A new energy vehicle charging data real-time monitoring system based on a cloud platform

Abstract

The application provides a new energy vehicle charging data real-time monitoring system based on a cloud platform, and relates to the technical field of charging management.The system comprises a data acquisition module, a performance stability analysis module, a control deviation evaluation module, a power curve correction module and a power regulation module.The data acquisition module is used to acquire voltage, current and power data.The performance stability analysis module is used to extract current voltage fluctuation characteristics and power change rate, and to construct a stability index.The control deviation evaluation module is used to quantify charging state deviation.The power curve correction module is used to adjust the power output curve according to the stability index and the control deviation.The power regulation module is used to issue control instructions according to the corrected curve and to upload real-time data to the cloud platform, thereby realizing visual management of charging piles.The application scheme can realize real-time monitoring and dynamic regulation of the running state of charging piles in the charging process based on the cloud platform.

Description

Technical Field

[0001] This application relates to the field of charging management technology, and more specifically, to a real-time monitoring system for charging data of new energy vehicles based on a cloud platform. Background Technology

[0002] As a crucial carrier of green transportation, the charging management of new energy vehicles directly impacts battery life, vehicle safety, and charging efficiency. With the rapid increase in the number of charging piles and the growing complexity of charging loads, traditional manual monitoring and single-machine data collection are insufficient to meet the refined management needs of large-scale charging networks. Real-time data collection, operational status analysis, and performance evaluation of charging piles during the charging process are essential for ensuring charging safety, extending battery life, and optimizing grid load. Furthermore, centralized management based on a cloud platform can provide cross-regional, multi-pile data integration and visualization analysis, offering operators intelligent control and decision-making support, thereby driving the development of charging infrastructure towards intelligence and efficiency.

[0003] In existing charging management technologies, most charging piles rely solely on partial data collection or offline data analysis, lacking the ability to dynamically evaluate performance and regulate power throughout the entire charging process. Under complex operating conditions, fluctuations in charging pile output power, abnormal current and voltage, and deviations in charging strategy execution often fail to be detected and corrected in a timely manner, causing the battery charging state to deviate from preset targets and posing risks of overcharging or abnormal temperature. Furthermore, existing technologies generally fail to systematically quantify charging performance stability and control deviation indicators, resulting in a lack of scientific basis for charging power regulation and hindering precise control and safety assurance during the charging process. Therefore, how to achieve real-time monitoring and dynamic regulation of the charging pile's operating status throughout the charging process based on a cloud platform has become a significant challenge for the industry. Summary of the Invention

[0004] This application provides a cloud-based real-time monitoring system for new energy vehicle charging data, which can realize real-time monitoring and dynamic control of the charging pile's operating status during the charging process based on the cloud platform.

[0005] Firstly, this application provides a real-time monitoring system for new energy vehicle charging data based on a cloud platform, the system specifically comprising:

[0006] The data acquisition module is used to collect the operating data of the charging pile during the charging process. The operating data includes charging voltage data, charging current data, and charging power data.

[0007] The performance stability analysis module is used to extract the fluctuation characteristics of charging current and voltage during the charging process from the operating data, and to determine the stability index of charging performance of the charging pile during the charging process based on the fluctuation characteristics and the rate of change of charging power.

[0008] The control deviation assessment module is used to determine the control deviation of the charging pile in controlling the charging status of the vehicle battery during the charging process.

[0009] The power curve correction module is used to acquire the power output curve of the charging pile during the charging process, and to perform correlation correction on the power output curve based on the stability index and the control deviation to obtain the power correction curve of the charging pile.

[0010] The power regulation module is used to pre-regulate the output power of the charging pile during the charging process according to the power correction curve, and at the same time upload the collected operating data and regulation information to the cloud platform in real time for monitoring the operating status of the charging pile.

[0011] In this embodiment, extracting the fluctuation characteristics of charging current and voltage during the charging process from the operational data specifically includes:

[0012] The charging stage division nodes for each charging session of the charging pile are determined, including the starting point of the pre-charging stage, the starting point of the constant current charging stage, the starting point of the constant voltage charging stage, and the charging termination point.

[0013] The charging current and charging voltage data for each charging stage are filtered from the operational data, and the current-voltage data segments within a set time window before and after each division node are extracted.

[0014] The current fluctuation amplitude for the corresponding charging stage is calculated based on the difference between the maximum and minimum current values ​​for each current data segment, and the voltage fluctuation amplitude for the corresponding charging stage is calculated based on the difference between the maximum and minimum voltage values ​​for each voltage data segment.

[0015] The set of current fluctuation amplitudes and the set of voltage fluctuation amplitudes across all charging stages are used together as the fluctuation characteristics of charging current and voltage during the charging process.

[0016] In this embodiment, determining the stability index of charging performance of the charging pile during the charging process based on the fluctuation characteristics and the rate of change of charging power specifically includes:

[0017] The reference power variation range for each charging stage is determined based on the charging data from the charging pile.

[0018] The real-time rate of change of charging power in each charging stage is determined, and the real-time rate of change is calculated by the ratio of the power difference between adjacent time nodes to the time interval.

[0019] Based on the current fluctuation amplitude, voltage fluctuation amplitude, real-time power change rate at each stage, and reference power change range in the fluctuation characteristics, an evaluation model is constructed to characterize the charging performance stability of the charging pile.

[0020] The evaluation model outputs a dimensionless score, which is used as a stability indicator of the charging performance of the charging pile during the charging process.

