Electric vehicle charging system and charging method suitable for European standard charging pile

Abstract

The invention discloses an electric vehicle charging system and charging method suitable for a European standard charging pile, and relates to the technical field of electric vehicle charging, and the method comprises the following steps: collecting various parameter information generated by the charging pile and a vehicle control unit in a power negotiation process in real time before charging is started, and transmitting related data to a monitoring system; according to the invention, the key parameters of the charging pile and the vehicle control unit are collected in real time, and intelligent analysis is carried out by using the machine learning model, so that abnormal conditions in power negotiation can be found in time, and battery damage caused by mismatching of current and voltage is avoided. The adaptive adjustment function automatically adjusts parameters when inconsistent signals are detected, optimizes the negotiation process, reduces power fluctuation, improves the negotiation success rate, and ensures the charging safety and efficiency. The flexibility of the system and a real-time feedback mechanism significantly enhance the stability and adaptive ability of the charging process, reduce the maintenance cost, and prolong the service life of the battery.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging, and in particular to an electric vehicle charging system and a charging method suitable for a European standard charging pile. Background Art

[0002] The electric vehicle charging method suitable for European standard charging piles refers to the electric vehicle charging technology and specifications designed and used in accordance with European standards (IEC 61851, IEC62196, etc.). European standard charging piles are divided into two categories: alternating current (AC) and direct current (DC). AC charging is mostly slow charging, suitable for homes and public parking lots, with a voltage range of usually between 230V and 400V, and commonly used interfaces include Type 2. DC charging is fast charging, with a voltage of up to 1000V or even higher, and is often used in places such as highway service areas. Typical interfaces include Combo 2 (CCS). These standardized interfaces and technologies not only ensure the safety of charging, but also ensure that electric vehicles of different brands and models are compatible with the same charging infrastructure.

[0003] The European standard charging method also includes unified requirements for energy transfer and communication protocols during the charging process. Not only must the physical parameters such as current and voltage be matched between the charging pile and the electric vehicle, but real-time data exchange must also be achieved through the communication protocol to ensure charging safety. For example, before charging begins, the charging pile will authenticate and negotiate power with the vehicle control unit to determine the maximum charging power, while monitoring the entire charging process and stopping charging in time to prevent problems such as overheating or short circuits. Through these standardized methods, European standard charging piles improve the reliability and efficiency of charging and facilitate the popularization of electric vehicles throughout Europe.

[0004] The prior art has the following deficiencies: Before charging begins, the charging pile and the vehicle control unit will perform identity authentication and power negotiation to determine the maximum charging power, and monitor the entire process in real time to prevent problems such as overheating or short circuits. If an abnormality occurs during the power negotiation process, causing the charging pile to fail to correctly match the voltage or current, it may damage the single cells in the battery pack, causing imbalance and reducing battery life. In extreme cases, the BMS system may not be able to repair these batteries, causing the battery pack to fail, or even requiring the entire battery pack to be replaced, resulting in high repair costs and significant economic losses to car owners and manufacturers.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide an electric vehicle charging system and charging method suitable for European standard charging piles. By real-time acquisition of key parameters of the charging pile and the vehicle control unit, and intelligent analysis using a machine learning model, abnormal conditions in power negotiation can be discovered in a timely manner to avoid battery damage caused by current and voltage mismatch. The adaptive adjustment function automatically adjusts parameters when inconsistent signals are detected, optimizes the negotiation process, reduces power fluctuations, improves the negotiation success rate, and ensures charging safety and efficiency. The flexibility of the system and the real-time feedback mechanism significantly enhance the stability and adaptability of the charging process, reduce maintenance costs, and extend the battery life to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a method for charging an electric vehicle suitable for a European standard charging pile, comprising the following steps: Before charging begins, various parameter information generated by the charging pile and vehicle control unit during the power negotiation process is collected in real time, and the relevant data is transmitted to the monitoring system; In the monitoring system, the pre-processed data is analyzed for abnormalities, and the trained machine learning model is used to intelligently evaluate the parameter information that has undergone abnormal processing. Based on the output results of the machine learning model, it is determined whether the current power negotiation is stable. If it is determined that there is an unstable state in the current power negotiation process, a real-time warning signal will be sent to the charging pile and the vehicle to indicate that there are potential risks in the power negotiation process. At the same time, the automatic system will make adaptive adjustments, and the charging pile and the vehicle control unit will try to renegotiate to obtain stable power parameters; If the power negotiation process is determined to be in a stable state, the charging pile is allowed to provide power to the electric vehicle, while continuously monitoring various parameters of the power negotiation to ensure the safety of the electric vehicle charging process.

[0008] Preferably, the various parameter information generated by the charging pile and the vehicle control unit during the power negotiation process includes current flow direction control information and electrical safety monitoring information. The current flow direction control information is mainly used to monitor whether the current flows in the reverse direction during the charging process, and the electrical safety monitoring information is used to detect whether there is current that is not normally consumed or diverted in the charging system.

[0009] Preferably, after obtaining the current flow direction control information and electrical safety monitoring information generated by the charging pile and the vehicle control unit during the power negotiation process, the current flow direction control information is analyzed for abnormalities to generate a reverse current detection index, and a residual current index is generated after abnormal analysis of the electrical safety monitoring information. The reverse current detection index and the residual current index are input into a pre-learned machine learning model, and the charging pile and the vehicle control unit are intelligently evaluated during the power negotiation process through the machine learning model. Based on the output results of the machine learning model, it is determined whether the current power negotiation is stable.

[0010] Preferably, the negotiation consistency coefficient generated by the power negotiation process between the charging pile and the vehicle control unit under the detection window is compared and analyzed with a preset negotiation consistency coefficient reference threshold, and the analysis results are as follows: If the negotiation consistency coefficient is greater than or equal to a preset negotiation consistency coefficient reference threshold, the power negotiation process between the charging pile and the vehicle control unit under the detection window is classified as an unstable state; If the negotiation consistency coefficient is less than a preset negotiation consistency coefficient reference threshold, the power negotiation process between the charging pile and the vehicle control unit under the detection window is classified as a stable state.

