High-precision calculation method for charging capacity measurement error in charging stations

Abstract

The present application relates to the technical field of charge metering and proposes a high-precision calculation method for charge metering errors at charging stations, including: collecting the charge capacity and cumulative operating time of the charging equipment at the charging station, as well as the battery state of charge, charging voltage, charging current, ambient temperature, charging temperature, and charging power during the operation of the charging station; defining the input matrix, process noise matrix, state transition matrix, and input vector at each data collection moment, and determining the state vector at each data collection moment; calculating the observed power at the data collection moment and processing it using Kalman filtering to obtain the optimal state vector and theoretical power correction value at the data collection moment; obtaining the metering error of the charge capacity of the charging station at all collection moments based on the charge capacity, charging power, and battery state of charge directly displayed by the charging equipment at all collection moments. The present application aims to improve the charge metering accuracy of charging stations.

Description

Technical Field

[0001] The present application relates to the technical field of charge quantity measurement, and in particular to a high-precision calculation method for charge quantity measurement errors of a charging station. Background Art

[0002] Accurately measuring charging capacity at charging stations is a core requirement for ensuring fair trading and optimizing operational management. This increases user trust in charging services, helps operators evaluate equipment energy efficiency and analyze grid load, and facilitates optimal resource allocation. During charging at charging stations, the charging power exhibits a complex pattern of high-frequency fluctuations and low-frequency attenuation, influenced by factors such as equipment aging, drastic changes in ambient temperature, and grid harmonic interference. Traditional average power integration methods and filtering techniques such as Kalman filtering are unable to effectively suppress noise and compensate for physical parameter variations, easily leading to charging capacity measurement errors exceeding national measurement standards.

[0003] Specifically, the Kalman filter assumes that both system noise and observation noise are Gaussian white noise, which cannot adapt to power surges caused by drastic temperature changes during charging at the charging station. It is prone to problems such as excessive noise suppression or signal tracking delays, resulting in insufficient charging metering accuracy. Summary of the Invention

[0004] This application provides a high-precision calculation method for charging capacity metering errors in charging stations to address the problem of insufficient charge capacity metering accuracy caused by power surges caused by sudden temperature changes during charging in the Kalman filter method. The technical solutions adopted are as follows:

[0005] One embodiment of the present application provides a high-precision calculation method for charging amount measurement error of a charging station, the method comprising the following steps:

[0006] Collect the charging capacity and cumulative operating time directly displayed by the charging equipment at the charging station, as well as the battery state of charge, charging voltage, charging current, ambient temperature, charging temperature, and charging power at different data collection moments during the operation of the charging station;

[0007] Defining an input matrix at each data collection moment, defining a process noise matrix and a state transition matrix based on the charging temperature, defining an input vector at each data collection moment based on the ambient temperature, charging temperature, and cumulative operating time of the charging device, and determining a state vector at the same data collection moment based on the state transition matrix, input vector, input matrix, and process noise matrix;

[0008] Calculating the observed power at the same data collection moment based on the charging voltage, charging current, and charging power at the same data collection moment, processing the observed power at the data collection moment using a Kalman filter to obtain an optimal state vector at the data collection moment, and determining a theoretical power correction value at the data collection moment based on the optimal state vector at the data collection moment;

[0009] According to the charging capacity, theoretical power correction value and battery state of charge directly displayed by the charging equipment at all collection moments, the measurement error of the charging capacity of the charging station at all collection moments is obtained.

[0010] Furthermore, the input matrix and the input vector are specifically:

[0011] No. The input matrix at each data collection moment ,in, Indicates the preset first correction amount; Indicates the preset first correction amount;

[0012] The input vector at the data collection time is a column vector consisting of the ambient temperature, charging temperature, and the cumulative operating time of the charging equipment at the charging station at the data collection time.

[0013] Furthermore, the process noise matrix and the state transition matrix are specifically:

[0014] No. The process noise matrix at the data acquisition moment ,in, Indicates the preset temperature threshold; Indicates the preset nonlinear index; Indicates the preset adjustment range; Represents the optimal operating temperature of the battery of the charging equipment of the charging station; the process noise matrix at the first data collection moment ; represents a diagonal matrix;

[0015] No. The state transition matrix at the data collection moment ,in, Indicates the battery temperature sensitivity coefficient obtained by performing a constant current charging test on the charging equipment of the charging station; Indicates the The data collection time and The absolute value of the difference in charging temperature at each data collection moment; Indicates the drift rate of the voltage sensor obtained by performing an aging test on the voltage sensor; Indicates the drift rate of the current sensor obtained by performing an aging test on the current sensor; Indicates the data collection time Data collection time time interval; Indicates the design life of the charging equipment of the charging station; Indicates the average 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.

