Heavy truck charging pile power regulation method and device, electronic equipment and storage medium

Abstract

The application provides a heavy truck charging pile power regulation method and device, electronic equipment and storage medium, belonging to the technical field of charging pile regulation, comprising: collecting charging information of each charging group through a power regulation module; calculating dynamic impedance describing the degree of hindering of the heavy truck battery group to the charging current; determining the upper limit of the output power of the heavy truck charging pile based on the dynamic impedance, combined with the covariance matrix and the Loeve inequality; determining the charging urgency of each heavy truck charging pile according to the heavy truck charging demand data corresponding to each heavy truck battery group; under the constraints of the upper limit of the output power of the heavy truck charging pile and the charging urgency, optimizing the output power of each heavy truck charging pile, and outputting the optimal heavy truck charging pile output power; and regulating the current heavy truck charging pile output power to the corresponding optimal heavy truck charging pile output power for charging. The overall charging efficiency of the heavy truck charging pile is improved under the premise of reducing the impact on the power grid and ensuring the safety of the power grid.

Description

Technical Field

[0001] This application belongs to the field of charging pile control technology, and more specifically, it relates to a power control method and device for heavy truck charging piles, electronic equipment, and storage medium. Background Technology

[0002] Heavy-duty truck charging stations are charging facilities specifically designed for heavy-duty trucks (such as large freight vehicles). They have higher power output to meet the charging needs of heavy-duty trucks. Compared with ordinary electric vehicle charging stations, heavy-duty truck charging stations have higher requirements in terms of charging power, charging speed, and adaptability. They can support high-power charging equipment to shorten charging time and improve transportation efficiency.

[0003] Due to the high-power battery packs of heavy-duty truck charging stations, concentrated charging can easily lead to localized overloads of the power grid, causing significant impacts. Therefore, power regulation of heavy-duty truck charging stations is particularly important. Dynamically adjusting the charging power can smooth out load fluctuations, avoid overload and energy waste, and ensure timely charging for vehicles. Reasonable power regulation not only improves charging efficiency and ensures a safe and stable charging process, but also extends the lifespan of charging stations and batteries, optimizes power grid resource allocation, reduces negative impacts on the power grid, and provides crucial support for the large-scale popularization and development of electric heavy-duty trucks.

[0004] However, existing technologies typically employ static power regulation methods, which ignore dynamically changing charging demands and battery impedance characteristics, resulting in low charging efficiency and the risk of overload. Summary of the Invention

[0005] The purpose of this application is to provide a power regulation method and device for heavy-duty truck charging piles, electronic equipment, and storage medium to solve the reliability problems of existing technologies that typically use static power regulation methods, which ignore dynamically changing charging demands and battery impedance characteristics, resulting in low charging efficiency and overload risk.

[0006] A first aspect of this application provides a power regulation method for heavy-duty truck charging piles, applied to a power regulation system for heavy-duty truck charging piles, wherein the power regulation system for heavy-duty truck charging piles includes power regulation modules connected to the power grid and each heavy-duty truck charging pile respectively; the method includes:

[0007] The charging information of each charging group is collected through the power control module. The charging group includes a heavy-duty truck charging pile and a heavy-duty truck battery pack that are one-to-one and interconnected.

[0008] Based on the charging information, calculate the dynamic impedance describing the degree of resistance of the heavy truck battery pack to the charging current;

[0009] Based on dynamic impedance, the upper limit of output power of heavy truck charging pile is determined by combining the covariance matrix and Loeve's inequality.

[0010] Based on the charging demand data of each heavy-duty truck battery pack, the charging urgency of each heavy-duty truck charging station is determined.

[0011] Under the constraints of the upper limit of the output power of heavy-duty truck charging piles and the urgency of charging, the output power of each heavy-duty truck charging pile is optimized to output the optimal output power of the heavy-duty truck charging pile.

[0012] The output power of the current heavy-duty truck charging pile is adjusted to the corresponding optimal output power for charging.

[0013] A second aspect of this application provides a power regulation device for heavy-duty truck charging piles, applied to a power regulation system for heavy-duty truck charging piles, wherein the power regulation system for heavy-duty truck charging piles includes power regulation modules respectively connected to the power grid and each heavy-duty truck charging pile; the device includes:

[0014] The data acquisition module is used to collect charging information for each charging group through the power control module. The charging group includes a heavy-duty truck charging pile and a heavy-duty truck battery pack that are one-to-one and interconnected.

[0015] The calculation module is used to calculate the dynamic impedance describing the degree of obstruction of the heavy truck battery pack to the charging current based on the charging information.

[0016] The first determining module is used to determine the upper limit of the output power of the heavy truck charging pile based on dynamic impedance, combined with the covariance matrix and Loeve's inequality.

[0017] The second determining module is used to determine the charging urgency of each heavy truck charging pile based on the heavy truck charging demand data corresponding to each heavy truck battery pack.

[0018] The output module is used to optimize the output power of each heavy-duty truck charging pile under the constraints of the upper limit of the output power of the heavy-duty truck charging pile and the charging urgency, and output the optimal output power of the heavy-duty truck charging pile.

