V2G intelligent bidirectional charging and discharging pile state evaluation method and system

Abstract

The invention relates to the field of V2G charging pile detection, in particular to a V2G intelligent bidirectional charging and discharging pile state evaluation method and system, and the method comprises the steps: data collection: obtaining the type of equipment connected to a charging pile, and outputting a charging state evaluation instruction or a discharging state evaluation instruction according to the type of the equipment; a discharge state evaluation step of comparing and outputting a discharge state abnormal condition according to the actual discharge capacity and the predicted discharge capacity; a charging state evaluation step of comparing and outputting a charging state abnormal condition according to the actual received electric quantity and the predicted received electric quantity; and a fault type judgment step: judging to obtain the fault type, and performing accurate overhaul positioning by intelligently identifying the fault type of the charging pile, thereby effectively reducing the misjudgment rate, improving the state evaluation efficiency and reducing the maintenance cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of V2G charging pile detection, in particular to a V2G intelligent bidirectional charging and discharging pile state evaluation method and system. BACKGROUND

[0002] With the popularization of electric vehicles, V2G (Vehicle-to-Grid) technology has gradually become an important part of the smart grid. V2G intelligent bidirectional charging and discharging pile as a key device to realize the energy flow between electric vehicles and the grid, its state evaluation is crucial to ensure the stable operation of the charging pile and the safety of the grid. In the working process of the V2G charging pile, the discharging state involves the conversion of electric energy from the external power supply through the pile and then input into the vehicle battery, while the charging state involves the reverse output of the vehicle battery's electric energy through the pile for use by other devices or feedback to the grid. However, the existing V2G charging pile state evaluation method has many shortcomings, such as lack of systematicness and accuracy in data collection, discharging state evaluation, charging state evaluation, and fault type judgment, which leads to the inability to comprehensively and accurately evaluate the working state of the charging pile, thereby affecting the reliability of the charging pile and the stability of the grid.

[0003] Therefore, in order to solve the above problems, the present application provides a V2G intelligent bidirectional charging and discharging pile state evaluation method and system. SUMMARY

[0004] In view of the deficiencies of the prior art, the purpose of the present application is to provide a V2G intelligent bidirectional charging and discharging pile state evaluation method and system.

[0005] To achieve the above purpose, the present application provides the following technical solutions: A V2G intelligent bidirectional charging and discharging pile state evaluation method, comprising the following steps: A data collection step, obtaining the type of equipment connected to the charging pile, and outputting a charging state evaluation instruction or a discharging state evaluation instruction according to the type of equipment; A discharging state evaluation step, when the connected equipment is of the load type, obtaining the load data and load type of the load through the load communication protocol, calculating the predicted discharging capacity of the charging pile when the load is fully charged according to the load data, measuring the actual discharging capacity of the charging pile, and outputting the discharging state abnormality according to the comparison of the actual discharging capacity and the predicted discharging capacity; A charging state evaluation step, when the connected equipment is of the battery type, obtaining the battery data through the battery communication protocol, calculating the predicted receiving capacity of the charging pile according to the current energy storage capacity of the battery combined with the battery data, measuring the actual discharging capacity of the charging pile, and outputting the charging state abnormality according to the comparison of the actual receiving capacity and the predicted receiving capacity; The fault type judgment step, when the charging abnormality and the discharging abnormality are simultaneously received, judges the fault type according to the charging state evaluation result and the discharging state evaluation result through a fault feature evaluation strategy.

[0006] As a further improvement of the application, the discharging state evaluation step further comprises that the load data comprises a load rated power and a current demand power, a corresponding historical charging loss rate of the charging pile is called, a predicted discharging amount is calculated according to the historical charging loss in combination with the rated power and the current demand power, a discharging amount difference between the actual discharging amount and the predicted discharging amount is compared with a size of a preset discharging deviation threshold value, and a discharging abnormality result is output when the discharging amount difference exceeds the discharging deviation threshold value.

[0007] As a further improvement of the application, the charging state evaluation step further comprises that the battery data comprises a current SOC value, a target SOC value and a battery capacity of the battery, a corresponding historical electric energy conversion loss rate of the charging pile is called, a predicted received electric energy is calculated according to the historical electric energy conversion loss rate in combination with the current SOC value and the target SOC value, a received electric energy difference between the actual received electric energy and the predicted received electric energy is compared with a size of a preset charging deviation threshold value, and a data charging result is abnormal when the received electric energy difference exceeds the charging deviation threshold value.

[0008] As a further improvement of the application, the fault feature evaluation strategy comprises that a discharging fault feature value, a charging fault feature value and a common fault feature value are respectively calculated according to the load data, the battery data and the charging pile parameter data, and a final fault type is output according to a comparison result of the discharging fault feature value, the charging fault feature value and the common fault feature value with a size of a preset first threshold value and a second threshold value.

[0009] As a further improvement of the application, the charging fault feature value calculation is configured with a charging fault feature value calculation model, the charging fault feature value calculation model comprises that an electric energy deviation is nonlinearly processed, an identifiable property of an abnormal charging state is amplified, a situation of battery pack heat dissipation abnormality is reflected through a temperature change rate parameter, an accumulated effect caused by power insufficiency is calculated by using an exponential function, and the charging fault feature value is calculated by a way of weakening communication interference by replacing an original Boolean value.

