Intelligent charging pile metering method and system based on Internet of Things technology

Abstract

The invention relates to the technical field of electric energy consumption monitoring, in particular to an intelligent charging pile metering method and system based on the Internet of Things technology. The method comprises the following steps: acquiring charging data of a plurality of vehicles in a charging process; obtaining a battery aging parameter at each moment according to the difference between the charging duration corresponding to each moment in each charging process and the theoretical charging duration and the difference between the accumulated electric energy consumption and the theoretical electric energy demand, and further obtaining a capacity change factor in combination with the change condition of the battery temperature and the environment temperature; clustering the charging processes according to the difference of the electric energy consumption in every two charging processes and the difference of the similar conditions of the capacity change factor and the SOC value; and correcting the electric energy consumption of the current charging process of the target vehicle by using the difference between the electric energy consumption of the current charging process of the target vehicle and the electric energy consumption of the charging process in the cluster to obtain the corrected electric energy consumption. The accuracy of the electric energy consumption metering result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power consumption monitoring, and specifically to an intelligent charging pile metering method and system based on Internet of Things technology. Background Art

[0002] With the popularization of electric vehicles and the development of new energy technologies, the construction of electric vehicle charging facilities has become an important part of the development of the new energy vehicle industry. As the core component of electric vehicle charging facilities, the accuracy of the metering method and the stability of the system of intelligent charging piles are of great significance to the use experience of electric vehicles and the operation and management of charging facilities.

[0003] In the existing charging piles during the metering process, an intelligent electronic watt-hour meter is usually used to measure the current and voltage flowing through the charging pile during the charging process, record various parameters during the charging process, such as charging time, charging amount, etc., so as to calculate the consumed electric energy. Through the existing Internet of Things technology, all the charging pile-related data in the area are centrally collected and analyzed, and then the electric energy consumption data of a single charging pile are uniformly analyzed and managed.

[0004] For the batteries installed in electric two-wheelers and three-wheelers themselves, during their long-term use, aging problems will occur, affecting the battery storage capacity of electric vehicles and the electric energy loss during the charging process, resulting in a reduction in the cruising range of electric vehicles and more electric energy consumption during the charging process. Therefore, when evaluating only based on the collected electric energy consumption data, there is no reference, which easily leads to an increase in electric energy consumption and errors in the metering results. Summary of the Invention

[0005] In order to solve the problem of errors in the metering results when the existing method meters the electric energy loss, the purpose of the present invention is to provide an intelligent charging pile metering method and system based on Internet of Things technology, and the specific technical solutions adopted are as follows: In the first aspect, the present invention provides a method for detecting anomalies in multivariate time series data based on a multi-head graph attention network, and the method includes the following steps: Obtain the charging duration, electric energy consumption, battery temperature, ambient temperature, and SOC value during the charging process of several vehicles, where the several vehicles include the target vehicle; According to the difference between the charging duration corresponding to each moment during each charging process and the theoretical charging duration, and the difference between the accumulated electric energy consumption corresponding to each moment and the theoretical electric energy demand, obtain the battery aging parameter corresponding to each moment during each charging process; combine the change situation of the battery temperature corresponding to each moment during each charging process, the ambient temperature, and the battery aging parameter to obtain the capacity change factor corresponding to the moment. Calculate the difference index between every two charging processes based on the difference in power consumption during every two charging processes and the difference in the similarity between the capacity change factor and the SOC value; cluster all charging processes using the difference index; Use the difference in power consumption between the current charging process of the target vehicle and the charging processes within its cluster to correct the power consumption of the current charging process of the target vehicle, and obtain the corrected power consumption.

[0006] Preferably, the battery aging parameter at each moment during each charging process is obtained based on the difference between the charging duration corresponding to each moment during each charging process and the theoretical charging duration, and the difference between the accumulated power consumption at each moment and the theoretical power demand, including: Calculate the first difference between the charging duration corresponding to the candidate moment and the theoretical charging duration, and the second difference between the accumulated power consumption at the candidate moment and the theoretical power demand; Perform normalization processing on the arithmetic square root of the sum of the squares of the first difference and the second difference to obtain the battery aging parameter at the candidate moment; The candidate moment is any moment during any charging process of any vehicle among the several vehicles.

[0007] Preferably, the capacity change factor corresponding to each moment is obtained by combining the change in battery temperature at each moment during each charging process, the ambient temperature, and the battery aging parameter, including: For any charging process: Perform curve fitting on the SOC values at all moments during the any charging process to obtain the corresponding SOC curve; use the first derivative to process the SOC curve to obtain the instantaneous slope of the SOC value at each moment; record the moment with an instantaneous slope of 0 as the first characteristic moment; Use the battery aging parameter at the first characteristic moment as the weight to perform weighted summation on the battery temperatures at all first characteristic moments during the any charging process to obtain the critical temperature of the any charging process; For any first characteristic moment: combine the battery temperature, the ambient temperature, and the critical temperature at the any first characteristic moment to determine the capacity change factor at the any first characteristic moment.