[0019] The control module is used to adjust the output power of the current heavy-duty truck charging pile to the corresponding optimal output power for charging.

[0020] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described heavy-duty truck charging pile power control method.

[0021] In a fourth aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described heavy-duty truck charging pile power control method.

[0022] The beneficial effects of the heavy-duty truck charging pile power regulation method and device, electronic equipment, and storage medium provided in this application embodiment are as follows:

[0023] In this embodiment of the invention, by calculating the dynamic impedance reflecting the true state of the battery in real time and using Loeve's inequality to analyze the covariance matrix, the most severe fluctuation scenarios are accurately captured to set a strict upper limit for the output power of heavy-duty truck charging piles. This solves the overload risk caused by neglecting the time-varying characteristics of the battery in traditional static methods, thus improving charging safety. Furthermore, charging urgency is introduced as another key constraint to ensure that the limited grid capacity can be rationally allocated according to the urgent needs of vehicles. Ultimately, under these dual constraints, the optimal output power of the heavy-duty truck charging pile is output, achieving improved overall charging efficiency of the heavy-duty truck charging pile while reducing grid impact and ensuring grid safety. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating a method for regulating the power of a heavy-duty truck charging pile, provided as an embodiment of this application;

[0026] Figure 2 A flowchart illustrating a power control system for heavy-duty truck charging piles, provided in one embodiment of this application;

[0027] Figure 3 A structural block diagram of a heavy-duty truck charging pile power regulation device provided in an embodiment of this application;

[0028] Figure 4 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0029] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0030] It is understood that in the embodiments of this application, data such as user information are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0031] It should be noted that the terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0032] Before providing a further detailed description of the embodiments of this application, the nouns and terms involved in the embodiments of this application will be explained, and the nouns and terms involved in the embodiments of this application shall be interpreted as follows.

[0033] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0034] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for regulating the power of a heavy-duty truck charging pile according to an embodiment of this application.

[0035] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating a power control system for a heavy-duty truck charging pile, as provided in one embodiment of this application.

[0036] Figure 2 In the diagram, the marker n corresponds to the total number of heavy-duty truck charging piles, heavy-duty truck battery packs, and charging groups that have a corresponding relationship.

[0037] The heavy-duty truck charging pile power regulation method provided in this application embodiment is applied to a heavy-duty truck charging pile power regulation system, wherein the heavy-duty truck charging pile power regulation system includes a power regulation module that is connected to the power grid and each heavy-duty truck charging pile respectively.

[0038] The power allocation module is a key component of the heavy-duty truck charging pile power control system, responsible for coordinating power distribution between the power grid and each charging pile. This module can collect and analyze charging information from the charging piles in real time, such as voltage, current, and battery status data, and adjust the output power of each charging pile based on this data. Simultaneously, the power allocation module also needs to consider the grid's load capacity, the urgency of charging demand, and the optimal charging state of the battery to ensure a safe and efficient charging process. Furthermore, the power allocation module is connected to the power grid and can dynamically adjust according to the actual load conditions of the grid, thereby avoiding grid overload and balancing the power output of the charging piles.

[0039] like Figure 1 As shown, the method may include:

[0040] The power control module collects charging information for each charging group.

[0041] The charging group includes a heavy-duty truck charging pile and a heavy-duty truck battery pack that are connected to each other in a one-to-one manner.

[0042] The charging information includes the output voltage of the heavy-duty truck charging pile, the output current of the heavy-duty truck charging pile, and the temperature of the heavy-duty truck battery pack.

[0043] The output voltage and current of the heavy-duty truck charging pile can be directly collected by sensors. The temperature of the heavy-duty truck battery pack is acquired in real time through the heavy-duty truck battery management system and then received through the communication interface of the power regulation module. By comprehensively collecting key parameters such as voltage, current, and temperature in real time, the charging status can be accurately monitored, ensuring the safety and efficiency of the charging process.

[0044] Based on the charging information, calculate the dynamic impedance describing the degree of resistance of the heavy truck battery pack to the charging current.

[0045] Dynamic impedance refers to the degree to which a heavy-duty truck battery pack impedes the charging current during charging. It considers not only the battery's internal resistance but also dynamically reflects changes in the battery pack's electrochemical characteristics and temperature at different charging stages. By calculating dynamic impedance, the charging capacity of the battery pack and potential current fluctuations during charging can be described more accurately, thereby optimizing power regulation. Real-time calculation of dynamic impedance accurately reflects the battery's state, improving the precision and efficiency of the charging process and reducing charging problems caused by changes in battery performance.

[0046] In one possible implementation, based on charging information, a dynamic impedance describing the degree of resistance of the heavy-duty truck battery pack to the charging current is calculated, specifically including:

[0047] The initial impedance of the heavy-duty truck battery pack is calculated according to Ohm's law, where the initial impedance is the ratio of the output voltage of the heavy-duty truck charging pile to the output current of the heavy-duty truck charging pile.

