Charging control signal multi-stage filtering detection method and system, storage medium and electric vehicle

Abstract

This application discloses a multi-level filtering detection method, system, storage medium, and electric vehicle for charging control signals. The method includes sampling the charging control signal using an analog-to-digital converter to obtain raw sampled data; performing median filtering on the raw sampled data to obtain filtered signal values; dividing the data into intervals according to a preset threshold and converting the filtered signal values ​​into actual voltage values ​​using corresponding linear transformation relationships; determining the current state identifier based on the actual voltage value and the status of associated control pins through multi-condition judgment; if the state identifier is normal, detecting state changes, updating and recording them after confirmation; if the state identifier is abnormal, counting consecutive abnormalities, triggering protection when a threshold is reached, and resetting the count when normal operation is restored. This application can improve the anti-interference capability of charging control signals in complex electromagnetic environments, thereby improving the accuracy and response speed of state judgment, and ultimately enhancing the continuity of the charging control process and the reliability of system operation.

Description

Technical Field

[0001] This application relates to the field of electric vehicle charging technology, specifically to a multi-level filtering detection method, system, storage medium, and electric vehicle for charging control signals. Background Technology

[0002] With the rapid development of the electric vehicle industry, charging technology is constantly evolving towards higher power and higher efficiency. Megawatt Charging Systems (MCS), as a new generation of high-power charging technology, can achieve charging power up to megawatt levels, significantly shortening charging time and gradually becoming an important charging method for heavy-duty electric commercial vehicles, electric trucks, and high-end passenger vehicles. During MCS charging, the system needs to operate stably under high voltage and high current conditions. The reliability of communication between the charging pile and the vehicle, as well as the accuracy of control status detection, are crucial for ensuring the safety and continuity of the charging process.

[0003] In an MCS charging system, charging control signals (such as the Charge Enable (CE) signal) are crucial for characterizing the working state and control relationship between the charging station and the vehicle. Changes in their voltage amplitude reflect different charging stages and connection states. These control signals need to be acquired and processed in a complex electromagnetic environment during actual operation. When the MCS system is working, the high-frequency switching of power devices generates strong electromagnetic interference. Simultaneously, the contact impedance between the charging gun and the vehicle interface may experience instantaneous changes due to mechanical vibration. All of these factors can cause fluctuations, distortions, or short-term anomalies in the control signals, thus increasing the difficulty of determining the control state.

[0004] Currently, for the detection and processing of charging control signals, fixed-parameter filtering methods are typically used to smooth the acquired signals in order to reduce the impact of noise interference on the judgment results. For example, a low-pass filter with fixed coefficients is used to smooth the signal, or a mean filter is used to average the sampled data. These methods can achieve certain results in scenarios where the signal is relatively stable and the noise characteristics are simple.

[0005] However, existing charging control signal detection schemes have insufficient adaptability in terms of filtering and status judgment, making it difficult to achieve a reasonable balance between noise suppression, response speed and status recognition accuracy. Furthermore, they lack an effective mechanism for identifying short-term interference, which affects the continuity and reliability of the charging control process. Summary of the Invention

[0006] This application provides a multi-level filtering detection method, system, storage medium, and electric vehicle for charging control signals. It can improve the anti-interference capability of charging control signals in complex electromagnetic environments, thereby improving the accuracy of state judgment and response speed, and further enhancing the continuity of the charging control process and the reliability of system operation.

[0007] In a first aspect, embodiments of this application provide a multi-level filtering detection method for charging control signals, including: The charging control signal is sampled using an analog-to-digital converter to obtain the raw sampled data; The original sampled data is filtered using a median filtering method based on a sampling window to obtain the filtered signal value; The filtered signal value is divided into different intervals according to a preset threshold, and a corresponding linear transformation relationship is applied to each interval to convert the filtered signal value into an actual voltage value. Based on the actual voltage value and the status information of the associated control pin, a multi-condition judgment is performed on the working state corresponding to the charging control signal to determine the current status identifier. This includes: matching the actual voltage value with the voltage range in the status mapping table, and simultaneously obtaining the current status of the associated control pin; the associated control pin includes the SS3 pin; if the actual voltage value falls within a preset voltage range in the status mapping table and the current status of the associated control pin matches the status of the associated control pin corresponding to the preset voltage range, then a normal status identifier corresponding to the preset voltage range and the status of the associated control pin is output; if the actual voltage value does not fall within any preset voltage range, then an abnormal status identifier is output. When the current status identifier is the normal status identifier, it is detected whether the charging control status has changed. When a status change is detected, the status is confirmed, and the current status is updated and the status transition information is recorded after confirmation. When the current status is identified as an abnormal status, the system counts the number of consecutive abnormal statuses. When the number of abnormal statuses reaches a preset threshold, protection processing is triggered. The abnormality count is reset when the system returns to normal.