[0010] As a further improvement of the application, the discharging fault feature value calculation is configured with a discharging fault feature value calculation model, the discharging fault feature value calculation model comprises that a calculation range of a power difference is constrained through a hyperbolic tangent function calculation of a power grid frequency fluctuation term in combination with the load data, and the discharging fault feature value is calculated by introducing a bus voltage deviation ratio.

[0011] As a further improvement of the present application, the common fault characteristic value calculation configuration is provided with a common fault characteristic value calculation model, which includes: weighting and evaluating the high-temperature risk of the charging pile by the battery temperature value, and constructing an aging factor by the number of charge and discharge cycles and the health degree of the MCU, and then calculating the common fault characteristic value by the cumulative effect of the voltage deviation from the rated value, the high-temperature risk evaluation weight, and the aging factor.

[0012] As a further improvement of the present application, the fault type includes charging state fault, discharging state fault, and charging and discharging common fault, and a first threshold value and a second threshold value are set to represent the fault characteristic degree, when the charging fault characteristic value is greater than the preset first threshold value, and the common fault characteristic value is greater than the second threshold value and less than the first threshold value, and the discharging fault characteristic value is less than the second threshold value, the output result is charging state fault; when the discharging fault characteristic value is greater than the preset first threshold value, and the common fault characteristic value is greater than the second threshold value and less than the first threshold value, and the charging fault characteristic value is less than the second threshold value, the output result is discharging state fault; when the common fault characteristic value is greater than the first threshold value, and the charging fault characteristic value and the discharging fault characteristic value are less than the second threshold value, the output result is charging and discharging common state fault.

[0013] A V2G intelligent bidirectional charging and discharging pile state evaluation system, comprising: A data acquisition module acquires the type of equipment connected to the charging pile, and outputs a charging state evaluation instruction or a discharging state evaluation instruction according to the type of equipment; A discharging state evaluation module, when the connected equipment is of the load type, obtains the load data and load type of the load through the load communication protocol, calls the corresponding historical charging loss rate of the charging pile, calculates the predicted discharge amount of the charging pile when the load is fully charged according to the load data and the historical charging loss rate, measures the actual discharge amount of the charging pile, and outputs the discharging state abnormality according to the comparison between the actual discharge amount and the predicted discharge amount; A charging state evaluation module, when the connected equipment is of the battery type, obtains the battery data through the battery communication protocol, calls the corresponding historical electrical energy conversion loss rate of the charging pile, and calculates the predicted received electrical energy of the charging pile in combination with the current stored electrical energy of the battery, measures the actual discharge amount of the charging pile, and outputs the charging state abnormality according to the comparison between the actual received electrical energy and the predicted received electrical energy; A fault type judgment module, when the charging abnormality and the discharging abnormality are accepted at the same time, judges the fault type according to the charging state evaluation result and the discharging state evaluation result through the fault characteristic evaluation strategy.

[0014] The beneficial effects of the present application are: automatically triggering the charging / discharging evaluation mode by intelligently identifying the device type, dynamically combining the load rated power, the required power and the historical loss rate to generate the predicted discharge amount in the discharge evaluation, calculating the predicted received power based on the battery SOC difference, the capacity and the conversion loss rate in the charging evaluation, and realizing high-sensitivity abnormal detection; using nonlinear power deviation processing to strengthen the charging thermal runaway identification, using hyperbolic tangent function to constrain the discharge power fluctuation, integrating temperature square weighting, voltage integral accumulation and cycle aging factor to construct the system-level fault fingerprint, accurately distinguishing the three states of charging, discharging and common faults through the hierarchical threshold strategy (T1>T2), combining the communication quality quantization and the MCU health degree correction, effectively reducing the transient interference misjudgment rate, improving the evaluation accuracy, improving the fault positioning efficiency, accurately finding the charging pile fault module to avoid disassembly and maintenance, reducing the maintenance cost, and significantly enhancing the grid interaction reliability of the V2G system. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is a method flowchart of a V2G intelligent bidirectional charging and discharging pile state evaluation method of the present application. DETAILED DESCRIPTION

[0016] The present application will be further described in detail below in combination with the drawings and embodiments. Identical parts are denoted by identical reference numerals in the following description. It should be noted that the words "front", "back", "left", "right", "up" and "down" used in the following description refer to the directions in the drawings, and the words "bottom surface" and "top surface", "inner" and "outer" refer to the directions towards or away from the geometric center of a particular part.

[0017] The present application embodiment proposes a V2G intelligent bidirectional charging and discharging pile state evaluation method, as shown in Figure 1 , comprising the following steps: A data acquisition step acquires the type of equipment connected to the charging pile, and outputs a charging state evaluation instruction or a discharging state evaluation instruction according to the type of equipment; A discharging state evaluation step, when the connected equipment is of the load type, obtains the load data and the load type of the load through the load communication protocol, calculates the predicted discharge amount of the charging pile when the load is fully charged according to the load data, measures the actual discharge amount of the charging pile, and outputs the discharging state abnormal condition according to the comparison between the actual discharge amount and the predicted discharge amount; A charging state evaluation step, when the connected equipment is of the battery type, obtains the battery data through the battery communication protocol, calculates the predicted received power of the charging pile according to the current energy storage power of the battery combined with the battery data, measures the actual discharge amount of the charging pile, and outputs the charging state abnormal condition according to the comparison between the actual received power and the predicted received power; The fault type judgment step judges the fault type according to the charging state evaluation result and the discharging state evaluation result through a fault feature evaluation strategy when the charging abnormality and the discharging abnormality are simultaneously accepted.

