Intelligent correction method, device and equipment for electric energy metering of charging pile and medium

Abstract

The invention relates to the technical field of electric energy metering, in particular to an intelligent correction method, device and equipment for charging pile electric energy metering and a medium, and the method comprises the steps: collecting the current and voltage of the output end of a charging pile, and decomposing the characteristics of a current signal into a DC signal and an unsteady state signal; respectively carrying out electric energy metering on the direct-current signal and the unsteady-state signal, and adding the electric energy to obtain a preliminary electric energy metering result; establishing a temperature error correction model; establishing a self-adaptive correction model; and correcting the initial electric energy metering result through a temperature error model, a self-adaptive correction model, the obtained charging electric energy data recorded by the electric vehicle and the obtained standard load electric energy metering result in the charging pile to obtain a final electric energy metering result. The charging pile electric energy metering result can be intelligently corrected, the fault report rate of electric energy metering is reduced, the regular checking period is prolonged, the service life of an electric energy metering module electronic component in the charging pile is prolonged, and the metering precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy metering, and in particular to an intelligent correction method, device, equipment and medium for charging pile electric energy metering. Background Art

[0002] The charging pile power metering module refers to the module inside the charging pile used to measure and record the power consumption of electric vehicles during charging. The charging pile power metering module contains a large number of electronic components. The performance of each component directly affects the metering performance of the DC charger. The impact of power metering abnormalities on the charging device cannot be ignored.

[0003] There are many factors that affect the accuracy of energy metering, such as instantaneous fluctuations in output current and voltage during charging, changes in ambient temperature of the energy meter, accuracy of the energy meter itself, dynamic response capability, etc. Extreme temperatures can cause abnormal performance of electronic components or rapid degradation of the life of electronic components, thus affecting the accuracy of energy metering and further leading to abnormal energy metering.

[0004] Abnormal energy metering may cause the charging pile to fail to accurately measure energy consumption, affecting the user's charging fee settlement and the economic operation and benefits of the charging pile. In addition, accurate energy metering is the basis for ensuring the economic operation of the charging pile. By reducing the number of abnormal failures, the economic losses caused by metering errors and the additional expenses of customers' charging fees can be reduced.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides an intelligent correction method, device, equipment and medium for charging pile electric energy metering, thereby effectively solving the problems in the background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: an intelligent correction method for charging pile electric energy metering, comprising the following steps: Collect the current and voltage at the output end of the charging pile, and decompose the current signal characteristics into DC signals and non-steady-state signals; The DC signal and the non-steady-state signal are measured for electric energy respectively and added to obtain a preliminary electric energy measurement result; Obtain the ambient temperature and establish a temperature error correction model; obtain historical data and establish an adaptive correction model; The preliminary energy measurement result is corrected by using the temperature error model, the adaptive correction model, the acquired charging energy data recorded by the electric vehicle, and the acquired standard load energy measurement result inside the charging pile to obtain the final energy measurement result.

[0008] Furthermore, the decomposition of the current signal characteristics into a DC signal and a non-steady-state signal comprises the following steps: Use current sensors and voltage sensors to collect current and voltage in real time during the charging process, and obtain digital signals through digital-to-analog conversion; A low-pass filter is used to extract a DC signal from the digital signal; A differential operation is performed on the DC signal and the digital signal to extract a non-steady-state signal, and a waveform feature of the non-steady-state signal is obtained.

[0009] Furthermore, when calculating the preliminary electric energy measurement result, the electric energy is measured by an average value method for the DC signal and by an integration method for the non-steady-state signal.

[0010] Furthermore, when the temperature error model corrects the preliminary electric energy metering result, the following steps are included: Setting the temperature range of the temperature error correction model; Obtaining the ambient temperature through a temperature sensor, and determining whether the ambient temperature exceeds the temperature range; If the temperature exceeds the stated range, no temperature error correction will be performed and an alarm message will be issued; If the temperature does not exceed the temperature range, the temperature error model is used to perform temperature correction, and the correction result is uploaded.

