A Direct and Alternating Current Intelligent Regulation System and Method for Charging Piles

Abstract

The present invention discloses a direct and alternating current intelligent regulation system and method for charging piles, which relates to the technical field of charging pile control. By calculating the proportion of each charging mode in the charging station to generate the overall information entropy, calculating the proportion of each charging mode under different regulation strategies to generate the conditional information entropy, calculating the information gain of each regulation strategy based on the conditional information entropy and the overall information entropy, and obtaining the degree of anomaly reduction of the charging station under each regulation strategy, after analyzing the information gain and the degree of anomaly reduction through a joint algorithm, a performance value is generated for each regulation strategy, and the regulation strategy is selected according to the strategy list to regulate the DC charging piles and AC charging piles in the charging station. The regulation system dynamically generates the optimal regulation strategy according to the current situation of the charging station, effectively reduces the uncertainty of the grid load of the charging station, improves the regulation efficiency of the charging piles, meets the user needs and protects the grid stability.

Description

Technical Field

[0001] The present invention relates to the technical field of charging pile control, and particularly relates to a DC and AC intelligent regulation system and method for charging piles. Background Art

[0002] With the rapid development of the electric vehicle (EV) market, the charging demand has increased sharply, posing higher requirements for the intelligence of charging infrastructure. Charging piles are divided into two categories: direct current (DC) and alternating current (AC) charging piles, which are used for fast charging and slow charging respectively. The DC charging pile directly converts the high-voltage alternating current from the power grid into direct current to quickly replenish the battery power; the AC charging pile provides alternating current for the electric vehicle and converts it into direct current through an on-vehicle charger for charging.

[0003] The prior art has the following deficiencies:

[0004] 1. In the existing charging pile regulation, the load distribution often depends on fixed power settings or single regulation rules, lacking a dynamic adjustment mechanism, resulting in uneven or overloaded power grid loads during the charging peak period;

[0005] 2. The regulation methods of traditional charging piles respond slowly to external environments (such as power grid fluctuations and vehicle owner demands), cannot effectively adapt to changes, lack a dynamic regulation mechanism based on real-time data analysis, and the adaptability of regulation strategies is limited, unable to meet user needs.

[0006] Based on this, the present invention proposes a DC and AC intelligent regulation system and method for charging piles, which dynamically generates an optimal regulation strategy according to the current situation of the charging station, effectively reduces the uncertainty of the power grid load of the charging station, improves the regulation efficiency of the charging pile, meets user needs, and protects the stability of the power grid. Summary of the Invention

[0007] The purpose of the present invention is to provide a DC and AC intelligent regulation system and method for charging piles to solve the deficiencies in the background art.

[0008] To achieve the above purpose, the present invention provides the following technical solution: A DC and AC intelligent regulation method for charging piles, the regulation method includes the following steps:

[0009] The regulation system obtains the historical charging modes 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 it is analyzed that the charging station has an anomaly, calculate the proportion of each charging mode under different regulation strategies and generate the conditional information entropy, and calculate the information gain of each regulation strategy based on the conditional information entropy and the overall information entropy;

[0011] Obtain the degree of anomaly reduction of the charging station under each regulation strategy. After analyzing the information gain and the degree of anomaly reduction through a joint algorithm, generate a performance value for each regulation strategy. Sort all the regulation strategies according to the performance value to generate a strategy list, and select a regulation strategy according to the strategy list to regulate the DC charging piles and AC charging piles in the charging station.

[0012] In a preferred embodiment, calculate the overall information entropy after obtaining the proportion of each charging mode in the charging station, including the following steps:

[0013] Obtain the number of fast charging mode times, medium-speed charging mode times, and slow charging mode times in the charging station over a period of history. After summing up the number of fast charging mode times, medium-speed charging mode times, and slow charging mode times, obtain the total number of charging mode times. Obtain the proportion of the fast charging mode by dividing the number of fast charging mode times by the total number of charging mode times, obtain the proportion of the medium-speed charging mode by dividing the number of medium-speed charging mode times by the total number of charging mode times, and obtain the proportion of the slow charging mode by dividing the number of slow charging mode times by the total number of charging mode times;

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

[0015] In a preferred embodiment, perform anomaly analysis on the charging station, including the following steps:

[0016] Obtain the grid load, energy consumption increase deviation, and power factor of the charging station. Calculate the anomaly coefficient by comprehensively calculating the grid load, energy consumption increase deviation, and power factor. The expression is:

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

[0018] Compare the anomaly coefficient with a preset anomaly threshold. If the anomaly coefficient is less than or equal to the anomaly threshold, it is analyzed that the charging station has no anomaly. If the anomaly coefficient is greater than the anomaly threshold, it is analyzed that the charging station has an anomaly.