[0048] Calculate the temperature compensation term acting on the initial impedance at the temperature of the heavy truck battery pack.

[0049] Multiplying the initial impedance and the temperature compensation term yields the dynamic impedance.

[0050] The specific formula for calculating dynamic impedance is as follows:

[0051] .

[0052] in, express t Dynamic impedance at time t, and They represent t The output voltage and output current of the heavy-duty truck charging pile at any given time. This represents the temperature compensation coefficient of the heavy-duty truck battery pack, calibrated through battery impedance testing experiments at different temperatures. This indicates the safe temperature threshold for heavy-duty truck battery packs. express t The temperature of the heavy truck battery pack at any given time. Indicates the temperature compensation term. This represents the initial impedance.

[0053] The temperature compensation coefficient of the heavy-duty truck battery pack is a pre-calibrated parameter that can be extracted based on the equipment information of the heavy-duty truck battery pack.

[0054] It should be noted that this process assesses the charging state of the heavy-duty truck battery pack by calculating its dynamic impedance. First, the initial impedance, i.e., the ratio of voltage to current at the charging pile output, is calculated according to Ohm's law. Then, the influence of battery pack temperature on the impedance is considered, and a temperature compensation term is used to correct for this, resulting in a more accurate dynamic impedance. The temperature compensation coefficient is experimentally calibrated and adjusts the battery's charging impedance based on actual temperature changes. If the battery pack temperature exceeds a safety threshold, the system will issue a warning and interrupt charging to ensure safety during the charging process. This method dynamically reflects the actual charging state of the battery, making real-time adjustments based on temperature, improving charging accuracy and safety, and effectively avoiding charging instability or battery damage caused by temperature changes.

[0055] Based on dynamic impedance, the upper limit of the output power of heavy-duty truck charging piles is determined by combining the covariance matrix and Loeve's inequality.

[0056] The Loeve inequality, derived from the fundamental probability inequality (Chebyshev inequality), has the following standard form: For any random variable X with finite mean and finite variance, we have: ,in, and Let X represent the mean and variance, respectively. Let the margin coefficient be denoted as , and correspondingly, the complement of this inequality (i.e., the probability that the random variable falls within the interval near the mean) is: Transform the absolute value condition on the left side of the inequality into a square condition (because...). Equivalent to ,in, a and b (All are intermediate variables), resulting in Replace X with , Replace with For scalar random variables Its covariance matrix degenerates into variance. Since the covariance matrix of a scalar has only one eigenvalue, the largest eigenvalue is... Therefore, we obtain Loeve's inequality.

[0057] The covariance matrix is ​​a mathematical tool used to describe the correlation between multiple variables. Loeve's inequality is a mathematical inequality used to estimate the fluctuations of random variables. While the classic Loeve's inequality applies to scalar random variables, in this method, it is extended to the eigenvalue boundary of the covariance matrix. By calculating the largest eigenvalue of the covariance matrix, the worst-case fluctuation scenarios during charging are captured. This method determines the upper limit of the output power of the heavy-duty truck charging pile, ensuring that the charging process does not exceed the grid's capacity or the battery's charging safety range. Utilizing the extended Loeve's inequality and the covariance matrix, the power upper limit of the charging pile can be calculated more accurately and rigorously, fully considering the impact of multiple dynamic factors on the charging process and avoiding the underestimation of complex fluctuation scenarios by traditional methods.

[0058] In one possible implementation, the upper limit of the output power of the heavy-duty truck charging pile is determined based on dynamic impedance, combined with the covariance matrix and Loeve's inequality, specifically including:

[0059] Multiple dynamic impedances and temperature drift values ​​are obtained within a preset time window. The temperature drift value is the difference between the temperature of the heavy truck battery pack and the pre-calibrated optimal operating temperature of the heavy truck battery pack. Each dynamic impedance forms a dynamic impedance sequence, and each temperature drift value forms a temperature offset sequence.

[0060] Among them, the dynamic impedance sequence and the temperature offset sequence are both time-series sequences, that is, sequences spliced ​​together in time order.

[0061] Calculate the correlation coefficient between dynamic impedance and temperature offset.

[0062] The correlation coefficient is specifically the ratio of the numerator to the denominator. The numerator is the covariance between the dynamic impedance sequence and the temperature offset sequence, and the denominator is the product of the standard deviation of the dynamic impedance sequence and the standard deviation of the temperature offset sequence.

[0063] Establish the covariance matrix for dynamic impedance and temperature offset.

[0064] The covariance matrix is ​​a two-row, two-column matrix. The first row and first column of the covariance matrix is ​​the variance of the dynamic impedance sequence. The first row and second column of the covariance matrix are the products of the correlation coefficient, the standard deviation of the dynamic impedance sequence, and the standard deviation of the temperature offset sequence. The second row and second column of the covariance matrix is ​​the variance of the temperature offset sequence.

[0065] The formula for the covariance matrix is ​​as follows:

[0066] .