[0011] Preferably, the power negotiation process performed by the charging pile and the vehicle control unit under the detection window is divided into an unstable state and a real-time warning signal is sent to the charging pile and the vehicle, indicating that there is a potential risk in the power negotiation process, and then a number of negotiation consistency coefficients generated by the subsequent process of power negotiation performed by the charging pile and the vehicle control unit under the detection window are continuously obtained to establish an analysis set, and the negotiation consistency coefficients in the data set are compared and analyzed with the pre-set first gradient negotiation consistency reference threshold and the second gradient negotiation consistency reference threshold and the negotiation consistency coefficient reference threshold, wherein the second gradient negotiation consistency reference threshold is greater than the first gradient negotiation consistency reference threshold, and the first gradient negotiation consistency reference threshold is greater than the negotiation consistency coefficient reference threshold, and the negotiation consistency coefficient that is less than the first gradient negotiation consistency reference threshold and greater than or equal to the negotiation consistency coefficient reference threshold is calibrated as , the consensus coefficient that is less than the second gradient consensus reference threshold and greater than or equal to the first gradient consensus reference threshold is calibrated as , the consensus coefficient greater than or equal to the second consensus reference threshold is calibrated as .

[0012] Will , , Conduct comprehensive analysis to generate negotiation unification coefficients , based on the formula: , where , , They are , , The preset scaling factor.

[0013] Preferably, the generated negotiation unification coefficient is compared and analyzed with a preset negotiation unification coefficient reference threshold, and the comparison and analysis results are as follows: If the negotiation uniformity coefficient is greater than or equal to the preset negotiation uniformity coefficient reference threshold, a negotiation inconsistency signal is generated, indicating that the power negotiation results of the charging pile and the vehicle control unit are inconsistent under the detection window, and the charging pile cannot charge the electric vehicle; If the negotiation unification coefficient is less than the preset negotiation unification coefficient reference threshold, a negotiation consensus signal is generated, indicating that the power negotiation results of the charging pile and the vehicle control unit are consistent under the detection window, and the charging pile is allowed to charge the electric vehicle.

[0014] Preferably, the logic of performing abnormal analysis on the current flow direction control information and generating the reverse flow detection index is as follows: In the detection window, real-time acquisition of forward current and reverse current ,in, is the forward current, which indicates the current from the charging pile to the electric vehicle battery. is the reverse current, which means the current flowing back from the electric vehicle battery to the charging station; Detection in each The change of current direction within the time interval defines the current direction change function. The current direction change function represents the instantaneous change of current from forward flow to reverse flow. The calculation expression is as follows: , where Represents the function of current direction change, which is the basis for analyzing the reverse flow trend. When , the system is in a stable state, the current is completely positive, and When , it indicates that the current has a tendency to change in the reverse direction; The current direction stability factor is introduced to indicate the degree of current direction stability. The calculation expression is as follows: , where is the current direction stability factor, is a very small positive value, is a parameter that adjusts the exponential scaling; Combining the instantaneous current direction change function and the current direction stability factor, the comprehensive intensity of the reverse flow is obtained, and the calculation expression is as follows: , where is the comprehensive strength of countercurrent, Indicates the start time of the detection window, Indicates the end time of the detection window. is the instantaneous reverse current, is a time variable, indicating that Instantaneous reverse current; Based on the instantaneous fluctuation of current, the reverse current fluctuation factor is defined to measure the fluctuation of reverse current in the detection window. The calculation expression is as follows: , where represents the countercurrent fluctuation factor; Based on the comprehensive strength of countercurrent , countercurrent fluctuation factor And the current direction change function Generate the backflow detection index, the calculation expression is as follows: , where is the backflow detection index, is the adjustment coefficient.

[0015] Preferably, the logic of performing abnormal analysis on the electrical safety monitoring information and generating the residual current index is as follows: In the detection window, real-time acquisition of electrical parameters in the charging system, including the instantaneous current input by the charging pile , the instantaneous current consumed by the vehicle battery , the instantaneous voltage output by the charging pile and the instantaneous impedance of the charging system ; Combined with the instantaneous current input from the charging pile and the instantaneous current consumed by the vehicle battery , calculate the residual current to reflect the current balance state in the system. The calculation expression is as follows: , where Indicates residual current; The residual current At every time The value under is compared with the residual current threshold preset by the system to generate the residual current abnormal factor. The calculation expression is as follows: , where is the residual current anomaly factor, is the residual current threshold preset by the system, is the system tolerance coefficient; Considering the cumulative effect of residual current during the entire detection window, the residual current abnormality factor Perform time series weighted integration to generate an overall weighted anomaly index. The calculation expression is as follows: , where represents the weighted anomaly index, Indicates the start time of the detection window. Indicates the end time of the detection window, where , is the time weighting function, is the time decay constant, which controls the decay rate of the weighting function; By weighted anomaly index Normalization is performed and amplified to a readable index range to finally generate the residual current index. The calculation expression is as follows: , where Represents the residual current index, is the system adjustment coefficient.

[0016] An electric vehicle charging system suitable for European standard charging piles, characterized by comprising a data acquisition module, an abnormality analysis and evaluation module, an early warning and adaptive adjustment module, and a charging monitoring and safety assurance module; The data acquisition module collects the various parameter information generated by the charging pile and the vehicle control unit during the power negotiation process in real time before charging begins, and transmits the relevant data to the monitoring system; The abnormality analysis and evaluation module performs abnormality analysis on the pre-processed data in the monitoring system, applies the trained machine learning model to intelligently evaluate the parameter information after abnormal processing, and judges whether the current power negotiation is stable based on the output results of the machine learning model; The early warning and adaptive adjustment module sends a real-time early warning signal to the charging pile and the vehicle if it determines that there is an unstable state in the current power negotiation process, indicating that there are potential risks in the power negotiation process. At the same time, the automatic system performs adaptive adjustments, and the charging pile and the vehicle control unit will try to renegotiate to obtain stable power parameters. The charging monitoring and safety assurance module allows the charging pile to provide power to the electric vehicle if the power negotiation process is determined to be in a stable state, while continuously monitoring various parameters of the power negotiation to ensure the safety of the electric vehicle charging process.