[0011] Furthermore, in the acquisition of historical data and establishment of the adaptive correction model, the acquired historical data includes electric energy metering time, grid-side voltage and frequency, remaining power SOC on the battery management system BMS side, and battery temperature; By decoupling the influencing factors of the electric energy metering data in the historical data, the influence characteristics of the aging of electronic components on the electric energy metering are obtained, and the adaptive correction model is established.

[0012] Furthermore, the adaptive correction model also includes: By inputting the power gear selected during charging, the voltage and frequency on the grid side, the SOC on the BMS side and the battery temperature, the non-error factors affecting the energy metering are decoupled, so that the historical correction parameters of the energy metering are only related to the energy metering time; By inputting the electric energy measurement result and the electric energy measurement time, the adaptive correction model outputs the historical correction result; When the electric energy metering exceeds the set threshold, the adaptive correction model is trained iteratively according to the input parameters of the current round of adaptive correction model.

[0013] Furthermore, the electric energy metering time is the difference between the last iteration time of the adaptive correction model and the time of collecting the current and voltage at the output end of the charging pile.

[0014] Furthermore, the correction of the preliminary electric energy measurement result by acquiring the charging electric energy data recorded by the electric vehicle includes: The battery capacity, voltage and current information of the electric vehicle are obtained through the BMS to determine whether the BMS is faulty; when the BMS is faulty, it is determined that no correction is made; When the BMS is in normal state, compare the difference between the power change before and after charging reported by the BMS and the power measurement result, divide it by the dimensionless value of the power measurement result, and get the correction ratio difference; Determine whether the correction ratio difference exceeds the set correction range. If so, it is determined that no correction is required and an alarm message is issued; If it does not exceed the limit, the electricity metering result will be corrected through the BMS correction model based on the vehicle-side big data.

[0015] Furthermore, the obtained standard load electric energy measurement result inside the charging pile is used to correct the preliminary electric energy measurement, including: Record the measured values ​​of the electric energy meter under standard load, including current, voltage and electric energy measurement results; Compare the recorded electric energy meter measurement value with the known value of the standard load, calculate the standard load measurement error, and output the corrected electric energy measurement result; After the correction is completed, the difference between the electric energy measurement results before and after the correction is calculated and divided by the dimensionless value of the electric energy measurement result before the correction. If it exceeds the set threshold, an alarm message is issued.

[0016] The present invention also includes an intelligent correction device for charging pile electric energy metering, using the above method, including: The electric energy data acquisition module is used to collect the current and voltage at the output end of the charging pile and decompose the current signal characteristics into a DC signal and a non-steady-state signal; The electric energy metering module is used to measure the electric energy of the DC signal and the non-steady-state signal respectively and add them to obtain a preliminary electric energy metering result; The control module is used to obtain the ambient temperature and establish a temperature error correction model; obtain historical data and establish an adaptive correction model; The correction module is used to correct the preliminary energy measurement result through the temperature error model, the adaptive correction model, the acquired charging energy data recorded by the electric vehicle, and the acquired standard load energy measurement result inside the charging pile to obtain the final energy measurement result.

[0017] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0018] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0019] The beneficial effects of the present invention are as follows: the present invention can intelligently correct the electric energy metering results of the charging pile, reduce the fault reporting rate of the electric energy metering, extend the regular inspection cycle, extend the service life of the electronic components of the electric energy metering module inside the charging pile, reduce the electric energy metering errors caused by non-steady-state signals such as pulsating voltage / current and high-frequency ripple voltage / current, ambient temperature and component aging, and improve the metering accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 is a flow chart of the method in Example 1; Figure 2 It is a structural schematic diagram of the device in Example 1; Figure 3 It is a structural schematic diagram of the device in Example 2; Figure 4 It is a structural schematic diagram of the method in Example 2; Figure 5 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0023] Embodiment 1: like Figure 1As shown: An intelligent correction method for charging pile electric energy metering includes the following steps: Collect the current and voltage at the output end of the charging pile, and decompose the current signal characteristics into DC signals and non-steady-state signals; The DC signal and the non-steady-state signal are measured for electric energy respectively and added to obtain a preliminary electric energy measurement result; Obtain the ambient temperature and establish a temperature error correction model; obtain historical data and establish an adaptive correction model; The preliminary energy measurement results are corrected by using the temperature error model, the adaptive correction model, the charging energy data recorded by the electric vehicle, and the standard load energy measurement results inside the charging pile to obtain the final energy measurement results.