[0067] in, Represents the covariance matrix. Represents the correlation coefficient. and These represent the standard deviation of the dynamic impedance sequence and the standard deviation of the temperature offset sequence, respectively. and These represent the variance of the dynamic impedance sequence and the variance of the temperature offset sequence, respectively.

[0068] By combining the covariance matrix, the generalized Loeve inequality is introduced to determine the lower limit of dynamic impedance.

[0069] In one possible implementation, the lower limit of the dynamic impedance is determined by combining the covariance matrix and introducing the generalized Loeve inequality, specifically including:

[0070] Extract the largest eigenvalue of the covariance matrix.

[0071] By combining the maximum eigenvalue, the impedance constraint formula is determined according to the generalized Loeve inequality.

[0072] The impedance constraint formula is as follows:

[0073] .

[0074] in, express t Dynamic impedance at all times This represents the mean dynamic impedance in the dynamic impedance sequence. This represents the safety margin coefficient. Represents the covariance matrix The largest eigenvalue, Indicates an event The probability of occurrence.

[0075] Among them, the event This indicates whether the square of the difference between the dynamic impedance and its mean is less than or equal to a threshold related to the maximum eigenvalue and the safety margin factor. Specifically, it measures the deviation of the dynamic impedance from its mean at a given moment. The probability within this deviation range reflects the uncertainty and volatility during charging, thus determining whether the system can operate within a safe range.

[0076] Let the probability lower bound term of the impedance constraint be a preset confidence level, and solve the impedance constraint to obtain the impedance lower bound value, where the preset confidence level is the difference between the numerical value and the significance level.

[0077] It should be noted that those skilled in the art can set the preset confidence level according to actual needs, and this invention does not limit this. Optionally, the preset confidence level can be set to 0.95, that is, a significance level of 0.05.

[0078] .

[0079] .

[0080] .

[0081]

[0082] in, Let the probability lower bound term of the impedance constraint expression be... To preset the credit level The impedance range obtained below, Indicates the significance level. This represents the average dynamic impedance in the dynamic impedance sequence. This indicates the lower limit of impedance.

[0083] Specifically, this process accurately determines the lower limit of the impedance of heavy-duty truck charging piles by introducing the generalized Loeve inequality and combining it with the eigenvalues ​​of the covariance matrix to ensure the safety of the charging process. In this process, the largest eigenvalue of the covariance matrix is ​​first extracted, and an impedance constraint is defined using the generalized Loeve inequality. This constraint determines the safe range of dynamic impedance by calculating the probability of events occurring. By setting a preset confidence level, the safe range of dynamic impedance can be obtained, and the lower limit of impedance can be further determined. Since charging power is inversely proportional to battery impedance, setting this lower limit effectively avoids excessive power due to insufficient impedance, thus preventing the safety risk of overpower during battery charging. This not only precisely controls the output power of the charging pile to prevent overcharging, but also dynamically adjusts based on actual temperature changes and battery characteristics to optimize charging efficiency and ensure the safe operation of the power grid and battery, avoiding the shortcomings of traditional methods that cannot fully consider complex environmental factors.

[0084] The lower limit of impedance is mapped to the upper limit of the output power of the heavy truck charging pile.

[0085] The specific formula for calculating the upper limit of the output power of heavy-duty truck charging piles is as follows:

[0086] .

[0087] in, This indicates the upper limit of the output power of the heavy-duty truck charging station. This indicates the output voltage of the heavy-duty truck charging station.

[0088] Specifically, this process precisely controls the charging pile's output power by combining dynamic impedance and temperature drift analysis, ensuring safe and efficient charging. First, dynamic impedance and temperature drift data are collected at multiple times, reflecting the relationship between the battery pack's current resistance and temperature changes. Then, by calculating their correlation coefficients, the impact of temperature changes on impedance is analyzed, quantifying the relationship between the two. Next, the covariance matrix is ​​used to further describe the joint variation characteristics of impedance and temperature drift, providing more accurate parameters for subsequent power calculations. Using the generalized Loeve inequality, considering the worst-case fluctuation scenarios, a lower impedance limit is set to avoid overestimating the charging pile's power output and ensure safe operation. Finally, this lower impedance limit is converted into a higher power limit for the charging pile, ensuring the output power is within the range of battery and grid load capacity. By comprehensively considering the impact of dynamic temperature changes and battery characteristics, the upper power limit can be safely and accurately adjusted, thereby achieving optimized control of the grid load, avoiding overload, improving charging efficiency, and extending equipment lifespan.

[0089] Based on the charging demand data of each heavy-duty truck battery pack, the charging urgency of each heavy-duty truck charging station is determined.

[0090] The heavy-duty truck charging demand data refers to the charging needs of each heavy-duty truck's battery pack. This data reflects the charging priority and urgency of each heavy-duty truck. Charging urgency is a metric determined based on this demand data, representing the level of urgency of each heavy-duty truck's battery pack's charging needs. In other words, it corresponds to the charging urgency of the heavy-duty truck charging station. Based on the charging urgency, the charging tasks of each heavy-duty truck charging station can be rationally scheduled, prioritizing heavy-duty trucks with more urgent charging needs. Dynamic evaluation based on actual charging demand ensures that charging resources are rationally allocated according to the urgency of each heavy-duty truck, avoiding resource waste and improving charging efficiency.

