Charging facility metering performance monitoring system based on various charging station scene simulation

Abstract

The invention provides a charging facility metering performance monitoring system based on simulation of various charging station scenes, and relates to the field of charging performance monitoring. The charging facility metering performance monitoring system comprises a scene simulation module, a data acquisition module, a metering performance monitoring module, a data analysis module and a man-machine interaction interface output module, and the scene simulation module comprises a climate simulation module, an electric power disturbance module and a charging load module. The climate simulation module can simulate the temperature ranging from-30 DEG C to 80 DEG C. Through the multi-scene simulation module, typical application scenes such as a city fast charging station, a high-speed service area charging station and a community slow charging station can be simulated, more than 95% of actual charging working conditions are covered, the problem that a traditional system is single in scene is solved, the actual charging scene of a new energy automobile is fitted to the maximum extent, and the charging efficiency is improved. And a large amount of data support is provided for research on the metering performance of the charging facility of the new energy automobile.

Description

Technical Field

[0001] This invention relates to the field of charging performance monitoring systems, specifically to a system for monitoring the metering performance of charging facilities based on simulations of various charging station scenarios. Background Technology

[0002] Against the backdrop of achieving carbon peaking and carbon neutrality, my country's new energy vehicle industry, especially the electric vehicle industry, has developed rapidly. However, the rapid increase in the number of charging piles has limited the development of electric vehicles. This rapid increase has also brought about the issue of accurate metering. Currently, many cities in China have installed a large number of public and private charging piles. As a newly emerging electricity metering device, charging piles, whether DC, AC, vehicle-mounted, or non-vehicle-mounted, all involve electricity trade settlement and the settlement of electricity costs. Therefore, it is essential to implement mandatory metering verification for electric vehicle charging piles to ensure their accurate metering performance. Currently, there are still problems in accounting methods and emission reduction certification, mainly due to inconsistent accounting methods, a lack of fair certification of emission reductions, and a lack of accounting for the carbon emissions of charging and battery swapping stations themselves. Factors such as equipment and line losses at charging stations, charging pile conversion efficiency, and the application of green electricity all affect carbon emissions. There is still no unified standard or specification for carbon emission accounting throughout the entire charging service process.

[0003] Therefore, this invention proposes a charging facility metering performance monitoring system based on simulation of multiple charging station scenarios. Through multi-scenario simulation, it achieves accurate monitoring of the metering performance of new energy charging cabinet facilities, thereby effectively solving the above-mentioned problems and difficulties. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a charging facility metering performance monitoring system based on simulation of various charging station scenarios. Through multi-scenario simulation modules, it can simulate typical application scenarios such as urban fast charging stations, highway service area charging stations, and community slow charging stations, covering more than 95% of actual charging conditions. This solves the problem of single-scenario application in traditional systems, and maximizes the fit with the actual charging scenarios of new energy vehicles, providing a large amount of data support for the research on the metering performance of charging facilities for new energy vehicles.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A system for monitoring the metering performance of charging facilities based on simulation of various charging station scenarios is proposed. The system comprises five modules: a scenario simulation module, a data acquisition module, a metering performance monitoring module, a data analysis module, and a human-computer interaction interface output module.

[0007] The scenario simulation module includes a climate simulation module, a power disturbance module, and a charging load module. The climate simulation module can simulate a climate environment with a temperature of -30℃ to 80℃ and a humidity of 5% to 95%. The power disturbance module can adjust and generate voltage surges and drops, and simulate a power grid signal with a harmonic distortion rate of 0% to 20%. The charging load module can support dynamic power adjustment from 0 to 100kW to simulate single / multiple new energy vehicle charging scenarios.

[0008] The data acquisition module includes a current sensor, a voltage sensor, a power sensor, and an environmental data acquisition device. The current sensor and the voltage sensor are both high-precision sensors. The environmental data acquisition device synchronously acquires ambient temperature and humidity data. The power sensor acquires the power measurement data of the charging facility in real time.

[0009] The metering and monitoring module includes functions such as error analysis, data filtering, and performance index recording. Error analysis is used to calculate the basic error, short-term repeatability, and temperature drift of the charging facility in real time. Data filtering preprocesses noise data to improve the accuracy of error calculation.

[0010] The data analysis module constructs a historical database, establishes a metrology performance prediction model based on trend analysis algorithms, and performs trend analysis and correlation analysis on metrology data in multiple scenarios.

[0011] The human-computer interaction module provides a graphical interface that supports scene parameter settings, real-time data monitoring, report generation and data export. It also has a threshold alarm mechanism that automatically triggers an audible and visual alarm when the error exceeds the allowable range.