[0024] This embodiment can intelligently correct the electric energy metering results of the charging pile, reduce the fault reporting rate of the electric energy metering, extend the regular inspection cycle, extend the service life of the electronic components of the electric energy metering module inside the charging pile, reduce the electric energy metering errors caused by non-steady-state signals such as pulsating voltage / current and high-frequency ripple voltage / current, ambient temperature and component aging, and improve the metering accuracy.

[0025] In this embodiment, decomposing the current signal characteristics into a DC signal and a non-steady-state signal includes the following steps: Use current sensors and voltage sensors to collect current and voltage in real time during the charging process, and obtain digital signals through digital-to-analog conversion; A low-pass filter is used to extract the DC signal from the digital signal; The DC signal and the digital signal are differentially operated to extract the non-steady-state signal and obtain the waveform characteristics of the non-steady-state signal.

[0026] Among them, when calculating the preliminary electric energy measurement results, the average value method is used to measure the electric energy for the DC signal, and the integration method is used to measure the electric energy for the non-steady-state signal.

[0027] When the temperature error model is used to correct the preliminary energy metering results, the following steps are included: Set the temperature range of the temperature error correction model; Obtain the ambient temperature through the temperature sensor and determine whether the ambient temperature exceeds the temperature range; If the temperature exceeds the range, no temperature error correction will be performed and an alarm message will be issued; If the temperature does not exceed the range, the temperature error model is used to perform temperature correction and the correction result is uploaded.

[0028] Acquire historical data and establish an adaptive correction model. The acquired historical data includes energy metering time, grid voltage and frequency, remaining power SOC on the battery management system BMS side, and battery temperature; By decoupling the influencing factors of electric energy metering data in historical data, the impact characteristics of electronic component aging on electric energy metering are obtained, and an adaptive correction model is established.

[0029] In this embodiment, the adaptive correction model also includes: By inputting the power gear selected during charging, the voltage and frequency on the grid side, the SOC on the BMS side and the battery temperature, the non-error factors affecting the energy metering are decoupled, so that the historical correction parameters of the energy metering are only related to the energy metering time; By inputting the electric energy metering results and the electric energy metering time, the adaptive correction model outputs the historical correction results; When the electric energy metering exceeds the set threshold, the adaptive correction model is trained iteratively according to the input parameters of the current round of adaptive correction model.

[0030] The energy metering time is the difference between the last adaptive correction model iteration time and the time of collecting the current and voltage at the output end of the charging pile.

[0031] Among them, the correction of the preliminary energy measurement results by obtaining the charging energy data recorded by the electric vehicle includes: The battery capacity, voltage and current information of the electric vehicle are obtained through the BMS to determine whether the BMS is faulty; when the BMS is faulty, it is determined that no correction is made; When the BMS is in normal state, compare the difference between the power change before and after charging reported by the BMS and the power measurement result, divide it by the dimensionless value of the power measurement result, and get the correction ratio difference; Determine whether the correction ratio difference exceeds the set correction range. If so, it is determined that no correction is required and an alarm message is issued; If it does not exceed the limit, the electricity metering result will be corrected through the BMS correction model based on the vehicle-side big data.

