Charging state data accurate metering method and system suitable for electric vehicle

Abstract

The invention relates to the field of accurate metering of charging state data, and discloses an accurate metering method and system for charging state data of an electric automobile, and the method comprises the steps: dividing a charging process into a plurality of time grid segments according to a fixed time interval, and arranging the time grid segments according to a time sequence to form a charging response curve corresponding to the time grid segments; aligning with a reference charging response curve pre-established and stored for different battery types, environment temperature intervals and charging stages on the same time coordinate axis, and generating a curve deviation sequence corresponding to the time grid segments; calculating a deviation credibility parameter in combination with a change trend between adjacent time grid segments; inputting the curve deviation and the deviation credibility parameter into a preset deviation-error mapping relation, generating a complete electric quantity error sequence, and forming a charging power instruction value; and forming an accumulated electric quantity data sequence according to the charging power instruction value and the duration of the corresponding time grid segment. The method has the advantage that the metering precision is improved.

Description

Technical Field

[0001] This invention relates to the field of accurate measurement of charging status data, specifically to a method and system for accurate measurement of charging status data applicable to electric vehicles. Background Technology

[0002] With the rapid growth of electric vehicle ownership, higher requirements are being placed on the metering accuracy of the charging process in various scenarios such as public charging stations, large commercial complexes, and residential communities. In actual operation, charging facilities generally adopt conventional metering methods based on data collected by the charging pile-end meter, accumulating and statistically analyzing data such as voltage, current, and power. However, in the high-concurrency charging scenarios of urban centralized charging stations, due to factors such as fluctuations in the contact resistance of the charging gun, frequent dynamic adjustments of the vehicle's on-board charger, and transient noise interference in the charging pile-end data acquisition link, conventional metering results often show inconsistencies with the actual amount of electricity absorbed by the vehicle. Especially during low-power charging, peak-valley switching, and high SOC buffering phases, the charging pile-end metering data is easily affected by the cumulative impact of transient disturbances, leading to aggravated metering accuracy deviations. This not only affects the accuracy of billing for operators but also makes it difficult for users to obtain reliable charging status assessments. Existing technologies cannot accurately measure charging status data, making it difficult to meet the needs of high-precision charging status monitoring and intelligent operation and maintenance. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method and system for accurate measurement of charging status data for electric vehicles, which has the advantage of improving measurement accuracy and solves the problems mentioned in the background technology.

[0004] To achieve the aforementioned goal of improving measurement accuracy, this invention provides the following technical solution: a method for accurately measuring charging status data of electric vehicles, comprising the following steps: The charging process is divided into multiple time grid segments according to fixed time intervals. In each time grid segment, the charging current value, charging voltage value and corresponding timestamp are collected and arranged in chronological order to form the charging response curve corresponding to the time grid segment. Align the charging response curves corresponding to the time grid segments with the reference charging response curves that have been pre-established and stored for different battery types, ambient temperature ranges and charging stages on the same time axis, and quantify the differences between the two to generate a sequence of curve deviations corresponding to continuous time grid segments. Based on the curve deviation sequence corresponding to the time grid segment, combined with the changing trend between adjacent time grid segments and the stability index within the preset time window, the deviation confidence parameter is calculated. Input the curve deviation and deviation confidence parameters into the preset deviation-error mapping relationship to generate a complete power error sequence, and correct the charging power command value of the current time grid segment to form the charging power command value. Based on the charging power command value and the duration of the corresponding time grid segment, the corrected power value of the time grid segment is calculated, and the corrected power value of each time grid segment is accumulated in chronological order to form a cumulative power data sequence.

[0005] Preferably, the process of arranging the charging response curves corresponding to the time grid segments in chronological order is as follows: Real-time acquisition of current, voltage, and timestamp data via the charger's communication interface; The continuously collected data is divided into independent time grid segments according to a preset fixed time interval; The data for each time grid segment is sorted and standardized, including missing data interpolation, outlier removal, and unit standardization; The processed data points are connected in chronological order to form the charging response curve corresponding to the time grid segment.

[0006] Preferably, the process of aligning the charging response curve corresponding to the time grid segment with the reference charging response curves that have been pre-established and stored for different battery types, ambient temperature ranges, and charging stages on the same time axis is as follows: Based on different battery types, ambient temperature ranges, and charging stages, matching curves are extracted from a pre-established reference curve database. Map the response curve corresponding to the time grid segment to the time coordinate corresponding to the reference curve, and interpolate to fill in the missing sampling points; After alignment, a pair of actual data and reference curve data is generated for each time grid segment.