[0021] In this embodiment, determining the control deviation of the charging pile in controlling the charging state of the vehicle battery during the charging process specifically includes:

[0022] Acquire the target charging state curve of the vehicle battery during the charging process, wherein the target charging state curve includes the target state of charge value and the target battery temperature value at different charging time points;

[0023] Extract the actual charging state curve of the battery during the charging process. The actual charging state curve includes the actual state of charge value and the actual battery temperature value corresponding to different charging time nodes.

[0024] Calculate the deviations between the target state of charge and the actual state of charge, and the deviations between the target battery temperature and the actual battery temperature, respectively.

[0025] The control deviation of the charging pile in controlling the charging state of the vehicle battery during the charging process is determined by the weighted sum of the two types of deviations. The weights of the state of charge deviation and the battery temperature deviation are set according to the priority of battery safe operation.

[0026] In this embodiment, the power output curve is correlated and corrected based on the stability index and the control deviation to obtain the power correction curve of the charging pile, specifically including:

[0027] Determine the safe adjustment range of the power output curve, which is based on the rated power range of the charging pile and the allowable charging power range of the battery.

[0028] Based on the safety adjustment range, the stability index, and the control deviation, determine the power compensation ratio corresponding to each charging stage;

[0029] According to the power compensation ratio of each charging stage, the power value of the corresponding stage in the original power output curve is corrected.

[0030] The corrected power values ​​for each stage are spliced ​​together in order of charging time to obtain the power correction curve of the charging pile.

[0031] In this embodiment, the pre-regulation of the charging pile's output power during the charging process based on the power correction curve specifically includes:

[0032] The power correction curve is decomposed into multiple power sub-targets corresponding to multiple time segments along the charging time axis;

[0033] A corresponding power control instruction is generated for each time segment's power sub-target, and the power control instruction includes the power target value and the instruction execution duration;

[0034] During the charging process, power control commands for the corresponding time segments are sent to the power control unit of the charging pile in advance according to the time sequence.

[0035] The system monitors the deviation between the actual output power of the charging pile and the power correction curve in real time. If the deviation exceeds the preset threshold, the power control command for the next time segment is finely adjusted.

[0036] In this embodiment, uploading the collected operational data and control information to the cloud platform in real time for monitoring the charging pile's operational status specifically includes:

[0037] The collected operational data and control information are processed in a standardized manner;

[0038] Standardized operational data and control information are uploaded to the cloud platform in real time via encrypted communication links;

[0039] After receiving the data, the cloud platform classifies and stores the data, and establishes a database indexed by the charging pile number and charging operation data.

[0040] The cloud platform generates a real-time dashboard of charging pile operation status based on uploaded data, displaying the current charging power, stability indicators, control deviations, and power regulation execution.

[0041] In this embodiment, the charging pile's operational data during the charging process is collected by a sensing and detection unit deployed inside the charging pile.

[0042] In this embodiment, the sensing and detection unit includes a voltage sensor, a current sensor, and a power metering module.

[0043] In this embodiment, the power output curve of the charging pile during the charging process is obtained from the interaction data between the charging pile and the vehicle battery management unit.

[0044] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0045] First, operational data of the charging pile during the charging process is collected, including charging voltage data, charging current data, and charging power data. Second, the fluctuation characteristics of the charging current and voltage during the charging process are extracted from the operational data, and the stability index of the charging pile's charging performance during the charging process is determined based on the fluctuation characteristics and the rate of change of charging power. Further, the control deviation of the charging pile in controlling the vehicle battery charging state during the charging process is determined through a control deviation evaluation module. Then, the power output curve of the charging pile during the charging process is obtained, and the power output curve is correlated and corrected according to the stability index and the control deviation to obtain the power correction curve of the charging pile. Finally, the output power of the charging pile during the charging process is pre-regulated according to the power correction curve, and the collected operational data and regulation information are uploaded to the cloud platform in real time for monitoring the charging pile's operational status.

[0046] Therefore, this application's solution, through the organic synergy of a data acquisition module, performance stability analysis module, control deviation evaluation module, power curve correction module, and power regulation module, achieves real-time monitoring and dynamic regulation of new energy vehicle charging piles throughout the charging process. Its overall beneficial effects manifest as multi-level and multi-dimensional improvements in safety and efficiency. Firstly, the data acquisition module, through high-frequency, timestamp-bound voltage, current, and power acquisition, ensures the integrity and accuracy of the original operating data during the charging process, providing a reliable foundation for subsequent performance analysis. It also preprocesses abnormal data, reducing noise interference and ensuring data quality; this step directly supports the scientific nature of charging performance evaluation and control deviation analysis. Secondly, the performance stability analysis module extracts current and voltage fluctuation characteristics and calculates charging stability indicators based on the power change rate, achieving a quantitative characterization of the power output pattern and abnormal fluctuations during the charging process. This allows for timely reflection of the charging pile's output stability at different stages, providing a scientific basis for power regulation. Based on this, its function is to reduce the potential damage risk to the battery caused by voltage and current fluctuations during charging, while improving charging balance. Secondly, the control deviation assessment module quantifies the battery SOC and temperature deviation by comparing the target state of charge curve with the actual charging state curve, achieving accurate feedback on the execution effect of the charging strategy, providing a decision basis for power correction, and helping to ensure the safe operation of the battery and extend its life. Then, the power curve correction module, based on stability indicators and control deviations, formulates the power compensation ratio according to the charging stage and performs curve correction, so that the output power transitions smoothly within a safe range, effectively avoiding power sudden changes and overload risks, while improving the controllability of the charging process. Finally, the power regulation module decomposes the corrected power curve into executable instructions and issues them in advance, while monitoring the deviation between the actual output and the target in real time to achieve closed-loop control. At the same time, it uploads operating data and regulation information in real time through the cloud platform, realizing visualized management and remote monitoring of charging piles, greatly enhancing the intelligence and response speed of charging operation and maintenance. In summary, this invention enables real-time monitoring and dynamic control of the charging pile's operational status during the charging process based on a cloud platform. Through modular collaboration and index quantification, it not only ensures the stability and safety of the charging pile's output power but also achieves dynamic control and remote visual monitoring of the charging process. This effectively improves charging efficiency, ensures battery safety, and provides complete technical support for the intelligent management of charging networks. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart of a cloud-based real-time monitoring system for charging data of new energy vehicles, provided in this application.