[0008] Preferably, the combining of the battery temperature, the ambient temperature, and the critical temperature at the any first characteristic moment to determine the capacity change factor at the any first characteristic moment includes: Calculate the third difference between the battery temperature at the any first characteristic moment and the critical temperature; Calculate the sum of the ambient temperature at any of the first characteristic moments and the preset adjustment parameter, and determine the ratio between the third difference and the sum as the capacity change factor at any of the first characteristic moments; wherein the preset adjustment parameter is a value greater than 0.

[0009] Preferably, calculating the difference index between every two charging processes according to the difference in power consumption during every two charging processes and the difference in the similarity between the capacity change factor and the SOC value includes: For any two charging processes: Calculate the Pearson correlation coefficient between the capacity change factor sequence and the SOC value sequence corresponding to each charging process, and record it as the first correlation coefficient corresponding to each charging process; wherein, the capacity change factor sequence corresponding to each charging process is composed of the capacity change factors at all moments during each charging process, and the SOC value sequence corresponding to each charging process is composed of the SOC values at all moments during each charging process; Based on the matching relationship between the instantaneous slopes corresponding to each moment on the SOC curves corresponding to any two charging processes, obtain the characteristic difference value corresponding to any two charging processes; According to the first difference between the first correlation coefficients corresponding to any two charging processes, the second difference between the power consumption amounts during any two charging processes, and the characteristic difference value, obtain the difference index between any two charging processes, and the first difference, the second difference, and the characteristic difference value are all positively correlated with the difference index.

[0010] Preferably, the obtaining the characteristic difference value corresponding to any two charging processes based on the matching relationship between the instantaneous slopes corresponding to each moment on the SOC curves corresponding to any two charging processes includes: Change the matching relationship of the values in the first slope sequence and the second slope sequence by translation, and respectively obtain the difference between the two data in each matching pair; the first slope sequence and the second slope sequence are respectively composed of the instantaneous slopes corresponding to all moments on the SOC curve corresponding to one of any two charging processes; Calculate the average value of the differences corresponding to all matching pairs obtained after each translation, and record it as the difference value of each translation; wherein the step size of each translation is a preset step size; Determine the smallest difference value of all translations as the characteristic difference value corresponding to any two charging processes.

[0011] Preferably, clustering all charging processes using the difference index includes: clustering all charging processes using the K-means clustering algorithm based on the difference index between every two charging processes.

[0012] Preferably, the method of correcting the power consumption of the current charging process of the target vehicle by using the difference between the power consumption of the current charging process of the target vehicle and that of the charging processes within its cluster to obtain the corrected power consumption includes: Calculating the average value of the power consumption of all charging processes within the cluster where the current charging process of the target vehicle is located; denoting the difference between the average value and the power consumption of the current charging process of the target vehicle as the fourth difference; Taking the product of the normalization result of the minimum value of all the feature difference values and the fourth difference as the adjustment value; Using the adjustment value to correct the power consumption of the current charging process of the target vehicle to obtain the corrected power consumption.

[0013] Preferably, the method of using the adjustment value to correct the power consumption of the current charging process of the target vehicle to obtain the corrected power consumption includes: Determining the sum of the power consumption of the current charging process of the target vehicle and the adjustment value as the corrected power consumption.

[0014] In a second aspect, the present invention provides a multivariate time series data anomaly detection system based on a multi-head graph attention network, which includes: A data acquisition module, configured to obtain the charging duration, power consumption, battery temperature, ambient temperature, and SOC value during the charging processes of a plurality of vehicles, where the plurality of vehicles include the target vehicle; A first calculation module, configured to obtain the battery aging parameter at each moment during each charging process according to the difference between the charging duration corresponding to each moment during each charging process and the theoretical charging duration, and the difference between the accumulated power consumption and the theoretical power demand at each moment; combining the change of the battery temperature at each moment during each charging process, the ambient temperature, and the battery aging parameter to obtain the capacity change factor at the corresponding moment; A clustering module, configured to calculate the difference index between every two charging processes according to the difference in power consumption between every two charging processes and the difference in the similarity between the capacity change factor and the SOC value; clustering all charging processes by using the difference index; A correction module, configured to correct the power consumption of the current charging process of the target vehicle by using the difference between the power consumption of the current charging process of the target vehicle and that of the charging processes within its cluster to obtain the corrected power consumption.

[0015] The present invention has at least the following beneficial effects: The present invention first collects the charging duration, power consumption, battery temperature, ambient temperature, and SOC value during multiple charging processes of multiple vehicles. It analyzes the difference between the charging duration corresponding to each moment during each charging process and the theoretical charging duration, as well as the difference between the cumulative power consumption at each moment and the theoretical power demand, and obtains the battery aging parameter at each moment during each charging process. This operation avoids errors in power consumption data caused by the increase in the actual parameters of electric vehicles over time and the differences between different electric vehicles themselves, making the elements within the cluster more representative during subsequent clustering. Then, by combining the change in battery temperature with the ambient temperature at each moment during each charging process, the capacity change factor is determined. Based on the difference in power consumption between every two charging processes and the difference in the similarity between the capacity change factor and the SOC value, the difference index between different charging processes is determined, and thus the charging processes are clustered. This operation analyzes the impact of battery loss differences and temperature influence differences of different electric vehicles on the basic battery parameters, avoiding data distortion that easily occurs when obtaining the relationship between the SOC value and temperature based on a single moment. Finally, according to the difference in power consumption between the current charging process of the target vehicle and the charging processes within its cluster, the power consumption data is cleaned in real time, improving the accuracy of cleaning and the credibility of the charging pile metering process. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for description in the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a flowchart of an intelligent charging pile metering method based on Internet of Things technology provided by an embodiment of the present invention; Figure 2 It is a structural block diagram of an intelligent charging pile metering system based on Internet of Things technology provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the intelligent charging pile metering method and system based on Internet of Things technology proposed according to the present invention as follows.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0020] The following specifically describes the specific solutions of the intelligent charging pile metering method and system based on the Internet of Things technology provided by the present invention in conjunction with the accompanying drawings.