[0091] In one possible implementation, the heavy-duty truck charging demand data includes the truck's available waiting time and the target battery level.

[0092] Among them, the waiting time for heavy-duty trucks refers to the maximum time a heavy-duty truck driver is willing to stay at a charging station (i.e., the set charging time for the heavy-duty truck). The target battery level for heavy-duty trucks is the desired battery capacity level.

[0093] Based on the charging demand data corresponding to each heavy-duty truck battery pack, the charging urgency of each heavy-duty truck charging station is determined, specifically including:

[0094] A time urgency factor is established based on the waiting time of heavy trucks.

[0095] Based on the charging demand of heavy trucks, a power urgency factor is established.

[0096] By combining the time urgency factor and the power urgency factor, the charging urgency is obtained.

[0097] The specific formula for calculating charging urgency is as follows:

[0098]

[0099] in, Indicates the first i Heavy truck charging piles t The urgency of constantly charging Represents the natural logarithm. Indicates the first i The waiting time for each heavy-duty truck charging station corresponds to a specific heavy-duty truck. This indicates the maximum allowable charging time for heavy-duty truck charging stations. Indicates the first i Each heavy-duty truck charging station corresponds to the charging demand of heavy-duty trucks. Indicates the first i Each heavy-duty truck charging station corresponds to the target battery level of the heavy-duty truck. The charging demand of the heavy-duty truck is the difference between the target battery level and the current battery level. Indicates the time urgency factor. This indicates the urgency factor of the battery charge.

[0100] Among them, the maximum allowable charging time for heavy-duty truck charging piles is the longest charging time limit set by the charging station for a single heavy-duty truck.

[0101] It should be noted that this process establishes a charging urgency factor by considering the charging demand and available waiting time of heavy-duty trucks to dynamically optimize the allocation of charging resources. First, the time urgency factor reflects the urgency of the charging demand based on the ratio between the available waiting time and the maximum charging time of the heavy-duty trucks. Next, the energy urgency factor assesses the urgency of energy replenishment by comparing the charging demand of the heavy-duty trucks with the target energy level. By combining these two factors, the overall charging urgency of each heavy-duty truck can be accurately calculated, ensuring that vehicles with the most urgent charging needs are prioritized for charging. This method efficiently allocates charging resources, avoids resource waste, improves charging efficiency, and simultaneously ensures battery charging safety and grid load balance, thereby achieving an efficient and safe charging process.

[0102] Under the constraints of the upper limit of the output power of heavy-duty truck charging piles and the urgency of charging, the output power of each heavy-duty truck charging pile is optimized to output the optimal output power.

[0103] It should be noted that by optimizing power output under the dual constraints of upper limit of output power and charging urgency, the optimal output power can be accurately determined for each charging station. This not only ensures grid safety during charging but also guarantees efficient allocation of charging resources, avoids overcharging and energy waste, and prioritizes emergency charging needs, thereby improving overall charging efficiency and resource utilization.

[0104] In one possible implementation, under the constraints of the upper limit of the output power of heavy-duty truck charging piles and the urgency of charging, the output power of each heavy-duty truck charging pile is optimized to output the optimal output power, specifically including:

[0105] Obtain the expected charging power of each heavy-duty truck at each charging station.

[0106] The expected charging power is the ideal charging power of the heavy-duty truck battery pack corresponding to the heavy-duty truck, which is the factory calibration parameter.

[0107] An objective function based on the sum of weighted squared differences is established by combining the charging urgency, where the weighted squared difference is the weighted squared difference between the actual allocated charging power and the expected charging power of each heavy-duty truck corresponding to each heavy-duty truck charging pile.

[0108] Based on the upper limit of the output power of each heavy-duty truck charging pile, an objective function optimization constraint is established. The objective function optimization constraint includes a first constraint and a second constraint. The first constraint is that the actual allocated charging power of the same heavy-duty truck is less than the upper limit of the output power of the heavy-duty truck charging pile. The second constraint is that the sum of the actual allocated charging power of each heavy-duty truck is less than the maximum output power of the power grid.

[0109] Under the constraint of objective function optimization, the output power of heavy-duty truck charging piles is optimized by minimizing the objective function, and the optimal output power of each heavy-duty truck charging pile is output.

[0110] Among them, the optimal output power of the heavy-duty truck charging pile is the actual allocated charging power of the corresponding heavy-duty truck charging pile obtained through optimization.

[0111] The specific formulas for the objective function and its optimization constraints are as follows:

[0112] Objective function: .

[0113] First constraint: .

[0114] Second constraint: .

[0115] in, Indicates minimization. n This indicates the total number of charging stations for heavy-duty trucks. and They represent the first i The actual allocated charging power and expected charging power of each heavy-duty truck charging station Indicates the maximum output power of the power grid. Indicates the first i The upper limit of the output power of each heavy-duty truck charging pile.