[0012] Furthermore, the scenario simulation module links various environmental simulation units through a PLC programmable logic controller and connects to the charging facility under test through a hardware interface to achieve composite simulation of climate, power disturbance and load scenarios.

[0013] Furthermore, the data acquisition module is deployed at the output end of the device under test and inside the environmental chamber, and collects real-time data through a sensor network and transmits the data to the measurement and monitoring module in real time.

[0014] Furthermore, the metering and monitoring module decomposes the charging data into approximate and detail components through multi-resolution analysis, removes high-frequency noise components from the current and voltage data during charging, and processes impulse noise or data distortion during non-stationary charging processes through discrete wavelet transform.

[0015]

[0016] Where ψ j,k (n)=2 j / 2 ψ(2 jnk) is the wavelet basis function, and f(n) is the charging data sequence.

[0017] Furthermore, the data analysis module, based on a trend analysis algorithm, captures long-term trends and short-term fluctuations by processing the time-series dependencies of charging metering data, predicts metering error trends in different seasons and time periods, and uses a time-series trend analysis algorithm to take the mean of a sliding window on the time series to eliminate short-term fluctuations and highlight the trend.

[0018]

[0019] Where n is the window size, x i For the measurement data at the i-th time point:

[0020] Then, an exponentially decreasing weight is assigned to the historical data using an exponential smoothing algorithm:

[0021] S t =αx t +(1-α)S t-1 :

[0022] Where α is the smoothing coefficient (0 < α < 1), S t The smoothed value at time t.

[0023] Furthermore, the data analysis module establishes an ARIMA prediction model for econometric performance based on trend analysis algorithms, combining autoregression (AR), difference (I), and moving average (MA), which is suitable for non-stationary time series.

[0024] φ(B)(1-B) d X t =θ(B)ε t :

[0025] Where B is the lag operator, φ(B) and θ(B) are polynomials, d is the difference order, and ε t For white noise, the ARIMA prediction model determines the model order (p, d, q) by differentiating and stabilizing the time series, thereby fitting the model:

[0026]

[0027] in, This is the predicted value for time t+h based on time t.

[0028] Furthermore, the data analysis module uses the ARIMA prediction model combined with a historical database to perform trend analysis and correlation analysis on multi-scenario measurement data. The ARIMA prediction model is evaluated using the mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R²). 2Data trend analysis and evaluation are conducted from three aspects;

[0029] Mean Squared Error (MSE):

[0030] Mean Absolute Error (MAE):

[0031] Coefficient of determination (R) 2 ):

[0032] By combining evaluation results from different scenarios, the weights of data from each scenario are determined using information entropy to eliminate subjective bias. Then, by fusing multi-source sensor data, a comprehensive evaluation of metrological performance under complex environments is conducted.

[0033]

[0034] Where m1 and m2 are the basic probability assignments of multi-source data, A i B j For multi-source scene coefficient constants, the conflict coefficient is:

[0035]

[0036] Furthermore, the human-computer interaction interface includes interface modules such as a real-time data monitoring area, a scene parameter setting area, a historical data display area, and an alarm information prompting area. The real-time data monitoring area dynamically displays voltage / current waveforms, real-time power curves, current error values, and environmental parameter dashboards. The scene parameter setting area supports combined settings for temperature and harmonic distortion rate parameters. The historical data display area supports comparison of historical data with multiple parameters and generation of data graphs and text. The alarm information prompting area displays alarms according to priority and records the alarm time and type.

[0037] This invention provides a system for monitoring the metering performance of charging facilities based on simulations of various charging station scenarios. It offers the following advantages:

[0038] 1. This invention provides a charging facility metering performance monitoring system based on simulation of various charging station scenarios. Through multi-dimensional scenario simulation (climate, power disturbance, load), it can simulate more than 90% of actual charging conditions, ensuring that the metering monitoring results closely match real applications. Furthermore, through multi-scenario simulation modules, it can simulate typical application scenarios such as urban fast charging stations, highway service area charging stations, and community slow charging stations, covering more than 95% of actual charging conditions. This solves the problem of single scenario in traditional systems, and maximizes the fit with the actual charging scenarios of new energy vehicles, providing a large amount of data support for the research on the metering performance of charging facilities for new energy vehicles.