[0021] Embodiment of the intelligent charging pile metering method based on the Internet of Things technology: The specific scenario targeted by this embodiment is as follows: During the charging process of two-wheeled electric vehicles or three-wheeled electric vehicles, due to the aging problem that occurs in the batteries installed in two-wheeled electric vehicles or three-wheeled electric vehicles during long-term use, it affects the battery power storage of the electric vehicle and the power loss during the charging process, resulting in a reduction in the battery life of the electric vehicle and more power consumption during the charging process. At this time, when evaluating it only based on the power consumption data of a single electric vehicle itself, there is no reference, which easily leads to an increase in power consumption and errors in the metering results. Therefore, it is necessary to clean the actual power loss data to ensure the accuracy and reliability of the metering results.

[0022] This embodiment proposes an intelligent charging pile metering method based on the Internet of Things technology, as Figure 1 shown. The intelligent charging pile metering method based on the Internet of Things technology in this embodiment includes the following steps: Step S1, obtain the charging duration, power consumption, battery temperature, ambient temperature, and SOC value during the charging process of several vehicles, where the several vehicles include the target vehicle.

[0023] During the charging process of two-wheeled electric vehicles or three-wheeled electric vehicles, the BMS system used will be simpler than that of large electric vehicles because it manages fewer battery units, but its core functions are similar. The BMS system, that is, the Battery Management System (abbreviated as BMS), is an electronic system that monitors and protects the battery pack. It can monitor relevant data such as the voltage, current, and temperature of the battery in real time, and perform balanced management and protection on the battery components.

[0024] First, collect the charging duration of each charging process, the cumulative power consumption at each moment during each charging process, the battery temperature, the ambient temperature, and the SOC (State of Charge) value within a preset time period for multiple vehicles. Among the multiple vehicles collected, there is a target vehicle, that is, the vehicle for which the power consumption needs to be corrected. It should be noted that: in this embodiment, the multiple vehicles are all of the same type, the same model, and have the same relevant parameters such as the rated maximum storage capacity of the battery, the maximum rated current, and the voltage. In this embodiment, the preset time period is the most recent month. In specific applications, the implementer can set the preset time period according to specific circumstances. In this embodiment, the data collection frequency is once per second, that is, for any charging process, a charging duration, power consumption, battery temperature, ambient temperature, and SOC value are collected every second during this charging process. The number of vehicles is 200. In specific applications, the implementer can set it according to specific circumstances.

[0025] So far, this embodiment has obtained the charging duration, power consumption, battery temperature, ambient temperature, and SOC value at each moment during each charging process of multiple vehicles. It should be noted that: the charging duration at each moment is the duration from the start moment of this charging to this moment, and the power consumption at each moment is the cumulative power consumption from the start moment of this charging to this moment.

[0026] Step S2: Obtain the battery aging parameter at each moment during each charging process based on the difference between the charging duration corresponding to each moment during each charging process and the theoretical charging duration, and the difference between the cumulative power consumption at each moment and the theoretical power demand; combine the change situation of the battery temperature at each moment during each charging process, the ambient temperature, and the battery aging parameter to obtain the capacity change factor at the corresponding moment.

[0027] When measuring the power consumption of different electric vehicles, since the actual parameters of electric vehicles change with the increase of use time and the change degrees of different electric vehicles are different, there are certain errors in the power consumption data generated during the comparison process, resulting in a reduction in the accuracy of the power consumption data for a certain charging process corresponding to the current charging pile.

[0028] Since the total driving ranges of different electric vehicles are different in reality, certain battery losses occur during the continuous charging and discharging process of their batteries, resulting in a decrease in their actual power storage capacity. At the same time, it causes a change in the magnitude of the current actually generated by the charging pile and passing through the battery, thereby leading to a change in the actual charging efficiency. To avoid the impact of battery self-loss on the actual measurement of the power consumption of the charging pile, it is necessary to correct the collected power consumption to eliminate the impact brought by the loss. At the same time, since the remaining power storage capacities of actual electric vehicles before charging are different, the difference between the rated power storage capacity of the battery and the remaining power storage capacity before charging is obtained, thereby obtaining the theoretical power demand and the theoretical charging duration deduced when the parameters such as the power of the charging pile are the same.

[0029] Next, this embodiment will be described by taking a moment during the charging process of a vehicle as an example. The method provided in this embodiment can be used to process all other moments of this vehicle and all moments during the charging processes of other vehicles.