[0019] In a preferred embodiment, calculate the information gain of each regulation strategy 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, calculate the information gain of each control strategy. The expression is: H(K|Z) i = H(Z) - H(K) i , where H(K|Z) i is the information gain of the i-th control strategy, H(Z) is the overall information entropy, and H(K) i is the conditional information entropy of the i-th control strategy.

[0021] In a preferred embodiment, obtain the degree of anomaly reduction of the charging station under each control strategy. After analyzing the information gain and the degree of anomaly reduction through a joint algorithm, generate a performance value for each control strategy, including the following steps:

[0022] Obtain the initial anomaly coefficient of the charging station. The initial anomaly coefficient is the anomaly coefficient when analyzing that the charging station has an anomaly. Obtain the current anomaly coefficient of the charging station after adopting the current control strategy, and subtract the current anomaly coefficient from the initial anomaly coefficient to obtain the anomaly reduction value;

[0023] After analyzing the information gain and the degree of anomaly reduction through a joint algorithm, generate a performance value for each control strategy. The expression is: bxz i = 0.6 * ycj i + 0.4 * H(K|Z) i , where bxz i is the performance value of the i-th control strategy, ycj i is the anomaly reduction value of the i-th control strategy, and H(K|Z) i is the information gain of the i-th control strategy;

[0024] Sort all control strategies according to the performance value from large to small to generate a strategy list, and select the control strategy ranked first in the strategy list 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, first obtain the initial charging speed according to the current charging stage, and obtain the warning factor during the charging process. Compare the warning factor with a preset first warning threshold and a second warning threshold. The first warning threshold is used to determine whether there is an anomaly in the current charging process, and the second warning threshold is used to determine the severity of the anomaly 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 anomaly 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 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 as follows: In the formula, sd new is the adjusted charging speed, sd old is the initial charging speed, and jsz is the warning factor. That is, the larger the warning factor, the more necessary it is 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 continued charging is not supported. 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, it is determined that the charging pile is in the fast charging mode. If the adjusted charging speed is less than or equal to the second charging speed threshold and greater than the first charging speed threshold, it is determined that the charging pile is in the medium charging mode. If the adjusted charging speed is less than or equal to the first charging speed threshold, the charging pile is in the slow charging mode;

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

[0032] In a preferred embodiment, the processing logic of the warning factor is as follows: real-time monitor the voltage volatility of the charging pile, the real-time temperature of the vehicle battery, and the temperature rise rate, perform normalization processing on the voltage volatility, real-time temperature, and temperature rise rate, map the value ranges of the voltage volatility, real-time temperature, and temperature rise rate to between [0, 1], and sum the normalized voltage volatility, real-time temperature, and temperature rise rate to obtain the warning factor.

[0033] An intelligent regulation system for direct and alternating current of a charging pile includes a charging station analysis module, a calculation module, and a regulation module;

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

[0035] Calculation module: Conduct abnormal analysis on the charging station. When it is analyzed that there is an abnormality in the charging station, calculate the proportion of each charging mode under different regulation strategies and generate the conditional information entropy, and calculate the information gain of each regulation strategy based on the conditional information entropy and the overall information entropy;

[0036] Regulation module: Obtain the degree of anomaly mitigation of the charging station under each regulation strategy. After analyzing the information gain and the degree of anomaly mitigation through a joint algorithm, generate a performance value for each regulation strategy. Sort all the regulation strategies according to the performance value to generate a strategy list, and select a regulation strategy according to the strategy list to regulate 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 as follows:

