Charging pile metering error online monitoring method and system based on magnitude transmission and standard vehicle identification

Abstract

The invention discloses a charging pile metering error online monitoring method and system based on magnitude transmission and standard vehicle identification. According to the method, a high-precision remote measurement standard module is deployed on a charging pile with an extremely low proportion (less than or equal to 5%), first charging process data are acquired, and second charging process data of a charging operation platform are acquired at the same time. Based on the first charging process data, analyzing the stability of the vehicle metering performance by using an algorithm, and dynamically identifying and authenticating a movable standard vehicle with stable metering performance from the vehicles charged on the standard pile; and when the standard vehicle is charged on a target charging pile without a module, calculating a metering error of the target charging pile through a magnitude transfer algorithm based on the second charging process data, and aggregating multiple monitoring results to perform consistency analysis and early warning. The intelligent metering network composed of the standard pile nodes, the pile nodes to be measured and the standard vehicle nodes is constructed, online intelligent metering monitoring of the charging piles in the whole network is achieved with extremely low hardware deployment cost, and the system has the remarkable advantages of being low in cost, wide in coverage, high in reliability, high in real-time performance and the like.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging facility metering technology, and in particular to an online monitoring method and system for charging pile metering errors based on value transfer and standard vehicle identification. Background Technology

[0002] With the increasing popularity of electric vehicles, the accuracy of charging piles as measuring instruments for trade settlement is crucial. Currently, the metrological verification of charging piles mainly faces the following challenges: traditional manual on-site verification methods are costly, inefficient, and time-consuming; while existing metrological testing and verification technologies also have many limitations.

[0003] For example, Chinese invention patent CN113406557A discloses a "remote calibration method for charging piles." This method calibrates a subset of charging piles (standard piles) and then utilizes the charging behavior of electric vehicles to transmit standard values ​​through the charging network of "standard pile → electric vehicle → uncalibrated pile," thereby achieving remote pre-calibration. However, this method has the following inherent drawbacks:

[0004] 1. Fixed transmission medium and limited network construction: The transmission of standard values ​​depends entirely on the charging piles themselves, and the transmission path of standard values ​​is limited by the physical connection between charging piles (i.e., which vehicles are charging between which piles). If a "standard pile" is used infrequently or the vehicles connected to it do not use other uncalibrated piles, the transmission network will be interrupted, and the coverage will be limited.

[0005] 2. Failure to leverage the mobility advantage of vehicles: Although this method mentions vehicles, it does not systematically utilize and dynamically manage vehicles as a mobile and verifiable metrological standard. Vehicles are merely passive, one-time transmission nodes, and their long-term metrological stability is not assessed or utilized.

[0006] For example, existing solutions present a contradiction between monitoring costs and coverage: patents such as CN116184058A and CN117250427A require the installation of dedicated detection equipment or transmitters on the charging pile side, resulting in high hardware costs and making it difficult to achieve large-scale deployment and full network coverage monitoring.

[0007] For example, the patent with publication number CN115542235A estimates the error by analyzing the power probability density function of multiple charging guns, but its method relies on static data comparison of homogeneous charging guns and cannot achieve cross-pile, dynamic, and real-time online monitoring.

[0008] Furthermore, existing technologies generally suffer from insufficient credibility verification of a single data source. They rely on a single data source, lack credible data sources, and lack cross-validation mechanisms for multi-source data, making it difficult to ensure the credibility and reliability of monitoring results.

[0009] Therefore, existing technologies lack an effective solution that can achieve wide coverage, high reliability, and real-time online monitoring of charging pile metering errors with extremely low hardware deployment costs. Summary of the Invention

[0010] The purpose of this invention is to overcome the aforementioned deficiencies of the prior art and provide an online monitoring method and system for charging pile metering errors based on value transfer and standard vehicle identification. This method, by deploying remote metering standard modules at a low ratio and combining them with dynamically identified and authenticated mobile standard vehicles, constructs a flexible, reliable, and low-cost nationwide metering error monitoring network, solving problems such as high cost, difficulty in coverage, and rapid decay of reliability in the prior art.