[0030] Specifically, any moment during the charging process of any vehicle among the several vehicles is denoted as a candidate moment. The first difference between the charging duration corresponding to the candidate moment and the theoretical charging duration, and the second difference between the cumulative power consumption at the candidate moment and the theoretical power demand are calculated respectively; the arithmetic square root of the sum of the squares of the first difference and the second difference is normalized to obtain the battery aging parameter at the candidate moment. It should be noted that: the duration corresponding to the candidate moment is the duration from the start of this charging to the candidate moment. The theoretical charging duration and the theoretical power demand are set by the implementer according to the battery parameters.

[0031] In this embodiment, a specific calculation formula for the battery aging parameter is given. The battery aging parameter at the r-th moment during the i-th charging process of any vehicle can be expressed as: where represents the battery aging parameter at the r-th moment during the i-th charging process of this vehicle, represents the difference between the charging duration corresponding to the r-th moment during the i-th charging process of this vehicle and the theoretical charging duration, that is, the first difference; represents the difference between the cumulative power consumption at the r-th moment during the i-th charging process of this vehicle and the theoretical power demand, that is, the second difference; norm( ) represents the normalization function.

[0032] It represents the arithmetic square root of the sum of the squares of the first difference and the second difference, that is, the eigenvalue is combined using the Euclidean norm and normalized. When the difference between the charging duration corresponding to the r-th moment during the i-th charging process of the vehicle and the theoretical charging duration is larger, and the difference between the accumulated power consumption and the theoretical power demand is also larger, it indicates that the health of the battery is lower, that is, the battery aging parameter is larger.

[0033] By adopting the above method, the battery aging parameter of the vehicle at each moment during each charging process can be obtained.

[0034] For each charging process, the value of SOC should theoretically increase linearly. However, during the actual charging process, it is affected by external factors such as current and voltage fluctuations and temperature generated when the inverter of the charging pile converts alternating current to direct current. Among them, the influence of environmental temperature on the actual power consumption of the charging pile is the most obvious. For example, at a lower temperature, the chemical reaction rate inside the battery decreases, resulting in a weakened charge and discharge ability of the battery, thereby reducing the available capacity of the battery, leading to a decrease or even tending to 0 in the power demand, shortening the actual charging time. As the charging progresses, the temperature of the electric vehicle battery gradually becomes higher than the environmental temperature, and the capacity of the battery gradually approaches the actual level. However, it takes time in this process, resulting in an increase in the required duration during the charging process, thereby increasing the power consumption. At this time, since the influence of temperature on the battery capacity has a corresponding time, it is easy to have data distortion when obtaining the relationship between it and temperature based only on the SOC value at a single moment, resulting in external factor interference still existing in the final result.

[0035] There is a corresponding threshold between the capacity change of the battery during the charging process and the temperature, that is, only when the temperature is lower than a certain temperature threshold, the capacity of the battery will change with the temperature.

[0036] Next, this embodiment will be described by taking a charging process as an example. The method provided in this embodiment can be used to process other charging processes.

[0037] Specifically, for any charging process: Perform curve fitting on the SOC values at all moments during the any charging process to obtain the corresponding SOC curve. The abscissa of the SOC curve is the acquisition moment, and the ordinate is the SOC value corresponding to the acquisition moment; Curve fitting is a prior art and will not be elaborated here. Use the first derivative to process the SOC curve to obtain the instantaneous slope of the SOC value at each moment; Denote the moment with an instantaneous slope of 0 as the first characteristic moment, that is, multiple first characteristic moments are obtained.

[0038] Use the battery aging parameter at the first characteristic moment as the weight to perform weighted summation on the battery temperatures at all first characteristic moments during the any charging process to obtain the critical temperature of the any charging process.

[0039] For any first characteristic moment: Calculate the difference between the battery temperature at the any first characteristic moment and the critical temperature, and denote this difference as the third difference; Determine the ratio of the third difference to the ambient temperature at the any first characteristic moment as the capacity change factor at the any first characteristic moment.

[0040] In this embodiment, a specific calculation formula for the capacity change factor is given. The capacity change factor at the j-th first characteristic moment in the i-th charging process can be expressed as: Where, represents the capacity change factor at the j-th first characteristic moment in the i-th charging process, represents the battery temperature at the j-th first characteristic moment in the i-th charging process, N represents the number of first characteristic moments in the i-th charging process, represents the battery aging parameter at the j-th first characteristic moment in the i-th charging process, represents the ambient temperature at the j-th first characteristic moment in the i-th charging process, represents the preset adjustment parameter.

[0041] In this embodiment, a preset adjustment parameter is introduced into the calculation formula of the capacity change factor to prevent the denominator from being 0. The preset adjustment parameter is a value greater than 0. In specific applications, the implementer of the preset adjustment parameter sets it according to the actual ambient temperature. characterizes the critical temperature of the i-th charging process. represents the third difference, that is, the difference between the battery temperature and the critical temperature at the j-th first characteristic moment in the i-th charging process. This value has positive and negative. When it is less than 0, it means that the battery temperature is less than the critical temperature, and the probability of capacity change is greater. When the battery temperature is greater than or equal to 0, the probability of capacity change is smaller. At the same time, the lower the ambient temperature, the greater the probability of capacity change, that is, the greater the capacity change factor.