[0017] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention collects the key parameters of the charging pile and the vehicle control unit in real time, and transmits these data to the monitoring system for intelligent analysis, ensuring that abnormal situations can be discovered early during the power negotiation process. By applying machine learning models for intelligent evaluation, the stability of the current power negotiation can be effectively judged to ensure that the power matching is within a safe range. Once the power negotiation is detected to be unstable, the system will immediately take measures to prevent current and voltage mismatches from causing damage to the vehicle battery. This solution can effectively reduce the potential safety risks caused by problems such as battery overcharging, overvoltage, and backflow, prevent battery pack failure, and significantly reduce battery maintenance and replacement costs, ensuring the service life of the battery pack and the stability of system operation.

[0018] The adaptive adjustment function in the present invention is another major advantage of the system. When a negotiation inconsistency signal is detected, the system will automatically analyze the reasons for the previous negotiation failure, adjust the power negotiation parameters, reduce power fluctuations, optimize the negotiation process, and reduce power deviation. In this way, the system can make adjustments quickly after the power negotiation fails, maximizing the probability of successful power negotiation. Through real-time feedback and monitoring, the system can continuously evaluate the adjustment results of the power parameters until the negotiation consistency coefficient returns to the normal range. In this way, not only is the impact of power negotiation failure on the charging process reduced, the charging efficiency is significantly improved, but it also ensures that the system can flexibly deal with power mismatch problems under different combinations of vehicles and charging piles, enhance the system's adaptive ability, and achieve an efficient and stable charging process. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments 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 also be obtained based on these drawings.

[0020] Figure 1 The present invention is a method flow chart of a method for charging an electric vehicle suitable for a European standard charging pile.

[0021] Figure 2 The present invention is a module schematic diagram of an electric vehicle charging system suitable for a European standard charging pile. DETAILED DESCRIPTION

[0022] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0023] The present invention provides Figure 1 The electric vehicle charging method shown is applicable to a European standard charging pile, comprising the following steps: Before charging begins, various parameter information generated by the charging pile and vehicle control unit during the power negotiation process is collected in real time, and the relevant data is transmitted to the monitoring system; The various parameter information generated by the charging pile and the vehicle control unit during the power negotiation process includes current flow control information and electrical safety monitoring information. The current flow control information is mainly used to monitor whether the current flows in the reverse direction during the charging process (from the vehicle back to the charging pile). The electrical safety monitoring information is used to detect whether there is current that is not normally consumed or diverted in the charging system (i.e. leakage current); In the monitoring system, the pre-processed data is analyzed for abnormalities, and the trained machine learning model is used to intelligently evaluate the parameter information that has undergone abnormal processing. Based on the output results of the machine learning model, it is determined whether the current power negotiation is stable. After obtaining the current flow control information and electrical safety monitoring information generated by the charging pile and the vehicle control unit during the power negotiation process, the current flow control information is analyzed for abnormalities to generate a reverse current detection index. The electrical safety monitoring information is analyzed for abnormalities to generate a residual current index. The reverse current detection index and the residual current index are input into a pre-learned machine learning model. The machine learning model is used to perform an intelligent evaluation of the charging pile and the vehicle control unit during the power negotiation process. Based on the output results of the machine learning model, it is determined whether the current power negotiation is stable. During the power negotiation process between the charging pile and the vehicle control unit, an abnormal current flows back from the vehicle to the charging pile, indicating that the charging pile and the vehicle control unit are in an unstable state during the power negotiation process. The core purpose of power negotiation is to ensure that the voltage, current and power output between the charging pile and the vehicle can match, so as to charge the vehicle battery safely and efficiently. When the current flows in the reverse direction, it means that the charging system fails to effectively control the flow of current, indicating that there is a problem with the collaboration between the charging pile and the vehicle control unit. This reverse flow phenomenon is usually caused by control logic failure, sensor failure or damage to power electronic components, and directly reflects the instability of communication or power transfer between the charging pile and the vehicle.

[0024] In addition, the backflow phenomenon will not only destroy the power balance between the charging pile and the vehicle battery, but also affect the real-time feedback and control of key parameters such as current and voltage. Under normal circumstances, the current should flow unidirectionally from the charging pile to the vehicle battery to ensure that the battery is charged according to the predetermined charging curve. When the current flows back from the vehicle to the charging pile, this abnormal phenomenon will not only affect the charging efficiency, but may also cause equipment overheating, short circuit or power device damage, increasing system failures and safety hazards. Therefore, the backflow phenomenon is an obvious signal that the charging pile and the vehicle control unit are in an unstable state during the power negotiation process, indicating that there is a technical problem inside the system, and timely measures need to be taken to repair or protect it to ensure the safety and stability of the charging process.

[0025] The logic of abnormal analysis of current flow control information and generating reverse current detection index is as follows: In the detection window, real-time acquisition of forward current and reverse current ,in, is the forward current, which indicates the current from the charging pile to the electric vehicle battery. is the reverse current, which means the current flowing back from the electric vehicle battery to the charging station; Detection in each The change of current direction within the time interval defines the current direction change function. The current direction change function represents the instantaneous change of current from forward flow to reverse flow. The calculation expression is as follows: , where Represents the function of current direction change, which is the basis for analyzing the reverse flow trend. When , the system is in a stable state, the current is completely positive, and When , it indicates that the current has a tendency to change in the reverse direction; It should be noted that the above The time selection is a relatively short period of time. The time within the period is not specifically limited and can be set according to actual conditions. Its purpose is to monitor the network operation process. time, so as to understand the network operation process at different time periods ( Monitor the situation within the time period; The current direction stability factor is introduced to indicate the degree of current direction stability. The calculation expression is as follows: , where is the current direction stability factor, is a very small positive value, avoiding the situation where the denominator is zero. is a parameter that adjusts the exponential scaling to control the effect of backflow on the stability factor; It should be noted that the current direction stability factor is a parameter used to measure whether the current flow is stable during the charging process, reflecting the balance between the forward current (from the charging pile to the vehicle) and the reverse current (from the vehicle back to the charging pile). This factor is determined by calculating the ratio of the forward and reverse currents and combining the current flow trend of the system. When the forward current is dominant and the reverse current is rare, the current direction stability factor is close to 1, indicating that the current flow is stable; when the reverse current increases or the current frequently switches direction, the stability factor will decrease significantly, indicating that the current flow is unstable.