[0038] The present invention generates the overall information entropy by calculating the proportion of each charging mode in the charging station, and performs anomaly analysis on the charging station. When it is analyzed that the charging station has an anomaly, calculate the proportion of each charging mode under different regulation strategies to generate the conditional information entropy. Calculate the information gain of each regulation strategy based on the conditional information entropy and the overall information entropy, and obtain the degree of anomaly mitigation of the charging station under each regulation strategy. After analyzing the information gain and the degree of anomaly mitigation through a joint algorithm, generate a performance value for each regulation strategy. Sort all the regulation strategies according to the performance value to generate a strategy list, and select a regulation strategy according to the strategy list to regulate the DC charging piles and AC charging piles in the charging station. The regulation system dynamically generates the optimal regulation strategy according to the current situation of the charging station, effectively reduces the uncertainty of the power grid load of the charging station, improves the regulation efficiency of the charging piles, meets the user's needs, and protects the stability of the power grid. Description of the Drawings

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

[0040] Figure 1 It is a flowchart of the method of the present invention. Detailed Embodiments

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0042] Embodiment 1: Please refer to Figure 1 As shown, in this embodiment, a method for intelligent regulation of DC and AC power of charging piles is provided, and the regulation method includes the following steps:

[0043] The control system obtains the historical charging modes of the charging station. The charging modes include that the charging pile is in the fast charging mode, the charging pile is in the medium-speed charging mode, and the charging pile is in the slow charging mode (the charging piles here include AC charging piles and DC charging piles). 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. Anomaly analysis is performed on the charging station. When it is analyzed that there is an anomaly in the charging station, 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 anomaly reduction of the charging station under each control strategy is obtained. After analyzing the information gain and the degree of anomaly reduction through a joint algorithm, a performance value is generated 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.

[0044] In this application, after calculating the proportion of each charging mode in the charging station, the overall information entropy is generated. Anomaly analysis is performed on the charging station. When it is analyzed that there is an anomaly in the charging station, 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 anomaly reduction of the charging station under each control strategy is obtained. After analyzing the information gain and the degree of anomaly reduction through a joint algorithm, a performance value is generated 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. The control system dynamically generates the optimal control strategy according to the current situation of the charging station, effectively reducing the uncertainty of the power grid load of the charging station and improving the control efficiency of the charging piles, which not only meets the user's needs but also protects the stability of the power grid.

[0045] Embodiment 2: The control system obtains the historical charging modes of the charging station. The charging modes include that the charging pile is in the fast charging mode, the charging pile is in the medium-speed charging mode, and the charging pile is in the slow charging mode (the charging piles here include AC charging piles and DC charging piles), and the method includes the following steps:

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

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

[0048] When there is a vehicle charging at the charging pile, first obtain the initial charging speed according to the current charging stage. During the charging process, monitor the voltage volatility of the charging pile, the real-time temperature of the vehicle battery, and the temperature rising rate in real time. Normalize the voltage volatility, real-time temperature, and temperature rising rate so that the value ranges of the voltage volatility, real-time temperature, and temperature rising rate are mapped to between [0, 1]. Sum the normalized voltage volatility, real-time temperature, and temperature rising rate to obtain a warning factor. The larger the warning factor, the more serious the abnormality in the current charging process. Compare the warning factor with a preset first warning threshold and a 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;

[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 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 through the warning factor. The adjustment algorithm is: In the formula, sd new is the adjusted charging speed, sd old is the initial charging speed, and jsz is the warning factor, that is, the larger the warning factor, the more the initial charging speed needs to be reduced;

[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 continued charging is not supported. Control the charging pile to stop charging the vehicle and send a warning signal to the administrator.

[0052] After adjusting the initial charging speed of the charging pile, compare the adjusted charging speed with a preset first charging speed threshold and a 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, it is determined that the charging pile is in the fast charging mode. If the adjusted charging speed is less than or equal to the second charging speed threshold and greater than the first charging speed threshold, it is determined that the charging pile is in the 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 the slow charging mode.

[0053] In this application, the judgment methods for the charging modes of AC charging piles and DC charging piles are the same as above, and will not be elaborated one by one here.

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

[0055] Taking AC charging piles and DC charging piles as examples, the initial charging speeds at each charging stage are as follows:

[0056] 1) AC charging pile

[0057] AC charging piles usually have relatively low power (ranging from 3.3 kW to 22 kW for example), and the following are common initial charging speed configurations:

[0058] 1.1) Pre-charging 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 as the initial current. Power range: 0.3 kW - 1 kW. Purpose: To avoid using too large a current when the battery has a low charge and gradually increase the battery voltage.

