Insulation resistance dynamic detection circuit and method based on adaptive compensation under charging working condition
Patent Information
- Application Number
- CN202611122443.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-28
- Publication Date
- 2026-10-13
AI Technical Summary
在电池充电工况下，充电回路的电压波动、充放电电流变化、电化学产热与电解液状态改变，会与绝缘检测信号形成强耦合干扰，直接影响绝缘电阻检测的准确性与可靠性
剥离充电工况干扰，检测精度提升本发明构建充电工况绝缘耦合模型，精准区分绝缘检测信号中的有效分量与工况干扰分量，可完全消除充电电压波动、电流变化、温度漂移、电化学扰动等工况带来的信号偏移与畸变，从根源解决传统检测技术工况干扰下数值失真、误判漏判的问题，提升绝缘电阻检测的准确性与稳定性。
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Figure CN122901522A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of battery management system technology, specifically relating to a dynamic insulation resistance detection circuit and method based on adaptive compensation under charging conditions. Background Technology
[0002] Lead-acid batteries are core energy storage devices in scenarios such as vehicle starting, industrial energy storage, and backup power. Their insulation safety is a key indicator for the electrical protection and safe operation of the battery system during charging and discharging. Insulation resistance testing is a crucial technical means to determine battery system leakage, insulation failure, and to prevent the risks of electric shock and thermal runaway. During battery charging, voltage fluctuations in the charging circuit, changes in charging and discharging current, electrochemical heat generation, and changes in the electrolyte state can create strong coupling interference with the insulation resistance testing signal, directly affecting the accuracy and reliability of the insulation resistance test.
[0003] Current traditional insulation resistance testing technologies mostly employ static testing schemes, such as the balanced bridge method and the low-frequency signal injection method. These methods can only collect basic insulation parameters in static or offline states and do not include interference adaptation and compensation designs for dynamic charging conditions, resulting in significant technical shortcomings. Operating interference cannot be separated, resulting in insufficient detection accuracy. During the charging process, factors such as constant current / constant voltage switching, current ripple, temperature drift (combined with the temperature-sensitive characteristics of lead-acid batteries), and electrochemical reactions of the plates can cause deviation, distortion, and noise superposition of the original insulation detection signal. Existing technologies cannot distinguish between effective detection components and operating interference components, which can easily lead to distortion of insulation resistance values, false alarms, or missed detections.
[0004] Without an adaptive compensation mechanism, the detection results cannot be dynamically corrected. Traditional detection methods use fixed calculation logic and benchmark parameters, and do not establish a coupled mapping relationship between charging conditions and insulation detection. They cannot iterate the compensation coefficient according to the real-time charging status. Under complex conditions such as high-temperature charging and high-rate charging, the deviation of the calculation results continues to increase, and they cannot reflect the true insulation status.
[0005] The detection and charging systems are disconnected, and there is no closed-loop safety management. The existing insulation detection only outputs parameters and does not link with the charging strategy. It cannot adjust the charging current, voltage and temperature control logic in real time according to the insulation fault status. At the same time, it lacks the ability to dynamically track faults and control them in a closed loop. When the insulation is abnormal, it cannot intervene in time, which can easily lead to safety accidents such as leakage, thermal runaway or even battery explosion.
[0006] Poor adaptability to complex operating conditions and limited application scenarios: For battery types such as lead-acid batteries and water batteries that are highly sensitive to temperature and charging voltage, traditional testing technologies do not take into account the characteristics of electrolyte evaporation, sulfation, and temperature compensation. Under extreme operating conditions such as high and low temperatures and high-rate charging and discharging, the stability and effectiveness of testing decrease, making it difficult to meet the long-term safety operation and maintenance needs of industrial and automotive scenarios.
[0007] In summary, existing insulation resistance detection technologies cannot adapt to the interference characteristics of dynamic charging conditions, resulting in problems such as low detection accuracy, lack of adaptive compensation, and lack of linkage control, making it difficult to ensure the insulation safety and operational reliability of lead-acid battery systems during charging. Therefore, there is an urgent need for a dynamic insulation resistance detection circuit and method based on adaptive compensation under charging conditions to address the technical pain points of interference stripping, real-time compensation correction, accurate calculation, and closed-loop safety control. Summary of the Invention
[0008] To address the aforementioned problems in the prior art, this invention provides a dynamic insulation resistance detection circuit and method based on adaptive compensation under charging conditions.