[0008] In the multi-level filtering detection method for charging control signals provided in this application embodiment, the step of dividing the filtered signal value into different intervals according to a preset threshold, and applying a corresponding linear transformation relationship to each interval to convert the filtered signal value into an actual voltage value includes: Based on the first threshold, the filtered signal value is divided into a first interval and a second interval; When the filtered signal value is within the first interval, the actual voltage value is calculated using the first linear transformation formula; When the filtered signal value is within the second interval, the actual voltage value is calculated using the second linear transformation formula.

[0009] In the multi-level filtering detection method for charging control signals provided in this application embodiment, the coefficients of the first linear transformation formula and the second linear transformation formula are obtained in advance based on the nonlinear characteristics of the front-end signal conditioning circuit.

[0010] In the multi-level filtering detection method for charging control signals provided in this application embodiment, the charging control state includes one or more of CE_STATE_A, B0, B0_AUX, B, B_AUX, C_AUX, EC_AUX, and E.

[0011] In the multi-level filtering detection method for charging control signals provided in this application embodiment, the step of detecting whether the charging control state has changed when the current state identifier is a normal state identifier, performing state confirmation when a state change is detected, updating the current state and recording state transition information after confirmation, includes: When the current status identifier is a normal status identifier, the normal status identifier is compared with the historical status identifier recorded in the previous cycle; If the normal status identifier is inconsistent with the historical status identifier, the status will not be updated immediately, and the confirmation window will be entered. Within the confirmation window, obtain the normal status indicator that is output multiple times consecutively; If the normal state identifier is output multiple times in a row and points to the same new state, the state transition is confirmed to be valid, the current state is updated to the new state, and the state transition information is recorded.

[0012] In the multi-level filtering detection method for charging control signals provided in this application embodiment, the step of counting multiple consecutive abnormal state identifiers when the current state identifier is an abnormal state identifier, triggering protection processing when the number of abnormal identifiers reaches a preset threshold, and resetting the abnormal count when the system returns to normal includes: Set the exception counter to an initial value of zero; Each time the current status identifier is an abnormal status identifier, the abnormality counter is incremented by one count unit; Each time the current status identifier is a normal status identifier, the abnormality counter is reduced by one count unit or cleared to zero; When the count value of the anomaly counter reaches a preset threshold, an anomaly alert or protection action is triggered. When the system returns to normal, the fault counter is reset to zero.

[0013] In the multi-level filtering detection method for charging control signals provided in this application embodiment, before determining the current state identifier by performing multi-condition judgment on the operating state corresponding to the charging control signal based on the actual voltage value and the state information of the associated control pin, the method further includes: A preset state mapping table is provided, which records the correspondence between multiple voltage ranges, multiple associated control pin states, and multiple charging control states.

[0014] Secondly, embodiments of this application provide a multi-level filtering and detection system for charging control signals, comprising: The signal acquisition module is used to sample the charging control signal through an analog-to-digital converter to obtain the raw sampled data; The primary filtering module is used to filter the original sampled data using a median filtering method based on a sampling window to obtain the filtered signal value; The segmented calibration module is used to divide the filtered signal value into different intervals according to a preset threshold, and to apply a corresponding linear transformation relationship to each interval to convert the filtered signal value into an actual voltage value. The status judgment module is used to perform multi-condition judgment on the working state corresponding to the charging control signal based on the actual voltage value and the status information of the associated control pin, so as to determine the current status identifier. This includes: matching the actual voltage value with the voltage range in the status mapping table, and simultaneously obtaining the current status of the associated control pin; the associated control pin includes the SS3 pin; if the actual voltage value falls within a preset voltage range in the status mapping table and the current status of the associated control pin matches the status of the associated control pin corresponding to the preset voltage range, then a normal status identifier corresponding to the preset voltage range and the associated control pin status is output; if the actual voltage value does not fall within any preset voltage range, then an abnormal status identifier is output. The status confirmation module is used to detect whether the charging control status has changed when the current status identifier is the normal status identifier. When a status change is detected, the status is confirmed, and the current status is updated and the status transition information is recorded after confirmation. The exception handling module is used to count the number of consecutive exceptions when the current status is an exception status. When the number of exceptions reaches a preset threshold, protection processing is triggered, and the exception count is reset when the system returns to normal.

[0015] Thirdly, this application provides a storage medium storing a plurality of instructions adapted for loading by a processor to execute the multi-level filtering detection method for charging control signals described in any of the preceding claims.

[0016] Fourthly, this application provides an electric vehicle, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-level filtering detection method for charging control signals described in any of the above claims.