[0011] To achieve the above objectives, the present invention adopts the following technical solution:

[0012] A method for online monitoring of charging pile metering errors based on value transfer and standard vehicle identification is proposed. Its core lies in constructing a three-layer architecture: "low-density fixed standard nodes + highly mobile standard carriers + cloud-based data analysis platform." (See attached image.) Figure 1 Specifically, it includes the following steps:

[0013] S1: Low-density fixed standard node deployment and data acquisition

[0014] Data acquisition steps: Data is acquired from two data sources. The first data source is the first charging process data collected by remote metering standard modules deployed on a portion of the charging piles at a first preset ratio. Charging piles with remote metering standard modules installed are designated as standard piles. The second data source is the second charging process data reported by the charging operation platform. High-precision remote metering standard modules are installed on a selected portion of the charging piles at a first preset ratio (e.g., but not limited to 2%). These modules have an energy metering accuracy of no less than 0.5 level and can collect parameters such as voltage, current, energy, and vehicle-pile charging communication data in real time during the charging process, forming the first data stream. Simultaneously, the charging operation platform reports all order data and vehicle-pile communication data for the entire charging process according to a unified standard, forming the second data stream. The order data includes: charging start time, charging pile ID, charging amount, etc., and the vehicle-pile communication data includes: vehicle VIN code, charging start time, vehicle-side measured charging voltage, vehicle-side measured current, and acquisition time, etc.

[0015] S2: Dynamic Identification and Authentication of Standard Vehicles. This is one of the core differences between this invention and existing technologies (such as CN113406557A). Instead of simply treating all vehicles that have been charged at standard charging stations as transmission media, it actively screens and authenticates "mobile standard vehicles" with stable metering performance through the following steps:

[0016] • Statistical analysis of historical data of vehicles that have completed charging at charging stations equipped with remote metering standard modules.

[0017] • For each vehicle, calculate and analyze the relative deviation sequence between the vehicle's BMS metering value and the remote metering standard module metering value during multiple charging processes.

[0018] If the fluctuation range (such as standard deviation, range) of the relative deviation sequence is less than the preset stability threshold (e.g., 0.5%), the metering performance of the vehicle battery management system is considered stable and reliable, and it is dynamically certified as a "mobile standard vehicle" and its unique identity is marked in the system database.

[0019] • For each charging process on a standard pile for all standard vehicles, the relative deviation is tracked and analyzed. If the continuous deviation of a standard vehicle reaches the set exit threshold, the standard vehicle is marked as out of tolerance and exits the standard vehicle status, no longer participating in the metering monitoring and analysis of the pile to be tested, thus realizing the dynamic certification and management of standard vehicles.

[0020] S3: Error Analysis The error analysis process is triggered when a certified "mobile standard vehicle" is charged at any target charging station that does not have a remote metering standard module installed.

[0021] • Obtain the metering value of the target charging pile in this charging order from the second data stream of the charging operation platform. Measurement values ​​of BMS in standard vehicles .

[0022] • Utilize a measurement transfer algorithm to calculate the relative measurement error of the target charging station relative to the standard vehicle.

[0023] S4: Error Assessment and Early Warning Steps

[0024] To improve the reliability of monitoring results, multiple error data points generated by charging the same target charging pile at different time points, along with those generated by charging different mobile standard vehicles, are aggregated. Through consistency analysis (such as calculating the mean and variance, or using statistical process control methods), a comprehensive assessment of the charging pile's metering performance is formed. When the assessment results exceed a preset error threshold or exhibit an abnormal trend, the system automatically generates an early warning message.

[0025] Accordingly, an online monitoring system for implementing the above method includes:

[0026] • A remote metering standard module is deployed on a portion of the charging piles at a first preset ratio to collect and upload high-precision first charging process data as a reliable metering reference benchmark.

[0027] • The data access subsystem is used to receive and preprocess the first charging process data from the remote metering standard module and the second charging process data from the charging operation platform;

[0028] • Data storage subsystem, used to store the first charging process data received from the remote metering standard module and the second charging process data received from the charging operation platform;

[0029] • The standard vehicle identification and authentication subsystem executes the standard vehicle identification steps and dynamically authenticates movable standard vehicles;

[0030] • Error calculation and early warning subsystem, used to perform the error analysis step, calculate the metering error of the target charging pile based on the charging data of the mobile standard vehicle on the target charging pile, and generate a metering early warning record when the calculation result exceeds a set threshold;

[0031] • Display subsystem, used to support the display needs of relevant process and result information of the method, and adapted to various display terminal devices including but not limited to: PC, large screen, mobile phone, tablet and other display terminal devices.

[0032] Compared with the prior art, the beneficial effects of the present invention are mainly reflected in:

[0033] The invention innovatively proposes the concept of "mobile standard vehicle": Unlike the simple value transfer in the CN113406557A patent, this invention establishes a reliable mobile metrology standard through continuous evaluation and dynamic certification of vehicle metrology performance, thus solving the problem of credibility decay in traditional transmission networks.

[0034] Extremely low deployment cost: By deploying remote metering standard modules at a low ratio (e.g., 2%), monitoring of all charging piles in the network can be achieved. The hardware cost is reduced by tens of times compared to the full coverage solution, which solves the problem that patents such as CN116184058A are difficult to apply on a large scale due to high hardware costs.