[0007] Preferably, the process of generating the curve deviation sequence corresponding to continuous time grid segments is as follows: For each time grid segment, the actual data is compared point-by-point with the reference curve data, and the absolute deviation is calculated. The current deviation is converted into a power deviation, and the power deviation value at each sampling point is calculated using the equivalent power formula. Calculate the average, variance, and peak value of the power deviation within the time grid segment to form a grid segment-level deviation feature vector; Arrange the grid segment-level deviation feature vectors according to the time series to form a curve deviation sequence for continuous time grid segments.

[0008] Preferably, the process for calculating the deviation confidence parameter is as follows: Using the deviation of the curve in a continuous time grid segment as input, the trend of deviation change in adjacent time grid segments is extracted, including the deviation increase / decrease rate, change gradient, and directional consistency. Within a preset time window, statistical analysis is performed on the deviation series, including mean, standard deviation, and outlier detection; By combining the trend indicators with the time window analysis results, the deviation confidence parameter for each time grid segment is calculated according to the preset weights.

[0009] Preferably, the process of generating a complete power error sequence is as follows: Input the curve deviation and deviation confidence parameters for each time grid segment into the preset deviation-error mapping relationship; Calculate the power error value for the corresponding time grid segment based on the mapping relationship; Output a sequence of power error values, arranged in chronological order to form a complete power error sequence.

[0010] Preferably, the process of generating the charging power command value is as follows: Based on the complete power error sequence, the corrected charging power command value is calculated according to the linear correction formula. In the correction calculation, the battery's allowable voltage, current and power thresholds are included in the constraints, and the power command is adjusted to meet the preset range in combination with the current charging stage and battery state parameters. The corrected power command sequence is smoothed, including by moving average or low-pass filtering, to generate continuous charging power command values.

[0011] Preferably, the process for calculating the corrected energy value for a time grid segment is as follows: Obtain the corrected charging power command value and corresponding duration for each time grid segment; The charging power command value is numerically integrated with the duration of time to calculate the initial corrected power value for each grid segment; The battery charge and discharge efficiency and power loss factor are introduced into the integral calculation to correct the initial corrected capacity value and form the final corrected capacity value.

[0012] Preferably, the process of forming the cumulative electricity data sequence is as follows: The corrected energy values ​​for each time grid segment are accumulated sequentially in chronological order to form a preliminary cumulative energy sequence; The accumulated values ​​for each time grid segment are normalized within a given interval. During the sequence accumulation process, a sliding window accumulation strategy is adopted for the corrected charge values ​​of continuous time grid segments to form local cumulative subsequences; The local cumulative subsequences are integrated in chronological order to generate a complete cumulative electricity data sequence.

[0013] A precise metering system for charging status data suitable for electric vehicles, comprising: Response generation module: Collects charging current, voltage and timestamp data, and generates charging response curves for each time grid segment in chronological order; Curve alignment module: Aligns the charging response curve with the reference charging response curve, quantifies the difference between the two, and generates a sequence of curve deviations; Deviation Calculation Module: Based on the curve deviation sequence and its changing trend and stability index within adjacent time grid segments, the deviation reliability parameter is calculated. Power correction module: Determines the power error value based on the deviation reliability parameter and curve deviation, and corrects the charging power command for the current time grid segment; Power accumulation module: Calculates the corrected power based on the corrected charging power command value and the duration of the time grid segment, and accumulates them sequentially to form a cumulative power data sequence.

[0014] Compared with the prior art, the present invention provides a method and system for accurate measurement of charging status data for electric vehicles, which has the following beneficial effects: This invention achieves dynamic calculation of power error and real-time correction of power commands by dividing the charging process into time grid segments and combining the charging response curve with a reference curve for deviation analysis. This allows the power in each time period to be adjusted and accumulated according to the actual state, thereby forming a continuous and accurate cumulative power data sequence. It can effectively reflect the subtle changes in battery charging behavior under different battery types, ambient temperatures, and charging stages, improving the accuracy and consistency of charging data. This provides a reliable data foundation for battery management, charging strategy optimization, and energy monitoring, avoiding measurement deviations caused by current fluctuations or environmental differences, and realizing refined quantification and traceable management of battery charging status. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the method of the present invention; Figure 2 This is a schematic diagram of the system of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: Please refer to Figure 1 As shown in the embodiment of the present invention, a method for accurately measuring charging status data suitable for electric vehicles includes the following steps: S1: Divide the charging process into multiple time grid segments according to fixed time intervals. Collect the charging current value, charging voltage value and corresponding timestamp in each time grid segment, and arrange them in chronological order to form the charging response curve corresponding to the time grid segment.