[0017] In summary, the multi-level filtering detection method for charging control signals provided in this application includes sampling the charging control signal using an analog-to-digital converter to obtain raw sampled data; filtering the raw sampled data using a median filtering method based on a sampling window to obtain a filtered signal value; dividing the filtered signal value into different intervals according to a preset threshold, and applying a corresponding linear transformation relationship to each interval to convert the filtered signal value into an actual voltage value; based on the actual voltage value and combined with the status information of the associated control pin, performing multi-condition judgment on the working state corresponding to the charging control signal to determine the current state identifier; when the current state identifier is a normal state identifier, detecting whether the charging control state has changed; when a state change is detected, performing state confirmation, updating the current state after confirmation, and recording state transition information; when the current state identifier is an abnormal state identifier, counting the number of consecutive abnormal state identifiers; triggering protection processing when the number of abnormal identifiers reaches a preset threshold; and resetting the abnormal count when the system returns to normal. This application embodiment employs median filtering for primary processing of the raw sampled data, effectively suppressing impulse noise and random interference, providing a stable input signal for subsequent voltage conversion. Through segmented calibration, corresponding linear transformation relationships are applied to different signal ranges, improving the accuracy of actual voltage value restoration and laying a solid data foundation for state judgment. Furthermore, combining the state information of associated control pins for multi-condition state judgment enables the differentiation of various charging control states, improving the accuracy of state identification. A state confirmation mechanism avoids frequent state jumps caused by instantaneous signal fluctuations, ensuring stable and reliable state switching. Anomaly counting and protection processing distinguishes between transient interference and persistent faults, preventing false alarms from affecting system operation. Therefore, this application embodiment enhances the anti-interference capability of charging control signals in complex electromagnetic environments, improves the accuracy and response speed of state judgment, and thus enhances the continuity of the charging control process and the reliability of system operation. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram illustrating an application scenario of the multi-level filtering and detection method for charging control signals provided in this application embodiment.

[0020] Figure 2 This is a flowchart illustrating the multi-level filtering and detection method for charging control signals provided in the embodiments of this application.

[0021] Figure 3 This is a schematic diagram of the structure of the multi-stage filtering and detection system for charging control signals provided in the embodiments of this application.

[0022] Figure 4 This is a schematic diagram of the structure of an electric vehicle provided in an embodiment of this application. Detailed Implementation

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of systems and methods consistent with some aspects of this application as detailed in the appended claims.

[0024] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0025] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0026] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0027] In the description of this application, it should be noted that the terms "upper," "lower," "left," "right," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and for simplifying the description, and do not indicate or imply that the system or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. In addition, terms such as "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0028] Existing charging control signal detection schemes have insufficient adaptability in terms of filtering and status judgment, making it difficult to achieve a reasonable balance between noise suppression, response speed and status recognition accuracy. Furthermore, they lack an effective mechanism for identifying short-term interference, which affects the continuity and reliability of the charging control process.

[0029] Based on this, embodiments of this application provide a multi-level filtering detection method, system, storage medium, and electric vehicle for charging control signals. Specifically, the multi-level filtering detection system for charging control signals can be integrated into an electric vehicle.

[0030] For example, such as Figure 1 As shown, an electric vehicle can sample the charging control signal through an analog-to-digital converter to obtain raw sampled data. The raw sampled data is then filtered using a median filter based on a sampling window to obtain a filtered signal value. The filtered signal value is divided into different intervals according to a preset threshold, and each interval is converted into an actual voltage value using a corresponding linear transformation relationship. Based on the actual voltage value and the status information of the associated control pins, a multi-condition judgment is performed on the operating state corresponding to the charging control signal to determine the current state identifier. When the current state identifier is a normal state identifier, the charging control state is checked for changes. If a change is detected, the state is confirmed, and the current state is updated and the state transition information is recorded. When the current state identifier is an abnormal state identifier, the number of consecutive abnormal state identifiers is counted. When the number of abnormal identifiers reaches a preset threshold, protection processing is triggered, and the abnormal count is reset when the system returns to normal.

[0031] The technical solutions shown in this application will be described in detail below through specific embodiments. It should be noted that the order of description of the following embodiments is not intended to limit the priority of the embodiments.

[0032] Please see Figure 2 , Figure 2 This is a flowchart illustrating the multi-stage filtering detection method for charging control signals provided in this application embodiment. The specific flow of this multi-stage filtering detection method for charging control signals can be as follows: 101. The charging control signal is sampled by an analog-to-digital converter to obtain the original sampled data.

