Charging quantity metering error high-precision calculation method for charging station

Abstract

The invention relates to the technical field of charging quantity metering, and provides a charging quantity metering error high-precision calculation method for a charging station, and the method comprises the steps: collecting the charging quantity and the cumulative operation time of charging equipment of the charging station, and the battery charge state, the charging voltage, the charging current, the environment temperature, the charging temperature and the charging power in the working process of the charging station; respectively defining an input matrix, a process noise matrix, a state transition matrix and an input vector at each data acquisition moment, and determining a state vector at each data acquisition moment; calculating observation power at a data acquisition moment and processing the observation power by using Kalman filtering to obtain an optimal state vector and a theoretical power correction value at the data acquisition moment; and according to the charging amount, the charging power and the battery charge state directly displayed by the charging equipment at all the acquisition moments, obtaining metering errors of the charging amount of the charging station at all the acquisition moments. The invention aims to improve the charging quantity metering precision of the charging station.

Description

Technical Field

[0001] This application relates to the technical field of charging quantity measurement, and particularly to a high-precision calculation method for the measurement error of charging quantity used in charging stations. Background Art

[0002] Accurately measuring the charging quantity of a charging station is the core requirement for ensuring fair transactions of charging services and optimizing operation management in the charging station. It can enhance users' trust in charging services, help operators evaluate the energy efficiency of equipment and analyze the grid load, and contribute to the optimal allocation of resources. During the charging process of a charging station, affected by various factors such as equipment aging, drastic changes in environmental temperature, and grid harmonic interference, the charging power exhibits a composite characteristic of high-frequency fluctuation and low-frequency attenuation. Traditional average power integration methods and filtering techniques such as Kalman filtering cannot effectively suppress noise and compensate for changes in physical parameters, easily resulting in the measurement error of the charging quantity exceeding the national measurement standard.

[0003] Specifically, the Kalman filter assumes that both the system noise and the observation noise are Gaussian white noise, and it cannot adapt to the sudden power change caused by drastic temperature changes during charging at the charging station, easily resulting in problems such as excessive noise suppression or signal tracking delay, leading to insufficient measurement accuracy of the charging quantity. Summary of the Invention

[0004] This application provides a high-precision calculation method for the measurement error of charging quantity used in charging stations to solve the problem that the Kalman filter method cannot adapt to the sudden power change caused by drastic temperature changes during charging at the charging station, resulting in insufficient measurement accuracy of the charging quantity. The specific technical solutions adopted are as follows: An embodiment of this application provides a high-precision calculation method for the measurement error of charging quantity used in charging stations. The method includes the following steps: Collect the charging quantity directly displayed by the charging equipment of the charging station and the cumulative operation time, as well as the state of charge of the battery, charging voltage, charging current, environmental temperature, charging temperature, and charging power at different data collection times during the operation of the charging station; Define the input matrix at each data collection time. According to the charging temperature, define the process noise matrix and the state transition matrix respectively. According to the environmental temperature, charging temperature, and the cumulative operation time of the charging equipment, define the input vector at each data collection time. According to the state transition matrix, input vector, input matrix, and process noise matrix at the same data collection time, determine the state vector at the same data collection time; According to the charging voltage, charging current, and charging power at the same data collection time, calculate the observed power at the same data collection time. Use Kalman filtering to process the observed power at the data collection time to obtain the optimal state vector at the data collection time. According to the optimal state vector at the data collection time, determine the theoretical power correction value at the data collection time; Obtain the measurement errors of the charging amount at the charging station at all acquisition times based on the directly displayed charging amount, charging power, and state of charge of the battery at all acquisition times of the charging device.

[0005] Further, the input matrix and the input vector are specifically as follows: The input matrix at the th data acquisition time, where represents a preset first correction amount; represents a preset first correction amount; The input vector at the data acquisition time is a column vector formed by arranging the ambient temperature, charging temperature, and cumulative operating time of the charging device at the charging station in sequence at the data acquisition time. Further, the process noise matrix and the state transition matrix are specifically as follows: The process noise matrix at the th data acquisition time, where represents a preset temperature threshold; represents a preset non - linear exponent; represents a preset adjustment amplitude; represents the optimal operating temperature of the battery of the charging device at the charging station; the process noise matrix at the first data acquisition time ; represents a diagonal matrix; The at the th data acquisition time, where represents the battery temperature sensitivity coefficient obtained by performing a constant - current charging test on the charging device at the charging station; represents the th data acquisition time and the th data acquisition time, the absolute value of the difference in charging temperature; represents the drift rate of the voltage sensor obtained by performing an aging test on the voltage sensor; represents the drift rate of the current sensor obtained by performing an aging test on the current sensor; represents the data acquisition time and the data acquisition time time interval; represents the designed service life duration of the charging device at the charging station; represents the average value of the aging rate constants of the input and output terminals obtained by performing an aging test on the charging device at the charging station.