[0026] Combining the instantaneous current direction change function and the current direction stability factor, the comprehensive intensity of the reverse flow is obtained, and the calculation expression is as follows: , where is the comprehensive strength of countercurrent, Indicates the start time of the detection window, Indicates the end time of the detection window. is the instantaneous reverse current, is a time variable, indicating that Instantaneous reverse current; Based on the instantaneous fluctuation of current, the reverse current fluctuation factor is defined to measure the fluctuation of reverse current in the detection window. The calculation expression is as follows: , where represents the countercurrent fluctuation factor; It should be noted that the purpose of measuring the volatility of reverse current is to evaluate the current stability and abnormality in the charging system. The volatility of reverse current reflects the intensity and frequent changes of current within a certain time range. Excessive volatility means that the reverse current phenomenon is not just accidental, but frequent and severe, which may cause the charging system to fail to regulate the current correctly, thereby increasing the risk of equipment damage, short circuit or overload. By analyzing the volatility of reverse current, we can have a more comprehensive understanding of the system's operating conditions, detect potential problems early, and ensure the safety and stability of the charging process.

[0027] Based on the comprehensive strength of countercurrent , countercurrent fluctuation factor And the current direction change function Generate the backflow detection index, the calculation expression is as follows: , where is the backflow detection index, is the adjustment coefficient; It should be noted that the role of the adjustment coefficient is to control the intensity of the influence of a certain variable on the final result. In the reverse current detection, the adjustment coefficient ensures that the system responds appropriately to the current direction changes of different degrees by amplifying or reducing the influence of the current direction change on the reverse current detection index. If the adjustment coefficient is large, the influence of the current direction change on the result will be amplified, and the system will be more sensitive; if the adjustment coefficient is small, the system will react less to the current direction change. The adjustment coefficient flexibly adjusts the model to cope with different working conditions and requirements.

[0028] It can be seen from the calculation expression of the reverse current detection index that the larger the reverse current detection index performance value generated after the abnormal analysis of the current flow control information, the greater the probability of abnormalities in the power negotiation process between the charging pile and the vehicle control unit. The reverse current detection index reflects the intensity and volatility of the reverse current phenomenon within the detection window. When the index value is high, it means that the reverse current is frequent and the intensity is large, indicating that the current flow direction of the system is unstable, and there may be a risk of power negotiation abnormalities or system failures. On the contrary, when the reverse current detection index performance value is smaller, it means that the reverse current phenomenon is not obvious, the current flow is stable, the power negotiation process is normal, and the possibility of abnormalities is small. Therefore, the high and low reverse current detection index can effectively determine whether there are potential problems in the power negotiation process.

[0029] The presence of unconsumed or unprocessed current during power negotiation between the charging pile and the vehicle control unit does indicate that the system may be in an unstable state. This unprocessed current is often called residual current, which may come from poor grounding, equipment aging, or failure of components within the system. If there is residual current in the charging system, it means that the current does not flow into the battery of the electric vehicle in the expected path, but is retained or leaked in the system. This situation will affect the accuracy of power negotiation because power negotiation relies on real-time measurement of current, voltage and other parameters to set the optimal charging power. Unprocessed residual current will prevent the BMS and charging pile from obtaining accurate current data, which will cause the charging pile to output power that does not meet the actual needs of the electric vehicle, and there is a risk of current matching errors.

[0030] In addition, the presence of residual current also reflects the hidden dangers of electrical safety in the charging system, further exacerbating the instability of the system. Residual current indicates that the current is not normally introduced into the vehicle battery, but may flow into the ground or other electronic components through an abnormal path, increasing the risk of electrical leakage and safety accidents. This electrical anomaly not only affects the charging efficiency, but may also cause overheating or damage to the electronic components of the charging equipment or vehicle. Therefore, in the case of residual current, the power negotiation between the charging pile and the vehicle control unit cannot be in a stable state, and power setting errors, low charging efficiency, and even potential safety threats may occur.

[0031] The logic for abnormal analysis of electrical safety monitoring information and generating residual current index is as follows: In the detection window, real-time acquisition of electrical parameters in the charging system, including the instantaneous current input by the charging pile , the instantaneous current consumed by the vehicle battery , the instantaneous voltage output by the charging pile and the instantaneous impedance of the charging system ; Combined with the instantaneous current input from the charging pile and the instantaneous current consumed by the vehicle battery , calculate the residual current to reflect the current balance state in the system. The calculation expression is as follows: , where Indicates residual current; The residual current At every time The value under is compared with the residual current threshold preset by the system to generate the residual current abnormal factor. The calculation expression is as follows: , where is the residual current anomaly factor, is the residual current threshold preset by the system, is the system tolerance factor, which defines the permissible residual current range under normal operating conditions; It should be noted that the residual current anomaly factor is a quantitative indicator used to measure the degree to which the residual current (i.e. the current that is not consumed or processed) in the charging system deviates from the normal range. During the charging process, ideally all input current should flow to the battery of the electric vehicle, but if residual current occurs, it may mean that there is an electrical problem in the system, such as current leakage or equipment aging. The residual current anomaly factor evaluates whether the current is abnormal and the severity of the anomaly by calculating the difference between the current residual current and the system preset threshold (normal residual current range).

[0032] The system tolerance coefficient is a parameter that reflects the charging system's tolerance to residual current fluctuations. It represents the current fluctuation range that the system can accept, avoiding oversensitivity to small current deviations. When the system tolerance coefficient is large, it means that the system can accept large residual current fluctuations without triggering alarms or protective measures; on the contrary, when the tolerance coefficient is small, the system will be more sensitive, and any small residual current fluctuation will trigger a system reaction. This coefficient ensures the stability of the system in actual operation, while balancing the contradiction between safety and overprotection.