[0049] Figure 2 This is an exemplary flowchart for determining stability metrics provided in this application;

[0050] Figure 3 This is an exemplary flowchart for determining control deviations provided in this application; Detailed Implementation

[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0052] This application provides a cloud-based real-time monitoring system for new energy vehicle charging data. Its core components include: a data acquisition module for acquiring voltage, current, and power data; a performance stability analysis module for extracting current and voltage fluctuation characteristics and power change rate to construct stability indicators; a control deviation evaluation module for quantifying charging state deviations; a power curve correction module for adjusting the power output curve based on stability indicators and control deviations; and a power regulation module for issuing control commands according to the correction curve and uploading data to the cloud platform in real time, enabling visualized management of charging piles. This invention allows for real-time monitoring and dynamic regulation of the charging pile's operating status throughout the charging process via a cloud platform.

[0053] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of a cloud-based real-time monitoring system for new energy vehicle charging data according to this embodiment of the present application. The system specifically includes:

[0054] The data acquisition module 100 is used to collect the operating data of the charging pile during the charging process. The operating data includes charging voltage data, charging current data, and charging power data.

[0055] It should be noted that the charging pile's operational data during the charging process is collected by a sensing and detection unit deployed inside the charging pile. This sensing and detection unit includes a voltage sensor, a current sensor, and a power metering module. Specifically, the hardware can employ Hall effect voltage / current sensors and dedicated metering chips to construct the detection unit, which is connected to the output circuit in parallel / series connections to convert high voltage / current signals into low-level analog signals and calculate power in real time. The microcontroller collects signals at a preset frequency through an analog-to-digital converter interface, binds timestamps, performs preprocessing including moving average filtering and outlier removal, encapsulates the data in JSON format, transmits it to the edge unit via the controller area network bus, caches it after cyclic redundancy check, and uploads it to the cloud platform. It should also be noted that the operational data collected in this application specifically includes charging voltage data, charging current data, and charging power data.

[0056] The performance stability analysis module 200 is used to extract the fluctuation characteristics of charging current and voltage during the charging process from the operating data, and to determine the stability index of charging performance of the charging pile during the charging process based on the fluctuation characteristics and the rate of change of charging power.

[0057] In this embodiment, extracting the fluctuation characteristics of charging current and voltage during the charging process from the operational data can be achieved through the following steps:

[0058] The charging stage division nodes for each charging session of the charging pile are determined, including the starting point of the pre-charging stage, the starting point of the constant current charging stage, the starting point of the constant voltage charging stage, and the charging termination point.

[0059] The charging current and charging voltage data for each charging stage are filtered from the operational data, and the current-voltage data segments within a set time window before and after each division node are extracted.

[0060] The current fluctuation amplitude for the corresponding charging stage is calculated based on the difference between the maximum and minimum current values ​​for each current data segment, and the voltage fluctuation amplitude for the corresponding charging stage is calculated based on the difference between the maximum and minimum voltage values ​​for each voltage data segment.

[0061] The set of current fluctuation amplitudes and the set of voltage fluctuation amplitudes across all charging stages are used together as the fluctuation characteristics of charging current and voltage during the charging process.

[0062] It should be noted that the fluctuation characteristics in this application are used to characterize the stability of power output and adaptability to battery charging needs during the charging process of the charging pile.