[0018] When the load device is accessed, the charging pile reads the rated power and the remaining charging demand of the load device, combines the historical average loss rate of the load device, calculates the theoretical discharging amount required for full charging, and monitors the output electric quantity on the alternating current side in real time. When the actual discharging amount exceeds the theoretical value by a deviation range, the discharging abnormality is marked. When the battery device is accessed, the charging pile obtains the difference between the current SOC and the target SOC of the battery device, combines the conversion efficiency curve to calculate the electric quantity threshold that should be received, monitors the actual input electric quantity through the ammeter on the direct current side, and triggers the charging abnormality when the actual input electric quantity exceeds the threshold. When the charging abnormality and the discharging abnormality occur simultaneously, the system extracts parameters such as the electric quantity deviation rate, the temperature change trend, and the communication error rate, and distinguishes the power module fault, the heat dissipation system fault, or the communication interference through multi-dimensional feature matching.

[0019] By using the above method, the application can accurately identify the pseudo abnormality caused by environmental interference and the real device fault, and avoid false shutdown caused by sudden change of power grid frequency. In the scene where the battery pack is poorly cooled, the system distinguishes the battery overheating and the charging module overload in time through the temperature change rate parameter. When the communication quality fluctuates, the quality factor is used to dynamically adjust the evaluation threshold to prevent false alarms caused by temporary interference. For the charging station with diversified load types, the scheme optimizes the prediction model through historical data learning to improve the evaluation stability in the mixed use scene of different devices.

[0020] Specifically, the discharging state evaluation step further includes that the load data includes a load rated power and a current demand electric quantity, a corresponding historical charging loss rate of the charging pile is called, a predicted discharging amount is calculated according to the historical charging loss in combination with the rated power and the current demand electric quantity, a discharging amount difference between the actual discharging amount and the predicted discharging amount is compared with the size of a preset discharging deviation threshold, and a discharging abnormality result is output when the discharging amount difference exceeds the discharging deviation threshold.

[0021] The historical charging loss rate refers to the efficiency loss of the charging pile in the historical charging process due to factors such as device aging and line loss, and is dynamically calculated by taking a weighted average of charging times, running time, and environmental temperature, so as to reflect the actual performance degradation of the device. The load rated power refers to the maximum input power value allowed by the load device under standard working conditions, which is obtained by reading the rated parameter field in the device communication protocol and is used to represent the upper limit of the power demand of the load; the current demand electric quantity refers to the total amount of electric energy required for charging the load device in the current state, which is calculated by the difference between the load remaining electric quantity and the battery capacity, and is used to determine the target energy output of the discharging process.

[0022] When the charging pile accesses the load device, the system obtains the rated power and the current demand power through the communication protocol, and calls the historical charging loss rate data. The predicted discharge amount is calculated by dividing the current demand power by (1-historical charging loss rate), for example, when the demand power is 50 kWh and the historical loss rate is 5%, the predicted discharge amount is calculated as 50 / (1-0.05)=52.63 kWh. The actual discharge amount is collected in real time by the electric energy metering module, and the difference between the actual discharge amount and the predicted value is calculated. The preset discharge deviation threshold can be an empirical value or a dynamically adjusted value, for example, set to ±8% of the predicted value. When the actual discharge amount exceeds the predicted value by ±8%, it is determined that the discharge state is abnormal. Thus, adaptive compensation is achieved for dynamic factors such as device aging and environmental changes, significantly improving the accuracy of the prediction model.

[0023] Through the above means, the application effectively solves the prediction deviation problem caused by not considering dynamic loss in traditional methods, significantly improving the reliability of discharge anomaly detection. In the scenario where battery aging leads to a decrease in charging efficiency, this scheme can accurately identify discharge anomalies caused by device wear and tear, avoiding misjudgment of normal performance degradation as a sudden failure. At the same time, based on the combined calculation mechanism of rated power and demand power, it can prevent the prediction model from failing due to load overload or inefficient charging.

[0024] Specifically, the charging state evaluation step further includes that the battery data includes a current SOC value, a target SOC value, and a battery capacity of the battery, a historical electric energy conversion loss rate corresponding to the charging pile is called, a predicted received power is calculated according to the historical electric energy conversion loss rate in combination with the current SOC value and the target SOC value, a received power difference between the actual received power and the predicted received power is compared with the size of a preset charging deviation threshold, and if the received power difference exceeds the charging deviation threshold, the data charging result is abnormal.

[0025] The current SOC value refers to the percentage of the real-time energy storage state of the battery relative to the full capacity state, which is calculated using the voltage-capacity mapping relationship collected by the battery management system in real time, and is used to reflect the energy reserve level at the charging starting point. The target SOC value refers to the target state of charge that the charging operation needs to reach, which is achieved by user setting or adaptive adjustment of the battery health state, and is used to determine the charging termination condition. The historical electric energy conversion loss rate refers to the statistical mean of the energy conversion efficiency of the charging pile in the historical operation, which is calculated by a sliding time window algorithm to weight average the actual loss of the last 100 charging operations, and is used to quantify the influence of inherent performance degradation of the device on the prediction model. The preset charging deviation threshold refers to the maximum difference range of the actual received power and the predicted value, which can be dynamically adjusted based on factors such as battery type, environmental temperature, and running time, and is used to distinguish between incidental fluctuations and persistent abnormalities.