[0018] The process of arranging the charging response curves corresponding to the time grid segments in S1 in chronological order is as follows: Real-time acquisition of current, voltage, and timestamp data via the charger's communication interface; By using a standardized charger communication interface, such as an in-vehicle communication protocol, a real-time data channel is established. The charging current, voltage, and corresponding timestamp information are continuously read at a preset sampling frequency. During the receiving process, the validity of the data is verified and the units are standardized. At the same time, outliers are marked or interpolated to ensure continuous data collection.

[0019] The continuously collected data is divided into independent time grid segments according to a preset fixed time interval; The continuous current, voltage, and timestamp data collected through the communication interface are divided into preset fixed time intervals, such as per second or per minute. The data within each time interval is treated as an independent time grid segment. During the division process, the timestamps are verified and sorted to ensure that the data within each grid segment is complete, continuous, and non-overlapping, providing a standardized time unit for response curve generation and deviation analysis.

[0020] The data for each time grid segment is sorted and standardized, including missing data interpolation, outlier removal, and unit standardization; For the collected data in each time grid segment, the data points are first sorted by timestamp to ensure the correct order. Missing data points are interpolated to fill in the gaps. Data that is obviously abnormal or exceeds the reasonable range is removed. Current and voltage data from different sources or with different dimensions are uniformly converted into standard units and ranges to ensure that the data in each time grid segment is complete, consistent, and can be directly used for subsequent analysis and response curve generation.

[0021] The processed data points are connected in chronological order to form the charging response curve corresponding to the time grid segment. The sorted and standardized current and voltage data points within each time grid segment are arranged sequentially according to timestamps, and then smoothly connected between consecutive time points using linear or higher-order interpolation methods to form a continuous charging response curve. This curve fully reflects the characteristics of the charging current and voltage changes over time within each time grid segment.

[0022] S2: Align the charging response curve corresponding to the time grid segment with the reference charging response curves that have been pre-established and stored for different battery types, ambient temperature ranges and charging stages on the same time axis, and quantify the difference between the two to generate a sequence of curve deviations corresponding to continuous time grid segments.

[0023] In S2, the process of aligning the charging response curve corresponding to the time grid segment with the reference charging response curves that have been pre-established and stored for different battery types, ambient temperature ranges, and charging stages on the same time axis is as follows: Based on different battery types, ambient temperature ranges, and charging stages, matching curves are extracted from a pre-established reference curve database. Based on the battery model used in the current charging process, the real-time ambient temperature, and the charging stage, the corresponding reference curve entries are retrieved from the pre-established reference charging response curve database. The curve that best matches the current conditions is selected as the standard for comparison, so as to perform alignment and deviation analysis of the charging response curves.

[0024] Map the response curve corresponding to the time grid segment to the time coordinate corresponding to the reference curve, and interpolate to fill in the missing sampling points; The actual charging response data collected in each time grid segment are mapped onto the time coordinate of the reference curve in chronological order. For the missing sample values ​​in the actual data at the reference time point, linear interpolation or higher-order interpolation methods are used to fill in the missing values, so as to ensure that the actual curve and the reference curve correspond completely under the same time coordinate.

[0025] After alignment, pairs of actual data and reference curve data are generated for each time grid segment; After completing the time coordinate mapping and missing data interpolation, the actual collected data in each time grid segment are paired with the reference curve data at the corresponding time point in a one-to-one correspondence relationship to form actual value-reference value data pairs, providing a basic data structure for deviation calculation and error analysis.

[0026] The process of generating the curve deviation sequence corresponding to the continuous time grid segment in S2 is as follows: For each time grid segment, the actual data is compared point-by-point with the reference curve data, and the absolute deviation is calculated. The actual value-reference value data pairs formed in each time grid segment are traversed. The actual measured current or voltage value is subtracted from the corresponding value of the reference curve at the same time point. The absolute value is taken as the deviation at that time point. The absolute deviations of all time points are recorded in sequence to form a deviation sequence at the time grid segment level, which is used for power deviation calculation.