[0042] By using the above method, the capacity change factor at each first characteristic moment in each charging process can be obtained.

[0043] Step S3: Calculate the difference index between every two charging processes according to the difference in power consumption between every two charging processes and the difference in the similarity between the capacity change factor and the SOC value; Cluster all charging processes by using the difference index.

[0044] Next, this embodiment evaluates the difference in power consumption between every two charging processes and the difference in the similarity between the capacity change factor and the SOC value.

[0045] Specifically, calculate the Pearson correlation coefficient between the capacity change factor sequence corresponding to each charging process and the SOC value sequence, denoted as the first correlation coefficient corresponding to each charging process, and there is a first correlation coefficient for each charging process; among them, the capacity change factor sequence corresponding to each charging process is obtained by arranging the capacity change factors at all times during each charging process in chronological order, and the SOC value sequence corresponding to each charging process is obtained by arranging the SOC values at all times during each charging process in chronological order. The calculation method of the Pearson correlation coefficient is a prior art and will not be elaborated here.

[0046] For any two charging processes: Arrange the instantaneous slopes of the SOC values at all times during one of the two charging processes in chronological order to obtain a first slope sequence, and arrange the instantaneous slopes of the SOC values at all times during the other charging process of the two charging processes in chronological order to obtain a second slope sequence.

[0047] In the initial state, the elements at the corresponding positions in the first slope sequence and the second slope sequence are made to correspond to each other. That is, the first element in the first slope sequence corresponds to the first element in the second slope sequence, and the first element in the first slope sequence and the first element in the second slope sequence form a matching pair. The second element in the first slope sequence corresponds to the second element in the second slope sequence, and the second element in the first slope sequence and the second element in the second slope sequence form a matching pair. The third element in the first slope sequence corresponds to the third element in the second slope sequence, and the third element in the first slope sequence and the third element in the second slope sequence form a matching pair. And so on, until the last element in the first slope sequence corresponds to the last element in the second slope sequence, and the last element in the first slope sequence and the last element in the second slope sequence form a matching pair. That is, multiple matching pairs are obtained. Then, keep the first slope sequence unchanged and translate the second slope sequence. In this embodiment, the moving step size each time is 1. That is, the second element in the first slope sequence corresponds to the first element in the second slope sequence, and the second element in the first slope sequence and the first element in the second slope sequence form a matching pair. The third element in the first slope sequence corresponds to the second element in the second slope sequence, and the third element in the first slope sequence and the second element in the second slope sequence form a matching pair. The fourth element in the first slope sequence corresponds to the third element in the second slope sequence, and the fourth element in the first slope sequence and the third element in the second slope sequence form a matching pair. And so on, until the last element in the first slope sequence corresponds to the penultimate element in the second slope sequence, and the last element in the first slope sequence and the penultimate element in the second slope sequence form a matching pair. That is, multiple matching pairs are obtained. Next, on the basis of the previous translation result, translate the second slope sequence again. In this embodiment, the moving step size each time is 1. That is, the third element in the first slope sequence corresponds to the first element in the second slope sequence, and the third element in the first slope sequence and the first element in the second slope sequence form a matching pair. The fourth element in the first slope sequence corresponds to the second element in the second slope sequence, and the fourth element in the first slope sequence and the second element in the second slope sequence form a matching pair. The fifth element in the first slope sequence corresponds to the third element in the second slope sequence, and the fifth element in the first slope sequence and the third element in the second slope sequence form a matching pair. And so on, until the last element in the first slope sequence corresponds to the third-to-last element in the second slope sequence, and the last element in the first slope sequence and the third-to-last element in the second slope sequence form a matching pair. That is, multiple matching pairs are obtained.According to this method, the second slope sequence is translated multiple times until there are no matching pairs between the first slope sequence and the second slope sequence. Multiple matching pairs are obtained for each translation. The matching relationship between the values in the first slope sequence and the second slope sequence is changed by translation, and the differences between the two data in each matching pair are obtained respectively. Among them, the method for obtaining the difference between the two data in each matching pair is: taking the absolute value of the difference between the two data as the difference between the two data. Calculate the average value of the differences corresponding to all the matching pairs obtained after each translation, and record it as the difference value of each translation. The step size for each translation is a preset step size. The preset step size in this embodiment is 1. Determine the smallest difference value among all translations as the characteristic difference value corresponding to any two charging processes. According to the first difference between the first correlation coefficients corresponding to any two charging processes, the second difference between the power consumption amounts during any two charging processes, and the characteristic difference value, obtain the difference index between any two charging processes. The first difference, the second difference, and the characteristic difference value are all positively correlated with the difference index.

[0048] Among them, a positive correlation means that the dependent variable increases as the independent variable increases, and the dependent variable decreases as the independent variable decreases. It can be an additive relationship, a multiplicative relationship, etc., which is determined by the actual application. A negative correlation means that the dependent variable decreases as the independent variable increases, and the dependent variable increases as the independent variable decreases. It can be a subtractive relationship, a divisive relationship, etc., which is determined by the actual application.