[0060] 1.2) Constant current charging stage

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

[0062] 1.3) Trickle charging stage

[0063] Initial charging speed: Usually 0.05C (5% multiple of the battery capacity) or lower. Power range: Approximately 0.1 kW - 0.3 kW. Purpose: To slow down the charging speed, reduce the current impact after the battery is full, and protect the battery life.

[0064] 2) DC charging pile

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

[0066] 2.1) Pre-charging stage

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

[0068] 2.2) Constant current charging stage

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

[0070] 2.3) Trickle Charging Stage

[0071] Initial charging speed: approximately 0.05C to 0.1C. Power range: 5kW - 10kW. Purpose: Ensure that the current slows down at the end of charging to prevent overcharging, while replenishing the last part of the charge to fully charge the battery.

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

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

[0074] Obtain the number of times of fast charging mode, medium charging mode, and slow charging mode in the charging station over a certain period of history (which can be one week or one month). After summing up the number of times of fast charging mode, medium charging mode, and slow charging mode, obtain the total number of charging modes. Obtain the proportion of fast charging mode by dividing the number of times of fast charging mode by the total number of charging modes, obtain the proportion of medium charging mode by dividing the number of times of medium charging mode by the total number of charging modes, and obtain the proportion of slow charging mode by dividing the number of times of slow charging mode by the total number of charging modes;

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

[0076] Conduct anomaly analysis on the charging station, including the following steps:

[0077] Obtain the grid load, energy consumption increase deviation, and power factor of the charging station. Calculate the anomaly coefficient by comprehensively calculating the grid load, energy consumption increase deviation, and power factor. The expression is:

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

[0079] Compare the anomaly coefficient with the preset anomaly threshold. If the anomaly coefficient is less than or equal to the anomaly threshold, analyze that there is no anomaly in the charging station. If the anomaly coefficient is greater than the anomaly threshold, analyze that there is an anomaly in the charging station.

[0080] When analyzing that there are abnormalities in the charging station, after calculating the proportion of each charging mode under different regulation strategies, conditional information entropy is generated, including the following steps:

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

[0082] Under the current regulation strategy, obtain the number of times of each charging mode, including the number of times of fast charging mode, medium-speed charging mode, and slow charging mode. After summing up the number of times of fast charging mode, medium-speed charging mode, and slow charging mode, obtain the total number of charging modes. Obtain the proportion of fast charging mode by dividing the number of times of fast charging mode by the total number of charging modes, obtain the proportion of medium-speed charging mode by dividing the number of times 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 times of slow charging mode by the total number of charging modes;

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

[0084] Calculate the information gain of each regulation strategy 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, calculate the information gain of each regulation strategy. The expression is: H(K|Z) i =H(Z)-H(K) i In the formula, H(K|Z) i is the information gain of the i-th regulation strategy, H(Z) is the overall information entropy, and H(K) i is the conditional information entropy of the i-th regulation strategy;

[0086] A larger information gain means that the strategy significantly optimizes the operation state of the charging station during the regulation process, reduces load fluctuations and resource waste, and has an obvious effect on improving the overall performance of the charging station.

[0087] A smaller information gain indicates that the optimization effect of the strategy on the system is limited. It may only slightly adjust some charging demands and fails to effectively relieve the grid load or the pressure of the charging station.

[0088] And obtain the degree of anomaly mitigation of the charging station under each regulation strategy. After analyzing the information gain and the degree of anomaly mitigation through a joint algorithm, generate a performance value for each regulation strategy, including the following steps:

[0089] Obtain the initial anomaly coefficient of the charging station. The initial anomaly coefficient is the anomaly coefficient when analyzing the existence of anomalies in the charging station. Obtain the current anomaly coefficient of the charging station after adopting the current regulation strategy, and subtract the current anomaly coefficient from the initial anomaly coefficient to obtain the anomaly mitigation value. The larger the anomaly mitigation value, the greater the degree of anomaly mitigation of the charging station after adopting the current regulation strategy;

[0090] After analyzing the information gain and the degree of anomaly mitigation through a joint algorithm, generate a performance value for each regulation strategy. The expression is: bxz i = 0.6 * ycj i + 0.4 * H(K|Z) i where, bxz i is the performance value of the i-th regulation strategy, ycj i is the anomaly mitigation value of the i-th regulation strategy, and H(K|Z) i is the information gain of the i-th regulation strategy.