[0063] In practice, the extraction of charging current and voltage fluctuation characteristics relies on time-series data segmentation and basic numerical calculation techniques, executed in four orderly steps: First, when determining the charging stage nodes, a voltage and current characteristic judgment method is used—the pre-charging stage starts when the charging pile output voltage rises from 0 to 20%-30% of the battery's rated voltage; the constant current charging stage starts when the current enters the stable range of ±5% of the rated charging current and the voltage continues to rise; the constant voltage charging stage starts when the voltage reaches ±2% of the battery's full charge voltage and the current begins to continuously decrease (its decrease exceeds 10% of the initial current); the charging termination point is determined when the current drops below 5% of the rated charging current for 3 seconds. Each node is determined by comparing the real-time collected voltage and current time-series data with preset thresholds. Second, when filtering and extracting data segments, based on the time nodes determined in the first step, the corresponding current and voltage data for each stage are filtered from the stored operating data (which can be sorted by timestamp); then a fixed time window of 10-30 seconds is set. The duration of each charging stage can be flexibly adjusted. For example, a 10-second window can be used for the pre-charging stage and a 30-second window for the constant current stage. Current-voltage data segments within the first and last 50% of the window duration for each node are extracted to ensure coverage of the transition data before and after the node. In the third step, when calculating the fluctuation amplitude, the extreme value difference method can be used. That is, for each extracted data segment, the current data is traversed point by point in chronological order, and the maximum and minimum current values ​​within the segment are recorded. The difference between the two is the current fluctuation amplitude for the corresponding charging stage. Similarly, the maximum and minimum voltage values ​​are obtained by traversing the voltage data segments, and the difference is used as the voltage fluctuation amplitude. It should be further noted that before calculation, invalid data that exceeds the rated output range of the charging pile needs to be removed from the data segments. Invalid data is replaced by the average of two adjacent valid data. In the fourth step, when integrating the fluctuation characteristics, the current fluctuation amplitude of each stage can be organized into a current fluctuation set and the voltage fluctuation amplitude into a voltage fluctuation set according to the stage order of pre-charging, constant current, and constant voltage. The two sets together constitute a complete current and voltage fluctuation characteristic, which can be directly used for the performance stability analysis of the charging pile.

[0064] In this embodiment, reference Figure 2 As shown, this figure is an exemplary flowchart for determining stability indicators in an embodiment of this application. In this embodiment, the determination of the stability indicators of the charging pile's charging performance during the charging process based on the fluctuation characteristics and the rate of change of charging power can be achieved through the following steps:

[0065] In step S21, the reference power variation range for each charging stage is determined based on the charging data of the charging pile;

[0066] In step S22, the real-time change rate of charging power in each charging stage is determined. The real-time change rate is calculated by the ratio of the power difference between adjacent time nodes to the time interval.

[0067] In step S23, an evaluation model for characterizing the charging performance stability of the charging pile is constructed based on the current fluctuation amplitude, voltage fluctuation amplitude, real-time power change rate at each stage, and reference power change range in the fluctuation characteristics.

[0068] In step S24, the evaluation model outputs a dimensionless score, which is used as a stability indicator of the charging performance of the charging pile during the charging process.

[0069] It should be noted that the reference power variation range in this application refers to the reference range used to characterize the power fluctuation range that should exist under normal operating conditions in each charging stage, and is used as a reference standard to judge whether the actual power variation is stable; the stability index of charging performance in this application is an indicator to measure the stability and reliability of the charging pile's power output and current and voltage control throughout the entire charging process.

[0070] In practical implementation, firstly, when determining the baseline power variation range for each charging stage, a historical data statistical method can be used. This involves collecting historical power data from the charging piles used for normal charging of new energy vehicle batteries over the past three months, categorizing it into pre-charging, constant current, and constant voltage stages, and calculating a 95% confidence interval for each category. This confidence interval can be obtained by removing outliers exceeding the mean ± 3 standard deviations. This interval represents the baseline power variation range for the corresponding stage. Secondly, when calculating the real-time power change rate for each stage, a fixed time interval of 10 seconds (synchronized with the data collection timestamp) is used, according to the formula... Real-time power change rate = (current power value - previous power value) / time interval. One change rate data point is generated every 10 seconds. If the power value changes abruptly due to acquisition error, for example, if the absolute value of the change rate exceeds 20% of the upper limit of the baseline power change range for that period, the arithmetic mean of two adjacent effective change rates is used instead to avoid outlier interference. Then, when constructing the stability assessment model, a weighted summation model can be used. This involves first normalizing each input index. The normalized value of the current fluctuation amplitude = (current fluctuation amplitude in the current period / maximum allowable current fluctuation in the current period). For fluctuation amplitude), the normalized value of voltage fluctuation amplitude is calculated similarly. The deviation rate of real-time power change rate from the reference range is calculated as: |Real-time change rate - Mean of reference range| / Half width of reference range. Then, weights are set for each indicator based on industry experience. For example, current fluctuation amplitude has a weight of 0.3, voltage fluctuation amplitude 0.2, and power change rate deviation rate 0.5. During the constant current stage, the weight of the power change rate deviation rate can be increased to 0.6. The initial stability score is based on the formula: (1 - Normalized value of current fluctuation) × 0.3 + (1 - Normalized value of voltage fluctuation) × 0.2 + (1 - Power deviation rate) × 0.5 Construct an evaluation model to ensure that each indicator contributes positively to the evaluation. Finally, when outputting the dimensionless score, multiply the initial stability score (range 0-1) by 100 to convert it into a dimensionless score of 0-100, with 100 being the theoretical optimal score. At the same time, set a score level calibration, such as 90-100 for excellent stability, 70-89 for good stability, and below 70 for unstable. Finally, verify the rationality of the score through three sets of charging data with known stability to ensure that the score is consistent with the actual stability. The final score is the stability index of the charging pile's charging performance.

[0071] The control deviation evaluation module 300 is used to determine the control deviation of the charging pile in controlling the charging status of the vehicle battery during the charging process.

[0072] In this embodiment, reference Figure 3 As shown, this figure is an exemplary flowchart for determining the control deviation in an embodiment of this application. In this embodiment, determining the control deviation of the charging pile in controlling the charging state of the vehicle battery during the charging process can be achieved by the following steps:

[0073] In step S31, the target charging state curve of the vehicle battery during the charging process is obtained. The target charging state curve includes the target state of charge value and the target battery temperature value at different charging time points.