[0033] In this embodiment, the charging control signal is specifically the CE signal in the MCS charging interface. This CE signal is used to characterize the connection and operating status between the charging pile and the vehicle. In the MCS charging system, due to factors such as high-frequency switching noise from power devices, electromagnetic interference, and mechanical vibration between the charging gun and the vehicle interface, the CE signal is easily affected by interference, resulting in fluctuations or distortion.

[0034] In this embodiment, the CE signal can be periodically sampled using an analog-to-digital converter to convert the analog voltage signal into a digital signal and obtain the original sampled data.

[0035] For example, in a specific application scenario, when an electric vehicle completes a physical connection with an MCS charging station and enters the handshake phase, the system samples the CE signal at a sampling rate of 1kHz to obtain continuous raw sampled data.

[0036] 102. The original sampled data is filtered using a median filtering method based on the sampling window to obtain the filtered signal value.

[0037] Since the original sampled data may contain impulse noise and random interference, this embodiment uses median filtering for primary filtering. Specifically, a fixed-length sampling window can be set, and then the continuous original sampled data within the sampling window can be sorted. The median is extracted as the filtered output, and the filtered signal value is obtained, denoted as ce_filtered.

[0038] For example, in a practical application embodiment, the sampling window length is set to 5 sampling points. When the continuous raw sampling data collected within the sampling window is [512, 518, 523, 517, 245], where 245 is impulse noise, after sorting, we get [245, 512, 517, 518, 523], and the median of 517 is taken as the filtered output. In this way, impulse interference can be effectively suppressed while preserving the signal edge characteristics, avoiding excessive smoothing of voltage changes at state transition moments.

[0039] 103. Divide the filtered signal value into different intervals according to the preset threshold, and apply the corresponding linear transformation relationship to each interval to convert the filtered signal value into the actual voltage value.

[0040] Understandably, since there may be a non-linear relationship between the digital value of the analog-to-digital converter and the actual voltage across the entire range, this embodiment employs a segmented calibration method to improve voltage conversion accuracy. Specifically, the filtered signal value can be divided into different intervals according to a preset threshold, and the filtered signal value in each interval can be converted into an actual voltage value (millivolt level) using a corresponding linear conversion relationship, denoted as ceVolt_mV.

[0041] In some embodiments, the filtered signal value can be divided into a first interval and a second interval according to a first threshold; when the filtered signal value is in the first interval, the actual voltage value is calculated using a first linear transformation formula; when the filtered signal value is in the second interval, the actual voltage value is calculated using a second linear transformation formula.

[0042] The coefficients of the first linear transformation formula and the second linear transformation formula are obtained in advance based on the nonlinear characteristics of the front-end signal conditioning circuit.

[0043] In one specific embodiment, the first threshold is set to 650. When ce_filtered ≥ 650, the filtered signal value is in the first interval, and the actual voltage value is calculated using Formula 1: ceVolt_mV = (ce_filtered × 1.9) - 240; When ce_filtered < 650, the filtered signal value is in the second interval. The actual voltage value is calculated using Formula 2: ceVolt_mV = (ce_filtered × 640) ÷ 525.

[0044] For example, when the filtered signal value is 700, the actual voltage value calculated using Formula 1 is (700 × 1.9) - 240 = 1090mV; when the filtered signal value is 500, the actual voltage value calculated using Formula 2 is (500 × 640) ÷ 525 ≈ 609.5mV. Through segmented calibration, the actual voltage value can be more accurately restored, providing a reliable data foundation for subsequent state judgment.

[0045] 104. Based on the actual voltage value and the status information of the associated control pin, perform multi-condition judgment on the working state corresponding to the charging control signal to determine the current status identifier.

[0046] In an MCS charging system, the voltage of the CE signal is not the sole determinant of its state; its combination with the SS3 pin state encodes the physical connection state and handshake phase information of the charging interface. This embodiment employs a multi-condition judgment method, combining the actual voltage value with the state information of the associated control pins, to accurately identify the current operating state.

[0047] Specifically, a state mapping table can be preset first, recording the correspondence between multiple voltage ranges, multiple associated control pin states, and multiple charging control states. Then, the actual voltage value is matched with the voltage range in the state mapping table, while the current state of the associated control pin is obtained. If the actual voltage value falls within a preset voltage range in the state mapping table and the current state of the associated control pin matches the state of the associated control pin corresponding to the preset voltage range, a normal state flag corresponding to the preset voltage range and the state of the associated control pin is output. If the actual voltage value does not fall within any preset voltage range, an abnormal state flag is output.

[0048] In this embodiment, the associated control pin includes the SS3 pin, and the charging control state includes one or more of CE_STATE_A, B0, B0_AUX, B, B_AUX, C_AUX, EC_AUX, and E.