[0039] 2. This invention provides a charging facility metering performance monitoring system based on simulation of various charging station scenarios. The entire process from scenario switching and data acquisition to error analysis is automated, which greatly improves the detection efficiency. Furthermore, the machine learning model based on trend analysis algorithm can realize the prediction of metering performance trends, reduce maintenance costs, and achieve accurate error source tracing. Through multi-parameter synchronous acquisition and correlation analysis, the source of error (such as sensor temperature drift, power harmonic interference, etc.) can be located, and the location accuracy is greatly improved. Attached Figure Description

[0040] Figure 1 This is an architecture diagram of the charging facility metering performance monitoring system of the present invention. Detailed Implementation

[0041] 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.

[0042] Example 1:

[0043] like Figure 1 As shown, this embodiment of the invention provides a charging facility metering performance monitoring system based on simulation of various charging station scenarios. The charging facility metering performance monitoring system includes five modules: scenario simulation module, data acquisition module, metering performance monitoring module, data analysis module, and human-computer interaction interface output module.

[0044] The scenario simulation module includes a climate simulation module, a power disturbance module, and a charging load module. The climate simulation module can simulate a climate environment with a temperature of -30℃ to 80℃ and a humidity of 5% to 95%. The power disturbance module can adjust and generate voltage surges and drops, and simulate power grid signals with a harmonic distortion rate of 0% to 20%. The charging load module can support dynamic power adjustment from 0 to 100kW to simulate single / multiple new energy vehicle charging scenarios.

[0045] The data acquisition module includes a current sensor, a voltage sensor, a power sensor, and an environmental data acquisition device. The current sensor and voltage sensor are both high-precision sensors. The environmental data acquisition device synchronously acquires ambient temperature and humidity data. The power sensor acquires the power measurement data of the charging facility in real time.

[0046] The metering and monitoring module includes functions such as error analysis, data filtering, and performance index recording. Error analysis is used to calculate the basic error, short-term repeatability, and temperature drift of the charging facility in real time. Data filtering preprocesses noise data to improve the accuracy of error calculation.

[0047] The data analysis module builds a historical database, establishes a metrology performance prediction model based on trend analysis algorithms, and performs trend analysis and correlation analysis on metrology data in multiple scenarios.

[0048] The human-computer interaction module provides a graphical interface that supports scene parameter settings, real-time data monitoring, report generation and data export. It also has a threshold alarm mechanism that automatically triggers an audible and visual alarm when the error exceeds the allowable range.

[0049] The scenario simulation module links various environmental simulation units through a PLC programmable logic controller and connects to the charging facility under test through a hardware interface to achieve composite simulation of climate, power disturbance, and load scenarios. The data acquisition module is deployed at the output end of the device under test and inside the environmental chamber. It collects real-time data through a sensor network and transmits the data to the metering and monitoring module in real time. The human-machine interface includes interface modules such as a real-time data monitoring area, a scenario parameter setting area, a historical data display area, and an alarm information prompting area. The real-time data monitoring area dynamically displays voltage / current waveforms, real-time power curves, current error values, and environmental parameter dashboards. The scenario parameter setting area supports the combined setting of parameters such as support temperature and harmonic distortion rate. The historical data display area supports the comparison of historical data for multiple parameters and the generation of data graphs and text. The alarm information prompting area displays alarms according to priority and records the alarm time and type.

[0050] Example 2:

[0051] like Figure 1 As shown, this embodiment of the invention provides a charging facility metering performance monitoring system based on simulations of various charging station scenarios. The metering monitoring module decomposes charging data into approximate and detail components through multi-resolution analysis, removes high-frequency noise components from the current and voltage data during charging, and processes impulse noise or data distortion during non-stationary charging processes through discrete wavelet transform.

[0052]

[0053] Where ψ j,k (n)=2 j / 2 ψ(2 j nk) is the wavelet basis function, and f(n) is the charging data sequence;

[0054] Example 3:

[0055] like Figure 1As shown, this embodiment of the invention provides a charging facility metering performance monitoring system based on simulations of various charging station scenarios. The data analysis module uses a trend analysis algorithm to capture long-term trends and short-term fluctuations by processing the time series dependencies of charging metering data, predicting metering error trends in different seasons and time periods. A time series trend analysis algorithm is used to take the mean of a sliding window on the time series to eliminate short-term fluctuations and highlight the trend.

[0056]

[0057] Where n is the window size, x i For the measurement data at the i-th time point:

[0058] Then, an exponentially decreasing weight is assigned to the historical data using an exponential smoothing algorithm:

[0059] S t =αx t +(1-α)S t-1 :

[0060] Where α is the smoothing coefficient (0 < α < 1), S t The smoothed value at time t;

[0061] The data analysis module builds an econometric performance ARIMA prediction model based on trend analysis algorithms, combining autoregression (AR), difference (I), and moving average (MA), which is suitable for non-stationary time series.