[0116] It should be noted that this process, under the dual constraints of the upper limit of the output power of heavy-duty truck charging piles and the urgency of charging, uses an objective function optimization method to accurately calculate the optimal output power of each charging pile. First, the desired charging power for each heavy-duty truck is obtained. Then, combined with the charging urgency, an objective function based on the weighted sum of squared differences is constructed to measure the gap between the actual allocated charging power and the desired charging power. By setting constraints, it is ensured that the actual output power of each charging pile does not exceed the upper limit, and the total power of all charging piles does not exceed the maximum output power of the power grid. This optimization process, by minimizing the objective function, accurately allocates charging power, prioritizing vehicles with the most urgent charging needs while avoiding overcharging and grid overload, thus improving overall charging efficiency and resource utilization. This optimization method ensures the high efficiency of the charging process and the safety of the power grid, achieving intelligent and balanced charging resource management.

[0117] Specifically, it can be solved directly using MATLAB tools. The specific steps are as follows: First, define the objective function. Then, set the optimization variables (the...). i The actual allocated charging power of each heavy-duty truck charging pile and the constraints are then determined. Next, the fmincon function is selected to specify the constraints for optimization, yielding the optimal charging pile output power.

[0118] The output power of the current heavy-duty truck charging pile is adjusted to the corresponding optimal output power for charging.

[0119] In practical applications, this method optimizes the charging process by comprehensively considering the upper limit of the output power of heavy-duty truck charging piles and the urgency of charging, and using objective function optimization to precisely adjust the output power of each charging pile. First, the system establishes an objective function based on the charging urgency, charging demand, and desired charging power, using a weighted sum of squared differences to minimize the difference between the actual allocated charging power and the desired power. Furthermore, the system sets upper limits on power and the maximum output power of the power grid as constraints to ensure that the actual output power of each charging pile does not exceed the limits and that the grid load is effectively managed. Through this optimization process, vehicles with the most urgent charging needs are prioritized for charging, while avoiding power overload and excessive grid load during the charging process. The optimized output power effectively improves charging efficiency, reduces energy waste, and ensures battery safety and stable grid operation. This intelligent resource scheduling improves charging efficiency while ensuring safe operation.

[0120] In this embodiment of the invention, by calculating the dynamic impedance reflecting the true state of the battery in real time and using Loeve's inequality to analyze the covariance matrix, the most severe fluctuation scenarios are accurately captured to set a strict upper limit for the output power of heavy-duty truck charging piles. This solves the overload risk caused by neglecting the time-varying characteristics of the battery in traditional static methods, thus improving charging safety. Furthermore, charging urgency is introduced as another key constraint to ensure that the limited grid capacity can be rationally allocated according to the urgent needs of vehicles. Ultimately, under these dual constraints, the optimal output power of the heavy-duty truck charging pile is output, achieving improved overall charging efficiency of the heavy-duty truck charging pile while reducing grid impact and ensuring grid safety.

[0121] Based on the same inventive concept, this application also provides a heavy-duty truck charging pile power regulation device for implementing the above-mentioned heavy-duty truck charging pile power regulation method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the heavy-duty truck charging pile power regulation device provided below can be found in the limitations of the heavy-duty truck charging pile power regulation method above, and will not be repeated here.

[0122] This application provides a power regulation device for heavy-duty truck charging piles, such as... Figure 3 As shown, the heavy-duty truck charging pile power regulation device 20 includes:

[0123] The acquisition module 201 is used to acquire the charging information of each charging group through the power regulation module. The charging group includes a heavy truck charging pile and a heavy truck battery pack that are one-to-one and interconnected.

[0124] The calculation module 202 is used to calculate the dynamic impedance describing the degree of obstruction of the heavy truck battery pack to the charging current based on the charging information.

[0125] The first determining module 203 is used to determine the upper limit of the output power of the heavy truck charging pile based on dynamic impedance, combined with the covariance matrix and Loeve's inequality.

[0126] The second determining module 204 is used to determine the charging urgency of each heavy truck charging pile based on the heavy truck charging demand data corresponding to each heavy truck battery pack.

[0127] The output module 205 is used to optimize the output power of each heavy-duty truck charging pile under the constraints of the upper limit of the output power of the heavy-duty truck charging pile and the charging urgency, and output the optimal output power of the heavy-duty truck charging pile.

[0128] The control module 206 is used to adjust the output power of the current heavy-duty truck charging pile to the corresponding optimal output power for charging.

[0129] See Figure 4 , Figure 4 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of each module / unit in the above-described device embodiments, for example... Figure 3 The functions of the acquisition module 201, calculation module 202, first determination module 203, second determination module 204, output module 205, and control module are shown.

[0130] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0131] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0132] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0133] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation method described in the heavy truck charging pile power control method provided in the embodiments of this application, or they can execute the implementation method of the electronic device described in the embodiments of this application, which will not be repeated here.