[0006] Further, the state vector is specifically as follows: The state vector at the th data acquisition time , where represents the state vector at the th data acquisition moment; represents the process noise vector at the th data acquisition moment, and the process noise vector at the th data acquisition moment obeys a Gaussian distribution with a mean vector of 0 and a covariance matrix of ; represents the input vector at the th data acquisition moment; The th data acquisition moment state vector calculation result is the temperature compensation coefficient, drift deviation of the voltage sensor, drift deviation of the current sensor, input side efficiency, and output side efficiency arranged in a column vector in sequence.

[0007] Furthermore, the method for obtaining the observed power at the data acquisition moment is as follows: The th data acquisition moment theoretical power ; where represents the th data acquisition moment theoretical power; represents the th data acquisition moment charging power; represents the th data acquisition moment charging current; represents the th data acquisition moment charging voltage; represents a preset charging input side aging coefficient; represents a preset charging output side aging coefficient; Determine the observed power at the data acquisition moment according to the theoretical power and the observation noise at the data acquisition moment.

[0008] Furthermore, the specific method for determining the observed power at the data acquisition moment according to the theoretical power and the observation noise at the data acquisition moment includes: Denote the sum of the theoretical power and the observation noise at the data acquisition moment as the observed power at the data acquisition moment; the observation noise obeys a Gaussian distribution with a mean of 0 and a covariance matrix of ; represents the observation noise variance.

[0009] Further, the optimal state vector at the data acquisition moment is specifically: A column vector formed by arranging in sequence the optimal estimated value of the temperature compensation coefficient at the data acquisition moment, the optimal estimated value of the drift deviation of the voltage sensor, the optimal estimated value of the drift deviation of the current sensor, the optimal estimated value of the input-side efficiency, and the optimal estimated value of the output-side efficiency.

[0010] Further, the calculation formula for the theoretical power correction value at the data acquisition moment is specifically: denotes the theoretical power correction value at the th data acquisition moment; denotes the optimal estimated value of the temperature compensation coefficient at the th data acquisition moment; denotes the optimal estimated value of the drift deviation of the voltage sensor at the th data acquisition moment; denotes the optimal estimated value of the drift deviation of the current sensor at the th data acquisition moment; denotes the optimal estimated value of the input-side efficiency at the th data acquisition moment; denotes the optimal estimated value of the output-side efficiency at the th data acquisition moment.

[0011] Further, the specific method for obtaining the measurement errors of the charging amounts of the charging station at all data acquisition moments according to the charging amounts, charging powers, and state of charge of the battery directly displayed by the charging device at all acquisition moments includes: When the state of charge of the battery at the data acquisition moment is less than the preset state of charge threshold, assign the charging measurement weight at the data acquisition moment as 1; when the state of charge of the battery at the data acquisition moment is greater than or equal to the state of charge threshold, assign the charging measurement weight at the data acquisition moment as 0.95; According to the charging powers and charging measurement weights at all data acquisition moments, determine the adjusted charging amounts at each acquisition moment respectively, and record the difference between the charging amount directly displayed by the charging device of the charging station at the same acquisition moment and the adjusted charging amount as the charging amount measurement error at the same acquisition moment.