[0033] Considering the cumulative effect of residual current during the entire detection window, the residual current abnormality factor Perform time series weighted integration to generate an overall weighted anomaly index. The calculation expression is as follows: , where represents the weighted anomaly index, Indicates the start time of the detection window. Indicates the end time of the detection window, where , is the time weighting function, is the time decay constant, which controls the decay rate of the weighting function; It should be noted that the cumulative impact of residual current during the entire detection window mainly includes the following aspects: 1. System current distribution is unbalanced Residual current reflects the current in the charging system that has not been fully consumed or introduced into the electric vehicle battery. Over time, the continuous accumulation of residual current will cause an imbalance in current distribution, that is, there is a difference between the current received by the electric vehicle and the current output by the charging station. This imbalance may lead to reduced charging efficiency, longer charging time, and affect the accuracy of power negotiation. Power negotiation relies on real-time current and voltage information. If residual current continues to accumulate, it will be difficult for the system to accurately calculate the actual load demand, which will affect the stability of charging.

[0034] 2. Equipment aging and damage risks If the residual current persists, the power electronic components of the charging system may be subject to additional current loads, especially the power transmission part of the charging pile and the charging control unit of the electric vehicle. This unstable current distribution will accelerate the aging of system equipment and shorten the service life of electronic components. If the accumulated residual current is too high, it may cause component overheating or even electrical failure, resulting in equipment damage. In addition, excessive residual current may also increase the risk of leakage, which may lead to serious electrical safety hazards such as electric shock or fire.

[0035] 3. Electrical safety hazards The accumulation of residual current will increase electrical safety risks, especially in long-term operation. The current that is not properly handled in the charging system may leak through the grounding system or flow into other parts of the system, increasing the risk of leakage. If leakage persists, it may cause electrical accidents. This hidden danger will be more obvious, especially for charging piles with poor grounding systems or aging equipment, and in serious cases may cause electrical fires or electric shocks.

[0036] Why time weighting? 1. Highlight the impact of recent moments The core purpose of time weighting is to highlight abnormal conditions in the current time period when analyzing the cumulative impact of residual current. Current fluctuations usually have a greater impact in a short period of time, so the weighting function uses time decay to make the residual current anomaly factor closer to the current moment contribute more to the overall index, while the impact of anomalies in more distant time periods is reduced. This ensures that the system takes more recent current fluctuations into account when evaluating the current electrical state, without being overly disturbed by past abnormal information.

[0037] 2. Adapt to the residual current changes in dynamic environment During the charging process, the state of the system changes dynamically, and the residual current may fluctuate significantly in different time periods. Through time weighting, the system can effectively adapt to this dynamic change and respond agilely to the current system state. Without time weighting, the cumulative effect of historical residual current may obscure the current true state of the system, resulting in the system being unable to respond to the current abnormal situation in a timely manner, which in turn affects the stability and safety of the charging process.

[0038] 3. Reduce the noise interference of historical data During the long-term operation of the charging system, there will inevitably be some instantaneous residual current fluctuations, which may be temporary, accidental and have no long-term impact on the entire system. Through time weighting, the system can effectively "dilute" the impact of these historical data, avoid excessive impact of short-term abnormal data on the final result, reduce noise interference, and improve the overall detection accuracy of the system for residual current anomalies.

[0039] The cumulative impact of residual current includes imbalanced current distribution in the system, increased risk of equipment aging and damage, and electrical safety hazards. Through time weighting, the system can pay more attention to abnormal conditions at the current moment, adapt to changes in the dynamic environment, and reduce the interference of historical noise data, thereby more accurately evaluating the safety and stability of the current system.

[0040] By weighted anomaly index Normalization is performed and amplified to a readable index range to finally generate the residual current index. The calculation expression is as follows: , where Represents the residual current index, is the system regulation coefficient; It should be noted that the purpose of normalizing the weighted anomaly index and enlarging it to a readable index range is to make the results more consistent, easier to interpret and compare. Normalization can standardize the anomaly index in different time periods and environments, eliminate the differences caused by different dimensions or measurement units, and ensure that the indices are compared on the same scale. At the same time, enlarging to a readable index range (such as 0-100) can enable technicians or system users to more intuitively understand the size of the index and the degree of anomaly it represents, so as to quickly respond or make decisions and improve the operability and application value of the system.

[0041] It can be seen from the residual current index calculation expression that the larger the residual current index performance value generated after abnormal analysis of electrical safety monitoring information, the greater the probability of abnormalities in the charging pile and vehicle control unit during power negotiation, because a high residual current index means that more current is not processed or consumed in the system, reflecting that the system may have problems such as unbalanced current distribution, equipment failure or electrical safety hazards. These abnormal current conditions will interfere with the normal power negotiation, resulting in parameter mismatch or system instability. When the residual current index performance value is smaller, it means that the current distribution in the system is more balanced, the abnormal current is less, the probability of abnormalities in the charging pile and vehicle control unit during power negotiation is correspondingly reduced, and the system operation is more stable.

[0042] The machine learning model is not limited here and can realize the backflow detection index and residual current index After comprehensive analysis, the consensus coefficient is generated In order to realize the technical solution of the present invention, the present invention provides a specific implementation method; Coefficient of consensus The resulting calculation formula is: , where , Backflow detection index and residual current index The preset scaling factor of , Both are greater than 0.

[0043] It can be seen from the calculation expression of the negotiation consistency coefficient that the larger the performance value of the reverse current detection index generated after the abnormal analysis of the current flow direction control information, and the larger the performance value of the residual current index generated after the abnormal analysis of the electrical safety monitoring information, the larger the performance value of the negotiation consistency coefficient, which means that the probability of an abnormality occurring between the charging pile and the vehicle control unit during the power negotiation process is greater, and vice versa. The smaller the probability of an abnormality occurring between the charging pile and the vehicle control unit during the power negotiation process.