[0016] Furthermore, the state vector is specifically:

[0017] No. The state vector at the data collection moment ,in, Indicates the The state vector at the moment of data collection; Indicates the The process noise vector at the data collection moment, The process noise vector at the data collection moment The mean vector is 0 and the covariance matrix is Gaussian distribution; Indicates the The input vector at each data collection moment;

[0018] No. The state vector at the data collection moment The calculation result is Temperature compensation coefficient at each data collection moment , drift deviation of voltage sensor , drift deviation of current sensor , input side efficiency and output side efficiency Arranged sequentially into column vectors.

[0019] Furthermore, the method for obtaining the observed power at the data collection moment is:

[0020] No. Theoretical power at the data collection moment ;in, Indicates the Theoretical power at the time of data collection; Indicates the Charging power at the time of data collection; Indicates the The charging current at the time of data collection; Indicates the The charging voltage at the time of data collection; Indicates the preset charging input side aging coefficient; Indicates the preset charging output side aging coefficient;

[0021] The observed power at the data collection moment is determined based on the theoretical power and the observed noise at the data collection moment.

[0022] Furthermore, the method of determining the observed power at the data collection moment based on the theoretical power and the observed noise at the data collection moment includes the following specific methods:

[0023] The sum of the theoretical power and the observation noise at the time of data collection is recorded as the observation power at the time of data collection; the observation noise has a mean of 0, and the covariance matrix is Gaussian distribution, represents the observation noise variance.

[0024] Furthermore, the optimal state vector at the time of data collection is specifically:

[0025] The optimal estimated value of the temperature compensation coefficient at the time of data collection, 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 are arranged in sequence into a column vector.

[0026] Furthermore, the calculation formula of the theoretical power correction value at the data collection time is specifically as follows:

[0027]

[0028] Indicates the Theoretical power correction value at each data collection moment; Indicates the The optimal estimated value of the temperature compensation coefficient at each data collection moment; Indicates the The optimal estimate of the drift deviation of the voltage sensor at each data collection moment; Indicates the The optimal estimate of the drift deviation of the current sensor at each data collection moment; Indicates the The optimal estimate of the input side efficiency at each data collection moment; Indicates the The optimal estimate of the output side efficiency at each data collection moment.

[0029] Furthermore, the method of obtaining the measurement error of the charging capacity of the charging station at all collection moments based on the charging capacity, theoretical power correction value and battery state of charge directly displayed by the charging equipment at all collection moments includes the following specific methods:

[0030] When the battery state of charge at the time of data collection is less than the preset state of charge threshold, the charge measurement weight at the time of data collection is assigned a value of 1; when the battery state of charge at the time of data collection is greater than or equal to the state of charge threshold, the charge measurement weight at the time of data collection is assigned a value of 0.95;

[0031] The adjusted charge capacity at each collection moment is determined based on the theoretical power correction value and the charge metering weight at all collection moments. The difference between the charge capacity directly displayed by the charging equipment at the charging station at the same collection moment and the adjusted charge capacity is recorded as the charge capacity metering error at the same collection moment.

[0032] Furthermore, the calculation formula for adjusting the charging amount at each acquisition moment is:

[0033]

[0034] in, Indicates the data collection time Adjust the charging capacity; Indicates the data collection time The charge capacity, where and The values ​​of are all preset to 0; Indicates the Theoretical power correction value at each data collection moment; Indicates the data collection time The charging metering weight.

