and power factor to obtain the abnormal coefficient. The expression is: In the formula, dzy s is the abnormal coefficient, gls is the power factor, dwz is the grid load, nhp is the energy consumption increase deviation, α, β, γ are the adjustment coefficients of power factor, grid load, and energy consumption increase deviation, and α, β, γ are all greater than 0; The abnormality coefficient is compared with the preset abnormality threshold. If the abnormality coefficient is less than or equal to the abnormality threshold, it is analyzed that there is no abnormality in the charging station. If the abnormality coefficient is greater than the abnormality threshold, it is analyzed that there is an abnormality in the charging station.

4. The method for intelligently controlling direct and alternating current of a charging pile according to claim 3, characterized in that: The information gain of each control strategy is calculated based on the conditional information entropy and the overall information entropy, including the following steps: After obtaining the conditional information entropy and the overall information entropy, the information gain of each control strategy is calculated, and the expression is: H(K|Z) i =H(Z)-H(K) i , where H(K|Z) i is the information gain of the ith control strategy, H(Z) is the overall information entropy, and H(K)i is the conditional information entropy of the ith control strategy.

5. The method for intelligently controlling direct and alternating current of a charging pile according to claim 4, characterized in that: Obtain the degree of abnormality reduction of the charging station under each control strategy, analyze the information gain and the degree of abnormality reduction through a joint algorithm, and generate a performance value for each control strategy, including the following steps: Obtaining an initial abnormality coefficient of the charging station, which is an abnormality coefficient when analyzing the abnormality of the charging station, obtaining a current abnormality coefficient of the charging station after adopting the current control strategy, and subtracting the current abnormality coefficient from the initial abnormality coefficient to obtain an abnormality reduction value; After analyzing the information gain and the degree of abnormality reduction through a joint algorithm, a performance value is generated for each control strategy, expressed as: bxz i =0.6*ycj i +0.4*H(K|Z) i , where bxz i is the performance value of the ith control strategy, ycj i is the abnormality reduction value of the ith control strategy, H(K|Z) i is the information gain of the i-th control strategy; All control strategies are sorted from large to small according to their performance values ​​to generate a strategy list, and the control strategy ranked first in the strategy list is selected to control the DC charging piles and AC charging piles in the charging station.

6. A charging pile direct and alternating current intelligent control method according to claim 5, characterized in that: The control system obtains the historical charging mode of the charging station, including the following steps: When there is a vehicle charging at the charging pile, the initial charging speed is first obtained according to the current charging stage, and the warning factor is obtained during the charging process, and the warning factor is compared with the preset first warning threshold and second warning threshold. The first warning threshold is used to determine whether there is an abnormality in the current charging process, and the second warning threshold is used to determine the severity of the abnormality in the current charging process, and the first warning threshold is less than the second warning threshold; If the warning factor is less than or equal to the first warning threshold, it is determined that there is no abnormality in the current charging process, and the charging pile charges the vehicle at the initial charging speed; If the warning factor is greater than the first warning threshold, and the warning factor is less than or equal to the second warning threshold, it is determined that there is a slight abnormality in the current charging process, and the initial charging speed is dynamically adjusted according to the warning factor. The adjustment algorithm is: In the formula, sd new is the adjusted charging speed, sd old is the initial charging speed, jsz is the warning factor, that is, the larger the warning factor is, the more it is necessary to reduce the initial charging speed; If the warning factor is greater than the second warning threshold, it is determined that there is a serious abnormality in the current charging process, indicating that further charging is not supported, and the charging pile is controlled to stop charging the vehicle, and a warning signal is sent to the administrator; After the initial charging speed of the charging pile is adjusted, the adjusted charging speed is compared with the preset first charging speed threshold and the second charging speed threshold, and the first charging speed threshold is less than the second charging speed threshold. If the adjusted charging speed is greater than the second charging speed threshold, the charging pile is judged to be in fast charging mode. If the adjusted charging speed is less than or equal to the second charging speed threshold, and the adjusted charging speed is greater than the first charging speed threshold, the charging pile is judged to be in medium-speed charging mode. If the adjusted charging speed is less than or equal to the first charging speed threshold, the charging pile is in slow charging mode. After obtaining the historical charging modes of all charging piles in the charging station, the number of times each charging mode is used is recorded.

7. A charging pile direct and alternating current intelligent control method according to claim 6, characterized in that: The processing logic of the warning factor is: real-time monitoring of the voltage fluctuation rate of the charging pile and the real-time temperature and temperature rise rate of the vehicle battery, normalizing the voltage fluctuation rate, real-time temperature and temperature rise rate, mapping the value range of the voltage fluctuation rate, real-time temperature and temperature rise rate to [0,1], and summing the normalized voltage fluctuation rate, real-time temperature and temperature rise rate to obtain the warning factor.

8. An intelligent control system for direct and alternating current of a charging pile, used to implement the control method according to any one of claims 1 to 7, characterized in that: It includes charging station analysis module, calculation module and control module; Charging station analysis module: obtain the historical charging mode of the charging station, calculate the proportion of each charging mode in the charging station, and generate the overall information entropy; Calculation module: Perform abnormal analysis on the charging station. When the charging station is abnormal, calculate the proportion of each charging mode under different control strategies to generate conditional information entropy. Calculate the information gain of each control strategy based on the conditional information entropy and the overall information entropy. Control module: obtain the degree of abnormality reduction of the charging station under each control strategy, analyze the information gain and the degree of abnormality reduction through a joint algorithm, generate a performance value for each control strategy, sort all the control strategies according to the performance value and generate a strategy list, and select the control strategy according to the strategy list to control the DC charging piles and AC charging piles in the charging station.

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