[0074] In step S32, the actual charging state curve of the battery during the charging process is extracted. The actual charging state curve includes the actual state of charge value and the actual battery temperature value corresponding to different charging time nodes.

[0075] In step S33, the deviations between the target state of charge value and the actual state of charge value, and the deviations between the target battery temperature value and the actual battery temperature value are calculated respectively.

[0076] In step S34, the control deviation of the charging pile on the charging state of the vehicle battery during the charging process is determined based on the weighted sum of the two types of deviations. The weights of the state of charge deviation and the battery temperature deviation are set according to the priority of battery safe operation.

[0077] It should be noted that the control deviation in this application is a characteristic indicator reflecting the accuracy of the charging pile in tracking the target charging state of the vehicle battery during the charging process.

[0078] In practice, firstly, when acquiring the target charging state curve, the charging pile communicates with the vehicle's Battery Management System (BMS) via the Controller Area Network (CAN) bus to receive charging strategy data from the BMS in real time. The target State of Charge (SOC) curve is labeled with target values ​​at 1-minute intervals, for example, a linearly increasing sequence from 20% to 80%. The target temperature curve has a preset safety range based on the battery type, and the data is stored as an array with timestamps. Secondly, when extracting the actual state curve, the real-time SOC value and the highest temperature of each battery cell are also collected via the CAN bus from the BMS. The temperature of each battery cell is determined by the battery's built-in NTC. Thermistor detection shows that communication can be established between the charging pile and the vehicle data interface. Interpolation along the time axis completes the data into a sequence synchronized with the target curve, at a rate of 1 minute per point, ensuring time alignment. Then, when calculating deviations, the absolute value method can be used: for the same time point, SOC deviation = |target SOC - actual SOC|, and temperature deviation = |target temperature - actual temperature|. If data is missing at a certain moment, the average deviation of adjacent moments is used as a substitute. Finally, when calculating control deviations, weights can be set according to safety priorities. For example, the temperature deviation weight can be set to 0.6, and the SOC deviation weight to 0.4. These can be fine-tuned according to battery type; for example, the temperature weight for lithium iron phosphate batteries can be reduced to 0.5. The control deviation is further determined using the following formula: Control deviation = Average of all SOC deviations × 0.4 + Average of all temperature deviations × 0.6. The final value reflects the degree of deviation of the charging pile from the battery state control; the smaller the value, the higher the control accuracy.

[0079] It should be noted that, compared with existing technologies, this application's solution, by simultaneously introducing the target state of charge (SOC) curve and target temperature curve of the vehicle battery for comparison, can not only monitor the control accuracy of the charging pile in terms of power replenishment, but also reflect its ability to regulate battery thermal management. This solves the problem of traditional methods that only focus on power deviation and ignore battery temperature safety. On this basis, by weighted summing of SOC deviation and temperature deviation, and dynamically setting the weight according to battery safety priority, it further avoids safety hazards of the battery when charging at high temperatures or high rates. Ultimately, it achieves a comprehensive and refined evaluation of the charging pile control performance, thereby improving the technical effect of improving the safety and reliability of the charging process.

[0080] The power curve correction module 400 is used to acquire the power output curve of the charging pile during the charging process, and to perform correlation correction on the power output curve based on the stability index and the control deviation to obtain the power correction curve of the charging pile.

[0081] It should be noted that this application can obtain the power output curve of the charging pile during the charging process from the interaction data between the charging pile and the vehicle's battery management unit. Specifically, firstly, the CAN bus communication link between the charging pile and the vehicle's BMS is used to exchange data in real time during the charging process, which includes real-time feedback information on the charging pile's output power. In the communication data, the power information is encapsulated in a 16-bit binary format in a specific data frame (such as a charging status frame), which needs to be parsed into an actual power value according to the protocol and bound with a precise timestamp (synchronized to the charging pile's local clock, with an error ≤1ms). Then, the parsed power values ​​are arranged in chronological order, and a linear interpolation method is used to fill any possible communication packet loss gaps. If the interval between two consecutive valid data exceeds 500ms, a linear fitting between the two points is used to supplement the intermediate value. Finally, the processed power-time data is integrated along the entire charging time axis to form a continuous power output curve, with the horizontal axis representing the charging time and the vertical axis representing the output power, which can be directly used in the subsequent power curve correction stage.

[0082] In this embodiment, the power output curve is correlated and corrected based on the stability index and the control deviation to obtain the power correction curve of the charging pile. This can be achieved through the following steps:

[0083] Determine the safe adjustment range of the power output curve, which is based on the rated power range of the charging pile and the allowable charging power range of the battery.

[0084] Based on the safety adjustment range, the stability index, and the control deviation, determine the power compensation ratio corresponding to each charging stage;

[0085] According to the power compensation ratio of each charging stage, the power value of the corresponding stage in the original power output curve is corrected.

[0086] The corrected power values ​​for each stage are spliced ​​together in order of charging time to obtain the power correction curve of the charging pile.

[0087] It should be noted that the proposed solution addresses the issues of lagging charging power adjustment, lack of specificity, and phased optimization in existing technologies by correlating stability indicators and control deviations with the original power output curve. Through safety adjustment range constraints and calculation of power compensation ratios at each stage, it achieves phased and refined power output correction, avoiding the risks of sudden power changes and exceeding the battery's allowable range. The resulting power correction curve ensures that the charging pile outputs stable power throughout the entire charging process, improving charging stability, control accuracy, and battery safety protection capabilities, thus achieving the technical effect of optimized charging performance.