[0044] The negotiation consistency coefficient generated by the power negotiation process between the charging pile and the vehicle control unit under the detection window is compared and analyzed with the preset negotiation consistency coefficient reference threshold. The analysis results are as follows: If the negotiation consistency coefficient is greater than or equal to a preset negotiation consistency coefficient reference threshold, the power negotiation process between the charging pile and the vehicle control unit under the detection window is classified as an unstable state; If the negotiation consistency coefficient is less than a preset negotiation consistency coefficient reference threshold, the power negotiation process between the charging pile and the vehicle control unit under the detection window is classified as a stable state; If it is determined that there is an unstable state in the current power negotiation process, a real-time warning signal will be sent to the charging pile and the vehicle to indicate that there are potential risks in the power negotiation process. At the same time, the automatic system will make adaptive adjustments, and the charging pile and the vehicle control unit will try to renegotiate to obtain stable power parameters; The power negotiation process performed by the charging pile and the vehicle control unit under the detection window is divided into an unstable state and a real-time warning signal is sent to the charging pile and the vehicle, indicating that there is a potential risk in the power negotiation process. A number of negotiation consistency coefficients generated by the subsequent process of power negotiation performed by the charging pile and the vehicle control unit under the detection window are continuously obtained to establish an analysis set, and the negotiation consistency coefficients in the data set are compared and analyzed with the pre-set first gradient negotiation consistency reference threshold and the second gradient negotiation consistency reference threshold and the negotiation consistency coefficient reference threshold, wherein the second gradient negotiation consistency reference threshold is greater than the first gradient negotiation consistency reference threshold, and the first gradient negotiation consistency reference threshold is greater than the negotiation consistency coefficient reference threshold, and the negotiation consistency coefficient that is less than the first gradient negotiation consistency reference threshold and greater than or equal to the negotiation consistency coefficient reference threshold is calibrated as , the consensus coefficient that is less than the second gradient consensus reference threshold and greater than or equal to the first gradient consensus reference threshold is calibrated as , the consensus coefficient greater than or equal to the second consensus reference threshold is calibrated as .

[0045] Will , , Conduct comprehensive analysis to generate negotiation unification coefficients , based on the formula: , where , , They are , , The preset scaling factor.

[0046] It can be seen from the calculation expression of the negotiation unification coefficient that the larger the value of the negotiation unification coefficient, the greater the probability that an abnormality will occur between the charging pile and the vehicle control unit during the power negotiation process. Conversely, the smaller the probability that an abnormality will occur between the charging pile and the vehicle control unit during the power negotiation process.

[0047] The generated negotiation unification coefficient is compared and analyzed with the preset negotiation unification coefficient reference threshold. The comparison and analysis results are as follows: If the negotiation uniformity coefficient is greater than or equal to the preset negotiation uniformity coefficient reference threshold, a negotiation inconsistency signal is generated, indicating that the power negotiation results of the charging pile and the vehicle control unit are inconsistent under the detection window, and the charging pile cannot charge the electric vehicle; If the negotiation unification coefficient is less than the preset negotiation unification coefficient reference threshold, a negotiation consensus signal is generated, indicating that the power negotiation results of the charging pile and the vehicle control unit are consistent under the detection window, and the charging pile is allowed to charge the electric vehicle; When a negotiation inconsistency signal is generated, it indicates that the power negotiation results of the charging pile and the vehicle control unit are inconsistent within the detection window, and the charging pile cannot charge the electric vehicle. The charging pile and the vehicle control unit will try to renegotiate through the automatic system to obtain stable power parameters. The steps are as follows: After the negotiation inconsistency signal is generated, the charging pile immediately stops supplying power to the electric vehicle to prevent continued charging under unstable or incorrect power parameters, thereby avoiding damage to the battery or charging system; Send a warning to the user interface (such as the charging pile display screen or vehicle display system) to indicate potential risks in the current power negotiation process, and the charging pile and vehicle control unit will start the adaptive adjustment program; Automatically adjust the power negotiation parameters according to the current negotiation inconsistency signal. The automatic adjustment scheme includes: before re-negotiation, the system will adjust its negotiation strategy based on the previous failure analysis, such as adjusting the initial current or voltage setting range, modifying the negotiation step size, etc., to increase the success rate. At the same time, the stability of power negotiation is optimized by reducing the negotiation range or reducing power fluctuations to avoid large parameter deviations; The charging pile and the vehicle will exchange relevant parameter information (such as battery status, maximum tolerable current, voltage range, etc.) again, hoping to re-establish stable charging power negotiation under the new parameter settings; During the renegotiation process, the negotiation status is continuously monitored, and if there is still inconsistency, the adjustment procedure may be triggered again; If the renegotiation is successful and the negotiation consistency coefficient returns to the normal range, the system will confirm that the power parameters are consistent, indicating that the negotiation between the charging pile and the vehicle control unit has been stably completed. At this point, the charging pile can safely continue to provide power to the electric vehicle, and the charging process officially begins.

[0048] If the power negotiation process is determined to be in a stable state, the charging pile is allowed to provide power to the electric vehicle, while continuously monitoring various parameters of the power negotiation to ensure the safety of the electric vehicle charging process.

[0049] When the power negotiation process is determined to be in a stable state, the system allows the charging pile to provide power to the electric vehicle, which means that a matching power output agreement has been reached between the charging pile and the vehicle control unit; this indicates that the voltage, current and other related parameters are within an acceptable range and will not cause damage to the battery or system; During the charging process, although power negotiation has been successful and power supply has begun, the system will continue to monitor various parameters of power negotiation to ensure that the charging process of the electric vehicle is always within a safe range; this includes real-time monitoring of key electrical parameters such as voltage, current, power changes, and temperature; once an abnormality is detected, such as current exceeding the limit, temperature being too high, or power fluctuations being too large, the system will immediately take action, such as adjusting power output or suspending charging, to ensure the safety of the entire charging process and the stable operation of the equipment.