[0035] Full network coverage that breaks geographical limitations: By utilizing the natural mobility of standard vehicles, the monitoring range can be naturally extended as the vehicles move, overcoming the coverage limitations of the fixed transmission network in patent CN113406557A.

[0036] The traceability of the measurement value transfer chain has high reliability: Through remote measurement standard module and dynamic identification and continuous certification of standard vehicles, the reliability of the measurement value transfer source is ensured, the accumulation rate of uncertainty is effectively suppressed, and the problem of reliability decay caused by uncontrollable transfer medium in patents such as CN113406557A is solved.

[0037] Real-time online monitoring and early warning: Based on daily charging order data, it operates in real time and can promptly detect metering anomalies, realizing the transformation from "periodic verification" to "continuous monitoring". Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the system architecture and value transmission in an embodiment of the present invention.

[0039] Figure 2 This is a flowchart of the dynamic identification and authentication of a mobile standard vehicle in an embodiment of the present invention.

[0040] Figure 3 This is a flowchart of online monitoring of charging pile metering errors in an embodiment of the present invention. Detailed Implementation

[0041] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0042] Example 1: System Deployment and Data Acquisition

[0043] Online monitoring system for metering errors of charging piles

[0044] As attached Figure 1 As shown, the online monitoring of charging pile metering errors includes, but is not limited to: a data access subsystem, a data storage subsystem, a calculation subsystem, an application subsystem, and a display subsystem.

[0045] Data access subsystem: Enables unified access and standardized processing of multi-source heterogeneous charging data.

[0046] Multi-protocol adaptation: Build a data access gateway with multi-protocol adaptation capabilities, supporting connection with charging operation platforms of different manufacturers through API interfaces, database middleware, etc., to receive second charging process data; at the same time, receive first charging process data uploaded by remote metering standard module.

[0047] Data cleaning and formatting: Perform preprocessing operations such as parsing, deduplication, outlier filtering, and timestamp alignment on the incoming raw data, and convert it into a unified standardized data format within the system.

[0048] Data distribution: The processed clean data is distributed in real time to the data storage subsystem for persistence, and simultaneously provided to the standard vehicle identification and authentication subsystem and the error calculation and early warning subsystem for real-time analysis.

[0049] The data storage subsystem constructs a data foundation that supports the storage and efficient querying of massive amounts of charging data.

[0050] Storage architecture design: A hybrid storage architecture is adopted. Time-series databases (such as InfluxDB and TDengine) are used to efficiently store and process timestamped charging process sequence data; relational databases (such as MySQL and PostgreSQL) are used to store structured data such as vehicle files, charging pile static information, standard vehicle identification, system configuration parameters, and warning records.

[0051] Data partitioning and indexing: Time-series data is partitioned by time range (e.g., by month) and charging pile ID, and a composite index is created to support efficient backtracking and aggregation analysis of historical charging records.

[0052] Data lifecycle management: Develop data retention strategies, archive or clean up raw detailed data that exceeds the specified period, and retain only the aggregated analysis results needed in the long term to control storage costs.

[0053] The calculation subsystem, based on a standard vehicle, calculates the metering error and provides status warnings for the target charging pile.

[0054] Error calculation module:

[0055] Real-time monitoring of charging order data stream. When a charging event is identified as involving a "mobile standard vehicle" and the charging station is a target charging station without a remote metering standard module installed, the calculation process is triggered.

[0056] The metering value of the target charging station is extracted from the data of the second charging process in this charging session. Measurement values ​​of BMS in standard vehicles .

[0057] Call the value transfer algorithm, according to the formula Calculate the instantaneous relative error of the charging pile during this charging.

[0058] Error assessment and early warning mechanism:

[0059] Data aggregation: For the same target charging pile, aggregate multiple instantaneous error data calculated based on different standard vehicles and at different time points.

[0060] Consistency analysis: Calculate the average value and standard deviation of the error of the pile, or use the statistical process control (SPC) method to draw a control chart to analyze the central tendency and dispersion of the error.

[0061] Threshold warning: Set a preset error threshold (e.g., ±2.0%). When the absolute value of the average error of a charging pile exceeds this threshold, or when the standard deviation of the error sequence is too large, indicating unstable metering, the system will automatically generate a "metering out of tolerance" warning record.

[0062] Trend Warning: By analyzing time series data, the long-term trend of charging pile errors is monitored. If a continuous unidirectional shift or abrupt change in error is detected, even if the absolute value threshold is not exceeded, a "trend anomaly" warning is triggered, enabling early intervention.