[0049] The state mapping table is as follows:

[0050] For example, in a practical application scenario, when the converted actual voltage value is 2.8V and the SS3 pin is in the OPEN state, the system matches the state mapping table and determines the current state as B0; when the SS3 pin state corresponding to the same voltage value of 2.8V is CLOSED, it is determined to be in the B_AUX state. If the actual voltage value is 0.8V, which does not fall within any preset voltage range, an abnormal state indicator is output. Through this multi-condition judgment method, the control state under different charging stages can be accurately distinguished, avoiding misidentification caused by single-dimensional judgment.

[0051] 105. When the current status is marked as normal, check whether the charging control status has changed. When a status change is detected, confirm the status, update the current status and record the status transition information after confirmation.

[0052] Since residual noise may still exist in the CE signal after primary filtering, the state judgment result may switch back and forth instantaneously within a short period of time. If the state is updated directly, it will affect the system stability. To address this, this application introduces a state verification mechanism to verify state changes and ensure stable and reliable state switching.

[0053] Specifically, when the current status identifier is a normal status identifier, the normal status identifier is compared with the historical status identifier recorded in the previous cycle. If the normal status identifier is inconsistent with the historical status identifier, the status is not updated immediately, and the process enters the confirmation window. In the confirmation window, the normal status identifiers output multiple times consecutively are obtained. If the normal status identifiers output multiple times consecutively all point to the same new status, the status switch is confirmed to be valid, the current status is updated to the new status, and the status transition information is recorded.

[0054] For example, in a specific application embodiment, the confirmation window is set to three consecutive judgments. The system currently records the state as B0. When state B is output for the first time in step 104, the system does not update immediately but enters the confirmation window. If the subsequent two consecutive judgments are both state B, that is, state B is output three times consecutively, the state switch is confirmed to be valid, the current state is updated to B, and the state transition information is recorded, including the state before the state switch (B0), the state after the state switch (B), the switching time, and the corresponding actual voltage value. If a judgment result inconsistent with state B appears in the confirmation window, the confirmation fails, the system does not update the state, and the state confirmation window is reset. Through this mechanism, state glitches with a duration shorter than the confirmation window can be effectively filtered out, ensuring the stability and reliability of state switching.

[0055] 106. When the current status is marked as an abnormal status, count the number of consecutive abnormal statuses. When the number of abnormal statuses reaches a preset threshold, trigger protection processing. Reset the abnormal count when the system returns to normal.

[0056] In MCS charging scenarios, the electromagnetic environment is complex, and occasional transient interference can lead to single abnormal states. If each abnormality immediately triggers protection actions, the system's availability will be reduced due to frequent false protection. To address this, this application introduces an anomaly counting mechanism to distinguish between transient interference and persistent faults.

[0057] In some embodiments, an exception counter can be initially set to zero; each time the current status identifier is an exception status identifier, the exception counter is incremented by one count unit; each time the current status identifier is a normal status identifier, the exception counter is decremented by one count unit or cleared to zero; when the count value of the exception counter reaches a preset threshold, an exception prompt or protection action is triggered; when the system returns to normal status, the exception counter is reset to zero.

[0058] For example, in one specific embodiment, the preset threshold is set to 3 times. When the system consecutively identifies an abnormal state three times, the abnormal counter reaches the threshold, triggering protection processing, such as reporting the abnormal state to the upper-level control system, stopping the charging process, recording a fault log, or illuminating a fault indicator light. When the system returns to normal (i.e., a normal state indicator is output in step 104), the abnormal counter is reset to zero. If the system returns to normal after only one or two abnormal state indicators, the abnormal counter is reduced or cleared accordingly, and no protection action is triggered. Through this mechanism, the system can respond to real faults in a timely manner and maintain high operational availability in complex electromagnetic environments.

[0059] In summary, the multi-level filtering detection method for charging control signals provided in this application includes sampling the charging control signal using an analog-to-digital converter to obtain raw sampled data; filtering the raw sampled data using a median filtering method based on a sampling window to obtain filtered signal values; dividing the filtered signal values ​​into different intervals according to a preset threshold, and applying a corresponding linear transformation relationship to each interval to convert the filtered signal values ​​into actual voltage values; based on the actual voltage values ​​and combined with the status information of the associated control pins, performing multi-condition judgments on the working state corresponding to the charging control signal to determine the current state identifier; when the current state identifier is a normal state identifier, detecting whether the charging control state has changed; when a state change is detected, performing state confirmation, updating the current state after confirmation, and recording state transition information; when the current state identifier is an abnormal state identifier, counting the number of consecutive abnormal state identifiers; triggering protection processing when the number of abnormal identifiers reaches a preset threshold; and resetting the abnormal count when the system returns to normal.