[0027] The current deviation is converted into a power deviation, and the power deviation value at each sampling point is calculated using the equivalent power formula. For the current deviation calculated within each time grid segment, the power deviation value is calculated by comparing it with the actual current at the corresponding time point. This calculation is repeated for all sampling points to generate a power deviation sequence within that time grid segment, providing a data basis for deviation feature extraction.

[0028] Calculate the average, variance, and peak value of the power deviation within the time grid segment to form a grid segment-level deviation feature vector; For the power deviation sequence within each time grid segment, its mean, variance, and maximum value are calculated sequentially, and these three statistical indicators are combined into a vector in a fixed order to form the deviation characteristic vector of that time grid segment, so as to perform continuous deviation analysis and trend assessment on the time series.

[0029] Arrange the grid segment-level deviation feature vectors according to the time series to generate a curve deviation sequence for continuous time grid segments; The deviation feature vectors calculated for each time grid segment are arranged sequentially according to their corresponding time order to form a continuous feature vector sequence. This sequence can fully reflect the power deviation change trend of each time period in the entire charging process, thereby generating a curve deviation sequence of continuous time grid segments.

[0030] S3: Based on the curve deviation sequence corresponding to the time grid segment, combined with the changing trend between adjacent time grid segments and the stability index within the preset time window, calculate the deviation confidence parameter.

[0031] The process for calculating the deviation confidence parameter in S3 is as follows: Using the deviation of the curve in a continuous time grid segment as input, the trend of deviation change in adjacent time grid segments is extracted, including the deviation increase / decrease rate, change gradient, and directional consistency. For the curve deviation sequence of continuous time grid segments, the deviation difference between each adjacent grid segment is calculated in turn, and the deviation increase / decrease rate and change gradient are further obtained. At the same time, it is analyzed whether the direction of deviation change is consistent, so as to extract the deviation change trend characteristics of each time grid segment.

[0032] Within a preset time window, statistical analysis is performed on the deviation series, including mean, standard deviation, and outlier detection; Within a set time window, the deviation sequence of curves in continuous time grid segments is traversed and calculated to obtain the average value and standard deviation of all deviations within the window, which are used to reflect the overall deviation level and fluctuation amplitude. Combined with statistical thresholds or outlier detection algorithms, abnormal deviation points within the window are identified and marked, thereby obtaining the statistical characteristics and abnormal distribution of each time window, providing input for deviation confidence calculation.

[0033] By combining the trend indicators with the time window analysis results, the deviation confidence parameter for each time grid segment is calculated according to the preset weights. For each time grid segment, the deviation trend indicators of adjacent grid segments are obtained, including the deviation increase / decrease rate, change gradient, and directional consistency. At the same time, the statistical analysis results of the grid segment within a preset time window are obtained, such as the mean, standard deviation, and outlier distribution. Based on the preset weighting coefficients, the trend indicators and the statistical results of the time window are weighted and combined to obtain the comprehensive deviation reliability parameter of each time grid segment. This parameter is used to quantify the reliability and stability of the deviation of the grid segment and provide a basis for power error correction.

[0034] S4: Input the curve deviation and deviation confidence parameters into the preset deviation-error mapping relationship to generate a complete power error sequence, and correct the charging power command value of the current time grid segment to form the charging power command value.

[0035] The process of generating the complete charge error sequence in S4 is as follows: Input the curve deviation and deviation confidence parameters for each time grid segment into the preset deviation-error mapping relationship; For each time grid segment, the calculated curve deviation and deviation confidence parameters are obtained, and these two are used as input values ​​and substituted into a pre-established deviation-error mapping function or lookup table. This mapping relationship is predefined based on historical experimental data or model calibration results, and the deviation and confidence are combined to convert into the power error value, realizing a quantitative correspondence from the deviation to the power error.

[0036] Calculate the power error value for the corresponding time grid segment based on the mapping relationship; After inputting the curve deviation and deviation confidence parameter into the deviation-error mapping relationship, the corresponding power error value is calculated point by point for each time grid segment according to the mapping function or lookup table rules. The calculation takes into account the deviation magnitude, confidence weight and battery characteristic parameters. The error value obtained by mapping is uniformly converted into power unit to form the precise quantified power error of each grid segment.