[0035] The beneficial effects of this application are:

[0036] In order to avoid the power mutation caused by the sudden temperature change during the charging process of the charging station, which leads to the problem of excessive noise suppression and signal tracking delay, the present application converts the complex interference factors such as the nonlinear change of the battery internal resistance of the charging equipment with temperature, the zero drift and sensitivity drift of the sensor, and the efficiency attenuation caused by the aging of the IGBT device into state variables that can be estimated in real time, and obtains the state vector of each data collection moment respectively. In the process of constructing the state vector, adaptive noise adjustment is performed, and the multi-source interference of the charging quantity metering is modeled and dynamically compensated to improve the charging quantity metering accuracy. Among them, the adaptive noise adjustment is realized through the process noise matrix at the data collection moment; further, in order to achieve accurate modeling of power metering errors and real-time suppression of noise in complex environments, the charging quantity metering is corrected, and the noise covariance driven by temperature changes is dynamically adjusted. The observed power at the data collection moment is processed using Kalman filtering to obtain data According to the optimal state vector at the data collection moment, and based on the optimal state vector at the data collection moment, the theoretical power correction value at the data collection moment is determined. The theoretical power correction value is the result of correcting the charging power of the charging station at each collection moment; finally, according to the charging capacity directly displayed by the charging equipment at all collection moments, the theoretical power correction value and the battery state of charge, combined with the stage in which the charging equipment of the charging station is in the charging process, the noise accumulation error caused by the current fluctuation in the constant voltage stage of the charging process is avoided to be amplified and accumulated into the final charging capacity, resulting in the problem of falsely high measurement. The integral weight of each collection moment in the charging capacity measurement process is dynamically adjusted to suppress the charging capacity measurement error caused by the noise sensitive stage, and the charging capacity measurement error of the charging station at all collection moments is obtained. The problem of insufficient charging capacity measurement accuracy caused by the power mutation caused by the sudden temperature change during charging of the Kalman filter method when adapting to the charging station is solved, thereby improving the accuracy of charging capacity measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0038] Figure 1 A schematic flow chart of a high-precision calculation method for charging capacity measurement error at a charging station provided in one embodiment of the present application;

[0039] Figure 2 A state vector acquisition flow chart provided in one embodiment of the present application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0041] See also Figure 1 , which shows a flow chart of a high-precision calculation method for charging amount measurement error of a charging station provided by an embodiment of the present application, the method comprising the following steps:

[0042] Step S001: Collect the charging capacity and cumulative operating time directly displayed by the charging equipment of the charging station, as well as the battery state of charge, charging voltage, charging current, ambient temperature, charging temperature and charging power at different data collection moments during the operation of the charging station.

[0043] Connect a voltage sensor in parallel to the output terminal inside the charging station, a current sensor in series with the positive circuit inside the charging station, and a temperature sensor inside the ventilation holes on the side panels of the charging station cabinet. During charging at the charging station, the voltage sensor collects the charging voltage, the current sensor collects the charging current, and the temperature sensor collects the ambient temperature. Simultaneously, the charging interface communication protocol reads the charging temperature and battery state of charge (SOC) from the temperature sensor built into the charging device's battery management system (BMS). The charging station's MCU's built-in clock chip also records the charging device's cumulative operating time. This allows for direct reading of the charging level displayed by the charging station's charging device.

[0044] Preferably, in one embodiment of the present application, when collecting the charge capacity, battery state of charge, charging voltage, charging current, ambient temperature, and charging temperature directly displayed by the charging device, the charging station MCU timer is used to uniformly trigger sampling, and data streams of different frequencies are aligned through timestamp marking and linear interpolation algorithm. In this embodiment, the data sampling frequency of the charge capacity, battery state of charge, charging voltage, and charging current directly displayed by the charging device is 1kHz, and the data sampling frequency of the ambient temperature and charging temperature is 100Hz. In actual application, as other implementation methods, the implementer can determine the data sampling frequency according to actual conditions, and this application does not impose any special restrictions.

[0045] The charging power at each data collection moment is calculated based on the charging voltage and charging current.

[0046] Any data collection moment is recorded as the target data collection moment, and the product of the charging power at the previous adjacent collection moment of the target data collection moment and 0.2 is recorded as the deviation threshold of the target data collection moment. When the absolute value of the difference between the charging power at the target data collection moment and the previous adjacent collection moment of the target data collection moment is greater than the deviation threshold of the target data collection moment, it is determined that a power mutation occurs at the target data collection moment. In order to avoid the interference of the noise accumulation effect caused by the aggravated current fluctuation during the charging process on the charging amount measurement, the charging power at the target data collection moment is assigned to the average of the charging powers of the three adjacent collection moments before the target data collection moment.

[0047] It is understandable that when the target data collection moment does not have a previous adjacent collection moment, the charging power at the target data collection moment is not assigned a value.

[0048] Among them, aligning data streams of different frequencies and calculating charging power according to charging voltage and charging current are both well-known technologies and will not be described in detail.