[0134] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0135] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., provided on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0136] Those skilled in the art will recognize that the modules / units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0137] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules, units, or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or modules / units, or it may be an electrical, mechanical, or other form of connection.

[0139] The modules / units described as separate components may or may not be physically separate. Similarly, the components shown as modules / units may or may not be physical modules / units; they may be located in one place or distributed across multiple network modules / units. Some or all of the modules / units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0140] Furthermore, the functional modules / units in the various embodiments of this application can be integrated into one processing module / unit, or each module / unit can exist physically separately, or two or more modules / units can be integrated into one module / unit. The integrated modules / units described above can be implemented in hardware or in the form of software functional modules / units.

[0141] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for power regulation of heavy-duty truck charging piles, characterized in that, An application to a power regulation system for heavy-duty truck charging piles, wherein the power regulation system for heavy-duty truck charging piles includes power regulation modules connected to the power grid and each heavy-duty truck charging pile respectively; the method includes: The power control module collects charging information for each charging group, wherein the charging group includes a heavy-duty truck charging pile and a heavy-duty truck battery pack that are one-to-one correspondence and interconnected. Based on the charging information, calculate the dynamic impedance describing the degree of resistance of the heavy truck battery pack to the charging current; Based on the dynamic impedance, the upper limit of the output power of the heavy truck charging pile is determined by combining the covariance matrix and Loeve's inequality. Based on the heavy truck charging demand data corresponding to each heavy truck battery pack, the charging urgency of each heavy truck charging pile is determined. Under the constraints of the upper limit of the output power of the heavy-duty truck charging pile and the charging urgency, the output power of each heavy-duty truck charging pile is optimized, and the optimal output power of the heavy-duty truck charging pile is output. The output power of the current heavy-duty truck charging pile is adjusted to the corresponding optimal output power of the heavy-duty truck charging pile for charging; The charging information includes the output voltage of the heavy-duty truck charging pile, the output current of the heavy-duty truck charging pile, and the temperature of the heavy-duty truck battery pack. The step of calculating the dynamic impedance describing the degree of resistance of the heavy-duty truck battery pack to the charging current based on the charging information specifically includes: The initial impedance of the heavy-duty truck battery pack is calculated according to Ohm's law, wherein the initial impedance is the ratio of the output voltage of the heavy-duty truck charging pile to the output current of the heavy-duty truck charging pile. Calculate the temperature compensation term applied to the initial impedance at the temperature of the heavy truck battery pack; Multiplying the initial impedance and the temperature compensation term yields the dynamic impedance; Specifically, determining the upper limit of the output power of the heavy-duty truck charging pile based on the dynamic impedance, combined with the covariance matrix and Loeve's inequality, includes: Multiple dynamic impedances and temperature drift values ​​are obtained within a preset time window. The temperature drift value is the difference between the temperature of the heavy truck battery pack and the pre-calibrated optimal operating temperature of the heavy truck battery pack. Each of the dynamic impedances forms a dynamic impedance sequence, and each of the temperature drift values ​​forms a temperature offset sequence. Calculate the correlation coefficient between the dynamic impedance and the temperature offset; Establish the covariance matrix for dynamic impedance and temperature offset; Based on the covariance matrix, the generalized Loeve inequality is introduced to determine the lower limit of the dynamic impedance. The lower limit of impedance is mapped to the upper limit of the output power of the heavy truck charging pile; Specifically, determining the lower limit of the dynamic impedance by combining the covariance matrix and introducing the generalized Loeve inequality includes: Extract the largest eigenvalue of the covariance matrix; Based on the maximum eigenvalue, the impedance constraint formula is determined according to the generalized Loeve inequality; Let the probability lower bound term of the impedance constraint equation be a preset confidence level, and solve the impedance constraint equation to obtain the impedance lower bound value, wherein the preset confidence level is the difference between the numerical value and the significance level. Specifically, the process of optimizing the output power of each heavy-duty truck charging pile under the constraints of the upper limit of the output power of the heavy-duty truck charging pile and the charging urgency, and outputting the optimal output power of the heavy-duty truck charging pile, includes: Obtain the expected charging power of each heavy-duty truck corresponding to each of the aforementioned heavy-duty truck charging piles; An objective function based on the sum of weighted squared differences is established in conjunction with the charging urgency, wherein the weighted squared differences are the weighted squared differences between the actual allocated charging power and the expected charging power of each heavy truck charging pile corresponding to the heavy truck. Based on the upper limit of the output power of each heavy-duty truck charging pile, an objective function optimization constraint is established. The objective function optimization constraint includes a first constraint and a second constraint. The first constraint is that the actual allocated charging power of the same heavy-duty truck is less than the upper limit of the output power of the heavy-duty truck charging pile. The second constraint is that the sum of the actual allocated charging power of each heavy-duty truck is less than the maximum output power of the power grid. Under the constraints of the objective function optimization, the output power of heavy-duty truck charging piles is optimized with the goal of minimizing the objective function, and the optimal output power of each heavy-duty truck charging pile is output.