[0037] Output the power error value sequence, and arrange them in chronological order to form a complete power error sequence; The power error values ​​calculated for each time grid segment are arranged sequentially according to the sampling time to form a continuous power error value sequence. During the arrangement process, any missing or abnormal grid segment error values ​​are interpolated or corrected, while maintaining the temporal consistency and integrity of the sequence, thereby obtaining a complete power error sequence covering the entire charging process, providing a continuous and usable data foundation for subsequent charging power correction.

[0038] The process of generating the charging power command value in S4 is as follows: Based on the complete power error sequence, the corrected charging power command value is calculated according to the linear correction formula. Using the complete power error sequence as input, the charging power of each time grid segment is adjusted according to the preset linear correction formula. That is, the power error is multiplied by the preset gain coefficient and added to the original power command to generate a preliminary corrected power command value. Combined with the current battery voltage and current limits and charging stage characteristics, the corrected power value is ensured to be within the safe and allowable range, while maintaining the smoothness of power changes between continuous time periods, forming a corrected power command sequence that can be directly used to control the charger.

[0039] In the correction calculation, the battery's allowable voltage, current and power thresholds are included in the constraints, and the power command is adjusted to meet the preset range in combination with the current charging stage and battery state parameters. When calculating the corrected charging power command value, the battery's current allowable voltage, current, and power threshold are read as constraints. At the same time, the battery's current charging stage, such as constant current or constant voltage stage, and battery status parameters, such as SOC, temperature, and health status, are obtained. The initially corrected power command is compared with these constraints. Power commands that exceed the threshold are trimmed or proportionally adjusted to ensure that they strictly fall within the allowable range, maintaining the continuity and smoothness of power changes. This ensures that the corrected power command not only meets safety constraints but also smoothly responds to the correction requirements of power error.

[0040] The corrected power command sequence is smoothed, including by moving average or low-pass filtering, to generate continuous charging power command values. The corrected charging power command sequence is taken as input, and an appropriate smoothing algorithm is selected, such as a fixed-window moving average or a digital low-pass filter. In the moving average processing, the power command at each time point is replaced with the weighted average of several sampling points before and after it to eliminate instantaneous fluctuations. In the low-pass filtering processing, the power sequence is filtered according to a preset cutoff frequency to suppress high-frequency interference and retain low-frequency trends, thus maintaining the time order and amplitude continuity of the power sequence. Finally, a smooth, continuous charging power command value sequence that meets the dynamic response requirements is output for power correction calculation.

[0041] S5: Calculate the corrected power value of each time grid segment based on the charging power command value and the duration of the corresponding time grid segment, and accumulate the corrected power values ​​of each time grid segment in chronological order to form a cumulative power data sequence.

[0042] The process of calculating the corrected charge value for the time grid segment in S5 is as follows: Obtain the corrected charging power command value and corresponding duration for each time grid segment; For the smoothed charging power command sequence, the power command value of each segment is extracted segment by segment according to the pre-divided time grid segments, and the corresponding time span is recorded. For each time grid segment, its start and end time points are determined, and the duration of the segment is calculated. At the same time, all corrected power command values ​​in the segment are associated to form complete power-time information for each time grid segment, providing input for energy integral calculation.

[0043] The charging power command value is numerically integrated with the duration of time to calculate the initial corrected power value for each grid segment; For each time grid segment, the corrected charging power command value within that segment is numerically integrated with the corresponding time duration. Trapezoidal integration or rectangular integration methods are typically used to accumulate the power over time to obtain the preliminary corrected energy value for that time grid segment. This ensures that the integration steps are consistent with the time interval, and the power contributions of all sampling points are superimposed to accurately reflect the energy input of that grid segment.

[0044] The battery charge and discharge efficiency and power loss factor are introduced into the integral calculation to correct the initial corrected power value and form the final corrected power value. After obtaining the preliminary corrected energy value for each time grid segment, the preliminary energy value is multiplied and divided for correction based on the current charge and discharge efficiency of the battery and the system power loss factor. The actual available energy is adjusted with the theoretical integral value to form the final corrected energy value. This ensures that the final corrected energy value accurately reflects the energy actually received or released by the battery. At the same time, the dynamic adjustment of efficiency by factors such as temperature and SOC is considered to achieve precise correction.