[0012] Further, the calculation formula for the adjusted charging amount at each acquisition moment is: where denotes the adjusted charging amount at the data acquisition moment ; denotes the charging amount at the data acquisition moment , where and are both preset to 0; represents the charging power at the data acquisition moment ; represents the charging metering weight at the data acquisition moment ;

[0013] The beneficial effects of this application are as follows: In order to avoid problems such as power mutation caused by drastic temperature changes during the charging process of the charging station, resulting in excessive noise suppression and signal tracking delay, this application converts complex interference factors such as the non-linear change of the battery internal resistance of the charging device with temperature, sensor zero drift and sensitivity drift, and efficiency attenuation caused by IGBT device aging into state variables that can be estimated in real time, obtains the state vector at each data acquisition moment respectively, performs adaptive noise adjustment during the process of constructing the state vector, models and dynamically compensates for the multi-source interference received by the charging quantity metering, and improves the charging quantity metering accuracy. Among them, the adaptive noise adjustment is realized through the process noise matrix at the data acquisition moment; further, in order to achieve accurate modeling of the power metering error and real-time suppression of noise in complex environments, correct the charging quantity metering, and dynamically adjust the noise covariance driven by temperature changes, use the Kalman filter to process the observed power at the data acquisition moment, obtain the optimal state vector at the data acquisition moment, and determine the theoretical power correction value at the data acquisition moment according to the optimal state vector at the data acquisition moment. The theoretical power correction value is the result of correcting the charging power at each acquisition moment of the charging station; finally, according to the charging quantity, charging power, and battery state of charge directly displayed by the charging device at all acquisition moments, combined with the stage in which the charging device of the charging station is in during the charging process, avoid the noise cumulative error caused by the current fluctuation in the constant voltage stage during the charging process from being amplified and accumulated into the final charging quantity, resulting in the problem of inflated metering, dynamically adjust the integration weight at each acquisition moment during the charging quantity metering process, suppress the charging quantity metering error caused by the noise-sensitive stage, obtain the metering error of the charging quantity of the charging station at all acquisition moments respectively, solve the problem that the Kalman filter method is not suitable for the power mutation caused by drastic temperature changes during the charging of the charging station, resulting in insufficient charging quantity metering accuracy, and improve the accuracy of the charging quantity metering. Description of the Drawings

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0015] Figure 1Schematic flow chart of a high-precision calculation method for charging quantity measurement error of a charging station provided by an embodiment of the present application; Figure 2 Flow chart for obtaining a state vector provided by an embodiment of the present application. Detailed implementation manners

[0016] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0017] Please refer to Figure 1 , which shows a flow chart of a high-precision calculation method for charging quantity measurement error of a charging station provided by an embodiment of the present application. The method includes the following steps: Step S001: Collect the charging quantity directly displayed by the charging device of the charging station and the cumulative operation time, as well as the state of charge of the battery, charging voltage, charging current, ambient temperature, charging temperature, and charging power at different data collection times during the operation of the charging station.

[0018] Connect the voltage sensor in parallel to the output terminal inside the charging station, connect the current sensor in series to the positive electrode circuit inside the charging station, and fix the temperature sensor inside the ventilation hole on the side plate of the charging station cabinet. When charging at the charging station, use the voltage sensor to collect the charging voltage, use the current sensor to collect the charging current, use the temperature sensor to collect the ambient temperature. At the same time, through the charging interface communication protocol, read the charging temperature and the state of charge SOC of the built-in temperature sensor of the battery management system BMS of the charging device, and record the cumulative operation time of the charging device through the built-in clock chip of the charging station MCU. Directly read the charging quantity directly displayed by the charging device of the charging station.

[0019] Preferably, in an embodiment of the present application, when collecting the charging quantity directly displayed by the charging device, the state of charge of the battery, the charging voltage, the charging current, the ambient temperature, and the charging temperature, the charging station MCU timer is used to uniformly trigger sampling, and through the time stamp marking and linear interpolation algorithm, the data streams with different frequencies are aligned. In this embodiment, the data sampling frequencies of the charging quantity directly displayed by the charging device, the state of charge of the battery, the charging voltage, and the charging current are 1 kHz, and the data sampling frequencies of the ambient temperature and the charging temperature are 100 Hz. In the actual application process, as other implementation manners, the implementer can determine the data sampling frequency according to the actual situation by himself / herself, and the present application does not make special restrictions.

[0020] Calculate the charging power at each data acquisition moment according to the charging voltage and charging current.

[0021] Denote any data acquisition moment as the target data acquisition moment, and denote the product of the charging power at the previous adjacent acquisition moment of the target data acquisition moment and 0.2 as the deviation threshold of the target data acquisition moment. When the absolute value of the difference between the charging power at the target data acquisition moment and the charging power at the previous adjacent acquisition moment of the target data acquisition moment is greater than the deviation threshold of the target data acquisition moment, it is determined that a power mutation has occurred at the target data acquisition moment. To avoid the interference of the noise accumulation effect caused by the increased current fluctuation during the charging process due to the power mutation on the charging amount measurement, assign the charging power at the target data acquisition moment as the average value of the charging powers at the three previous adjacent acquisition moments of the target data acquisition moment.