2. The heavy-duty truck charging pile power regulation method as described in claim 1, characterized in that, The heavy-duty truck charging demand data includes the waiting time for the heavy-duty truck and the target battery level of the heavy-duty truck. The step of determining the charging urgency of each heavy-duty truck charging pile based on the heavy-duty truck charging demand data corresponding to each heavy-duty truck battery pack specifically includes: Based on the waiting time of the heavy truck, a time urgency factor is established; Based on the charging demand of the heavy trucks, a power urgency factor is established; The charging urgency is obtained by combining the time urgency factor and the power urgency factor.

3. A power regulation device for heavy-duty truck charging piles, characterized in that, An application in a power regulation system for heavy-duty truck charging piles, wherein the power regulation system for heavy-duty truck charging piles includes power regulation modules respectively connected to the power grid and each of the heavy-duty truck charging piles; the device includes: The acquisition module is used to acquire charging information of each charging group through the power regulation module, wherein the charging group includes the heavy truck charging pile and the heavy truck battery pack that are one-to-one and interconnected. The calculation module is used to calculate the dynamic impedance describing the degree of resistance of the heavy truck battery pack to the charging current based on the charging information. The first determining module is used to determine the upper limit of the output power of the heavy truck charging pile based on the dynamic impedance, combined with the covariance matrix and Loeve's inequality. The second determining module is used to determine the charging urgency of each heavy truck charging pile based on the heavy truck charging demand data corresponding to each heavy truck battery pack. The output module is used to optimize the output power of each heavy-duty truck charging pile under the constraints of the upper limit of the output power of the heavy-duty truck charging pile and the charging urgency, and output the optimal output power of the heavy-duty truck charging pile. The control module is used to adjust the current output power of the heavy-duty truck charging pile to the corresponding optimal output power of the heavy-duty truck charging pile for charging. The charging information includes the output voltage of the heavy-duty truck charging pile, the output current of the heavy-duty truck charging pile, and the temperature of the heavy-duty truck battery pack. The step of calculating the dynamic impedance describing the degree of resistance of the heavy-duty truck battery pack to the charging current based on the charging information specifically includes: The initial impedance of the heavy-duty truck battery pack is calculated according to Ohm's law, wherein the initial impedance is the ratio of the output voltage of the heavy-duty truck charging pile to the output current of the heavy-duty truck charging pile. Calculate the temperature compensation term applied to the initial impedance at the temperature of the heavy truck battery pack; Multiplying the initial impedance and the temperature compensation term yields the dynamic impedance; Specifically, determining the upper limit of the output power of the heavy-duty truck charging pile based on the dynamic impedance, combined with the covariance matrix and Loeve's inequality, includes: Multiple dynamic impedances and temperature drift values ​​are obtained within a preset time window. The temperature drift value is the difference between the temperature of the heavy truck battery pack and the pre-calibrated optimal operating temperature of the heavy truck battery pack. Each of the dynamic impedances forms a dynamic impedance sequence, and each of the temperature drift values ​​forms a temperature offset sequence. Calculate the correlation coefficient between the dynamic impedance and the temperature offset; Establish the covariance matrix for dynamic impedance and temperature offset; Based on the covariance matrix, the generalized Loeve inequality is introduced to determine the lower limit of the dynamic impedance. The lower limit of impedance is mapped to the upper limit of the output power of the heavy truck charging pile; Specifically, determining the lower limit of the dynamic impedance by combining the covariance matrix and introducing the generalized Loeve inequality includes: Extract the largest eigenvalue of the covariance matrix; Based on the maximum eigenvalue, the impedance constraint formula is determined according to the generalized Loeve inequality; Let the probability lower bound term of the impedance constraint equation be a preset confidence level, and solve the impedance constraint equation to obtain the impedance lower bound value, wherein the preset confidence level is the difference between the numerical value and the significance level. Specifically, the process of optimizing the output power of each heavy-duty truck charging pile under the constraints of the upper limit of the output power of the heavy-duty truck charging pile and the charging urgency, and outputting the optimal output power of the heavy-duty truck charging pile, includes: Obtain the expected charging power of each heavy-duty truck corresponding to each of the aforementioned heavy-duty truck charging piles; An objective function based on the sum of weighted squared differences is established in conjunction with the charging urgency, wherein the weighted squared differences are the weighted squared differences between the actual allocated charging power and the expected charging power of each heavy truck charging pile corresponding to the heavy truck. Based on the upper limit of the output power of each heavy-duty truck charging pile, an objective function optimization constraint is established. The objective function optimization constraint includes a first constraint and a second constraint. The first constraint is that the actual allocated charging power of the same heavy-duty truck is less than the upper limit of the output power of the heavy-duty truck charging pile. The second constraint is that the sum of the actual allocated charging power of each heavy-duty truck is less than the maximum output power of the power grid. Under the constraints of the objective function optimization, the output power of heavy-duty truck charging piles is optimized with the goal of minimizing the objective function, and the optimal output power of each heavy-duty truck charging pile is output.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 2.

5. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 2.

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