[0009] The objective of this invention can be achieved through the following technical solution: a dynamic insulation resistance detection circuit and method based on adaptive compensation under charging conditions, comprising: S1: Obtain the characteristic parameters of the battery system charging condition and the original signal of the ground insulation detection, construct the charging condition insulation coupling model, extract the condition interference factor, calibrate the compensation benchmark parameters and establish a compensation benchmark database. S2: Based on the charging condition insulation coupling model and the compensation benchmark database, perform feature analysis and attribute determination on the real-time charging condition, iteratively calculate the condition adaptation compensation coefficient, and generate dynamic compensation rules. S3: Based on the dynamic compensation rules, the working condition interference components are stripped off, and the working condition adaptation compensation coefficient is combined to correct the solution process in real time and verify the validity of the solution results. S4: Output the calibrated actual insulation resistance parameters, compare them with the preset safety threshold to determine the insulation fault status, link the charging system to implement adaptive control, and perform closed-loop safety management according to the fault status.
[0010] Specifically, the construction process of the charging condition insulation coupling model is as follows: The charging condition characteristic parameters and the original ground insulation detection signal are preprocessed; the causes of disturbances to the original ground insulation detection signal by the charging condition elements during operation are analyzed, and the effects of independent interference and compound superposition interference are distinguished; the interference influence weights corresponding to the condition elements are quantified, and a dynamic mapping relationship between the condition fluctuation and the offset of the original ground insulation detection signal is established. By combining the temporal variation characteristics of the charging process to correct the correlation mapping deviation, the signal distortion evolution law under multi-condition coupling is explored; the correlation characteristics between the condition variables and the insulation detection signal are solidified to generate the charging condition insulation coupling model.
[0011] Specifically, the process for extracting the operating condition interference factor is as follows: Based on the aforementioned charging condition insulation coupling model, the corresponding correlation between the charging condition characteristic parameters and the original signal of the ground insulation detection is established; the inherent causes of signal deviation caused by the condition elements are analyzed, and the inherent attribute components of the signal itself and the external condition disturbance components are separated; the influencing factors of the condition are screened according to the strength of the disturbance correlation, and the continuous and instantaneous effects of the condition disturbance are distinguished.
[0012] Specifically, the process of calibrating the compensation benchmark parameters and establishing the compensation benchmark database is as follows: Based on the aforementioned insulation coupling model for charging conditions, a standard charging scenario under operating conditions is matched; referring to the original ground insulation detection benchmark performance under the standard scenario, the corresponding compensation benchmark parameters under the calibration scenario are matched. The calibrated compensation benchmark parameters are categorized according to their working condition attributes, and the binding relationships between the parameters and their corresponding working conditions are clarified. The attribute identifiers and matching logic of the parameters are standardized, and the categorized compensation benchmark parameters are uniformly collected and stored to generate the compensation benchmark database.
[0013] Specifically, the process of performing feature analysis and attribute determination on real-time charging conditions is as follows: The charging condition characteristic parameters and the original ground insulation detection signal are acquired synchronously under real-time operating conditions. The inherent characteristic information of the operating conditions is compared with the charging condition insulation coupling model. The operating category to which the real-time operating conditions belong is divided, and the inherent attribute state during the operation of the operating conditions is identified. The operating condition information is classified and matched with the classification dimensions of the compensation benchmark database to lock the attribute category corresponding to the real-time operating conditions.
[0014] Specifically, the iterative calculation process for the working condition adaptation compensation coefficient is as follows: Based on the deviation characteristics of the charging condition characteristic parameters and the original signal of the ground insulation detection associated with the charging condition insulation coupling model, the initial matching coefficient is set with reference to the benchmark parameters in the compensation benchmark database; the coefficient matching deviation is continuously corrected by combining the signal offset degree of the real-time condition, the fit between the coefficient and the current condition is continuously corrected, the range of the convergence coefficient matching deviation is determined, and the condition adaptation compensation coefficient is output.
[0015] Specifically, the process of generating dynamic compensation rules through matching is as follows: Based on the benchmark constraints corresponding to the operating conditions in the compensation benchmark database, and combined with the determined operating condition adaptation compensation coefficients, the disturbance attributes of the charging operating condition characteristic parameters are associated; according to the offset performance of the original signal of the ground insulation detection, the corresponding adaptation compensation adjustment logic is used to integrate the compensation control orientation corresponding to the degree of disturbance; the compensation constraint boundary and adjustment logic are regularized under the operating condition, and the control paradigm of the matching operating condition fluctuation state is integrated to generate the dynamic compensation rule.
[0016] Specifically, the process of removing the interference component under the stripping condition is as follows: Based on the dynamic compensation rules and the charging condition insulation coupling model, the interference components contained in the original ground insulation detection signal are identified. By comparing the disturbance correlation characteristics of the condition interference factor, the effective detection component and the condition interference component in the original ground insulation detection signal are distinguished. Based on the condition adaptation compensation coefficient, the signal disturbance caused by the condition is offset, and the condition interference component is separated and filtered out.