[0022] It can be understood that when there is no previous adjacent acquisition moment for the target data acquisition moment, no assignment is made to the charging power at the target data acquisition moment.

[0023] Among them, aligning data streams of different frequencies and calculating the charging power according to the charging voltage and charging current are both well-known technologies and will not be elaborated here.

[0024] So far, obtain the charging amount and the cumulative operation time directly displayed by the charging device of the charging station, as well as the state of charge, charging voltage, charging current, ambient temperature, charging temperature, and charging power at different data acquisition moments during the operation of the charging station.

[0025] Step S002: Define the input matrix at each data acquisition moment, define the process noise matrix and the state transition matrix respectively according to the charging temperature, define the input vector at each data acquisition moment according to the ambient temperature, charging temperature, and the cumulative operation time of the charging device, and determine the state vector at the same data acquisition moment according to the state transition matrix, input vector, input matrix, and process noise matrix at the same data acquisition moment.

[0026] Considering that power mutations may occur during the charging process of the charging station due to sudden temperature changes, to avoid the problems of excessive noise suppression and signal tracking delay, convert complex interference factors such as the non-linear change of the battery internal resistance of the charging device with temperature, sensor zero drift and sensitivity drift, and efficiency decay caused by IGBT device aging into state variables that can be estimated in real time, and through adaptive noise adjustment, model and dynamically compensate for the multi-source interference received by the charging amount measurement to improve the accuracy of charging amount measurement. Among them, IGBT of the charging device, namely Insulated Gate Bipolar Transistor, represents an insulated gate bipolar transistor, which is a very important power electronic device in the charging device.

[0027] Define the state transition matrix at each data acquisition time. Specifically, at the th data acquisition time, , where represents a diagonal matrix, and the values separated by commas in the parentheses are all the elements on the diagonal of the diagonal matrix; represents the battery temperature sensitivity coefficient obtained by performing a constant current charging test on the charging equipment of the charging station. Specifically, in this embodiment, the constant current charging test is performed in an environment where the temperature is higher than -30°C and lower than 50°C. The value of the battery temperature sensitivity coefficient obtained in this embodiment is 0.02 ; represents the absolute value of the difference in charging temperature between the th data acquisition time and the th data acquisition time; represents the drift rate of the voltage sensor obtained by performing an aging test on the voltage sensor. Specifically, in this embodiment, the aging test is performed in an incubator with a temperature cycle of 25°C, 45°C, and 25°C, and the duration of the aging test is 100 hours. The drift rate of the voltage sensor obtained in this embodiment is per second; represents the drift rate of the current sensor obtained by performing an aging test on the current sensor. Specifically, in this embodiment, the aging test is performed in an incubator with a temperature cycle of 25°C, 45°C, and 25°C, and the duration of the aging test is 100 hours. The drift rate of the current sensor obtained in this embodiment is per second; represents the time interval between the data acquisition time and the data acquisition time ; represents the designed life duration of the charging equipment of the charging station; represents the average value of the aging rate constants of the input and output ends obtained by performing an aging test on the charging equipment of the charging station. Specifically, in this embodiment, when performing the aging test, an 80% rated load is applied to the charging equipment and it runs for 500 hours. The value of the aging rate constant obtained in this embodiment is 0.8.

[0028] It can be understood that the state transition matrix is a 5 5 diagonal matrix. Performing a constant current charging test on the charging equipment of the charging station, as well as performing aging tests on the voltage sensor, current sensor, and charging equipment of the charging station are all well-known technologies and will not be elaborated here.

[0029] Define the input vector at each data acquisition time. Specifically, the input vector at the th data acquisition time is , where represents the ambient temperature at the th data acquisition time; Indicates the Charging temperature at the time of data collection; Indicates the charging equipment of the charging station up to The cumulative running time at each data collection moment.

[0030] Define the input matrix at each data collection moment. Specifically, The input matrix at each data collection moment ,in, represents a preset first correction value, which represents an additional correction value for the aging of the IGBT device due to the cumulative operating time of the charging equipment of the charging station. The value of should be greater than or equal to 0.002 and less than or equal to 0.01. The value of is 0.005; Indicates the preset second correction value, which indicates the effect of temperature on the Temperature compensation coefficient at each data collection moment The additional correction amount, The value of should be greater than or equal to 0.005 and less than or equal to 0.02. The value of is 0.01.