[0088] In practical implementation, firstly, when determining the safe adjustment range, the parameter intersection method can be used. This involves extracting the rated power range from the charging pile's hardware parameters and simultaneously obtaining the battery's real-time allowable charging power range through the BMS. The intersection of these two parameters is taken as the safe range, with the upper and lower limits stored as array upper and lower limits, respectively. Secondly, when determining the power compensation ratio for each stage, a multi-parameter weighted method can be used. This involves setting calculation models for the pre-charging, constant current, and constant voltage stages. The specific formula is: Compensation ratio = (1 - Stability index / 100) × 0.05 + Control deviation × 0.02. The weighting coefficients can be adjusted according to the stage characteristics. For example, the stability weighting coefficient for the constant current stage can be increased to 0.08 to ensure the compensation ratio range is controlled within ±15%. The purpose is to avoid sudden power changes and ensure that the corrected power does not exceed the safe range. Then, for the power value at each time point in each stage of the original power curve, the correction value can be calculated according to the corresponding stage compensation ratio. For example, if the original power is P and the compensation ratio is r, then the corrected power is P = P × (1 + r). If the correction value exceeds the safe range, the boundary value of the range is taken. If it exceeds the upper limit, the upper limit value is taken. Finally, when splicing the curves, at the connection between adjacent stages (such as constant current to constant voltage), the power values ​​of the previous and next 5 time points can be processed by linear interpolation. For example, the first 3 points are corrected according to the original stage ratio, the last 2 points are corrected according to the new stage ratio, and the intermediate points are gradually transitioned, finally forming a continuous power correction curve without sudden changes. The horizontal axis is the charging time (minutes), and the vertical axis is the corrected power (kW).

[0089] The power regulation module 500 is used to pre-regulate the output power of the charging pile during the charging process according to the power correction curve, and at the same time upload the collected operating data and regulation information to the cloud platform in real time for monitoring the operating status of the charging pile.

[0090] In this embodiment, the pre-regulation of the charging pile's output power during the charging process based on the power correction curve can be achieved through the following steps:

[0091] The power correction curve is decomposed into multiple power sub-targets corresponding to multiple time segments along the charging time axis;

[0092] A corresponding power control instruction is generated for each time segment's power sub-target, and the power control instruction includes the power target value and the instruction execution duration;

[0093] During the charging process, power control commands for the corresponding time segments are sent to the power control unit of the charging pile in advance according to the time sequence.

[0094] The system monitors the deviation between the actual output power of the charging pile and the power correction curve in real time. If the deviation exceeds the preset threshold, the power control command for the next time segment is finely adjusted.

[0095] It should be noted that the solution in this application decomposes the power correction curve into time segments and generates corresponding power control commands to achieve pre-regulation of the charging pile's output power, effectively solving the problems of delayed power adjustment response and inability to accurately match charging demands in the prior art. By sending control commands in advance and combining them with real-time deviation monitoring, subsequent power output can be dynamically fine-tuned to avoid power fluctuations exceeding the safe range or deviating from the target curve. This achieves continuity and stability of power output during charging, improves charging accuracy, ensures battery safety, and optimizes the stability and controllability of overall charging performance.

[0096] In practical implementation, firstly, when decomposing the power correction curve, a time segment of 1-3 minutes can be used, which can be dynamically adjusted according to the charging stage. For example, 1 minute is used for the pre-charging stage and 3 minutes for the constant current stage. The power sub-target corresponding to each segment is extracted along the charging time axis, i.e., the average power value within that segment. This is obtained by calculating the arithmetic mean of all power points within the segment, ensuring that the power target for each segment is stable and conforms to the correction curve trend. Secondly, when generating power control commands, the charging pile control protocol, such as the Modbus-RTU protocol, is followed. The power sub-target (unit: kW) for each segment and the command execution duration (i.e., time segment length, unit: seconds) are encapsulated into a standard command frame. The frame contains the command type code, the power target value (converted to hexadecimal data according to the protocol format), the execution duration, and a checksum (CRC16 checksum can be used to ensure command integrity). Then, a preset lead time of 30-60 seconds is set, which can be adjusted according to... Communication delay adjustment: CAN bus communication takes 30 seconds. When the current time segment has a preset lead time remaining, the control command for the next segment is sent to the power control unit (such as the IGBT power module controller) via the charging pile's internal control bus (such as RS485 bus), reserving time for command reception and execution preparation. Finally, a real-time comparison and proportional correction method can be used, that is, the actual output power of the charging pile is collected every 5 seconds. The actual output power can be obtained through the power metering module, and then the deviation from the power value at the corresponding time point of the correction curve is calculated. The deviation = |actual power - target power|. If the deviation exceeds a preset threshold, which is usually ±5% of the target power, the adjustment value of the next segment's power target is calculated according to the fine adjustment amount = deviation × 0.2. For example, if the actual power is 5kW lower than the target, the target power of the next segment is increased by 1kW. After fine adjustment, it is still necessary to ensure that the power value is within the safe adjustment range, ultimately achieving precise pre-control of power output.

[0097] In this embodiment, the real-time uploading of collected operational data and control information to the cloud platform for monitoring the charging pile's operational status can be achieved through the following steps:

[0098] The collected operational data and control information are processed in a standardized manner;

[0099] Standardized operational data and control information are uploaded to the cloud platform in real time via encrypted communication links;

[0100] After receiving the data, the cloud platform classifies and stores the data, and establishes a database indexed by the charging pile number and charging operation data.