[0032] As a preferred embodiment of the above, the standard load electric energy measurement result inside the charging pile is obtained to correct the preliminary electric energy measurement, including: Record the measured values ​​of the electric energy meter under standard load, including current, voltage and electric energy measurement results; Compare the recorded electric energy meter measurement value with the known value of the standard load, calculate the standard load measurement error, and output the corrected electric energy measurement result; After the correction is completed, the difference between the electric energy measurement results before and after the correction is calculated and divided by the dimensionless value of the electric energy measurement result before the correction. If it exceeds the set threshold, an alarm message is issued.

[0033] This embodiment collects charging characteristic data, extracts and separates charging characteristics, and designs different metering methods according to DC signals and non-steady-state signals, thereby improving the accuracy of electric energy metering. The temperature error correction model, the adaptive correction model established based on the historical big data of electric energy metering, the charging electric energy data recorded by the obtained electric vehicle, and the standard load electric energy metering results obtained inside the charging pile are used to perform multiple error judgments and corrections on the electric energy metering of the charging pile, thereby extending the service life of the components of the electric energy metering module and reducing the fault reporting rate of the electric energy metering. The intelligent correction method of charging pile power metering detects and quantitatively analyzes the factors affecting the power metering results and self-checks the power metering error through standard loads. This method can make independent judgments on the power metering situation. When the factors affecting the power metering results exceed the limit or the power metering error is too large, the power metering module will send an alarm message to the fault detection and alarm module and report it to the maintenance personnel.

[0034] like Figure 2 As shown, this embodiment also includes an intelligent correction device for charging pile electric energy metering, using the above method, including: The electric energy data acquisition module is used to collect the current and voltage at the output end of the charging pile and decompose the current signal characteristics into a DC signal and a non-steady-state signal; The electric energy metering module is used to measure the electric energy of the DC signal and the non-steady-state signal respectively and add them to obtain a preliminary electric energy metering result; The control module is used to obtain the ambient temperature and establish a temperature error correction model; obtain historical data and establish an adaptive correction model; The correction module is used to correct the preliminary energy measurement result through the temperature error model, the adaptive correction model, the charging energy data recorded by the electric vehicle, and the standard load energy measurement result inside the charging pile to obtain the final energy measurement result.

[0035] Embodiment 2: Reference Figure 3 , a schematic diagram of the structure of a DC charger of an intelligent correction method for charging pile electric energy metering provided in this embodiment, the main structure and function of the DC charger are as follows: (1) Control module: The control module is mainly composed of the main control unit, power control unit, communication unit, safety protection unit, fault diagnosis unit, storage and processing unit. The control module is responsible for managing and controlling all aspects of the charging process, including ensuring normal power supply during the charging process, adjusting the output voltage and current according to the charging requirements and battery status of the electric vehicle, and recording important data during the charging process. The energy metering module measures the incoming charging characteristics, the ambient temperature from the temperature sensor, and the fault log from the fault detection and alarm module, and the information is stored in the data storage and processing unit for management and analysis.

[0036] (2) Energy metering module: The energy metering module is an important component to ensure accurate measurement of energy consumption during the charging process. It mainly includes an energy meter, a current sensor, a voltage sensor, a communication interface, a data processing unit, and a standard load. The standard load is used to reduce measurement deviations caused by the passage of time or environmental changes and to detect faults in the energy metering module.

[0037] (3) Fault detection and alarm module: The main function of the fault detection and alarm module is to identify the type of fault that occurs and classify it; generate fault codes or fault logs based on the detected abnormal conditions for subsequent fault diagnosis and maintenance. This module communicates with other modules through the CAN bus to obtain fault information during the charging process.

[0038] (4) Temperature sensor: measures the ambient temperature during charging and transmits it to the energy metering module and control module through the CAN bus. Temperature changes may cause changes in the parameters of the internal components of the energy meter, thereby affecting the measurement results of current and voltage.

[0039] Reference Figure 4 This embodiment discloses an intelligent correction method for charging pile electric energy metering, and the specific steps of the intelligent correction method are as follows: (1) Electric energy data collection: The electric energy metering module measures and records the current and voltage passing through the charger output terminal and transmits them to the control module; In this embodiment, the control module is mainly composed of a main control unit, a power control unit, a communication unit, a safety protection unit, a fault diagnosis unit, and a storage and processing unit; Among them, the electric energy metering module builds a data transmission channel with the storage and processing unit of the control module through the CAN bus.