[0031] The state vector at the same data collection moment is determined according to the state vector, the state transfer matrix, the input vector, the input matrix and the process noise matrix at the same data collection moment.

[0032] During the actual operation of a charging station's charging equipment, the dynamic characteristics of its hardware are significantly affected by the ambient and charging temperatures. Therefore, it is necessary to accurately characterize the influence of complex interference factors such as the nonlinear change of the battery's internal resistance with temperature, sensor zero drift and sensitivity drift, and efficiency degradation caused by IGBT device aging based on the ambient and charging temperatures. This allows the Kalman filter to adapt to power surges caused by drastic temperature changes during charging at the charging station, avoiding problems such as excessive noise suppression and signal tracking delays.

[0033] Specifically, the process noise matrix at each data collection moment allows the Kalman filter to make more drastic corrections when the charging temperature changes more drastically between adjacent collection moments to track rapidly changing power fluctuations, such as a sudden power drop caused by a sudden increase in battery internal resistance at low temperatures.

[0034] Define the process noise matrix at each data collection moment. Specifically, The process noise matrix at the data acquisition moment ,in, Indicates the preset temperature threshold. In this embodiment, the temperature threshold is set to °C; The process noise matrix at the first data acquisition moment selected in this embodiment ; represents a preset non - linear exponent, which is used to balance fast response and stability. The value of the non - linear exponent in this embodiment is 1.5; represents a preset adjustment amplitude. The value of the adjustment amplitude in this embodiment is 0.5; represents the optimal operating temperature of the battery of the charging equipment in the charging station. The value of the optimal operating temperature of the battery of the charging equipment in the charging station in this embodiment is 25 °C.

[0035] Define the state vectors at each data acquisition moment. Specifically, the state vector at the th data acquisition moment is the th temperature compensation coefficient at the data acquisition moment , the drift deviation of the voltage sensor , the drift deviation of the current sensor , the input - side efficiency , the output - side efficiency arranged in sequence as column vectors.

[0036] It can be understood that the state vectors at each data acquisition moment are all column vectors; In this embodiment, the state vector at the first data acquisition moment is set to . Among them, represents the temperature compensation coefficient at the th data acquisition moment. The temperature compensation coefficient is used to evaluate the non - linear influence of the charging temperature on the battery internal resistance and charging power. The value of the temperature compensation coefficient at the first data acquisition moment in this embodiment is set to 1; represents the drift deviation of the voltage sensor at the th data acquisition moment. The value of the drift deviation of the voltage sensor at the first data acquisition moment in this embodiment is set to 0; represents the drift deviation of the current sensor at the th data acquisition moment. The value of the drift deviation of the current sensor at the first data acquisition moment in this embodiment is set to 0; represents the input - side efficiency at the th data acquisition moment. The input - side efficiency is used to evaluate the input - side efficiency decay caused by the IGBT aging of the charging equipment. The value of the input - side efficiency at the first data acquisition moment in this embodiment is set to 1; represents the output - side efficiency at the [[ID=5s4]]th data acquisition moment. The output - side efficiency is used to evaluate the output - side efficiency decay caused by the IGBT aging of the charging equipment. The value of the output - side efficiency at the first data acquisition moment in this embodiment is set to 1; Indicates transposing a row vector into a column vector.

[0037] Preferably, as an embodiment of the present application, state vectors at each data acquisition moment are calculated. The state vector at the th data acquisition moment , where represents the state vector at the th data acquisition moment; represents the process noise vector at the th data acquisition moment. The process noise vector at the th data acquisition moment follows a Gaussian distribution with a mean vector of 0 and a covariance matrix of .

[0038] Specifically, it can be understood that the calculation result of the state vector at the th data acquisition moment is a column vector. The data values of this column vector from top to bottom are, in sequence, the temperature compensation coefficient at the th data acquisition moment , the drift deviation of the voltage sensor , the drift deviation of the current sensor , the input - side efficiency , and the output - side efficiency .

[0039] The flowchart for obtaining the state vector is as Figure 2 shown.

[0040] Thus far, the state vector at each data acquisition moment has been obtained.