[0045] The process of forming the cumulative electricity data sequence in S5 is as follows: The corrected energy values ​​for each time grid segment are accumulated sequentially in chronological order to form a preliminary cumulative energy sequence; The final corrected power value of each time grid segment is taken sequentially according to time order. During the accumulation process, the cumulative value of the previous grid segment is added to the corrected power value of the current grid segment to gradually form a continuous preliminary cumulative power sequence. At the same time, the consistency of the time sequence is maintained during the accumulation process to ensure that the cumulative power of each time node accurately corresponds to the corresponding charging time point.

[0046] The accumulated values ​​for each time grid segment are normalized within a given interval. Determine the upper and lower limits of the normalization interval, such as 0 to 1 or set a range based on the total charging amount. Then, perform a linear mapping on the accumulated charge value for each time grid segment to convert it proportionally into the normalization interval while maintaining the time order and relative size relationship of the original sequence, thereby obtaining the standardized accumulated charge sequence.

[0047] During the sequence accumulation process, a sliding window accumulation strategy is adopted for the corrected charge values ​​of continuous time grid segments to form local cumulative subsequences; A fixed-length sliding window is set on the corrected energy sequence of a continuous time grid segment, and the window is moved sequentially according to time order. The corrected energy values ​​within the continuous grid segment covered by the window are accumulated each time to form the corresponding local cumulative value. As the window slides, a series of local cumulative subsequences are generated, and the start and end time information of each subsequence is retained to reflect the trend of energy accumulation change in a short period of time.

[0048] The local cumulative subsequences are integrated in chronological order to generate a complete cumulative electricity data sequence. All local cumulative subsequences obtained by sliding window accumulation are arranged sequentially according to their time start and end order. The values ​​of overlapping segments between adjacent subsequences are smoothed or weighted averaged to eliminate the effects of duplicate calculations or abrupt changes. Finally, the local cumulative values ​​are continuously connected to form a complete cumulative power data sequence arranged in time order, which can accurately reflect the power changes throughout the charging process.

[0049] Example 2: As Figure 2 As shown, a precise charging status data measurement system suitable for electric vehicles includes: Response generation module: Collects charging current, voltage and timestamp data, and generates charging response curves for each time grid segment in chronological order; Curve alignment module: Aligns the charging response curve with the reference charging response curve, quantifies the difference between the two, and generates a sequence of curve deviations; Deviation Calculation Module: Based on the curve deviation sequence and its changing trend and stability index within adjacent time grid segments, the deviation reliability parameter is calculated. Power correction module: Determines the power error value based on the deviation reliability parameter and curve deviation, and corrects the charging power command for the current time grid segment; Power accumulation module: Calculates the corrected power based on the corrected charging power command value and the duration of the time grid segment, and accumulates them sequentially to form a cumulative power data sequence.

[0050] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0051] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for accurate measurement of state of charge data suitable for electric vehicles, characterized by, The method comprises the following steps: dividing the charging process into multiple time grid segments according to fixed time intervals, collecting charging current values, charging voltage values and corresponding time stamps in each time grid segment, and arranging the charging response curves corresponding to the time grid segments in time sequence to form the charging response curves corresponding to the time grid segments; aligning the charging response curves corresponding to the time grid segments with reference charging response curves pre-established and stored for different battery types, environmental temperature intervals and charging stages on the same time coordinate axis, and quantitatively processing the differences between the two to generate a curve deviation sequence corresponding to continuous time grid segments; calculating a deviation credibility parameter based on the curve deviation sequence corresponding to the time grid segments, in combination with the change trend between adjacent time grid segments and the stability index within a preset time window; inputting the curve deviation and the deviation credibility parameter into a preset deviation-error mapping relationship to generate a complete electric quantity error sequence, and correcting the charging power instruction value of the current time grid segment to form a charging power instruction value; calculating the corrected electric quantity value of the time grid segment according to the charging power instruction value and the duration of the corresponding time grid segment, and accumulating the corrected electric quantity values of each time grid segment in time sequence to form a cumulative electric quantity data sequence.

2. The method for precise measurement of state of charge data for electric vehicles according to claim 1, wherein, The process of arranging the charging response curves corresponding to the time grid segments in time sequence comprises: real-time collection of current, voltage and time stamp data through a charger communication interface; dividing the continuously collected data into independent time grid segments according to a preset fixed time interval; sorting and standardizing the data of each time grid segment, including interpolation of missing data, removal of abnormal values and unit unification; connecting the processed data points in time sequence to form the charging response curves corresponding to the time grid segments.