[0049] At this point, the charging capacity and cumulative operating time directly displayed by the charging equipment of the charging station are obtained, as well as the battery state of charge, charging voltage, charging current, ambient temperature, charging temperature and charging power at different data collection moments during the operation of the charging station.

[0050] Step S002: Define the input matrix at each data collection moment, define the process noise matrix and the state transfer matrix according to the charging temperature, define the input vector at each data collection moment according to the ambient temperature, charging temperature, and the cumulative operating time of the charging device, and determine the state vector at the same data collection moment based on the state transfer matrix, input vector, input matrix, and process noise matrix.

[0051] Considering the potential for sudden power fluctuations caused by temperature fluctuations during charging at charging stations, and to avoid issues such as excessive noise suppression and signal tracking delays, complex interference factors such as the nonlinear change in the charging device's battery internal resistance with temperature, sensor zero drift and sensitivity drift, and efficiency degradation due to IGBT device aging are converted into state variables that can be estimated in real time. Adaptive noise adjustment is then used to model and dynamically compensate for the multi-source interference affecting charge metering, improving charge metering accuracy. The IGBT (Insulated Gate Bipolar Transistor) in charging equipment is a crucial power electronic device in charging equipment.

[0052] Define the state transfer matrix at each data collection moment. Specifically, The state transition matrix at the data collection moment ,in, Represents a diagonal matrix. The values ​​separated by commas in the brackets are the elements on the diagonal of the diagonal matrix. Indicates the battery temperature sensitivity coefficient obtained by performing a constant current charging test on the charging equipment of the charging station. Specifically, this embodiment performs a constant current charging test in an environment above -30°C and below 50°C. The battery temperature sensitivity coefficient obtained in this embodiment is 0.02 ; Indicates the The data collection time and The absolute value of the difference in charging temperature at each data collection moment; The drift rate of the voltage sensor obtained by the aging test is shown in FIG. 1 . Specifically, the aging test is performed in a temperature chamber cycled at 25°C, 45°C, and 25°C. The aging test lasts for 100 hours. The drift rate of the voltage sensor obtained in this embodiment is per second; The drift rate of the current sensor obtained by the aging test is shown in FIG. 1 . Specifically, the aging test is performed in a temperature chamber cycled at 25°C, 45°C, and 25°C. The aging test lasts for 100 hours. The drift rate of the current sensor obtained in this embodiment is per second; Indicates the data collection time Data collection time time interval; Indicates the design life of the charging equipment of the charging station; Indicates the average 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, the charging equipment is loaded with 80% of the rated load and runs for 500 hours. The value of the aging rate constant obtained in this embodiment is 0.8.

[0053] It can be understood that the state transfer matrix is ​​5 The constant current charging test on the charging equipment of the charging station and the aging test on the voltage sensor, current sensor and the charging equipment of the charging station are well-known technologies and will not be described in detail.

[0054] Define the input vector at each data collection moment. Specifically, The input vector at the data collection moment ,in, Indicates the The ambient temperature at the time of data collection; 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.

[0055] Define the input matrix at each data collection moment. Specifically, The input matrix at each data collection moment ,in, It 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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 ℃; the process noise matrix of the first data collection moment selected in this embodiment is ; The nonlinear index is used to balance fast response and stability. In this embodiment, the nonlinear index is set to 1.5. Indicates the preset adjustment range. In this embodiment, the value of the adjustment range is 0.5; The optimal operating temperature of the battery of the charging equipment of the charging station is 25° C. in this embodiment.

[0060] Define the state vector at each data collection moment. Specifically, The state vector at the data collection moment It is Temperature compensation coefficient at each data collection moment , drift deviation of voltage sensor , drift deviation of current sensor , input side efficiency , output side efficiency Arranged in sequence into a column vector of .

[0061] It can be understood that the state vectors at each data collection moment are all column vectors; in this embodiment, the state vector at the first data collection moment is Set to .in, Indicates the The temperature compensation coefficient at each data collection moment is used to evaluate the nonlinear effect of charging temperature on the battery internal resistance and charging power. In this embodiment, the value of the temperature compensation coefficient at the first data collection moment is set to 1; Indicates that the voltage sensor is The drift deviation at the first data collection moment is set to 0 in this embodiment; Indicates that the current sensor is The drift deviation at the first data collection moment is set to 0 in this embodiment; Indicates the The input side efficiency at the first data collection moment is used to evaluate the input side efficiency attenuation caused by IGBT aging of the charging device. In this embodiment, the value of the input side efficiency at the first data collection moment is set to 1; Indicates the The output side efficiency at the first data collection moment is used to evaluate the output side efficiency attenuation caused by aging of the IGBT of the charging device. In this embodiment, the value of the output side efficiency at the first data collection moment is set to 1; Transposes a row vector to a column vector.