[0041] Step S003: Calculate the observed power at the same data acquisition moment based on the charging voltage, charging current, and charging power at the same data acquisition moment. Use Kalman filtering to process the observed power at the data acquisition moment to obtain the optimal state vector at the data acquisition moment. Determine the theoretical power correction value at the data acquisition moment according to the optimal state vector at the data acquisition moment.

[0042] In order to achieve accurate modeling of power measurement errors and real - time suppression of noise in complex environments, and effectively address the non - linear power fluctuations caused by the coupling of multiple factors such as temperature changes, sensor drift, and equipment aging in charging stations, the present application corrects the charging amount measurement from multiple dimensions and dynamically adjusts the noise covariance driven by temperature changes.

[0043] Calculate the observed power at the same data acquisition moment based on the charging voltage, charging current, and charging power at the same data acquisition moment.

[0044] Among them, represents the observed power at the -th data acquisition moment; represents the theoretical power at the -th data acquisition moment; represents the charging power at the -th data acquisition moment; represents the charging current at the -th data acquisition moment; represents the charging voltage at the -th data acquisition moment; represents the aging coefficient of the charging input side. The aging coefficient of the charging input side is used to evaluate the efficiency decay of the input side caused by the aging of the IGBT module. In this embodiment, the value of the aging coefficient of the charging input side is  1; represents the aging coefficient of the charging output side. The aging coefficient of the charging output side is used to evaluate the efficiency decay of the output side caused by the aging of the IGBT module. In this embodiment, the value of the aging coefficient of the charging output side is 1; represents the observed noise at the -th data acquisition moment. The observed noise at the -th data acquisition moment follows a Gaussian distribution with a mean of 0 and a covariance matrix of ; represents the observed noise variance, which is determined by the ADC conversion accuracy. In this embodiment, 16-bit ADC voltage and current are taken to obtain the observed noise variance, , it can be understood that is the unit of ADC conversion.

[0045] It can be understood that the observed power at each data acquisition moment is composed of the theoretical power and the observed noise at the corresponding data acquisition moment. Among them, represents the theoretical power at the -th data acquisition moment, represents the observed noise at the -th data acquisition moment.

[0046] Use Kalman filtering to process the observed power at the -th data acquisition moment to obtain the optimal state vector at the -th data acquisition moment.

[0047] The optimal state vector at the -th data acquisition moment is still a column vector. The data values of the column vector from top to bottom are respectively the The optimal estimated value of the temperature compensation coefficient at a data acquisition moment, the optimal estimated value of the drift deviation of the voltage sensor, the optimal estimated value of the drift deviation of the current sensor, the optimal estimated value of the input-side efficiency, and the optimal estimated value of the output-side efficiency.

[0048] Take the optimal estimated values of the temperature compensation coefficient, the drift deviation of the voltage sensor, the drift deviation of the current sensor, the input-side efficiency, and the output-side efficiency at the th data acquisition moment as the values of the corresponding parameters, substitute them into the above calculation formula of the theoretical power, and take the calculation result as the theoretical power correction value at the data acquisition moment . The calculation formula of the theoretical power correction value at the th data acquisition moment is specifically as follows: represents the theoretical power correction value at the th data acquisition moment; represents the optimal estimated value of the temperature compensation coefficient at the th data acquisition moment; represents the optimal estimated value of the drift deviation of the voltage sensor at the th data acquisition moment; represents the optimal estimated value of the drift deviation of the current sensor at the th data acquisition moment; represents the optimal estimated value of the input-side efficiency at the th data acquisition moment; represents the optimal estimated value of the output-side efficiency at the th data acquisition moment.

[0049] So far, the theoretical power correction values at each data acquisition moment are obtained.

[0050] Step S004: Obtain the measurement errors of the charging amounts at all acquisition moments of the charging station according to the charging amounts, charging powers, and state of charge of the battery directly displayed by the charging equipment at all acquisition moments.

[0051] The charging process of the charging equipment at the charging station includes a constant current stage and a constant voltage stage, a total of two stages. Among them, the current is constant and the voltage rises in the constant current stage; the voltage is constant and the current drops in the constant voltage stage. At the end of the constant voltage stage, the battery is close to saturation, and the charging current is vulnerable to noise interference, resulting in an increase in the fluctuation range of the charging power. In order to avoid the amplification of the end noise and its accumulation in the final charging amount, causing the problem of inflated measurement, it is necessary to dynamically adjust the integration weight at each acquisition moment in the charging amount measurement process according to the state of charge SOC of the battery to suppress the charging amount measurement error caused by the noise-sensitive stage.