[0040] (2) Charging feature extraction and separation: Analyze the collected electric energy data and decompose it into DC signal and non-steady-state signal according to the characteristics of the current signal; In this embodiment, the working state is to analyze the collected electric energy data and decompose it into a DC signal and a non-steady-state signal according to the signal characteristics. The specific collection and decomposition steps are as follows: Step 1: Use current sensors and voltage sensors to collect the current and voltage in real time during the charging process, and obtain digital signals through analog-to-digital conversion; Step 2: Use a low-pass filter to extract the DC signal; Step 3: Perform differential operation on the filtered signal and the signal before filtering to extract the non-steady-state signal, and further analyze and process the extracted non-steady-state signal to obtain its waveform characteristics; Step 4: Integrate the decomposed DC signal and non-steady-state signal into the electric energy metering system.

[0041] (3) Digital signal processing: The separated DC signal and the non-steady-state signal are measured for electric energy respectively and added to obtain the preliminary electric energy measurement result; The separated DC signal is measured by the average value method, and the non-steady-state signal is measured by the integration method.

[0042] (4) Temperature error correction: According to the ambient temperature transmitted by the temperature sensor, the preliminary energy metering result is corrected to obtain a corrected energy metering result, and the energy metering result and the corresponding charging power information are converted into storage data and transmitted to the control module; The temperature sensor builds a data transmission channel with the electric energy metering module through the CAN bus, the electric energy metering module builds a data transmission channel with the storage and processing unit of the control module through the CAN bus, and the electric energy metering module builds a data transmission channel with the fault detection and alarm module through the CAN bus; In addition, the electric energy metering module corrects the preliminary metering results through the internal preset temperature error correction model, and the application temperature range of the temperature error correction model is -20℃ to 40℃; The specific correction steps are as follows: Step 1: Determine whether the temperature input by the temperature sensor exceeds the application temperature range of the temperature error correction model; Step 2: If the temperature input by the temperature sensor exceeds the model application temperature range, an alarm message is sent to the fault detection and alarm module, no temperature error correction is performed, and the preliminary measurement results are uploaded to the storage and processing unit; Step 3: When the temperature input by the temperature sensor is within the temperature range, the preliminary metering result is corrected according to the temperature error correction model, and the corrected electric energy metering result is uploaded to the storage and processing unit.

[0043] (5) Correction of historical data: Based on the data transmitted by the control module, the primary corrected electric energy metering result in step (4) is corrected through the adaptive correction model to obtain the secondary corrected electric energy metering result; In this embodiment, the input data are the energy metering time, the voltage and frequency on the grid side, the SOC on the BMS side and the battery temperature. The adaptive correction model is established based on the historical big data of energy metering. By decoupling the influencing factors in the historical energy metering data, the influence characteristics of the aging of electronic components on the energy metering are obtained, and the energy metering error compensation due to the aging of components is realized. The energy metering time is the difference between the last adaptive correction model iteration time and the energy data collection time in step (1); In addition, the adaptive correction model takes the primary correction energy metering result, energy metering time, power gear selected during charging, grid-side voltage and frequency, BMS-side SOC and battery temperature as input, and the secondary correction energy metering result as output, and automatically iterates. The specific steps of the correction model operation are as follows: S1: By inputting the power gear selected during charging, the voltage and frequency on the grid side, the SOC on the BMS side and the battery temperature, the adaptive correction model decouples the non-error factors affecting the energy metering, so that the secondary correction parameters of the energy metering are only related to the energy metering time; S2: By inputting the primary corrected energy metering result and the energy metering time, the adaptive correction model outputs the secondary corrected energy metering result; S3: When the energy metering time exceeds 6 hours, the model is trained and iterated according to the input parameters of this round of model.