[0026] When the charging pile reversely outputs electric energy to the power grid or other devices, the current SOC value and the target SOC value are obtained in real time through the battery communication protocol, and the total amount of theoretical electric energy to be supplemented is calculated in combination with the battery capacity parameter. The historical electric energy conversion loss rate data of the corresponding type of the charging pile is called to obtain the predicted received electric energy by dividing the theoretical electric energy to be supplemented by (1-historical loss rate). The actual received electric energy is continuously monitored during the charging process, and when the charging operation reaches the target SOC value, the absolute difference between the actual received electric energy and the predicted received electric energy is calculated. By comparing the difference with the preset charging deviation threshold, the charging abnormal signal is triggered when the difference exceeds the threshold. The calculation model of the predicted received electric energy introduces the historical loss rate parameter, which can dynamically correct the decline in energy conversion efficiency caused by device aging and avoid the prediction deviation caused by using a fixed efficiency coefficient. The dynamic adjustment mechanism of the preset charging deviation threshold can adapt to the normal fluctuation range under different battery types and environmental temperatures, preventing false judgments caused by changes in working conditions.

[0027] Through the above means, the application solves the problem of inaccurate charging state abnormality detection caused by device performance degradation and changes in working conditions. By dynamically correcting the prediction model and adaptively adjusting the judgment threshold, the detection accuracy can be maintained under complex working conditions such as device aging and environmental temperature fluctuations, accurately identifying persistent charging abnormalities caused by a decline in energy conversion efficiency, abnormal heat dissipation of the battery pack, or communication interference, and avoiding false judgments of normal working condition fluctuations as system failures.

[0028] Specifically, the fault feature evaluation strategy includes calculating discharge fault feature values, charging fault feature values, and common fault feature values according to the load data, battery data, and charging pile parameter data, respectively, and outputting the final fault type according to the size comparison results of the discharge fault feature values, charging fault feature values, and common fault feature values with the preset first threshold and second threshold.

[0029] The discharge fault feature value is a quantitative indicator reflecting the abnormality degree in the discharging process, which is calculated by a nonlinear function through the difference between the rated power of the load and the actual discharging power, and the power grid frequency fluctuation parameter, and is used to capture the power instability phenomenon specific to the discharging side. The charging fault feature value is a quantitative indicator reflecting the abnormality degree in the charging process, which is realized by the combination operation of the battery temperature change rate and the communication quality parameter, and is used to identify the heat dissipation abnormality and signal interference problem specific to the charging side. The common fault feature value is a comprehensive indicator reflecting the abnormality degree of the charging and discharging shared device, which is realized by the integral operation of the voltage deviation duration combined with the device aging coefficient calculation, and is used to evaluate the system-level hardware fault risk. The first threshold and the second threshold are critical values for dividing the fault severity, which can be set by using the numerical range based on historical fault data statistics, and the first threshold is greater than the second threshold, and the fault level division is realized by double threshold setting.

[0030] When charging and discharging anomalies occur simultaneously at the charging pile, first, the power grid frequency fluctuation parameters are extracted from the load data, and the difference between the load rated power and the actual discharging power is combined to generate the discharging fault characteristic value after hyperbolic tangent function processing. The temperature change rate parameter is extracted from the battery data, and the charging power deviation is combined to perform exponential amplification operation to generate the charging fault characteristic value. At the same time, the bus voltage data is continuously collected from the charging pile operation parameters, the duration of the voltage deviation from the rated value is integrated and calculated, and the common fault characteristic value is generated after superimposing the device aging factor. When the charging fault characteristic value exceeds the first threshold value and the common characteristic value is between the second threshold value and the first threshold value, it is determined as an independent charging fault; when the discharging fault characteristic value exceeds the first threshold value and the common characteristic value is in the middle interval, it is determined as an independent discharging fault; when the common characteristic value exceeds the first threshold value and other characteristic values are lower than the second threshold value, it is determined as a device-level common fault. For example, when the battery temperature change rate suddenly increases to cause the charging fault characteristic value to reach 0.85 (exceeding the first threshold value 0.8), and the voltage continuously deviates to generate a common characteristic value of 0.75 (between the second threshold value 0.6 and the first threshold value), the common fault interference is excluded, and the charging side independent fault is accurately identified.

[0031] Through the above means, the application realizes accurate diagnosis in the charging and discharging mixed fault scene, and solves the actual problem that device aging and instantaneous anomaly are difficult to distinguish. When the temperature of the charging pile heat dissipation system abnormally rises, it can be accurately identified as a charging side fault rather than a common fault; when the power grid frequency suddenly changes to cause the discharging power to fluctuate, the power grid quality anomaly and device hardware fault can be effectively distinguished; when the voltage continuously deviates with device aging, it is timely determined as a system-level common fault, avoiding misjudgment as an independent charging and discharging fault. The scheme significantly improves the judgment accuracy of the fault diagnosis system under complex working conditions, ensuring that maintenance personnel can quickly locate the fault source.