[0052] Assign a charging measurement weight to the data collection time according to the state of charge of the battery at the data collection time.

[0053] When the state of charge of the battery at the data collection time is less than the state of charge threshold, assign the charging measurement weight at the data collection time to 1. At this time, the charging process of the charging equipment at the charging station is in the constant current stage; when the state of charge of the battery at the data collection time is greater than or equal to the state of charge threshold, assign the charging measurement weight at the data collection time to 0.95. At this time, the charging process of the charging equipment at the charging station is in the constant voltage stage.

[0054] Among them, the state of charge threshold is a preset parameter value. In this embodiment, the value of the state of charge threshold is 80%.

[0055] During the charging process of the charging equipment at the charging station, the interference of noise on power measurement is random, which can make the measured value too high or too low, and the current fluctuation in the constant voltage stage is likely to cause noise cumulative error. By reducing the charging measurement weight, the positive and negative deviations of the noise on the integral can be attenuated proportionally, so that the random fluctuations cancel each other out in the accumulation of the charging quantity measurement. At the same time, the main trend of the true power is retained, and the high-precision measurement of the charging quantity is realized without changing the cumulative trend of the true charging quantity, ensuring that the integral result of the charging quantity measurement is close to the actual charging quantity.

[0056] Determine the adjusted charging quantity at each collection time according to the charging power and charging measurement weight at all collection times.

[0057] Among them, represents the adjusted charging quantity at the data collection time ; represents the charging quantity at the data collection time . Among them, in this embodiment, and are both set to 0; represents the charging power at the data collection time ; represents the charging measurement weight at the data collection time .

[0058] Record the difference between the charging quantity directly displayed by the charging equipment at the charging station and the adjusted charging quantity at the same collection time as the charging quantity measurement error at the same collection time.

[0059] So far, obtain the calculation result of the charging quantity measurement error of the charging station.

[0060] The foregoing are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included within the protection scope of the present application.

Claims

1. A high-precision calculation method for the charging quantity measurement error of a charging station, characterized in that The method includes the following steps: Collect the charging amount and cumulative operation time directly displayed by the charging equipment of the charging station, as well as the state of charge of the battery, charging voltage, charging current, ambient temperature, charging temperature, and charging power at different data collection times during the operation of the charging station; Define the input matrix at each data collection time. According to the charging temperature, define the process noise matrix and state transition matrix respectively. According to the ambient temperature, charging temperature, and cumulative operation time of the charging equipment of the charging station, define the input vector at each data collection time. According to the state transition matrix, input vector, input matrix, and process noise matrix at the same data collection time, determine the state vector at the same data collection time; According to the charging voltage, charging current, and charging power at the same data collection time, calculate the observed power at the same data collection time. Use Kalman filtering to process the observed power at the data collection time to obtain the optimal state vector at the data collection time. According to the optimal state vector at the data collection time, determine the theoretical power correction value at the data collection time; According to the charging amount, charging power, and state of charge of the battery directly displayed by the charging equipment at all collection times, obtain the measurement errors of the charging amount of the charging station at all collection times; 2. The high-precision calculation method for the charging amount measurement error of a charging station according to claim 1, wherein The input matrix and the input vector are specifically as follows: The input matrix at the th data acquisition moment, where represents a preset first correction amount; represents a preset first correction amount; The input vector at the data collection time is a column vector formed by arranging the ambient temperature, charging temperature, and cumulative operation time of the charging equipment of the charging station at the data collection time in sequence; 3. The high-precision calculation method for the charging amount measurement error of a charging station according to claim 2, wherein The process noise matrix and the state transition matrix are specifically as follows: The process noise matrix at the first data acquisition moment , where represents a preset temperature threshold; represents a preset non - linear exponent; represents a preset adjustment amplitude; represents the optimal operating temperature of the battery of the charging device at the charging station; the process noise matrix at the first data acquisition moment ; represents a diagonal matrix; The data acquisition moment of , where represents the battery temperature sensitivity coefficient obtained by performing a constant current charging test on the charging equipment of the charging station; represents the absolute value of the difference in charging temperature between the th data acquisition moment and the th data acquisition moment; represents the drift rate of the voltage sensor obtained by performing an aging test on the voltage sensor; represents the drift rate of the current sensor obtained by performing an aging test on the current sensor; represents the time interval between the data acquisition moment and the data acquisition moment ; represents the designed service life duration of the charging equipment of the charging station; represents the average value of the aging rate constants of the input end and the output end obtained by performing an aging test on the charging equipment of the charging station.