[0062] Preferably, as an embodiment of the present application, the state vector at each data collection moment is calculated. The state vector at the data collection moment ,in, Indicates the The state vector at the moment of data collection; Indicates the The process noise vector at the data collection moment, The process noise vector at the data collection moment The mean vector is 0 and the covariance matrix is Gaussian distribution.

[0063] Specifically, it is understandable that The state vector at the data collection moment The result of the calculation is a column vector, the data values ​​of which are Temperature compensation coefficient at each data collection moment , drift deviation of voltage sensor , drift deviation of current sensor , input side efficiency , output side efficiency .

[0064] The state vector acquisition flow chart is as follows Figure 2 shown.

[0065] At this point, the state vector at each data collection moment is obtained.

[0066] Step S003: Calculate the observed power at the same data collection moment based on the charging voltage, charging current, and charging power at the same data collection moment, process the observed power at the data collection moment using a Kalman filter, obtain the optimal state vector at the data collection moment, and determine the theoretical power correction value at the data collection moment based on the optimal state vector at the data collection moment.

[0067] To achieve accurate modeling of power metering errors and real-time noise suppression in complex environments, and to effectively address nonlinear power fluctuations caused by the coupling of multiple factors such as temperature changes, sensor drift, and equipment aging at charging stations, this application corrects charging metering from multiple dimensions and dynamically adjusts the noise covariance driven by temperature changes.

[0068] The observed power at the same data collection moment is calculated based on the charging voltage, charging current, and charging power at the same data collection moment.

[0069]

[0070]

[0071] in, Indicates the The observed power at each data collection moment; Indicates the Theoretical power at the time of data collection; Indicates the Charging power at the time of data collection; Indicates the The charging current at the time of data collection; Indicates the The charging voltage at the time of data collection; The charging input side aging coefficient is used to evaluate the efficiency degradation of the input side caused by aging of the IGBT module. In this embodiment, the charging input side aging coefficient is set to 1. The charging output side aging coefficient is used to evaluate the output side efficiency degradation caused by IGBT module aging. In this embodiment, the charging output side aging coefficient is set to 1. Indicates the The observation noise at the data collection moment, The observation noise at each data collection moment has a mean of 0, and the covariance matrix is Gaussian distribution, Represents the observed noise variance, which is determined by the ADC conversion accuracy. In this embodiment, the voltage and current of the 16-bit ADC are used to obtain the observed noise variance. , understandably, The unit for ADC conversion.

[0072] It can be understood that the observed power at each data collection moment is composed of the theoretical power and the observed noise at the corresponding data collection moment, where Indicates the The theoretical power at the time of data collection, Indicates the The observation noise at each data collection moment.

[0073] Use Kalman filtering to The observed power at each data collection moment is processed to obtain the The optimal state vector at the data collection moment.

[0074] No. The optimal state vector at the data collection moment is still a column vector, and the data values ​​of the column vector from top to bottom are The optimal estimated value of the temperature compensation coefficient at each data collection 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.

[0075] The first The temperature compensation coefficient, drift deviation of the voltage sensor, drift deviation of the current sensor, optimal estimated values ​​of the input side efficiency and output side efficiency at the data collection time are respectively used as the values ​​of the corresponding parameters, substituted into the above theoretical power calculation formula, and the calculation results are used as the data collection time. Theoretical power correction value, The calculation formula of the theoretical power correction value at each data collection moment is as follows:

[0076]

[0077] Indicates the Theoretical power correction value at each data collection moment; Indicates the The optimal estimated value of the temperature compensation coefficient at each data collection moment; Indicates the The optimal estimate of the drift deviation of the voltage sensor at each data collection moment; Indicates the The optimal estimate of the drift deviation of the current sensor at each data collection moment; Indicates the The optimal estimate of the input side efficiency at each data collection moment; Indicates the The optimal estimate of the output side efficiency at each data collection moment.

[0078] At this point, the theoretical power correction value at each data collection moment is obtained.