[0017] Specifically, the process of real-time correction of the solution process based on the working condition adaptation compensation coefficient is as follows: Based on the pure detection signal after removing the interference components of the operating conditions, and relying on the operating condition adaptation compensation coefficient, combined with the mapping relationship of the insulation coupling model of the charging operating conditions, the residual deviations left in the insulation resistance calculation process are tracked; combined with the real-time fluctuation status of the characteristic parameters of the charging operating conditions, the correction amplitude of the calculation link is dynamically adjusted; the weak disturbance error remaining in the calculation link is offset, and the calculation logic of the insulation parameters is continuously optimized.
[0018] Specifically, the process of comparing the insulation fault state with the preset safety threshold is as follows: The actual insulation resistance parameters after calibration are retrieved and matched with a preset safety threshold; the deviation between the actual insulation resistance parameters and the safety threshold is compared, and occasional data fluctuation interference is eliminated by combining charging condition characteristic parameters; different insulation judgment states are divided according to the degree of parameter deviation, normal insulation state and abnormal fault state are identified, and the current insulation fault judgment result of the battery system is determined.
[0019] Specifically, the process of implementing adaptive control in the linked charging system is as follows: Based on the identified insulation fault state, the corresponding criteria for matching and adapting the control are matched with the characteristic parameters of the charging condition; the corresponding operation control orientation is matched according to the difference level of the insulation state, and the current operation adaptation logic of the charging system is adjusted; the control adaptation scale is matched synchronously with the real-time changes of the operating condition to match the system operation mode that is adapted to the current insulation state.
[0020] Specifically, the process of performing closed-loop safety management based on fault status is as follows: The system continuously acquires the charging condition characteristic parameters and the original signal of the ground insulation detection, and tracks the dynamic change trend of the insulation fault state in real time; it updates the control execution logic synchronously according to the changes in the fault state to maintain the coordinated adaptation between the charging system operation and insulation protection; it retains the correlation information between the state evolution and the control execution throughout the process, and continuously corrects the control execution logic based on real-time feedback.
[0021] This invention achieves fundamental breakthroughs in interference removal, accuracy improvement, dynamic adaptation, and safety control through a technical solution combining adaptive compensation under charging conditions and dynamic detection of insulation resistance. Compared with traditional insulation detection technologies, it has the following significant advantages: By eliminating interference from charging conditions, the accuracy of detection is improved. This invention constructs an insulation coupling model for charging conditions, which accurately distinguishes the effective components and interference components in the insulation detection signal. It can completely eliminate signal offset and distortion caused by charging voltage fluctuations, current changes, temperature drift, electrochemical disturbances and other conditions. It solves the problems of numerical distortion, misjudgment and missed judgment under the interference of conditions in traditional detection technology from the root, and improves the accuracy and stability of insulation resistance detection.
[0022] This invention achieves adaptive dynamic compensation under all operating conditions with extremely strong adaptability. Based on a compensation benchmark database, iteratively generates operating condition adaptation compensation coefficients. It can dynamically adjust the calculation logic and compensation rules according to the real-time charging status, without relying on fixed detection parameters. It can perfectly adapt to various charging stages, different ambient temperatures, and complex scenarios of full-rate charging, solving the technical shortcomings of traditional static detection in adapting to dynamic operating conditions.
[0023] By linking the detection and charging systems, this invention achieves closed-loop safety management. It deeply integrates insulation detection results with charging strategies, and can adjust the charging operation logic in real time according to the insulation fault status. When insulation is abnormal, it can promptly execute protective actions such as early warning, current limiting, and shutdown, forming a closed-loop safety management system of "detection-calculation-judgment-control". This effectively avoids safety accidents such as leakage and thermal runaway, and improves the charging safety of the battery system.
[0024] The solution process is corrected in real time, resulting in higher reliability of the test results. This invention introduces a dynamic correction mechanism in the insulation resistance solution process to continuously offset residual disturbance errors. At the same time, it verifies the validity of the test results, eliminates interference from occasional data fluctuations, ensures that the output insulation resistance parameters are true values, reduces the probability of false alarms and missed alarms, and guarantees the long-term reliability of the test results.
[0025] This invention is adapted to the characteristics of lead-acid and water-filled batteries, extending battery life. It fully combines the temperature sensitivity and charging voltage compensation characteristics of lead-acid batteries, adapting to the battery temperature control and charging compensation requirements while performing insulation detection. This avoids problems such as overcharging, undercharging, plate sulfation, and electrolyte evaporation caused by insulation detection failure, effectively delaying battery aging and extending the overall battery life.