[0062] φ(B)(1-B) d X t =θ(B)ε t :

[0063] Where B is the lag operator, φ(B) and θ(B) are polynomials, d is the difference order, and ε t For white noise, the ARIMA prediction model determines the model order (p, d, q) by differentiating and stabilizing the time series, thereby fitting the model:

[0064]

[0065] in, This is the predicted value for time t+h based on time t;

[0066] The data analysis module uses the ARIMA prediction model combined with a historical database to perform trend and correlation analysis on multi-scenario measurement data. The ARIMA prediction model is evaluated using the mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R²). 2 Data trend analysis and evaluation are conducted from three aspects;

[0067] Mean Squared Error (MSE):

[0068] Mean Absolute Error (MAE):

[0069] Coefficient of determination (R) 2 ):

[0070] By combining evaluation results from different scenarios, the weights of data from each scenario are determined using information entropy to eliminate subjective bias. Then, by fusing multi-source sensor data, a comprehensive evaluation of metrological performance under complex environments is conducted.

[0071]

[0072] Where m1 and m2 are the basic probability assignments of multi-source data, A i B j For multi-source scene coefficient constants, the conflict coefficient is:

[0073]

[0074] Working principle: The workflow steps of the charging facility metering performance monitoring system based on simulations of various charging station scenarios are as follows:

[0075] Step 1: Hardware system setup, including setting up a scenario simulation module, including an integrated constant temperature and humidity chamber (temperature control accuracy ±0.5℃), a power quality simulator (harmonic simulation range 0~30), and a programmable electronic load (supporting CC / CV / CP modes). The new energy charging pile under test is connected to the power quality simulator and electronic load via cables. Sensors are deployed at the output end of the charging pile and inside the environmental chamber. The data acquisition card communicates with the main control PC via a USB interface.

[0076] Step 2: Typical scenario testing. Users input parameter combinations through the human-computer interaction module to start the climate simulation unit, power disturbance simulation unit, and load simulation unit to build the simulation scenario.

[0077] Step 3: Raw data acquisition and processing. Voltage / current waveforms, power pulse signals and ambient temperature and humidity data are acquired at a sampling frequency of 10kHz to form raw data packets. Then, the raw data is denoised by recursively filtering the white noise in the voltage signal and the periodic interference in the current signal.

[0078] Step 3: Error measurement calculation and analysis. A measurement performance prediction model is established based on a trend analysis algorithm. Real-time data is compared with data in the historical database. Trend analysis is performed on real-time measurement data in multiple scenarios. The data processing module compares the simulation input value with the measured value.

[0079]

[0080] The data analysis module locates the cause of the error by comparing waveforms.

[0081] Step 4: Result report generation and display. The real-time error data obtained by the data analysis module is refreshed to the human-machine interface at 100ms intervals. Historical data is stored according to scenario type. When the error exceeds the allowable range, an audible and visual alarm is automatically triggered. The entire process is visualized and managed through the human-machine interaction module.

[0082] The following points should be noted in this article:

[0083] 1. The accompanying drawings of the embodiments disclosed herein only relate to the structures involved in the embodiments disclosed herein; other structures can be referred to in a general design.

[0084] 2. Where there is no conflict, the embodiments of this disclosure and the features in the embodiments can be combined with each other to obtain new embodiments.

[0085] 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.

Claims

1. A system for monitoring the metering performance of charging facilities based on simulations of various charging station scenarios, characterized in that: The charging facility metering performance monitoring system comprises five modules: a scenario simulation module, a data acquisition module, a metering performance monitoring module, a data analysis module, and a human-computer interaction interface output module. The scenario simulation module includes a climate simulation module, a power disturbance module, and a charging load module. The climate simulation module can simulate a climate environment with a temperature of -30℃ to 80℃ and a humidity of 5% to 95%. The power disturbance module can adjust and generate voltage surges and drops, and simulate a power grid signal with a harmonic distortion rate of 0% to 20%. The charging load module can support dynamic power adjustment from 0 to 100kW to simulate single / multiple new energy vehicle charging scenarios. The data acquisition module includes a current sensor, a voltage sensor, a power sensor, and an environmental data acquisition device. The current sensor and the voltage sensor are both high-precision sensors. The environmental data acquisition device synchronously acquires ambient temperature and humidity data. The power sensor acquires the power measurement data of the charging facility in real time. The metering and monitoring module includes functions such as error analysis, data filtering, and performance index recording. Error analysis is used to calculate the basic error, short-term repeatability, and temperature drift of the charging facility in real time. Data filtering preprocesses noise data to improve the accuracy of error calculation. The data analysis module constructs a historical database, establishes a metrology performance prediction model based on trend analysis algorithms, and performs trend analysis and correlation analysis on metrology data in multiple scenarios. The human-computer interaction module provides a graphical interface that supports scene parameter settings, real-time data monitoring, report generation and data export. It also has a threshold alarm mechanism that automatically triggers an audible and visual alarm when the error exceeds the allowable range.