[0060] This application embodiment employs median filtering for primary processing of the raw sampled data, effectively suppressing impulse noise and random interference, providing a stable input signal for subsequent voltage conversion. Through segmented calibration, corresponding linear transformation relationships are applied to different signal ranges, improving the accuracy of actual voltage value restoration and laying a solid data foundation for state judgment. Furthermore, combining the state information of associated control pins for multi-condition state judgment enables the differentiation of various charging control states, improving the accuracy of state identification. A state confirmation mechanism avoids frequent state jumps caused by instantaneous signal fluctuations, ensuring stable and reliable state switching. Anomaly counting and protection processing distinguishes between transient interference and persistent faults, preventing false alarms from affecting system operation. Therefore, this application embodiment enhances the anti-interference capability of charging control signals in complex electromagnetic environments, improves the accuracy and response speed of state judgment, and thus enhances the continuity of the charging control process and the reliability of system operation.

[0061] To facilitate better implementation of the multi-stage filtering detection method for charging control signals provided in this application, this application also provides a multi-stage filtering detection system for charging control signals. The meanings of the terms used are the same as in the above-described multi-stage filtering detection method for charging control signals, and specific implementation details can be found in the descriptions within the method embodiments.

[0062] Please see Figure 3 , Figure 3 This is a schematic diagram of the multi-stage filtering and detection system for charging control signals provided in an embodiment of this application. The multi-stage filtering and detection system for charging control signals may include a signal acquisition module 201, a primary filtering module 202, a segmented calibration module 203, a status judgment module 204, a status confirmation module 205, and an anomaly handling module 206. Signal acquisition module 201 is used to sample the charging control signal through an analog-to-digital converter to obtain raw sampled data; The primary filtering module 202 is used to filter the original sampled data using a median filtering method based on the sampling window to obtain the filtered signal value; The segmented calibration module 203 is used to divide the filtered signal value into different intervals according to a preset threshold, and to apply a corresponding linear transformation relationship to each interval to convert the filtered signal value into an actual voltage value. The status judgment module 204 is used to perform multi-condition judgment on the working status corresponding to the charging control signal based on the actual voltage value and the status information of the associated control pin, so as to determine the current status identifier. The status confirmation module 205 is used to detect whether the charging control status has changed when the current status identifier is the normal status identifier. When a status change is detected, the status is confirmed, and the current status is updated and the status transition information is recorded after confirmation. The exception handling module 206 is used to count the number of consecutive exceptions when the current status is an exception status. When the number of exceptions reaches a preset threshold, protection processing is triggered, and the exception count is reset when the system returns to normal.

[0063] For specific implementation methods of each of the above units, please refer to the embodiments of the above-described multi-level filtering detection method for charging control signals, which will not be repeated here.

[0064] In summary, the multi-level filtering and detection system for charging control signals provided in this application embodiment can obtain raw sampling data by sampling the charging control signal through an analog-to-digital converter via a signal acquisition module 201; the primary filtering module 202 filters the raw sampling data using a median filtering method based on a sampling window to obtain filtered signal values; the segmented calibration module 203 divides the filtered signal values ​​into different intervals according to a preset threshold, and applies a corresponding linear transformation relationship to each interval to convert the filtered signal values ​​into actual voltage values; the state judgment module 204 performs multi-condition judgment on the working state corresponding to the charging control signal based on the actual voltage value and the state information of the associated control pin to determine the current state identifier; the state confirmation module 205 detects whether the charging control state has changed when the current state identifier is a normal state identifier, and when a state change is detected, it performs state confirmation, updates the current state after confirmation, and records the state transition information; the anomaly handling module 206 counts the number of consecutive abnormal state identifiers when the current state identifier is an abnormal state identifier, triggers protection processing when the number of abnormal identifiers reaches a preset threshold, and resets the anomaly count when the system returns to normal. This application embodiment employs median filtering for primary processing of the raw sampled data, effectively suppressing impulse noise and random interference, providing a stable input signal for subsequent voltage conversion. Through segmented calibration, corresponding linear transformation relationships are applied to different signal ranges, improving the accuracy of actual voltage value restoration and laying a solid data foundation for state judgment. Furthermore, combining the state information of associated control pins for multi-condition state judgment enables the differentiation of various charging control states, improving the accuracy of state identification. A state confirmation mechanism avoids frequent state jumps caused by instantaneous signal fluctuations, ensuring stable and reliable state switching. Anomaly counting and protection processing distinguishes between transient interference and persistent faults, preventing false alarms from affecting system operation. Therefore, this application embodiment enhances the anti-interference capability of charging control signals in complex electromagnetic environments, improves the accuracy and response speed of state judgment, and thus enhances the continuity of the charging control process and the reliability of system operation.