[0032] Specifically, the charging fault characteristic value calculation is configured with a charging fault characteristic value calculation model, which includes nonlinear processing of the power deviation, amplifying the recognizability of abnormal charging state, and reflecting the battery pack heat dissipation anomaly through the temperature change rate parameter. After calculating the cumulative effect of power deficiency by using an exponential function, the charging fault characteristic value is calculated by replacing the original Boolean value to weaken communication interference.

[0033] The nonlinear processing power deviation refers to using a power function to operate the power deviation, and operating the ratio of the actual power to the predicted power to the power of 1.5. The ratio is amplified to enhance the recognition sensitivity of the abnormal state. The temperature change rate parameter refers to the ratio of the change amount of the battery temperature per unit time to the upper limit of the rated temperature. The temperature change rate parameter can be obtained by differentiating the real-time temperature data collected by the temperature sensor, and is used to reflect the temperature mutation caused by the abnormal heat dissipation of the battery. The cumulative effect of the power deficiency calculated by the exponential function refers to taking the difference between the rated power and the actual charging power as the input variable of the exponential function, for example, calculating the cumulative effect of power attenuation by using the natural exponential function. The alternative original Boolean value weakening communication interference refers to using a continuous quantitative communication quality parameter to replace the original binary judgment logic, for example, constructing a normalized parameter in the range of 0 to 1 based on the communication error rate or signal strength. The specific formula configuration is as follows: ; wherein F ch represents the charging fault feature value, represents the nonlinear power deviation coefficient, the actual received power and the predicted power are collected by the BMS in real time; k represents the power difference amplification coefficient, which is determined by regression analysis based on historical fault data, wherein, is the power difference alarm threshold, which is used to avoid abnormal fluctuations of the power; is the rated charging power of the charging pile, is the real-time charging power, is the battery temperature change rate, which is collected by the temperature sensor and calculated by the differential circuit or digital filtering algorithm. Comm represents the quantitative value of the communication quality, which is calculated by the RS495 communication module. The error rate is quantified, that is, the quantitative value. When Comm=0, it indicates that a fault occurs, and when Comm=1, it indicates that there is no abnormality. ω1 represents the sensitivity of the charging power deviation, which is the contribution of the power deviation to the fault in the historical charging abnormal case. The value is usually 0.4-0.6. ω2 represents the cumulative effect weight of the power deficiency, which is calculated according to the power sensor accuracy and the alarm threshold. ω3 represents the dynamic influence weight of the temperature change rate.

[0034] When the charging pile is in a charging state, battery temperature data is collected in real time and a change rate thereof is calculated, an exponential cumulative effect of a power difference is calculated in combination with measured data of a power sensor, and a channel quality parameter is acquired through a communication module. Nonlinear processing results of an electric quantity deviation, a temperature change rate parameter, an exponential term of the power difference, and the communication quality parameter are weighted and calculated to generate a charging fault characteristic value. For example, after the electric quantity deviation is amplified by a power function, normal fluctuations and abnormal deviations can be effectively distinguished, the temperature change rate parameter can capture a temperature rise mutation caused by a heat dissipation failure of the battery pack, the exponential calculation of the power difference can reflect a superimposed effect of long-term power deficiency, and the continuously quantized communication quality parameter can reduce the probability of misjudgment caused by temporary interference.

[0035] Through the above means, the application effectively solves the problem of missing heat dissipation anomaly detection caused by dynamic changes in battery temperature during the charging process, and avoids misjudgment caused by a discrete communication judgment mechanism in traditional methods. Through nonlinear modeling and dynamic parameter fusion, composite faults caused by power decay accumulation, temperature gradient anomalies, and communication quality fluctuations can be accurately identified, and the reliability of charging state evaluation is improved.

[0036] Specifically, the discharge fault characteristic value calculation is configured with a discharge fault characteristic value calculation model, and the discharge fault characteristic value calculation model includes: after the power difference is constrained by calculating the grid frequency fluctuation term in combination with the load data by a hyperbolic tangent function, the discharge fault characteristic value is calculated by introducing a bus voltage deviation ratio.

[0037] The grid frequency fluctuation term refers to the deviation amount between the actual operating frequency of the power grid and the standard frequency, which is realized by power function operation on the absolute value difference between the real-time collected grid frequency data and the reference frequency, and is used to quantify the influence degree of power grid fluctuations on the discharging process. The hyperbolic tangent function calculation refers to processing the power difference value by using the nonlinear characteristics of the hyperbolic tangent function, which is realized by inputting the difference value between the rated power and the actual discharging power into the hyperbolic tangent function after dividing the rated power, and is used to suppress numerical divergence in extreme working conditions and maintain sensitivity in the key interval. The bus voltage deviation ratio refers to the instantaneous deviation ratio of the charging pile DC bus voltage to the rated voltage, which is calculated by the ratio of the voltage data collected by the voltage sensor to the rated voltage, and is used to reflect the influence of power quality anomalies on the discharging process.