[0050] The present invention collects the key parameters of the charging pile and the vehicle control unit in real time, and transmits these data to the monitoring system for intelligent analysis, ensuring that abnormal situations can be discovered early during the power negotiation process. By applying machine learning models for intelligent evaluation, the stability of the current power negotiation can be effectively judged to ensure that the power matching is within a safe range. Once the power negotiation is detected to be unstable, the system will immediately take measures to prevent current and voltage mismatches from causing damage to the vehicle battery. This solution can effectively reduce the potential safety risks caused by problems such as battery overcharging, overvoltage, and backflow, prevent battery pack failure, and significantly reduce battery maintenance and replacement costs, ensuring the service life of the battery pack and the stability of system operation.

[0051] The adaptive adjustment function in the present invention is another major advantage of the system. When a negotiation inconsistency signal is detected, the system will automatically analyze the reasons for the previous negotiation failure, adjust the power negotiation parameters, reduce power fluctuations, optimize the negotiation process, and reduce power deviation. In this way, the system can make adjustments quickly after the power negotiation fails, maximizing the probability of successful power negotiation. Through real-time feedback and monitoring, the system can continuously evaluate the adjustment results of the power parameters until the negotiation consistency coefficient returns to the normal range. In this way, not only is the impact of power negotiation failure on the charging process reduced, the charging efficiency is significantly improved, but it also ensures that the system can flexibly deal with power mismatch problems under different combinations of vehicles and charging piles, enhance the system's adaptive ability, and achieve an efficient and stable charging process.

[0052] The present invention provides Figure 2An electric vehicle charging system suitable for European standard charging piles is shown, comprising a data acquisition module, an abnormality analysis and evaluation module, an early warning and adaptive adjustment module, and a charging monitoring and safety assurance module; The data acquisition module collects the various parameter information generated by the charging pile and the vehicle control unit during the power negotiation process in real time before charging begins, and transmits the relevant data to the monitoring system; The abnormality analysis and evaluation module performs abnormality analysis on the pre-processed data in the monitoring system, applies the trained machine learning model to intelligently evaluate the parameter information after abnormal processing, and judges whether the current power negotiation is stable based on the output results of the machine learning model; The early warning and adaptive adjustment module sends a real-time early warning signal to the charging pile and the vehicle if it determines that there is an unstable state in the current power negotiation process, indicating that there are potential risks in the power negotiation process. At the same time, the automatic system performs adaptive adjustments, and the charging pile and the vehicle control unit will try to renegotiate to obtain stable power parameters. The charging monitoring and safety assurance module allows the charging pile to provide power to the electric vehicle if the power negotiation process is determined to be in a stable state, while continuously monitoring various parameters of the power negotiation to ensure the safety of the electric vehicle charging process.

[0053] An electric vehicle charging method applicable to a European standard charging pile provided in an embodiment of the present invention is realized by the above-mentioned electric vehicle charging system applicable to a European standard charging pile. The specific method and process of an electric vehicle charging system applicable to a European standard charging pile are detailed in the above-mentioned embodiment of an electric vehicle charging method applicable to a European standard charging pile, which will not be repeated here.

[0054] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for charging an electric vehicle suitable for a European standard charging pile, characterized in that: The following steps are involved: Before charging begins, various parameter information generated by the charging pile and vehicle control unit during the power negotiation process is collected in real time, and the relevant data is transmitted to the monitoring system; In the monitoring system, the pre-processed data is analyzed for abnormalities, and the trained machine learning model is used to intelligently evaluate the parameter information that has undergone abnormal processing. Based on the output results of the machine learning model, it is determined whether the current power negotiation is stable. If it is determined that there is an unstable state in the current power negotiation process, a real-time warning signal will be sent to the charging pile and the vehicle to indicate that there are potential risks in the power negotiation process. At the same time, the automatic system will make adaptive adjustments, and the charging pile and the vehicle control unit will try to renegotiate to obtain stable power parameters; If the power negotiation process is determined to be in a stable state, the charging pile is allowed to provide power to the electric vehicle, while continuously monitoring various parameters of the power negotiation to ensure the safety of the electric vehicle charging process.

2. The method for charging an electric vehicle suitable for a European standard charging pile according to claim 1, characterized in that: The various parameter information generated by the charging pile and the vehicle control unit during the power negotiation process includes current flow direction control information and electrical safety monitoring information. The current flow direction control information is mainly used to monitor whether there is reverse flow of current during the charging process, and the electrical safety monitoring information is used to detect whether there is current that is not normally consumed or diverted in the charging system.

3. The method for charging an electric vehicle suitable for a European standard charging pile according to claim 2, characterized in that: After obtaining the current flow direction control information and electrical safety monitoring information generated by the charging pile and the vehicle control unit during the power negotiation process, the current flow direction control information is analyzed for abnormalities to generate a reverse current detection index. The electrical safety monitoring information is analyzed for abnormalities to generate a residual current index. The reverse current detection index and the residual current index are input into a pre-learned machine learning model. The machine learning model is used to perform an intelligent evaluation of the charging pile and the vehicle control unit during the power negotiation process. Based on the output results of the machine learning model, it is determined whether the current power negotiation is stable.

4. The method for charging an electric vehicle suitable for a European standard charging pile according to claim 3, characterized in that: The negotiation consistency coefficient generated by the power negotiation process between the charging pile and the vehicle control unit under the detection window is compared and analyzed with the preset negotiation consistency coefficient reference threshold. The analysis results are as follows: If the negotiation consistency coefficient is greater than or equal to a preset negotiation consistency coefficient reference threshold, the power negotiation process between the charging pile and the vehicle control unit under the detection window is classified as an unstable state; If the negotiation consistency coefficient is less than a preset negotiation consistency coefficient reference threshold, the power negotiation process between the charging pile and the vehicle control unit under the detection window is classified as a stable state.