[0063] Example 2: Standard vehicle identification and authentication (see attached) Figure 2 Taking an electric vehicle with the VIN code "LSVN000ABCD123456" as an example, the platform found that the vehicle had been charged eight times in the past three months at five different charging stations that had been equipped with remote metering standard modules.

[0064] The platform retrieves the "first data stream" (standard module data) and "second data stream" (operation platform data, including the vehicle's BMS data) from these eight charging sessions.

[0065] The relative deviation between the vehicle's BMS battery level and the standard module battery level was calculated for each charge, resulting in a set of deviation values: [0.05%, -0.08%, 0.12%, 0.03%, -0.04%, 0.09%, 0.06%, -0.02%].

[0066] The standard deviation of this data set was calculated to be 0.065%, and the range was 0.20%. This fluctuation is much smaller than the preset stability threshold (0.25%).

[0067] Therefore, the platform dynamically certified the vehicle as a "mobile standard vehicle" and added its VIN code to the standard vehicle list database.

[0068] Example 3: Charging Pile Error Monitoring and Early Warning (see attached) Figure 3 The certified standard vehicle "LSVN000ABCD123456" was charged once at a charging station (station ID: "ST12345") that did not have the module installed.

[0069] The platform obtained the following data from the operation platform: For this charging, the metering value of the ST12345 charging pile was 52.8 kWh, and the BMS metering value of the standard vehicle was 52.5 kWh.

[0070] Calculate the metering error of this charging of the pile: ε = (52.8 - 52.5) ​​ / 52.5 * 100% ≈ 0.57%.

[0071] The platform checked the historical error records of pile ST12345 and found that the average error of the last 10 monitoring tests conducted by different standard vehicles was 0.6%, and the standard deviation was very small and the status was stable. Therefore, it was determined that the pile measurement was normal.

[0072] Meanwhile, the platform discovered that another charging pile (pile ID: “ST67890”) had errors of 2.5%, 2.8%, and 3.1% respectively recently detected by three different standard vehicles, all exceeding the warning threshold of 2.0%. The platform immediately generated and pushed out a warning work order, prompting maintenance personnel to conduct on-site verification and repair of the pile.

[0073] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method and system for online monitoring of metering errors in charging piles based on value transfer and standard vehicle identification, characterized in that, Includes the following steps: Data collection steps: Obtain data from two data sources. The first data source is the first charging process data collected by the remote metering standard module deployed on a portion of the charging piles at a first preset low ratio. The charging piles with the remote metering standard module installed are used as standard piles. The second data source is the second charging process data reported by the charging operation platform. Standard vehicle identification steps: Based on the first charging process data, analyze the vehicles charging at the charging piles equipped with the remote metering standard module, identify vehicles with stable metering performance, and certify them as mobile standard vehicles. Error analysis steps: When the mobile standard vehicle is charging at a target charging pile where the remote metering standard module is not installed, based on the second charging process data, the metering value of the battery management system of the mobile standard vehicle and the metering value of the target charging pile are obtained, and the metering error of the target charging pile is calculated through the metering value transfer algorithm.

2. The method according to claim 1, characterized in that, The remote metering standard module has an energy metering accuracy better than 0.5 level and has the function of real-time acquisition and remote data reporting of voltage, current, energy and vehicle-pile charging communication data.

3. The method according to claim 1, characterized in that, The dynamic identification and authentication steps of the standard vehicle specifically include: collecting data on multiple charging sessions of a vehicle at a charging station equipped with the remote metering standard module; calculating the relative deviation between the battery management system metering value and the corresponding remote metering standard module metering value of the vehicle during each charging process; if the fluctuation range of the relative deviation is less than a preset stability threshold, the vehicle is authenticated as a mobile standard vehicle and assigned a standard vehicle identity identifier.

4. The method according to claim 1, characterized in that, In the error analysis step, the formula used to calculate the metering error of the target charging pile is: in, Due to measurement error, The measured electrical energy value of the target charging pile. The battery management system of the mobile standard vehicle measures the electrical energy value.

5. The method according to claim 1, characterized in that, It also includes: error assessment and early warning steps: aggregating multiple measurement errors calculated based on different mobile standard vehicles for the same target charging pile and performing consistency analysis; when the absolute value of the measurement error exceeds the preset error threshold, or the error change trend is abnormal, a measurement error warning signal is triggered.

6. A charging pile metering error online monitoring system for implementing the method as described in any one of claims 1 to 5, characterized in that, include: The remote metering standard module is deployed on a portion of the charging piles at a first preset ratio to collect and upload high-precision first charging process data, serving as a reliable metering reference benchmark.

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