[0038] When the load is detected to be connected, the power grid frequency monitoring module continuously collects the power grid operating frequency data, generates a frequency fluctuation correction coefficient by calculating the absolute value of the deviation of the frequency from the standard frequency and performing a 1.2 power operation, and simultaneously, the power monitoring unit obtains the rated power and the actual discharge power data of the load, inputs the power difference between the two into the hyperbolic tangent function after dividing the rated power, and obtains the constrained power difference parameter. The voltage acquisition circuit measures the operating voltage of the DC bus in real time, calculates the instantaneous deviation ratio of the voltage from the rated voltage as the voltage quality parameter. Finally, the three types of parameters are fused and calculated according to the preset weight, wherein the frequency fluctuation correction coefficient is used as the denominator to adjust the influence degree of the power difference parameter, and the voltage quality parameter is used as an independent term to participate in linear weighting, to form a comprehensive discharge fault characteristic value. The specific calculation model is configured with the formula: ; wherein, F dis is the discharge fault characteristic value, is the normalized discharge capacity deviation, representing the difference between the actual discharge capacity and the predicted discharge capacity ratio, i.e. 1-actual discharge capacity / predicted discharge capacity, is the power grid frequency fluctuation, which is collected in real time by the power grid frequency detection module, tanh(.) represents a power difference saturation function term, which is used to constrain the power deviation within the range of (-1, 1), and is the hyperbolic tangent function calculation, is the high-voltage bus voltage deviation absolute value, is the rated power, ω4 represents the sensitivity of the discharge capacity deviation, according to the power grid frequency fluctuation tolerance test, when the frequency deviation increases, the weight decreases, ω5 represents the non-linear weight of the discharge power difference, which is used to constrain the output range of the hyperbolic tangent function, and usually takes a value of 0.1-0.2, and ω6 represents the emergency weight of the bus voltage deviation, which is doubled when the voltage deviation value exceeds 5%, and usually takes a value of 0.1-0.15.

[0039] Through the above means, the application can accurately identify the discharge state abnormality under the condition of power grid frequency fluctuation, and avoid misjudgment caused by changes in the power grid environment. The application of the hyperbolic tangent function effectively suppresses the interference of power mutation on the evaluation result, while retaining the sensitivity of the key interval. The introduction of the bus voltage deviation ratio realizes the collaborative diagnosis of power quality abnormalities and equipment faults, and improves the recognition ability of composite discharge faults.

[0040] Specifically, the common fault characteristic value calculation is configured with a common fault characteristic value calculation model, which includes weight evaluation of the charging pile high temperature risk by the battery temperature value, construction of an aging factor by the number of charge and discharge cycles and the MCU health degree, and calculation of the common fault characteristic value by the cumulative effect of the voltage deviation from the rated value, the high temperature risk evaluation weight and the aging factor.

[0041] The weight evaluation of the battery temperature value refers to the square operation of the ratio of the real-time collected battery temperature and the maximum value of the rated temperature. The battery temperature data is obtained through the temperature sensor, and the square operation is performed through the calculation module to realize it, which can enhance the evaluation sensitivity of the influence of high temperature anomaly on system stability. The cumulative effect of voltage deviation from the rated value refers to the integral operation of the absolute value of the bus voltage deviation from the rated voltage. Real-time voltage data is obtained through the voltage sampling circuit, and time accumulation calculation is performed through the integrator module to realize it, which can capture the dynamic process of continuous degradation of voltage fluctuation. The number of charge and discharge cycles refers to the total number of times the battery completes a complete charge and discharge process, which is realized by adding and recording through the counter module in the battery management system, which can represent the life attenuation state of the battery system. The MCU health degree refers to the performance index calculated by monitoring the running error rate and clock stability of the microcontroller unit. The processor running log data is collected by using the hardware diagnosis module, and the dynamic coefficient is calculated by the health degree evaluation algorithm. This parameter can reflect the performance degradation of the control unit in real time. Specifically, the model is configured with the calculation formula: ; Wherein, F comm represents the total fault characteristic value, represents the battery temperature, represents the maximum temperature allowed for the battery to work, represents the voltage deviation value time accumulation, N cycle represents the number of charge cycles, which is recorded by the charge and discharge event counter, and each complete discharge is counted as 1, represents the MCU health degree adjustment coefficient, which is dynamically adjusted according to the MCU self-checking result, wherein it can be set as 1-(RAM error times / total detection times), MCU represents the health degree of the main control unit, which is also set as MCU=0, indicating failure, MCU=1, indicating normal, ω7 represents the overheat risk weight of the temperature square term, which is usually taken as 0.3-0.4; ω8 represents the aging weight of the voltage deviation accumulation, and the weight increases by 0.02 for every 10V·s of voltage stress; ω9 represents the life attenuation weight of the cycle number, which increases by 0.05 for every 100 cycles when the cycle aging test is performed.

[0042] When the charging pile is detected to have charging and discharging abnormalities at the same time, first, the battery temperature data is collected, and the influence weight of temperature abnormality on system stability is amplified through square operation. At the same time, the bus voltage data is collected in real time, the voltage deviation is integrated, and the cumulative voltage deviation in the continuous time period is obtained. Combined with the pre-recorded charging and discharging cycle number and the real-time calculated MCU health degree parameter, a comprehensive factor reflecting the aging degree of the equipment is constructed. Finally, the high-temperature risk weight, the cumulative voltage deviation and the aging factor are input into a nonlinear fusion model to generate a characteristic value that can represent system-level failure. The model replaces the single sampling value with integral operation to effectively identify persistent voltage abnormalities; the temperature abnormality signal is strengthened through square operation to improve the identification sensitivity of high-temperature risk; and the contribution of each parameter is dynamically adjusted by the aging factor to accurately quantify the impact of equipment wear on failure.