5. The method for charging an electric vehicle suitable for a European standard charging pile according to claim 4, characterized in that: The power negotiation process performed by the charging pile and the vehicle control unit under the detection window is divided into an unstable state and a real-time warning signal is sent to the charging pile and the vehicle, indicating that there is a potential risk in the power negotiation process. A number of negotiation consistency coefficients generated by the subsequent process of power negotiation performed by the charging pile and the vehicle control unit under the detection window are continuously obtained to establish an analysis set, and the negotiation consistency coefficients in the data set are compared and analyzed with the pre-set first gradient negotiation consistency reference threshold and the second gradient negotiation consistency reference threshold and the negotiation consistency coefficient reference threshold, wherein the second gradient negotiation consistency reference threshold is greater than the first gradient negotiation consistency reference threshold, and the first gradient negotiation consistency reference threshold is greater than the negotiation consistency coefficient reference threshold, and the negotiation consistency coefficient that is less than the first gradient negotiation consistency reference threshold and greater than or equal to the negotiation consistency coefficient reference threshold is calibrated as , the consensus coefficient that is less than the second gradient consensus reference threshold and greater than or equal to the first gradient consensus reference threshold is calibrated as , the consensus coefficient greater than or equal to the second consensus reference threshold is calibrated as ; Will , , Conduct comprehensive analysis to generate negotiation unification coefficients , based on the formula: , where , , They are , , The preset scaling factor.

6. The method for charging an electric vehicle suitable for a European standard charging pile according to claim 5, characterized in that: The generated negotiation unification coefficient is compared and analyzed with the preset negotiation unification coefficient reference threshold. The comparison and analysis results are as follows: If the negotiation uniformity coefficient is greater than or equal to the preset negotiation uniformity coefficient reference threshold, a negotiation inconsistency signal is generated, indicating that the power negotiation results of the charging pile and the vehicle control unit are inconsistent under the detection window, and the charging pile cannot charge the electric vehicle; If the negotiation unification coefficient is less than the preset negotiation unification coefficient reference threshold, a negotiation consensus signal is generated, indicating that the power negotiation results of the charging pile and the vehicle control unit are consistent under the detection window, and the charging pile is allowed to charge the electric vehicle.

7. The electric vehicle charging method applicable to European standard charging piles according to claim 3, characterized in that: The logic of abnormal analysis of current flow control information and generating reverse current detection index is as follows: In the detection window, real-time acquisition of forward current and reverse current ,in, is the forward current, which indicates the current from the charging pile to the electric vehicle battery. is the reverse current, which means the current flowing back from the electric vehicle battery to the charging station; Detection in each The change of current direction within the time interval defines the current direction change function. The current direction change function represents the instantaneous change of current from forward flow to reverse flow. The calculation expression is as follows: , where Represents the function of current direction change, which is the basis for analyzing the reverse flow trend. When , the system is in a stable state, the current is completely positive, and When , it indicates that the current has a tendency to change in the reverse direction; The current direction stability factor is introduced to indicate the degree of current direction stability. The calculation expression is as follows: , where is the current direction stability factor, is a very small positive value, is a parameter that adjusts the exponential scaling; Combining the instantaneous current direction change function and the current direction stability factor, the comprehensive intensity of the reverse flow is obtained, and the calculation expression is as follows: , where is the comprehensive strength of countercurrent, Indicates the start time of the detection window, Indicates the end time of the detection window. is the instantaneous reverse current, is a time variable, indicating that Instantaneous reverse current; Based on the instantaneous fluctuation of current, the reverse current fluctuation factor is defined to measure the fluctuation of reverse current in the detection window. The calculation expression is as follows: , where represents the countercurrent fluctuation factor; Based on the comprehensive strength of countercurrent , countercurrent fluctuation factor And the current direction change function Generate the backflow detection index, the calculation expression is as follows: , where is the backflow detection index, is the adjustment coefficient.

8. The method for charging an electric vehicle suitable for a European standard charging pile according to claim 3, characterized in that: The logic for abnormal analysis of electrical safety monitoring information and generating residual current index is as follows: In the detection window, real-time acquisition of electrical parameters in the charging system, including the instantaneous current input by the charging pile , the instantaneous current consumed by the vehicle battery , the instantaneous voltage output by the charging pile and the instantaneous impedance of the charging system ; Combined with the instantaneous current input from the charging pile and the instantaneous current consumed by the vehicle battery , calculate the residual current to reflect the current balance state in the system. The calculation expression is as follows: , where Indicates residual current; The residual current At every time The value under is compared with the residual current threshold preset by the system to generate the residual current abnormal factor. The calculation expression is as follows: , where is the residual current anomaly factor, is the residual current threshold preset by the system, is the system tolerance coefficient; Considering the cumulative effect of residual current during the entire detection window, the residual current abnormality factor Perform time series weighted integration to generate an overall weighted anomaly index. The calculation expression is as follows: , where represents the weighted anomaly index, Indicates the start time of the detection window. Indicates the end time of the detection window, where , is the time weighting function, is the time decay constant, which controls the decay rate of the weighting function; By weighted anomaly index Normalization is performed and amplified to a readable index range to finally generate the residual current index. The calculation expression is as follows: , where Represents the residual current index, is the system adjustment coefficient.

9. An electric vehicle charging system applicable to a European standard charging pile, used to implement an electric vehicle charging method applicable to a European standard charging pile as described in any one of claims 1 to 8, characterized in that: It includes data acquisition module, abnormal analysis and evaluation module, early warning and adaptive adjustment module, and charging monitoring and safety assurance module; The data acquisition module collects the various parameter information generated by the charging pile and the vehicle control unit during the power negotiation process in real time before charging begins, and transmits the relevant data to the monitoring system; The abnormality analysis and evaluation module performs abnormality analysis on the pre-processed data in the monitoring system, applies the trained machine learning model to intelligently evaluate the parameter information after abnormal processing, and judges whether the current power negotiation is stable based on the output results of the machine learning model; The early warning and adaptive adjustment module sends a real-time early warning signal to the charging pile and the vehicle if it determines that there is an unstable state in the current power negotiation process, indicating that there are potential risks in the power negotiation process. At the same time, the automatic system performs adaptive adjustments, and the charging pile and the vehicle control unit will try to renegotiate to obtain stable power parameters. The charging monitoring and safety assurance module allows the charging pile to provide power to the electric vehicle if the power negotiation process is determined to be in a stable state, while continuously monitoring various parameters of the power negotiation to ensure the safety of the electric vehicle charging process.

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