[0079] Step S004: Obtain the measurement error of the charging capacity of the charging station at all collection moments based on the charging capacity, theoretical power correction value, and battery state of charge directly displayed by the charging equipment at all collection moments.

[0080] The charging process of the charging equipment at the charging station includes two stages, the constant current stage and the constant voltage stage. In the constant current stage, the current is constant and the voltage increases; in the constant voltage stage, the voltage is constant and the current decreases. In the constant voltage stage, the terminal battery is close to saturation, and the charging current is easily affected by noise, resulting in an increase in the fluctuation amplitude of the charging power. In order to avoid the terminal noise being amplified and accumulated in the final charging amount, causing the problem of falsely high measurement, it is necessary to dynamically adjust the integral weight of each acquisition moment in the charging amount measurement process according to the battery state of charge SOC to suppress the charging amount measurement error caused by the noise-sensitive stage.

[0081] According to the battery state of charge at the time of data collection, the charging metering weight at the time of data collection is assigned.

[0082] When the battery state of charge at the time of data collection is less than the state of charge threshold, the charging metering weight at the time of data collection is assigned to 1. At this time, the charging process of the charging equipment of the charging station is in the constant current stage; when the battery state of charge at the time of data collection is greater than or equal to the state of charge threshold, the charging metering weight at the time of data collection is assigned to 0.95. At this time, the charging process of the charging equipment of the charging station is in the constant voltage stage.

[0083] The state of charge threshold is a preset parameter value. In this embodiment, the state of charge threshold is set to 80%.

[0084] During charging at a charging station, noise can randomly interfere with power measurement, causing measured values ​​to be falsely high or low. Current fluctuations during the constant voltage phase can easily lead to noise accumulation errors. By reducing the charge metering weight, the impact of positive and negative noise deviations on the integral is proportionally attenuated, allowing random fluctuations to offset each other in the cumulative charge metering. This also preserves the true power trend, achieving high-precision charge metering without changing the actual cumulative charge trend. This ensures that the integral result of charge metering closely matches the actual charge.

[0085] According to the theoretical power correction value and charging metering weight of all collection moments, the adjusted charging amount at each collection moment is determined respectively.

[0086]

[0087] in, Indicates the data collection time Adjust the charging capacity; Indicates the data collection time The charging capacity of and The values ​​of are all set to 0; Indicates the Theoretical power correction value at each data collection moment; Indicates the data collection time The charging metering weight.

[0088] The difference between the charging capacity directly displayed by the charging equipment of the charging station at the same collection time and the adjusted charging capacity is recorded as the charging capacity measurement error at the same collection time.

[0089] At this point, the calculation result of the charging amount measurement error of the charging station is obtained.

[0090] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A high-precision calculation method for charging capacity measurement error of a charging station, characterized in that: The method comprises the following steps: Collect the charging capacity and cumulative operating time directly displayed by the charging equipment at the charging station, as well as the battery state of charge, charging voltage, charging current, ambient temperature, charging temperature, and charging power at different data collection moments during the operation of the charging station; Define the input matrix at each data collection moment. Define the process noise matrix and state transition matrix based on the charging temperature. Define the input vector at each data collection moment based on the ambient temperature, charging temperature, and the cumulative operating time of the charging equipment. Determine the state vector at the same data collection moment based on the state transition matrix, input vector, input matrix, and process noise matrix at the same data collection moment. Calculate the observed power at the same data collection moment based on the charging voltage, charging current, and charging power at the same data collection moment, process the observed power at the data collection moment using a Kalman filter, obtain the optimal state vector at the data collection moment, and determine the theoretical power correction value at the data collection moment based on the optimal state vector at the data collection moment; Obtain the measurement error of the charging capacity of the charging station at all collection times based on the charging capacity, theoretical power correction value, and battery state of charge directly displayed by the charging equipment at all collection times; The process noise matrix and state transfer matrix are specifically: No. The process noise matrix at the data acquisition moment ,in, Indicates the preset temperature threshold; Indicates the preset nonlinear index; Indicates the preset adjustment range; Represents the optimal operating temperature of the battery of the charging equipment of the charging station; the process noise matrix at the first data collection moment ; represents a diagonal matrix; No. The state transition matrix at the data collection moment ,in, Indicates the battery temperature sensitivity coefficient obtained by performing a constant current charging test on the charging equipment of the charging station; Indicates the The data collection time and The absolute value of the difference in charging temperature at each data collection moment; Indicates the drift rate of the voltage sensor obtained by performing an aging test on the voltage sensor; Indicates the drift rate of the current sensor obtained by performing an aging test on the current sensor; Indicates the data collection time Data collection time time interval; Indicates the design life of the charging equipment of the charging station; Indicates 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; The state vector is specifically: No. The state vector at the data collection moment ,in, Indicates the The state vector at the moment of data collection; Indicates the The process noise vector at the data collection moment, The process noise vector at the data collection moment The mean vector is 0 and the covariance matrix is Gaussian distribution; Indicates the The input vector at each data collection moment; No. The state vector at the data collection moment The result of the calculation is a column vector, and the column vector is the data values ​​from top to bottom. Temperature compensation coefficient at each data collection moment , drift deviation of voltage sensor , drift deviation of current sensor , input side efficiency and output side efficiency Arranged in sequence into column vectors; The calculation formula of the theoretical power correction value at the time of data collection is as follows: Indicates the Theoretical power correction value at each data collection moment; Indicates the The optimal estimated value of the temperature compensation coefficient at each data collection moment; Indicates the The optimal estimate of the drift deviation of the voltage sensor at each data collection moment; Indicates the The optimal estimate of the drift deviation of the current sensor at each data collection moment; Indicates the The optimal estimate of the input side efficiency at each data collection moment; Indicates the The optimal estimate of the output side efficiency at each data collection moment.