[0049] In this embodiment, a specific calculation formula for the difference index is given. The difference index between the i-th charging process and the v-th charging process can be expressed as: Among them, represents the difference index between the i-th charging process and the v-th charging process, represents the power consumption amount of the i-th charging process, represents the power consumption amount of the v-th charging process, represents the second difference, represents the first correlation coefficient corresponding to the i-th charging process, represents the first correlation coefficient corresponding to the v-th charging process, represents the first difference, represents the characteristic difference value corresponding to the i-th charging process and the v-th charging process, represents the absolute value symbol.

[0050] When the first difference is smaller, the second difference is also smaller, and the characteristic difference value corresponding to the i-th charging process and the v-th charging process is also smaller, it indicates that the states of the batteries of the electric vehicles corresponding to the two charging processes are closer, that is, the difference index between the two charging processes is smaller, and the necessity of clustering them into one category is higher.

[0051] By using the above method, the difference index between every two charging processes can be obtained. The smaller the difference index, the closer the states of the batteries of the electric vehicles corresponding to the two charging processes, that is, the two charging processes should be clustered into the same category. Therefore, based on the difference index between every two charging processes, the K-means clustering algorithm is used to cluster all charging processes to obtain multiple clusters. The k value when using the K-means clustering algorithm is obtained by the elbow method, and the implementer can also set it according to specific circumstances. The K-means clustering algorithm is a prior art and will not be elaborated here.

[0052] Step S4: Use the difference between the power consumption of the current charging process of the target vehicle and the power consumption of the charging processes within its cluster to correct the power consumption of the current charging process of the target vehicle, and obtain the corrected power consumption.

[0053] The advantage of measuring the real-time charging process of the charging pile based on the Internet of Things is that it can analyze and process the real-time obtained power consumption data. When there is an error in the measurement data of the charging pile, a certain power consumption obtained can be cleaned by using the above-obtained historical charging data. Next, this embodiment will use the difference between the power consumption of the current charging process of the target vehicle and the power consumption of the charging processes within its cluster to clean the power consumption of the current charging process of the target vehicle.

[0054] Specifically, calculate the average value of the power consumption of all charging processes within the cluster where the current charging process of the target vehicle is located; record the difference between the average value and the power consumption of the current charging process of the target vehicle as the fourth difference; take the product of the normalized result of the minimum value of all the characteristic difference values and the fourth difference as the adjustment value; use the adjustment value to correct the computer power consumption of the current charging process of the target vehicle, and obtain the corrected power consumption. Specifically, determine the sum of the computer power consumption of the current charging process of the target vehicle and the adjustment value as the corrected power consumption. The corrected power consumption can be expressed as: Among them, represents the corrected power consumption, represents the power consumption of the current charging process of the target vehicle, represents the normalized result of the minimum value of all characteristic difference values, represents the average value of the power consumption of all charging processes within the cluster where the current charging process of the target vehicle is located. represents the fourth difference.

[0055] It should be noted that there are many methods for normalizing data. In this embodiment, the maximum - minimum normalization method is used to normalize the minimum value of all feature difference values. The maximum - minimum normalization method is a prior art and will not be elaborated here.

[0056] In this embodiment, in combination with a linear function, the current power consumption is cleaned. The difference between the average value of the power consumption of all charging processes within the cluster where the current charging process of the target vehicle is located and the power consumption of the current charging process of the target vehicle is used as the cleaning range, and the minimum value of the above - mentioned feature difference value is used as the coefficient. Thus, the original power consumption data is cleaned. Since it contains positive and negative values. When the power consumption of the current charging process is greater than the average value of the power consumption, this value is negative. Combining with the minimum value of the feature difference value, the reduction amount is obtained, and the original power consumption level is appropriately reduced. On the contrary, when it is positive, it is appropriately increased.

[0057] So far, this embodiment has obtained the corrected power consumption, that is, the cleaning of the power consumption is realized.

[0058] In this embodiment, first, the charging duration, power consumption, battery temperature, ambient temperature, and SOC value during multiple charging processes of multiple vehicles are collected. The difference between the charging duration corresponding to each moment during each charging process and the theoretical charging duration, as well as the difference between the accumulated power consumption and the theoretical power demand at each moment, are analyzed to obtain the battery aging parameters at each moment during each charging process. This operation avoids the error in power consumption data caused by the increase in the actual parameters of electric vehicles over time and the differences between different electric vehicles themselves, making the elements within the cluster more representative during subsequent clustering. Then, by combining the change in battery temperature with the ambient temperature at each moment during each charging process, the capacity change factor is determined. According to the difference in power consumption between every two charging processes and the difference in the similarity between the capacity change factor and the SOC value, the difference index between different charging processes is determined, and thus the charging processes are clustered. This operation analyzes the influence of battery loss differences and temperature - influence differences of different electric vehicles on the basic battery parameters, avoiding data distortion that is likely to occur when obtaining the relationship between the SOC value and temperature based on a single - moment SOC value. Finally, according to the difference in power consumption between the current charging process of the target vehicle and the charging processes within its cluster, the power consumption data is cleaned in real - time, improving the accuracy of cleaning and the credibility of the charging pile metering process.