[0065] This application also provides an electric vehicle, which may integrate the multi-level filtering and detection system for charging control signals according to this application, such as... Figure 4 As shown, it illustrates a structural schematic diagram of an electric vehicle according to an embodiment of this application. Specifically: The electric vehicle may include components such as a processor 301 with one or more processing cores and a memory 302 with one or more computer-readable storage media. Those skilled in the art will understand that... Figure 4The electric vehicle structure shown does not constitute a limitation on the electric vehicle and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. Wherein: The processor 301 is the control center of the electric vehicle. It connects various parts of the electric vehicle via various interfaces and lines. By running or executing software programs stored in the memory 302 and / or this application, and by calling data stored in the memory 302, it performs various functions and processes data of the electric vehicle, thereby providing overall monitoring of the electric vehicle. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operation of the storage medium, user interface, and application programs, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.

[0066] The memory 302 can be used to store software programs and this application. The processor 301 executes various functional applications and data processing by running the software programs and this application stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store applications required for operating the storage medium and at least one function; the data storage area may store data created based on the use of the electric vehicle. In addition, the memory 302 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0067] Although not shown, the electric vehicle may also include a display unit, an input unit, and a power supply, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electric vehicle loads the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 runs the application programs stored in the memory 302 to realize various functions, as follows: The charging control signal is sampled using an analog-to-digital converter to obtain the raw sampled data; The original sampled data is filtered using a median filtering method based on the sampling window to obtain the filtered signal value; The filtered signal value is divided into different intervals according to a preset threshold, and the corresponding linear transformation relationship is applied to each interval to convert the filtered signal value into the actual voltage value. Based on the actual voltage value and combined with the status information of the associated control pin, the working state corresponding to the charging control signal is judged under multiple conditions to determine the current status identifier. When the current status is normal, check whether the charging control status has changed. When a status change is detected, confirm the status, update the current status and record the status transition information after confirmation. When the current status is marked as abnormal, the system counts the number of consecutive abnormal statuses. When the number of abnormal statuses reaches a preset threshold, protection processing is triggered. The abnormality count is reset when the system returns to normal.

[0068] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0069] Therefore, embodiments of this application provide a storage medium storing a plurality of instructions that can be loaded by a processor to execute steps in any of the methods provided in embodiments of this application. For example, the instructions can execute the following steps: The charging control signal is sampled using an analog-to-digital converter to obtain the raw sampled data; The original sampled data is filtered using a median filtering method based on the sampling window to obtain the filtered signal value; The filtered signal value is divided into different intervals according to a preset threshold, and the corresponding linear transformation relationship is applied to each interval to convert the filtered signal value into the actual voltage value. Based on the actual voltage value and combined with the status information of the associated control pin, the working state corresponding to the charging control signal is judged under multiple conditions to determine the current status identifier. When the current status is normal, check whether the charging control status has changed. When a status change is detected, confirm the status, update the current status and record the status transition information after confirmation. When the current status is marked as abnormal, the system counts the number of consecutive abnormal statuses. When the number of abnormal statuses reaches a preset threshold, protection processing is triggered. The abnormality count is reset when the system returns to normal.

[0070] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0071] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0072] Since the instructions stored in the storage medium can execute the steps of any method provided in the embodiments of this application, the beneficial effects that any method provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0073] The above provides a detailed description of the multi-level filtering detection method, system, storage medium, and electric vehicle for charging control signals provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of this application. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for multi-stage filtering and detection of charging control signals, characterized in that, include: The charging control signal is sampled using an analog-to-digital converter to obtain the raw sampled data; The original sampled data is filtered using a median filtering method based on a sampling window to obtain the filtered signal value; The filtered signal value is divided into different intervals according to a preset threshold, and a corresponding linear transformation relationship is applied to each interval to convert the filtered signal value into an actual voltage value. Based on the actual voltage value and the status information of the associated control pin, a multi-condition judgment is performed on the working state corresponding to the charging control signal to determine the current status identifier. This includes: matching the actual voltage value with the voltage range in the status mapping table, and simultaneously obtaining the current status of the associated control pin; the associated control pin includes the SS3 pin; if the actual voltage value falls within a preset voltage range in the status mapping table and the current status of the associated control pin matches the status of the associated control pin corresponding to the preset voltage range, then a normal status identifier corresponding to the preset voltage range and the status of the associated control pin is output; if the actual voltage value does not fall within any preset voltage range, then an abnormal status identifier is output. When the current status identifier is the normal status identifier, it is detected whether the charging control status has changed. When a status change is detected, the status is confirmed, and the current status is updated and the status transition information is recorded after confirmation. When the current status is identified as an abnormal status, the system counts the number of consecutive abnormal statuses. When the number of abnormal statuses reaches a preset threshold, protection processing is triggered. The abnormality count is reset when the system returns to normal.