[0091] The larger the performance value, the greater the effect of the regulation strategy applied to the charging station. After sorting all the regulation strategies from largest to smallest according to the performance value, generate a strategy list. Select the regulation strategy ranked first in the strategy list to regulate the DC charging piles and AC charging piles in the charging station, and regularly monitor the status of the charging station to dynamically update the strategy list and the replacement of the regulation strategy.

[0092] Embodiment 3: An intelligent regulation system for DC and AC charging piles described in this embodiment includes a charging station analysis module, a calculation module, and a regulation module;

[0093] Charging station analysis module: Obtain the historical charging modes of the charging station. The charging modes include the charging pile being in a fast charging mode, the charging pile being in a medium-speed charging mode, and the charging pile being in a slow charging mode (the charging piles here include AC charging piles and DC charging piles). After calculating the proportion of each charging mode in the charging station, generate an overall information entropy. The overall information entropy reflects the overall uncertainty of the charging station, and the overall information entropy is sent to the regulation module;

[0094] Calculation module: Conduct anomaly analysis on the charging station. When analyzing that the charging station has anomalies, calculate the proportion of each charging mode under different regulation strategies to generate a conditional information entropy, and calculate the information gain of each regulation strategy based on the conditional information entropy and the overall information entropy. The information gain is sent to the regulation module;

[0095] Regulation module: Obtain the degree of anomaly mitigation of the charging station under each regulation strategy. After analyzing the information gain and the degree of anomaly mitigation through a joint algorithm, generate a performance value for each regulation strategy. Sort all the regulation strategies according to the performance value to generate a strategy list, and select a regulation strategy according to the strategy list to regulate the DC charging piles and AC charging piles in the charging station.

[0096] All the above formulas are dimensionless and only take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to get a formula that is closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0097] It should be understood that the term "and / or" in this article is only a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship. Specifically, it can be understood by referring to the context before and after.

[0098] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0099] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0100] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An intelligent regulation method for direct and alternating current of a charging pile, characterized in that: The regulation method includes the following steps: The regulation system obtains the historical charging modes of the charging station, calculates the proportion of each charging mode in the charging station, and generates the overall information entropy. The charging modes include fast charging mode, medium-speed charging mode, and slow charging mode. Perform anomaly analysis on the charging station. When it is analyzed that the charging station has an anomaly, calculate the proportion of each charging mode under different regulation strategies and generate the conditional information entropy. Calculate the information gain of each regulation strategy based on the conditional information entropy and the overall information entropy. Obtain the degree of anomaly mitigation of the charging station under each regulation strategy, and analyze the information gain and the degree of anomaly mitigation through a joint algorithm. The expression is: , where is the performance value of the th regulation strategy, is the anomaly mitigation value of the th regulation strategy, is the information gain of the th regulation strategy. Generate performance values for each regulation strategy, sort all regulation strategies according to the performance values to generate a strategy list, and select regulation strategies according to the strategy list to regulate the DC charging piles and AC charging piles in the charging station.

2. The intelligent regulation method for direct and alternating current of a charging pile according to claim 1, wherein: Calculating the overall information entropy by calculating the proportion of each charging mode in the charging station includes the following steps: Obtain the number of times of fast charging mode, medium-speed charging mode, and slow charging mode in the charging station during a certain period of history. Sum up the number of times of fast charging mode, medium-speed 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 times of fast charging mode by the total number of charging modes. Obtain the proportion of medium-speed charging mode by dividing the number of times of medium-speed charging mode by the total number of charging modes. Obtain the proportion of slow charging mode by dividing the number of times of slow charging mode by the total number of charging modes. Generate the overall information entropy based on the proportion of the fast charging mode, the medium-speed charging mode, and the slow charging mode. The expression is as follows: , where is the overall information entropy, is the number of charging modes, represents the proportion of the th charging mode.

3. A method for intelligent regulation of direct and alternating current of a charging pile according to claim 2, characterized in that: Performing anomaly analysis on the charging station includes the following steps: Obtain the grid load, energy consumption increase deviation, and power factor of the charging station. Calculate the anomaly coefficient by comprehensively calculating the grid load, energy consumption increase deviation, and power factor. The expression is: , where is the anomaly coefficient, is the power factor, is the power grid load, is the deviation of energy consumption increase, , , are the adjustment coefficients of the power factor, the power grid load, and the deviation of energy consumption increase respectively, and , , are all greater than 0; Compare the anomaly coefficient with the preset anomaly threshold. If the anomaly coefficient is less than or equal to the anomaly threshold, analyze that the charging station has no anomaly. If the anomaly coefficient is greater than the anomaly threshold, analyze that the charging station has an anomaly.