[0101] The cloud platform generates a real-time dashboard of charging pile operation status based on uploaded data, displaying the current charging power, stability indicators, control deviations, and power regulation execution.

[0102] In specific implementation, firstly, during data standardization, the collected operational data and control information can be uniformly converted according to preset field formats (such as JSON format, with fields including device ID, timestamp, parameter name, value, and unit). Numerical data is normalized in range, for example, converting voltage 0-800V to a normalized value of 0-1 using the formula: Normalized value = (Actual value - Minimum value) / (Maximum value - Minimum value). Textual information, such as instruction status, is encoded using enumerated values, for example, "Executing" is encoded as 1, and "Completed" as 2, ensuring uniformity in the format of different data types. Secondly, during encrypted transmission, an end-to-end encrypted link can be established using the TLS / SSL protocol. The charging pile port can encapsulate standardized data into message packets via the MQTT protocol. The MQTT protocol is a lightweight IoT communication protocol suitable for real-time data transmission. The message packet header carries a unique device identifier, and data is sent every 10 seconds. The frequency can be adjusted according to the importance of the data. Fault information is sent in real time, and a heartbeat mechanism (sent every 30 seconds) is used to maintain the communication connection during transmission to avoid data interruption. Then, when storing data in the cloud platform, a time-series database can be used to build the storage architecture, using "charging pile number + charging start time" as the main index. The data is stored in three categories: "raw operation data", "control command data", and "status analysis data". At the same time, data compression algorithms are used to reduce storage usage, and the retention time of a single data record is set according to business needs. Finally, when generating real-time dashboards, the front-end visualization framework ECharts can be used to design the interface, displaying the uploaded data in two categories: "real-time status of a single pile" and "regional pile group statistics". The single pile interface displays the power curve with a line graph and displays stability indicators and control deviations with digital cards. When the deviation exceeds the threshold, a color warning is triggered, for example, red when the threshold is exceeded. The pile group interface displays the regional charging pile operation status with a heat map and provides data query functions to ensure that maintenance personnel can keep track of the charging pile operation in real time.

[0103] It should be noted that this application's solution addresses the issues of data fragmentation, untimely transmission, and insufficient security in existing technologies by standardizing the processing of charging pile operation data and control information and uploading it to the cloud platform in real time with encryption. The cloud platform categorizes and stores the data and establishes an index, ensuring that historical and real-time data can be quickly queried and analyzed. At the same time, it displays power, stability indicators, control deviations, and control execution status in real time through a dashboard, realizing visualized monitoring and dynamic management of the charging pile's operating status. This significantly improves data management efficiency, monitoring accuracy, and operation and maintenance response capabilities, providing reliable support for the stable and safe operation of charging piles.

[0104] Therefore, this application's solution, through the organic synergy of a data acquisition module, performance stability analysis module, control deviation evaluation module, power curve correction module, and power regulation module, achieves real-time monitoring and dynamic regulation of new energy vehicle charging piles throughout the charging process. Its overall beneficial effects manifest as multi-level and multi-dimensional improvements in safety and efficiency. Firstly, the data acquisition module, through high-frequency, timestamp-bound voltage, current, and power acquisition, ensures the integrity and accuracy of the original operating data during the charging process, providing a reliable foundation for subsequent performance analysis. It also preprocesses abnormal data, reducing noise interference and ensuring data quality; this step directly supports the scientific nature of charging performance evaluation and control deviation analysis. Secondly, the performance stability analysis module extracts current and voltage fluctuation characteristics and calculates charging stability indicators based on the power change rate, achieving a quantitative characterization of the power output pattern and abnormal fluctuations during the charging process. This allows for timely reflection of the charging pile's output stability at different stages, providing a scientific basis for power regulation. Based on this, its function is to reduce the potential damage risk to the battery caused by voltage and current fluctuations during charging, while improving charging balance. Secondly, the control deviation assessment module quantifies the battery SOC and temperature deviation by comparing the target state of charge curve with the actual charging state curve, achieving accurate feedback on the execution effect of the charging strategy, providing a decision basis for power correction, and helping to ensure the safe operation of the battery and extend its life. Then, the power curve correction module, based on stability indicators and control deviations, formulates the power compensation ratio according to the charging stage and performs curve correction, so that the output power transitions smoothly within a safe range, effectively avoiding power sudden changes and overload risks, while improving the controllability of the charging process. Finally, the power regulation module decomposes the corrected power curve into executable instructions and issues them in advance, while monitoring the deviation between the actual output and the target in real time to achieve closed-loop control. At the same time, it uploads operating data and regulation information in real time through the cloud platform, realizing visualized management and remote monitoring of charging piles, greatly enhancing the intelligence and response speed of charging operation and maintenance. In summary, this invention enables real-time monitoring and dynamic control of the charging pile's operational status during the charging process based on a cloud platform. Through modular collaboration and index quantification, it not only ensures the stability and safety of the charging pile's output power but also achieves dynamic control and remote visual monitoring of the charging process. This effectively improves charging efficiency, ensures battery safety, and provides complete technical support for the intelligent management of charging networks.