[0059] Embodiment of an intelligent charging pile metering system based on Internet of Things technology: Refer to Figure 2 , which shows a block diagram of an intelligent charging pile metering system based on Internet of Things technology provided by an embodiment of the present invention. The system may include a data acquisition module, a first calculation module, a clustering module, and a correction module.

[0060] Among them, the data acquisition module is used to obtain the charging duration, power consumption, battery temperature, ambient temperature, and SOC value during the charging process of several vehicles, where the several vehicles include the target vehicle; The first calculation module is used to obtain the battery aging parameter at each moment during each charging process according to the difference between the charging duration corresponding to each moment and the theoretical charging duration during each charging process, and the difference between the accumulated power consumption and the theoretical power demand at each moment; combining the change of the battery temperature at each moment, the ambient temperature, and the battery aging parameter during each charging process, to obtain the capacity change factor at the corresponding moment; The clustering module is used to calculate the difference index between every two charging processes according to the difference in power consumption between every two charging processes and the difference in the similarity between the capacity change factor and the SOC value; clustering all charging processes by using the difference index; The correction module is used to correct the power consumption of the current charging process of the target vehicle by using the difference in power consumption between the current charging process of the target vehicle and the charging processes within its cluster, to obtain the corrected power consumption.

[0061] It should be understood that Figure 2 The block diagram of the intelligent charging pile metering system based on Internet of Things technology shown and its modules can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented by using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated designed hardware. Those skilled in the art can understand that the above methods and systems can be implemented by using computer-executable instructions and / or included in the processor control code. For example, such code is provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The systems and modules described in this specification can be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software implemented by various types of processors, or by a combination of the above hardware circuits and software (for example, firmware).

[0062] More details about each of the above modules can be found in other parts of this specification and will not be elaborated here.

[0063] In other embodiments, an intelligent charging pile metering device based on Internet of Things technology is also provided, including a memory and a processor. The memory is used to store executable program codes, and the processor is used to call and run the executable program codes from the memory, so that the device executes the welding control method applied to the pulse welder as described above. The device may specifically be a chip, a component or a module. The chip may include a processor and a memory connected thereto; wherein, the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute the intelligent charging pile metering method based on Internet of Things technology provided in the above embodiments.

[0064] In other embodiments, a computer program product is also provided. When the computer program product runs on a computer, the computer is caused to execute the above relevant steps to implement the intelligent charging pile metering method based on Internet of Things technology provided in the above embodiments.

[0065] In other embodiments, a computer-readable storage medium is also provided. Computer program codes are stored in the computer-readable storage medium, and when the computer program codes run on a computer, the computer is caused to execute the above relevant method steps to implement the intelligent charging pile metering method based on Internet of Things technology provided in the above embodiments.

[0066] Among them, the provided system, electronic device, computer program product, and computer-readable storage medium are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above and will not be elaborated here.

[0067] It should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent charging pile metering method based on Internet of Things technology, characterized in that, The method includes the following steps: Obtain the charging duration, power consumption, battery temperature, ambient temperature, and SOC value during the charging process of several vehicles, where the several vehicles include the target vehicle; Based on the difference between the charging duration corresponding to each moment during each charging process and the theoretical charging duration, and the difference between the accumulated power consumption at each moment and the theoretical power demand, obtain the battery aging parameter at each moment during each charging process; Combine the change of the battery temperature at each moment during each charging process, the ambient temperature, and the battery aging parameter to obtain the capacity change factor at the corresponding moment; Calculate the difference index between every two charging processes based on the difference in power consumption between every two charging processes and the difference in the similarity between the capacity change factor and the SOC value; Use the difference index to cluster all charging processes; Use the difference in power consumption between the current charging process of the target vehicle and the charging processes within its cluster to correct the power consumption of the current charging process of the target vehicle, and obtain the corrected power consumption.

2. The intelligent charging pile metering method based on Internet of Things technology according to claim 1, characterized in that, The obtaining the battery aging parameter at each moment during each charging process based on the difference between the charging duration corresponding to each moment during each charging process and the theoretical charging duration, and the difference between the accumulated power consumption at each moment and the theoretical power demand includes: Calculate the first difference between the charging duration corresponding to the candidate moment and the theoretical charging duration, and the second difference between the accumulated power consumption at the candidate moment and the theoretical power demand; Perform normalization processing on the arithmetic square root of the sum of the squares of the first difference and the second difference to obtain the battery aging parameter at the candidate moment; The candidate moment is any moment during any charging process of any vehicle among the several vehicles.

3. The intelligent charging pile metering method based on Internet of Things technology according to claim 1, characterized in that The combining the change of the battery temperature at each moment during each charging process, the ambient temperature, and the battery aging parameter to obtain the capacity change factor at the corresponding moment includes: For any charging process: Perform curve fitting on the SOC values at all moments during the any charging process to obtain the corresponding SOC curve; Use the first derivative to process the SOC curve to obtain the instantaneous slope of the SOC value at each moment; Denote the moment with an instantaneous slope of 0 as the first characteristic moment; Use the battery aging parameter at the first characteristic moment as the weight to perform weighted summation on the battery temperatures at all first characteristic moments during the any charging process to obtain the critical temperature of the any charging process; For any first characteristic moment: Combine the battery temperature, ambient temperature, and the critical temperature at the any first characteristic moment to determine the capacity change factor at the any first characteristic moment.