[0043] Through the above means, the application can accurately identify system stability problems caused by high-temperature risk, dynamically evaluate the continuous impact of voltage fluctuations on power quality, and quantify the correlation between equipment aging degree and failure probability, thereby improving the diagnostic accuracy of charging and discharging common faults.

[0044] Specifically, the fault type includes charging state fault, discharging state fault and charging and discharging common fault, a first threshold value and a second threshold value for indicating the degree of fault characteristics are set, the first threshold value is greater than the second threshold value, when the charging fault characteristic value is greater than the preset first threshold value, and the common fault characteristic value is greater than the second threshold value and less than the first threshold value, and the discharging fault characteristic value is less than the second threshold value, the output result is the charging state fault; when the discharging fault characteristic value is greater than the preset first threshold value, and the common fault characteristic value is greater than the second threshold value and less than the first threshold value, and the charging fault characteristic value is less than the second threshold value, the output result is the discharging state fault; when the common fault characteristic value is greater than the first threshold value, and the charging fault characteristic value and the discharging fault characteristic value are less than the second threshold value, the output result is the charging and discharging common state fault.

[0045] The first threshold value is a demarcation value for distinguishing between serious faults and ordinary abnormalities. The threshold value is calculated according to historical fault data by using a dynamic threshold adjustment algorithm, and is corrected according to environmental factors and charging pile use time limit and other factors. The second threshold value is a filtering threshold value for filtering secondary interference signals. The adaptive threshold value is calculated by a noise statistical model.

[0046] When the charging fault characteristic value exceeds the first threshold value, the system determines that the charging module has a serious fault, but needs to combine the common fault characteristic value between the second threshold value and the first threshold value, and the discharge fault characteristic value is lower than the second threshold value, so as to exclude the interference of the discharge module on the judgment. Similarly, the judgment of the discharge fault needs the discharge characteristic value to exceed the first threshold value, and the common characteristic value is in the middle interval and the charging characteristic value is lower than the secondary threshold value. For the judgment of the charging and discharging common fault, the common characteristic value needs to exceed the first threshold value, and the charging and discharging characteristic values are both lower than the second threshold value, for example, when the battery temperature continuously exceeds the safety threshold value and the charging and discharging cycle number reaches the life critical value, even if the charging and discharging characteristic values do not reach the serious fault standard, it is still judged as a system level fault. Through the hierarchical judgment mechanism of two-level threshold values and the logical mutual exclusion constraint of fault types, the source of the fault can be accurately identified.

[0047] A V2G intelligent bidirectional charging and discharging pile state evaluation system, comprising: A data acquisition module acquires the type of equipment connected to the charging pile and outputs a charging state evaluation instruction or a discharging state evaluation instruction according to the type of equipment; A discharging state evaluation module, when the connected equipment is a load type, obtains load data and a load type of the load through a load communication protocol, calls a corresponding historical charging loss rate of the charging pile, calculates a predicted discharge amount of the charging pile when the load is fully charged according to the load data and the historical charging loss rate, measures an actual discharge amount of the charging pile, and outputs a discharging state abnormality according to a comparison between the actual discharge amount and the predicted discharge amount; A charging state evaluation module, when the connected equipment is a battery type, obtains battery data through a battery communication protocol, calls a corresponding historical electrical energy conversion loss rate of the charging pile and calculates a predicted received electrical energy of the charging pile in combination with a current stored electrical energy of the battery, measures an actual discharge amount of the charging pile, and outputs a charging state abnormality according to a comparison between the actual received electrical energy and the predicted received electrical energy; A fault type judgment module, when the charging abnormality and the discharging abnormality are simultaneously accepted, judges a fault type according to a charging state evaluation result and a discharging state evaluation result through a fault characteristic evaluation strategy.

[0048] The above shows and describes the basic features, principles and advantages of the present application. It should be noted that the present application is not limited by the above examples, but only some examples, and any improvement and supplement made without departing from the spirit and scope of the present application is considered as the protection scope of the present application.