[0044] (6) BMS data correction: The charging energy data recorded by the electric vehicle BMS module transmitted via the CAN bus is used to correct the secondary corrected energy measurement result in step (5) to obtain a tertiary corrected energy measurement result; In this embodiment, the charging power data includes the battery power, voltage, and current information, and the correction steps are as follows: N1: The charging pile obtains the status information sent by the BMS and determines whether the BMS has a fault. When the BMS status is abnormal, it is determined that no BMS correction is required, and the three-corrected energy metering result is output as the second-corrected energy metering result; N2: When the BMS is in normal state, compare the power change before and after charging reported by the BMS with the secondary corrected power measurement result, and let the third correction ratio difference be the difference between the power change before and after charging reported by the BMS and the secondary corrected power measurement result divided by the dimensionless value of the secondary corrected power measurement result; N3: Determine the three-time correction ratio difference. When the three-time correction ratio difference is greater than -5% and less than 5%, it is determined that BMS correction is not required, and the three-time correction electric energy metering result is output as the second-time correction electric energy metering result, and an alarm message is sent to the fault detection and alarm module; N4: When the three-correction ratio difference is less than -5% or greater than 5%, it is determined that BMS correction is required. The secondary corrected energy metering result is corrected through the BMS correction model based on the vehicle-side big data, and the tertiary corrected energy metering result is output.

[0045] (7) Self-calibration correction: According to the electric energy measurement result on the standard load inside the charging pile, the three corrected electric energy measurement results in step (6) are corrected to obtain the four corrected electric energy measurement results; The standard load is integrated in the electric energy metering module to simulate the electric energy consumption when the charging pile charges the electric vehicle. The specific correction method is as follows: P1: Record the measured values ​​of the electric energy meter under standard load, including current, voltage and electric energy measurement results; P2: Compare the recorded electric energy meter measurement value with the known value of the standard load. According to the difference between the measured value and the known value, calculate the fourth-order correction factor and output the fourth-order corrected electric energy measurement result; P3: After the correction is completed, the four-time correction ratio difference is calculated, that is, the difference between the three-time correction electric energy measurement result and the four-time correction electric energy measurement result divided by the dimensionless value of the three-time correction electric energy measurement result. If the four-time correction ratio difference is greater than 0.1, the electric energy measurement module sends an alarm message to the fault detection and alarm module.

[0046] See also Figure 5 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0047] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0048] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0049] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0050] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0052] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0053] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0054] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0055] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0056] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An intelligent correction method for charging pile electric energy metering, characterized in that: The steps include: Collect the current and voltage at the output end of the charging pile, and decompose the current signal characteristics into DC signals and non-steady-state signals; The DC signal and the non-steady-state signal are measured for electric energy respectively and added to obtain a preliminary electric energy measurement result; Obtain the ambient temperature and establish a temperature error correction model; obtain historical data and establish an adaptive correction model; The preliminary energy measurement result is corrected by using the temperature error model, the adaptive correction model, the acquired charging energy data recorded by the electric vehicle, and the acquired standard load energy measurement result inside the charging pile to obtain the final energy measurement result.

2. The intelligent correction method for charging pile electric energy metering according to claim 1 is characterized in that: Decomposing the current signal characteristics into a DC signal and a non-steady-state signal comprises the following steps: Use current sensors and voltage sensors to collect current and voltage in real time during the charging process, and obtain digital signals through digital-to-analog conversion; A low-pass filter is used to extract a DC signal from the digital signal; A differential operation is performed on the DC signal and the digital signal to extract a non-steady-state signal, and a waveform feature of the non-steady-state signal is obtained.

3. The intelligent correction method for charging pile electric energy metering according to claim 2 is characterized in that: The method of measuring the electric energy of the DC signal and the non-steady-state signal respectively and adding them to obtain a preliminary electric energy measurement result also includes: measuring the electric energy of the DC signal using an average value method and measuring the electric energy of the non-steady-state signal using an integration method.