2. The multi-stage filtering and detection method for charging control signals as described in claim 1, characterized in that, The step of dividing the filtered signal value into different intervals according to a preset threshold, and applying a corresponding linear transformation relationship to each interval to convert the filtered signal value into an actual voltage value includes: Based on the first threshold, the filtered signal value is divided into a first interval and a second interval; When the filtered signal value is within the first interval, the actual voltage value is calculated using the first linear transformation formula; When the filtered signal value is within the second interval, the actual voltage value is calculated using the second linear transformation formula.

3. The multi-stage filtering and detection method for charging control signals as described in claim 2, characterized in that, The coefficients of the first linear transformation formula and the second linear transformation formula are obtained in advance based on the nonlinear characteristics of the front-end signal conditioning circuit.

4. The multi-stage filtering and detection method for charging control signals as described in claim 1, characterized in that, The charging control state includes one or more of CE_STATE_A, B0, B0_AUX, B, B_AUX, C_AUX, EC_AUX, and E.

5. The multi-stage filtering and detection method for charging control signals as described in claim 1, characterized in that, When the current state identifier is a normal state identifier, the system detects whether the charging control state has changed. When a state change is detected, it confirms the state, updates the current state, and records the state transition information, including: When the current status identifier is a normal status identifier, the normal status identifier is compared with the historical status identifier recorded in the previous cycle; If the normal status identifier is inconsistent with the historical status identifier, the status will not be updated immediately, and the confirmation window will be entered. Within the confirmation window, obtain the normal status indicator that is output multiple times consecutively; If the normal state identifier is output multiple times in a row and points to the same new state, the state transition is confirmed to be valid, the current state is updated to the new state, and the state transition information is recorded.

6. The multi-stage filtering and detection method for charging control signals as described in claim 1, characterized in that, When the current state identifier is an abnormal state identifier, the system counts the number of consecutive abnormal state identifiers. When the number of abnormal identifiers reaches a preset threshold, protection processing is triggered. When the system returns to normal, the abnormal count is reset, including: Set the exception counter to an initial value of zero; Each time the current status identifier is an abnormal status identifier, the abnormality counter is incremented by one count unit; Each time the current status identifier is a normal status identifier, the abnormality counter is reduced by one count unit or cleared to zero; When the count value of the anomaly counter reaches a preset threshold, an anomaly alert or protection action is triggered. When the system returns to normal, the fault counter is reset to zero.

7. The multi-stage filtering and detection method for charging control signals as described in claim 1, characterized in that, Before determining the current state identifier by performing multi-condition judgment on the operating state corresponding to the charging control signal based on the actual voltage value and the status information of the associated control pin, the method further includes: A preset state mapping table is provided, which records the correspondence between multiple voltage ranges, multiple associated control pin states, and multiple charging control states.

8. A multi-stage filtering and detection system for charging control signals, characterized in that, include: The signal acquisition module is used to sample the charging control signal through an analog-to-digital converter to obtain raw sampled data; The primary filtering module is used to filter the original sampled data using a median filtering method based on a sampling window to obtain the filtered signal value; The segmented calibration module is used to divide the filtered signal value into different intervals according to a preset threshold, and to apply a corresponding linear transformation relationship to each interval to convert the filtered signal value into an actual voltage value. The status judgment module is used to perform multi-condition judgment on the working state corresponding to the charging control signal based on the actual voltage value and the status information of the associated control pin, so as to determine the current status identifier. This includes: matching the actual voltage value with the voltage range in the status mapping table, and simultaneously obtaining the current status of the associated control pin; the associated control pin includes the SS3 pin; if the actual voltage value falls within a preset voltage range in the status mapping table and the current status of the associated control pin matches the status of the associated control pin corresponding to the preset voltage range, then a normal status identifier corresponding to the preset voltage range and the associated control pin status is output; if the actual voltage value does not fall within any preset voltage range, then an abnormal status identifier is output. The status confirmation module is used to detect whether the charging control status has changed when the current status identifier is the normal status identifier. When a status change is detected, the status is confirmed, and the current status is updated and the status transition information is recorded after confirmation. The exception handling module is used to count the number of consecutive exceptions when the current status is an exception status. When the number of exceptions reaches a preset threshold, protection processing is triggered, and the exception count is reset when the system returns to normal.

9. A storage medium, characterized in that, The storage medium stores multiple instructions, which are adapted for loading by a processor to execute the multi-level filtering and detection method for charging control signals according to any one of claims 1-7.

10. An electric vehicle, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-level filtering detection method for charging control signals as described in any one of claims 1-7.

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