2. The charging facility metering performance monitoring system based on simulation of multiple charging station scenarios as described in claim 1, characterized in that: The scenario simulation module links various environmental simulation units through a PLC programmable logic controller and connects to the charging facility under test through a hardware interface to achieve composite simulation of climate, power disturbance and load scenarios.

3. The charging facility metering performance monitoring system based on simulation of multiple charging station scenarios as described in claim 1, characterized in that: The data acquisition module is deployed at the output end of the device under test and inside the environmental chamber. It collects real-time data through a sensor network and transmits the data to the measurement and monitoring module in real time.

4. The charging facility metering performance monitoring system based on simulation of multiple charging station scenarios as described in claim 1, characterized in that: The metering and monitoring module decomposes charging data into approximate and detail components through multi-resolution analysis, removes high-frequency noise components from the current and voltage data during charging, and processes impulse noise or data distortion during non-stationary charging processes through discrete wavelet transform. Where ψ j,k (n)=2 j / 2 ψ(2 j nk) is the wavelet basis function, and f(n) is the charging data sequence.

5. A system for monitoring the metering performance of charging facilities based on simulations of various charging station scenarios, as described in claim 1, is characterized in that: The data analysis module, based on a trend analysis algorithm, captures long-term trends and short-term fluctuations by processing the time-series dependencies of charging metering data. It predicts metering error trends for different seasons and time periods, and uses a time-series trend analysis algorithm to take the mean of a sliding window on the time series data to eliminate short-term fluctuations and highlight the overall trend. Where n is the window size, x i For the measurement data at the i-th time point: Then, an exponentially decreasing weight is assigned to the historical data using an exponential smoothing algorithm: S t =αx t +(1-α)S t-1 : Where α is the smoothing coefficient (0 < α < 1), S t The smoothed value at time t.

6. The charging facility metering performance monitoring system based on simulation of multiple charging station scenarios as described in claim 1, characterized in that: The data analysis module is based on a trend analysis algorithm to build an ARIMA prediction model for econometric performance, which combines autoregression (AR), difference (I), and moving average (MA), and is suitable for non-stationary time series. φ(B)(1-B) d X t =θ(B)ε t : Where B is the lag operator, φ(B) and θ(B) are polynomials, d is the difference order, and ε t For white noise, the ARIMA prediction model determines the model order (p, d, q) by differentiating and stabilizing the time series, thereby fitting the model: in, This is the predicted value for time t+h based on time t.

7. A system for monitoring the metering performance of charging facilities based on simulations of various charging station scenarios, as described in claim 1, is characterized in that: The data analysis module uses the ARIMA prediction model combined with a historical database to perform trend analysis and correlation analysis on multi-scenario measurement data. The ARIMA prediction model is evaluated using the mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R²). 2 Data trend analysis and evaluation are conducted from three aspects; Mean Squared Error (MSE): Mean Absolute Error (MAE): Coefficient of determination (R) 2 ): By combining evaluation results from different scenarios, the weights of data from each scenario are determined using information entropy to eliminate subjective bias. Then, by fusing multi-source sensor data, a comprehensive evaluation of metrological performance under complex environments is conducted. Where m1 and m2 are the basic probability assignments of multi-source data, A i B j For multi-source scene coefficient constants, the conflict coefficient is:

8. A system for monitoring the metering performance of charging facilities based on simulations of various charging station scenarios, as described in claim 1, is characterized in that: The human-machine interface includes interface modules such as a real-time data monitoring area, a scene parameter setting area, a historical data display area, and an alarm information prompting area. The real-time data monitoring area dynamically displays voltage / current waveforms, real-time power curves, current error values, and environmental parameter dashboards. The scene parameter setting area supports combined settings for parameters such as temperature and harmonic distortion rate. The historical data display area supports comparison of historical data for multiple parameters and generation of data graphs and text. The alarm information prompting area displays alarms according to priority and records the alarm time and type.

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