4. The intelligent charging pile metering method based on Internet of Things technology according to claim 3, wherein, The combining the battery temperature, ambient temperature, and the critical temperature at the any first characteristic moment to determine the capacity change factor at the any first characteristic moment includes: Calculate the third difference between the battery temperature at the any first characteristic moment and the critical temperature; Calculate the sum of the ambient temperature at any one of the first characteristic moments and the preset adjustment parameter, and determine the ratio between the third difference and the sum value as the capacity change factor at any one of the first characteristic moments; wherein the preset adjustment parameter is a value greater than 0.

5. The intelligent charging pile metering method based on the Internet of Things technology according to claim 3, characterized in that, Calculating the difference index between every two charging processes according to the difference in power consumption during every two charging processes and the difference in the similarity between the capacity change factor and the SOC value includes: For any two charging processes: Calculate the Pearson correlation coefficient between the capacity change factor sequence and the SOC value sequence corresponding to each charging process, denoted as the first correlation coefficient corresponding to each charging process; wherein, the capacity change factor sequence corresponding to each charging process is composed of the capacity change factors at all moments during each charging process, and the SOC value sequence corresponding to each charging process is composed of the SOC values at all moments during each charging process; Based on the matching relationship between the instantaneous slopes corresponding to each moment on the SOC curves corresponding to any two charging processes, obtain the characteristic difference value corresponding to any two charging processes; According to the first difference between the first correlation coefficients corresponding to any two charging processes, the second difference between the power consumption amounts during any two charging processes, and the characteristic difference value, obtain the difference index between any two charging processes, and the first difference, the second difference, and the characteristic difference value are all positively correlated with the difference index.

6. The intelligent charging pile metering method based on Internet of Things technology according to claim 5, wherein, The obtaining the characteristic difference value corresponding to any two charging processes based on the matching relationship between the instantaneous slopes corresponding to each moment on the SOC curves corresponding to any two charging processes includes: Change the matching relationship of the values in the first slope sequence and the second slope sequence by translation, and respectively obtain the difference between the two data in each matching pair; the first slope sequence and the second slope sequence are respectively the instantaneous slopes corresponding to all moments on the SOC curve corresponding to one of any two charging processes; Calculate the average value of the differences corresponding to all matching pairs obtained after each translation, denoted as the difference value of each translation; wherein the step size of each translation is the preset step size; Determine the minimum difference value among all translations as the characteristic difference value corresponding to any two charging processes.

7. The intelligent charging pile metering method based on the Internet of Things technology according to claim 1, characterized in that The clustering of all charging processes using the difference index includes: clustering all charging processes using the K-means clustering algorithm based on the difference index between every two charging processes.

8. The intelligent charging pile metering method based on Internet of Things technology according to claim 6, wherein The correcting the power consumption amount of the current charging process of the target vehicle by using the difference between the power consumption amount of the current charging process of the target vehicle and the power consumption amounts of the charging processes within its cluster to obtain the corrected power consumption amount includes: Calculate the average value of the power consumption amounts of all charging processes within the cluster where the current charging process of the target vehicle is located; denote the difference between the average value and the power consumption amount of the current charging process of the target vehicle as the fourth difference; Take the product of the normalized result of the minimum value of all the characteristic difference values and the fourth difference as the adjustment value; The computer power consumption of the current charging process of the target vehicle is corrected using the adjustment value to obtain the corrected power consumption of electric energy.

9. The intelligent charging pile metering method based on the Internet of Things technology according to claim 8, characterized in that The step of using the adjustment value to correct the computer power consumption of the current charging process of the target vehicle to obtain the corrected power consumption of electric energy includes: Determining the sum of the computer power consumption of the current charging process of the target vehicle and the adjustment value as the corrected power consumption of electric energy.

10. An intelligent charging pile metering system based on Internet of Things technology, characterized in that, The system includes: A data acquisition module, configured to obtain the charging duration, power consumption of electric energy, battery temperature, ambient temperature, and SOC value during the charging process of a plurality of vehicles, where the plurality of vehicles includes the target vehicle; A first calculation module, configured to obtain the battery aging parameter at each moment during each charging process according to the difference between the charging duration corresponding to each moment during each charging process and the theoretical charging duration, and the difference between the cumulative power consumption of electric energy at each moment and the theoretical power demand of electric energy; combining the change of the battery temperature at each moment during each charging process, the ambient temperature, and the battery aging parameter to obtain the capacity change factor at the corresponding moment; A clustering module, configured to calculate the difference index between every two charging processes according to the difference in power consumption of electric energy between every two charging processes and the difference in the similarity between the capacity change factor and the SOC value; clustering all charging processes using the difference index; A correction module, configured to correct the power consumption of the current charging process of the target vehicle by using the difference in power consumption of electric energy between the current charging process of the target vehicle and the charging processes within its cluster to obtain the corrected power consumption of electric energy.

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