2. The high-precision calculation method for charging capacity measurement error of a charging station according to claim 1, characterized in that: The input matrix and input vector are specifically: No. The input matrix at each data collection moment ,in, Indicates the preset first correction amount; Indicates the preset second correction amount; The input vector at the data collection time is a column vector consisting of the ambient temperature, the charging temperature, and the cumulative operating time of the charging equipment of the charging station at the data collection time arranged in sequence.

3. The high-precision calculation method for charging capacity measurement error of a charging station according to claim 1, characterized in that: The method for obtaining the observed power at the time of data collection is: No. Theoretical power at the data collection moment ;in, Indicates the Theoretical power at the time of data collection; Indicates the Charging power at the time of data collection; Indicates the The charging current at the time of data collection; Indicates the The charging voltage at the time of data collection; Indicates the preset charging input side aging coefficient; Indicates the preset charging output side aging coefficient; The observed power at the data collection moment is determined based on the theoretical power and the observed noise at the data collection moment.

4. The high-precision calculation method for charging amount measurement error of a charging station according to claim 3, characterized in that: The observed power at the time of data collection is determined based on the theoretical power and the observed noise at the time of data collection. The specific method includes: The sum of the theoretical power and the observation noise at the time of data collection is recorded as the observation power at the time of data collection; the observation noise has a mean of 0, and the covariance matrix is Gaussian distribution, represents the observation noise variance.

5. The high-precision calculation method for charging capacity measurement error of a charging station according to claim 4, characterized in that: The optimal state vector at the time of data collection is: The optimal estimated value of the temperature compensation coefficient at the time of data collection, 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 are arranged in sequence into a column vector.

6. The high-precision calculation method for charging capacity measurement error of a charging station according to claim 1, characterized in that: Based on the charging capacity directly displayed by the charging equipment at all collection times, the theoretical power correction value, and the battery state of charge, the measurement error of the charging capacity of the charging station at all collection times is obtained. The specific method includes: When the battery state of charge at the time of data collection is less than the preset state of charge threshold, the charge measurement weight at the time of data collection is assigned a value of 1; when the battery state of charge at the time of data collection is greater than or equal to the state of charge threshold, the charge measurement weight at the time of data collection is assigned a value of 0.95; According to the theoretical power correction value and charging metering weight of all collection moments, the adjusted charging amount at each collection moment is determined respectively. The difference between the charging amount directly displayed by the charging equipment of the charging station at the same collection moment and the adjusted charging amount is recorded as the charging amount metering error at the same collection moment.

7. The high-precision calculation method for charging capacity measurement error of a charging station according to claim 6, characterized in that: The calculation formula for the adjusted charge amount at each collection moment is: in, Indicates the data collection time Adjust the charging capacity; Indicates the data collection time The charge capacity, where and The values ​​of are all preset to 0; Indicates the Theoretical power correction value at each data collection moment; Indicates the data collection time The charging metering weight.

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