4. The intelligent regulation method for direct and alternating current of a charging pile according to claim 3, characterized in that: Calculating the information gain of each regulation strategy based on the conditional information entropy and the overall information entropy includes the following steps: After obtaining the conditional information entropy and the overall information entropy, calculate the information gain of each control strategy. The expression is as follows: , where is the information gain of the -th control strategy, is the overall information entropy, is the conditional information entropy of the -th control strategy.

5. A method for intelligent regulation of direct and alternating current of a charging pile according to claim 4, characterized in that: Obtain the degree of anomaly reduction of the charging station under each regulation strategy. After analyzing the information gain and the degree of anomaly reduction through a joint algorithm, generate a performance value for each regulation strategy, including the following steps: Obtain the initial anomaly coefficient of the charging station. The initial anomaly coefficient is the anomaly coefficient when it is analyzed that the charging station has an anomaly. Obtain the current anomaly coefficient of the charging station after adopting the current regulation strategy. Subtract the current anomaly coefficient from the initial anomaly coefficient to obtain the anomaly reduction value. Sort all regulation strategies in descending order according to the performance value to generate a strategy list. Select the regulation strategy ranked first in the strategy list to regulate the DC charging piles and AC charging piles in the charging station.

6. A method for intelligent regulation of direct and alternating current of a charging pile according to claim 5, characterized in that: The regulation system obtains the historical charging modes of the charging station, including the following steps: When there is a vehicle charging at the charging pile, first obtain the initial charging speed according to the current charging stage, and obtain the warning factor during the charging process. Compare the warning factor with the preset first warning threshold and second warning threshold. The first warning threshold is used to judge whether there is an anomaly in the current charging process. The second warning threshold is used to judge the severity of the anomaly 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, judge that there is no anomaly 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 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 through the warning factor. The adjustment algorithm is as follows: , where is the adjusted charging speed, is the initial charging speed, is the warning factor, that is, the larger the warning factor, the more the initial charging speed needs to be reduced; 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 continued charging is not supported. The charging pile is controlled to stop charging the vehicle, and a warning signal is sent to the administrator. After adjusting the initial charging speed of the charging pile, 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, it is determined that the charging pile is in the fast charging mode. If the adjusted charging speed is less than or equal to the second charging speed threshold and greater than the first charging speed threshold, it is determined that the charging pile is in the medium charging mode. If the adjusted charging speed is less than or equal to the first charging speed threshold, the charging pile is in the slow charging mode. After obtaining the historical charging modes of all charging piles in the charging station, record the number of times of each charging mode.

7. A method for intelligent regulation of direct and alternating current of a charging pile according to claim 6, characterized in that: The processing logic of the warning factor is as follows: The voltage volatility of the charging pile, the real-time temperature of the vehicle battery, and the temperature rising rate are monitored in real time. The voltage volatility, real-time temperature, and temperature rising rate are normalized so that the value ranges of the voltage volatility, real-time temperature, and temperature rising rate are mapped to between [0, 1]. The normalized voltage volatility, real-time temperature, and temperature rising rate are summed to obtain the warning factor.

8. An intelligent direct and alternating current regulation system for charging piles, which is used to implement the regulation method described in any one of claims 1-7, and is characterized in that: It includes a charging station analysis module, a calculation module, and a regulation module. Charging station analysis module: Obtain the historical charging modes of the charging station, calculate the proportion of each charging mode in the charging station, and generate the overall information entropy. Calculation module: Conduct abnormal analysis on the charging station. When it is analyzed that there is an abnormality in the charging station, calculate the proportion of each charging mode under different regulation strategies and generate the conditional information entropy. Calculate the information gain of each regulation strategy based on the conditional information entropy and the overall information entropy. Regulation module: Obtain the degree of abnormal reduction of the charging station under each regulation strategy. After analyzing the information gain and the degree of abnormal reduction through a joint algorithm, generate a performance value for each regulation strategy. Sort all regulation strategies according to the performance value to generate a strategy list, and select a regulation strategy according to the strategy list to regulate the DC charging piles and AC charging piles in the charging station.

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