4. The high-precision calculation method for the charging amount measurement error of a charging station according to claim 3, wherein The state vector is specifically as follows: The state vector at the -th data acquisition time instant, where represents the state vector at the -th data acquisition time instant; represents the process noise vector at the -th data acquisition time instant. The process noise vector at the -th data acquisition time instant follows a Gaussian distribution with a mean vector of 0 and a covariance matrix of ; represents the input vector at the -th data acquisition time instant. The state vector at the th data acquisition moment is the temperature compensation coefficient at the th data acquisition moment, the drift deviation of the voltage sensor, the drift deviation of the current sensor, the input-side efficiency, and the output-side efficiency, which are arranged in sequence as a column vector.

5. The high-precision calculation method for the charging quantity measurement error of a charging station according to claim 4, characterized in that The method for obtaining the observed power at the data collection time is as follows: The theoretical power at the th data acquisition moment ; among them, represents the theoretical power at the th data acquisition moment; represents the charging power at the th data acquisition moment; represents the charging current at the th data acquisition moment; represents the charging voltage at the th data acquisition moment; represents the preset aging coefficient on the charging input side; represents the preset aging coefficient on the charging output side; Determine the observed power at the data collection time according to the theoretical power and observation noise at the data collection time; 6. The high-precision calculation method for the charging amount measurement error of a charging station according to claim 5, characterized in that, The method for determining the observed power at the data collection time according to the theoretical power and observation noise at the data collection time includes the following specific methods: Denote the sum of the theoretical power and the observation noise at the data acquisition moment as the observation power at the data acquisition moment; the observation noise follows a Gaussian distribution with a mean of 0 and a covariance matrix of , representing the observation noise variance.

7. The high-precision calculation method for the charging quantity measurement error of a charging station according to claim 5, characterized in that The optimal state vector at the data collection time is specifically as follows: A column vector formed by arranging the optimal estimated value of the temperature compensation coefficient, the optimal estimated value of the drift deviation of the voltage sensor, the optimal estimated value of the drift deviation of the current sensor, the optimal estimated value of the input-side efficiency, and the optimal estimated value of the output-side efficiency at the data collection time in sequence; 8. The high-precision calculation method for the charging amount measurement error of a charging station according to claim 7, characterized in that The calculation formula for the theoretical power correction value at the data collection time is specifically as follows: Indicates the theoretical power correction value at the th data acquisition moment; Indicates the optimal estimated value of the temperature compensation coefficient at the th data acquisition moment; Indicates the optimal estimated value of the drift deviation of the voltage sensor at the th data acquisition moment; Indicates the optimal estimated value of the drift deviation of the current sensor at the th data acquisition moment; Indicates the optimal estimated value of the input - side efficiency at the th data acquisition moment; Indicates the optimal estimated value of the output - side efficiency at the th data acquisition moment.

9. The high-precision calculation method for the charging quantity measurement error of a charging station according to claim 1, characterized in that The method for obtaining the measurement errors of the charging amount of the charging station at all collection times according to the charging amount, charging power, and state of charge of the battery directly displayed by the charging equipment at all collection times includes the following specific methods: When the state of charge of the battery at the data collection time is less than the preset state of charge threshold, assign a charging measurement weight of 1 to the data collection time; when the state of charge of the battery at the data collection time is greater than or equal to the state of charge threshold, assign a charging measurement weight of 0.95 to the data collection time; According to the charging power and charging measurement weights at all collection times, determine the adjusted charging amounts at each collection time respectively. Denote the difference between the charging amount directly displayed by the charging equipment of the charging station at the same collection time and the adjusted charging amount as the charging amount measurement error at the same collection time; 10. The method for calculating the high-precision charging amount measurement error for a charging station according to claim 9, wherein The calculation formula for the adjusted charging amount at each collection time is: Among them, represents the adjusted charging amount at the data collection time ; represents the charging amount at the data collection time , where and are both preset to 0; represents the charging power at the data collection time ; represents the charging measurement weight at the data collection time .

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