[0105] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0106] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compactdisc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0107] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A cloud platform-based new energy vehicle charging data real-time monitoring system, characterized in that, The system specifically comprises: a data acquisition module for acquiring operation data of the charging pile during the charging process, the operation data including charging voltage data, charging current data and charging power data; a performance stability analysis module for extracting fluctuation characteristics of charging current and voltage during the charging process from the operation data, determining a stability index of charging performance of the charging pile during the charging process based on the fluctuation characteristics and a change rate of the charging power; a control deviation evaluation module for determining a control deviation of the charging pile in controlling the charging state of the vehicle battery during the charging process; a power curve correction module for obtaining a power output curve of the charging pile during the charging process, and correcting the power output curve based on the stability index and the control deviation to obtain a power correction curve of the charging pile; a power regulation module for pre-regulating the output power of the charging pile during the charging process according to the power correction curve, and uploading the acquired operation data and regulation information to a cloud platform in real time for monitoring the operation state of the charging pile.

2. The system of claim 1, wherein, The extraction of the fluctuation characteristics of the charging current and voltage during the charging process specifically comprises: determining charging stage division nodes of each charging of the charging pile, the charging stage division nodes including a pre-charging stage starting point, a constant current charging stage starting point, a constant voltage charging stage starting point and a charging termination point; screening charging current data and charging voltage data in each charging stage from the operation data, and intercepting current-voltage data segments in a set time window before and after each division node; calculating a current fluctuation amplitude of the corresponding charging stage according to the difference between the maximum current value and the minimum current value of each current data segment, and calculating a voltage fluctuation amplitude of the corresponding charging stage according to the difference between the maximum voltage value and the minimum voltage value of each voltage data segment; collecting the current fluctuation amplitude set and the voltage fluctuation amplitude set of all charging stages as the fluctuation characteristics of the charging current and voltage during the charging process.

3. The system of claim 1, wherein, The determination of the stability index of the charging performance of the charging pile during the charging process based on the fluctuation characteristics and the change rate of the charging power specifically comprises: determining a reference power change range of each charging stage according to the charging data of the charging pile; determining a real-time change rate of the charging power in each charging stage, the real-time change rate being calculated by the ratio of the power difference value and the time interval of adjacent time nodes; constructing an evaluation model for representing the stability of the charging performance of the charging pile based on the current fluctuation amplitude, the voltage fluctuation amplitude, the real-time power change rate of each stage and the reference power change range in the fluctuation characteristics; outputting a dimensionless score from the evaluation model, and taking the score as the stability index of the charging performance of the charging pile during the charging process.

4. The system of claim 1, wherein, The determination of the control deviation of the charging pile in controlling the charging state of the vehicle battery during the charging process specifically comprises: obtaining a target charging state curve of the vehicle battery during the charging process, the target charging state curve including target state of charge values and target battery temperature values at different charging time nodes; extracting an actual charging state curve of the battery during the charging process, the actual charging state curve including actual state of charge values and actual battery temperature values corresponding to different charging time nodes; respectively calculate the deviation of the target state of charge value and the actual state of charge value, the deviation of the target battery temperature value and the actual battery temperature value; determine the control deviation of the charging pile in the charging process for the control of the state of charge of the vehicle battery according to the weighted sum of the two types of deviations, wherein the weights of the state of charge deviation and the battery temperature deviation are set according to the priority of safe operation of the battery.

5. The system of claim 1, wherein, According to the stability index and the control deviation, the power output curve is associated and corrected to obtain the power correction curve of the charging pile, which specifically includes: determine the safe adjustment interval of the power output curve, and the safe adjustment interval is jointly determined based on the rated power range of the charging pile and the allowed charging power range of the battery; determine the power compensation ratio corresponding to each charging stage according to the safe adjustment interval, the stability index and the control deviation; According to the power compensation ratio of each charging stage, the power value of the corresponding stage in the original power output curve is corrected; The power values of each stage after correction are spliced in the order of charging time to obtain the power correction curve of the charging pile.

6. The system of claim 1, wherein, According to the power correction curve, the output power of the charging pile in the charging process is pre-controlled, which specifically includes: The power correction curve is decomposed into power sub-targets corresponding to multiple time segments according to the charging time axis; For each time segment, a corresponding power control instruction is generated, and the power control instruction includes a power target value and an instruction execution time; In the charging process, the power control unit of the charging pile is sent the power control instruction of the corresponding time segment in advance at a preset time in time sequence; Real-time monitoring of the deviation between the actual output power of the charging pile and the power correction curve, if the deviation exceeds the preset threshold, the power control instruction of the next time segment is adjusted again.

7. The system of claim 1, wherein, The collected operation data and control information are uploaded to the cloud platform in real time for charging pile operation state monitoring, which specifically includes: standardizing the collected operation data and control information; Through an encrypted communication link, the standardized operation data and control information are uploaded to the cloud platform in real time; After receiving the data, the cloud platform classifies and stores the data, and establishes a database indexed by charging pile number and charging operation data; The cloud platform generates a charging pile operation state dashboard in real time based on the uploaded data, and displays the current charging power, stability index, control deviation and power control execution.

8. The system of claim 1, wherein, The operation data of the charging pile in the charging process are collected by a sensing detection unit deployed in the charging pile.

9. The system of claim 8, wherein, The sensing detection unit includes a voltage sensor, a current sensor and a power metering module.

10. The system of claim 1, wherein, The power output curve of the charging pile in the charging process is obtained from the interaction data between the charging pile and the vehicle battery management unit.

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