Claims

1. A V2G intelligent bidirectional charge-discharge pile state evaluation method, characterized in that, The method comprises the following steps: a data collection step, obtaining the type of equipment connected to the charging pile, and outputting a charging state evaluation instruction or a discharging state evaluation instruction according to the type of equipment; a discharging state evaluation step, when the connected equipment is of the load type, obtaining load data and a load type of the load through a load communication protocol, calculating a predicted discharging capacity of the charging pile when the load is fully charged according to the load data, measuring an actual discharging capacity of the charging pile, and outputting a discharging state abnormality according to a comparison between the actual discharging capacity and the predicted discharging capacity; a charging state evaluation step, when the connected equipment is of the battery type, obtaining battery data through a battery communication protocol, calculating a predicted receiving capacity of the charging pile according to a current energy storage capacity of the battery in combination with the battery data, measuring an actual receiving capacity of the charging pile, and outputting a charging state abnormality according to a comparison between the actual receiving capacity and the predicted receiving capacity; a fault type judgment step, when the charging abnormality and the discharging abnormality are simultaneously accepted, obtaining a fault type according to a charging state evaluation result and a discharging state evaluation result through a fault feature evaluation strategy. 2.The V2G intelligent bidirectional charge-discharge pile state evaluation method according to claim 1, characterized in that, The discharging state evaluation step further comprises that the load data comprises a load rated power and a current demand capacity, a corresponding historical charging loss rate of the charging pile is called, a predicted discharging capacity is calculated according to the historical charging loss in combination with the rated power and the current demand capacity, a discharging capacity difference between the actual discharging capacity and the predicted discharging capacity is compared with a size of a preset discharging deviation threshold value, and a discharging abnormality result is output when the discharging capacity difference exceeds the discharging deviation threshold value. 3.The V2G intelligent bidirectional charge-discharge pile state evaluation method according to claim 1, characterized in that, The charging state evaluation step further comprises that the battery data comprises a current SOC value, a target SOC value and a battery capacity of the battery, a corresponding historical electric energy conversion loss rate of the charging pile is called, a predicted receiving capacity is calculated according to the historical electric energy conversion loss rate in combination with the current SOC value and the target SOC value, a receiving capacity difference between the actual receiving capacity and the predicted receiving capacity is compared with a size of a preset charging deviation threshold value, and a data charging result is abnormal when the receiving capacity difference exceeds the charging deviation threshold value. 4.The V2G intelligent bidirectional charge-discharge pile state evaluation method according to claim 1, characterized in that, The fault feature evaluation strategy comprises that a discharging fault feature value, a charging fault feature value and a common fault feature value are respectively calculated according to the load data, the battery data and charging pile parameter data, and a final fault type is output according to a comparison result between the discharging fault feature value, the charging fault feature value and the common fault feature value and sizes of preset first and second threshold values. 5.The V2G intelligent bidirectional charge-discharge pile state evaluation method according to claim 1, characterized in that, The charging fault feature value calculation model is configured for the charging fault feature value calculation, the charging fault feature value calculation model comprises that the electric quantity deviation is nonlinearly processed, the identifiable property of the abnormal charging state is amplified, the heat dissipation abnormality of the battery pack is reflected through a temperature change rate parameter, the cumulative effect caused by the insufficient power is calculated through an exponential function, and the charging fault feature value is calculated through a method of replacing the original Boolean value to weaken the communication interference. 6.The V2G intelligent bidirectional charge-discharge pile state evaluation method according to claim 1, characterized in that, The discharge fault characteristic value calculation is configured with a discharge fault characteristic value calculation model, and the discharge fault characteristic value calculation model comprises: through the hyperbolic tangent function calculation of combining the power grid frequency fluctuation term with the load data to constrain the calculation range of the power difference, and then through the introduction of the bus voltage deviation ratio calculation to obtain the discharge fault characteristic value.

7. The V2G intelligent bidirectional charge and discharge pile state evaluation method according to claim 1, characterized in that, The common fault characteristic value calculation is configured with a common fault characteristic value calculation model, and the common fault characteristic value calculation model comprises: through the weight evaluation of the battery temperature value on the high-temperature risk of the charging pile, and through the construction of the aging factor by the number of charge and discharge cycles and the MCU health degree, the common fault characteristic value is calculated through the cumulative effect of the voltage continuous deviation from the rated value, the high-temperature risk evaluation weight and the aging factor. 8.The V2G intelligent bidirectional charge-discharge pile state evaluation method according to claim 4, characterized in that, The fault type includes charging state fault, discharging state fault and charging and discharging common fault, and the first threshold value and the second threshold value for indicating the fault characteristic degree are set, the first threshold value is greater than the second threshold value, when the charging fault characteristic value is greater than the preset first threshold value, and the common fault characteristic value is greater than the second threshold value and less than the first threshold value, and the discharge fault characteristic value is less than the second threshold value, the output result is the charging state fault; when the discharge fault characteristic value is greater than the preset first threshold value, and the common fault characteristic value is greater than the second threshold value and less than the first threshold value, and the charging fault characteristic value is less than the second threshold value, the output result is the discharging state fault; when the common fault characteristic value is greater than the first threshold value, and the charging fault characteristic value and the discharging fault characteristic value are less than the second threshold value, the output result is the charging and discharging common state fault.

9. A V2G intelligent bidirectional charge-discharge pile state evaluation system suitable for the V2G intelligent bidirectional charge-discharge pile state evaluation method of any one of claims 1 to 8, characterized in that, It comprises: A data acquisition module acquires the type of equipment connected to the charging pile, and outputs a charging state evaluation instruction or a discharging state evaluation instruction according to the type of equipment; A discharging state evaluation module, when the connected equipment is a load type, obtains the load data and load type of the load through the load communication protocol, calls the corresponding historical charging loss rate of the charging pile, calculates the predicted discharge amount of the charging pile when the load is fully charged according to the load data and the historical charging loss rate, measures the actual discharge amount of the charging pile, and compares the actual discharge amount and the predicted discharge amount to output the discharging state abnormality; A charging state evaluation module, when the connected equipment is a battery type, obtains the battery data through the battery communication protocol, calls the corresponding historical electric energy conversion loss rate of the charging pile and calculates the predicted received electric energy of the charging pile in combination with the current energy storage capacity of the battery, measures the actual discharge amount of the charging pile, and compares the actual received electric energy and the predicted received electric energy to output the charging state abnormality; A fault type judgment module, when the charging abnormality and the discharging abnormality are accepted at the same time, the fault type is judged according to the charging state evaluation result and the discharging state evaluation result through the fault characteristic evaluation strategy.

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