4. The intelligent correction method for charging pile electric energy metering according to claim 1 is characterized in that: The preliminary electric energy metering result is corrected by using the temperature error model, including the following steps: Setting the temperature range of the temperature error correction model; Obtaining the ambient temperature through a temperature sensor, and determining whether the ambient temperature exceeds the temperature range; If the temperature exceeds the stated range, no temperature error correction will be performed and an alarm message will be issued; If the temperature does not exceed the temperature range, the temperature error model is used to perform temperature correction, and the correction result is uploaded.

5. The intelligent correction method for charging pile electric energy metering according to claim 1 is characterized in that: The step of obtaining historical data and establishing an adaptive correction model includes: Obtain energy metering time, grid voltage and frequency, remaining capacity SOC and battery temperature on the battery management system BMS side as historical data; By decoupling the influencing factors of the electric energy metering data in the historical data, the influence characteristics of the aging of electronic components on the electric energy metering are obtained, and the adaptive correction model is established.

6. The intelligent correction method for charging pile electric energy metering according to claim 5 is characterized in that: The acquiring of historical data and establishing of the adaptive correction model further includes: establishing the adaptive correction model as: By inputting the power gear selected during charging, the voltage and frequency on the grid side, the SOC on the BMS side and the battery temperature, the non-error factors affecting the energy metering are decoupled, so that the historical correction parameters of the energy metering are only related to the energy metering time; Output the historical correction result through the input electric energy measurement result and electric energy measurement time; When the electric energy metering exceeds the set threshold, the adaptive correction model is trained iteratively according to the input parameters of the current round of adaptive correction model.

7. The intelligent correction method for charging pile electric energy metering according to claim 6 is characterized in that: The electric energy metering time is the difference between the last iteration time of the adaptive correction model and the time of collecting the current and voltage at the output end of the charging pile.

8. The intelligent correction method for charging pile electric energy metering according to claim 1 is characterized in that: The preliminary electric energy measurement result is corrected by acquiring the charging electric energy data recorded by the electric vehicle, including: The battery capacity, voltage and current information of the electric vehicle are obtained through the BMS to determine whether the BMS is faulty; when the BMS is faulty, it is determined that no correction is made; When the BMS is in normal state, compare the difference between the power change before and after charging reported by the BMS and the power measurement result, divide it by the dimensionless value of the power measurement result, and get the correction ratio difference; Determine whether the correction ratio difference exceeds the set correction range. If so, it is determined that no correction is required and an alarm message is issued; If it does not exceed the limit, the electricity metering result will be corrected through the BMS correction model based on the vehicle-side big data.

9. The intelligent correction method for charging pile electric energy metering according to claim 1 is characterized in that: The preliminary electric energy measurement is corrected by obtaining the standard load electric energy measurement result inside the charging pile, including: Record the measured values ​​of the electric energy meter under standard load, including current, voltage and electric energy measurement results; Compare the recorded electric energy meter measurement value with the known value of the standard load, calculate the standard load measurement error, and output the corrected electric energy measurement result; After the correction is completed, the difference between the electric energy measurement results before and after the correction is calculated and divided by the dimensionless value of the electric energy measurement result before the correction. If it exceeds the set threshold, an alarm message is issued.

10. An intelligent correction device for charging pile electric energy metering, characterized in that: Use of the method according to any one of claims 1 to 9, comprising: The electric energy data acquisition module is used to collect the current and voltage at the output end of the charging pile and decompose the current signal characteristics into a DC signal and a non-steady-state signal; The electric energy metering module is used to measure the electric energy of the DC signal and the non-steady-state signal respectively and add them to obtain a preliminary electric energy metering result; The control module is used to obtain the ambient temperature and establish a temperature error correction model; obtain historical data and establish an adaptive correction model; The correction module is used to correct the preliminary energy measurement result through the temperature error model, the adaptive correction model, the acquired charging energy data recorded by the electric vehicle, and the acquired standard load energy measurement result inside the charging pile to obtain the final energy measurement result.

11. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

12. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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