3. The method for precise measurement of state of charge data for electric vehicles according to claim 2, wherein, The process of aligning the charging response curves corresponding to the time grid segments with reference charging response curves pre-established and stored for different battery types, environmental temperature intervals and charging stages on the same time coordinate axis comprises: extracting matching curves from the pre-established reference curve database according to different battery types, environmental temperature intervals and charging stages; mapping the response curves corresponding to the time grid segments to the time coordinates of the reference curves to interpolate and complete the missing sampling points; after alignment, forming an actual data and reference curve data pair for each time grid segment.

4. The method for precise measurement of state of charge data for electric vehicles according to claim 3, wherein, The process of generating a curve deviation sequence corresponding to continuous time grid segments comprises: point-by-point comparison of the actual data and reference curve data pair of each time grid segment to calculate the absolute deviation; converting the current deviation into a power deviation, and calculating the power deviation value of each sampling point using the equivalent power formula; calculating the average value, variance and peak value of the power deviation value within the time grid segment to form a grid segment-level deviation feature vector; arranging the grid segment-level deviation feature vectors in time sequence to generate a curve deviation sequence for continuous time grid segments.

5. The method for precise measurement of state of charge data for electric vehicles according to claim 4, characterized in that, The process of calculating the deviation credibility parameter comprises: taking the curve deviation of continuous time grid segments as input, extracting the deviation trend of adjacent time grid segments, including the deviation increase / decrease rate, change gradient and direction consistency; statistical analysis of the deviation sequence within a preset time window, including average value, standard deviation and abnormal point detection; The trend index is combined with the time window analysis result, and a deviation credibility parameter of each time grid segment is calculated according to a preset weight.

6. The method for precise measurement of state of charge data for electric vehicles according to claim 5, wherein, The process of generating a complete power error sequence is as follows: The curve deviation of each time grid segment and the deviation credibility parameter are input into a preset deviation-error mapping relationship. The power error value of the corresponding time grid segment is calculated according to the mapping relationship. The power error value sequence is output, and a complete power error sequence is formed in chronological order.

7. The method for precise measurement of state of charge data for electric vehicles according to claim 6, wherein, The process of forming the charging power instruction value is as follows: According to the complete power error sequence, the corrected charging power instruction value is calculated according to a linear correction formula. In the correction calculation, the voltage, current and power thresholds allowed by the battery are included in the constraint condition, and the power instruction is adjusted to meet the preset range in combination with the current charging stage and the battery state parameter. The power instruction sequence after correction is smoothed, including moving average or low-pass filtering operation, to generate continuous charging power instruction value.

8. The method for precise measurement of state of charge data for electric vehicles according to claim 7, characterized in that, The process of calculating the corrected power value of the time grid segment is as follows: The corrected charging power instruction value and the corresponding duration of each time grid segment are obtained. The charging power instruction value and the time duration are numerically integrated to calculate the preliminary corrected power value of each grid segment. In the integral calculation, the battery charging and discharging efficiency and the power loss factor are introduced to correct the preliminary corrected power value to form the final corrected power value.

9. The method for precise measurement of state of charge data for electric vehicles according to claim 8, wherein, The process of forming the cumulative power data sequence is as follows: The corrected power value of each time grid segment is sequentially accumulated in chronological order to form a preliminary cumulative power sequence. The accumulated value of each time grid segment is interval normalized. In the sequence accumulation process, the corrected power value of the continuous time grid segment is accumulated by using a sliding window accumulation strategy to form a local cumulative subsequence. The local cumulative subsequence is integrated in chronological order to generate a complete cumulative power data sequence.

10. A precise state of charge data metering system for electric vehicles, applied to the method according to any one of claims 1 to 9, characterized in that, It includes: The response generation module collects charging current, voltage and timestamp data, and generates a charging response curve for each time grid segment in chronological order. The curve alignment module aligns the charging response curve with the reference charging response curve and quantifies the difference between the two to generate a curve deviation sequence. The deviation calculation module calculates the deviation credibility parameter based on the curve deviation sequence and the trend and stability indicators in the adjacent time grid segment. The power correction module determines the power error value according to the deviation credibility parameter and the curve deviation, and corrects the charging power instruction of the current time grid segment. The power accumulation module calculates the corrected power according to the corrected charging power instruction value and the time grid segment duration, and sequentially accumulates to form a cumulative power data sequence.

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