[0026] With strong technical versatility and wide application coverage, this invention does not require modification of existing battery and charging hardware structures. It achieves functional upgrades through the collaboration of algorithms and circuits, and can be adapted to various lead-acid battery charging systems such as vehicle starting batteries, industrial energy storage batteries, and backup power batteries. It balances low cost and high practicality, and has extremely strong engineering implementation and promotion value. Attached Figure Description
[0027] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.
[0028] Figure 1 This is a flowchart of the insulation resistance dynamic detection circuit and method based on charging condition adaptive compensation in this invention. Figure 2 This is a diagram illustrating the core technology module architecture for insulation detection in this invention. Detailed Implementation
[0029] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.
[0030] Please see Figures 1-2 A circuit and method for dynamic detection of insulation resistance based on adaptive compensation under charging conditions, including: S1: Obtain the characteristic parameters of the battery system charging condition and the original signal of the ground insulation detection, construct the charging condition insulation coupling model, extract the condition interference factor, calibrate the compensation benchmark parameters and establish a compensation benchmark database. S2: Based on the charging condition insulation coupling model and the compensation benchmark database, perform feature analysis and attribute determination on the real-time charging condition, iteratively calculate the condition adaptation compensation coefficient, and generate dynamic compensation rules. S3: Based on the dynamic compensation rules, the working condition interference components are stripped off, and the working condition adaptation compensation coefficient is combined to correct the solution process in real time and verify the validity of the solution results. S4: Output the calibrated actual insulation resistance parameters, compare them with the preset safety threshold to determine the insulation fault status, link the charging system to implement adaptive control, and perform closed-loop safety management according to the fault status.
[0031] Specifically, the construction process of the charging condition insulation coupling model is as follows: The charging condition characteristic parameters and the original ground insulation detection signal are preprocessed; the causes of disturbances to the original ground insulation detection signal by the charging condition elements during operation are analyzed, and the effects of independent interference and compound superposition interference are distinguished; the interference influence weights corresponding to the condition elements are quantified, and a dynamic mapping relationship between the condition fluctuation and the offset of the original ground insulation detection signal is established. By combining the temporal variation characteristics of the charging process to correct the correlation mapping deviation, the signal distortion evolution law under multi-condition coupling is explored; the correlation characteristics between the condition variables and the insulation detection signal are solidified to generate the charging condition insulation coupling model.
[0032] Specifically, the process for extracting the operating condition interference factor is as follows: Based on the aforementioned charging condition insulation coupling model, the corresponding correlation between the charging condition characteristic parameters and the original signal of the ground insulation detection is established; the inherent causes of signal deviation caused by the condition elements are analyzed, and the inherent attribute components of the signal itself and the external condition disturbance components are separated; the influencing factors of the condition are screened according to the strength of the disturbance correlation, and the continuous and instantaneous effects of the condition disturbance are distinguished.
[0033] Specifically, the process of calibrating the compensation benchmark parameters and establishing the compensation benchmark database is as follows: Based on the aforementioned insulation coupling model for charging conditions, a standard charging scenario under operating conditions is matched; referring to the original ground insulation detection benchmark performance under the standard scenario, the corresponding compensation benchmark parameters under the calibration scenario are matched. The calibrated compensation benchmark parameters are categorized according to their working condition attributes, and the binding relationships between the parameters and their corresponding working conditions are clarified. The attribute identifiers and matching logic of the parameters are standardized, and the categorized compensation benchmark parameters are uniformly collected and stored to generate the compensation benchmark database.
[0034] In this embodiment, the system is applied to a lead-acid battery energy storage and charging system. For the three-stage charging process, the system completes the construction of a charging condition insulation coupling model, extraction of condition interference factors, calibration of compensation benchmark parameters, and database establishment. The complete and detailed implementation process is as follows: Obtain basic data and preprocess it: This embodiment first acquires characteristic parameters A of the battery system charging condition, including charging voltage, charging current, battery temperature, charging stage, charge / discharge rate, and battery terminal ripple voltage; simultaneously, it acquires the raw signal B for ground insulation detection, which is output by the low-frequency AC injection insulation detection module and includes the amplitude signal and phase signal corresponding to the insulation resistance.
[0035] Specialized preprocessing is performed on parameter A and signal B: high-frequency electromagnetic noise is removed by moving average filtering, instantaneous abnormal data during charging start-up and shutdown and operating condition switching are removed by threshold judgment rules, and then normalization is performed to unify the dimensions, eliminating the interference of different parameter magnitude differences on subsequent modeling, and obtaining stable and usable preprocessed data.
[0036] Constructing an insulation coupling model for charging conditions: The analysis of disturbance causes and the differentiation of interference types are based on the preprocessed data. The disturbance mechanisms of each operating condition element during charging operation on the original signal B are analyzed one by one: the charging voltage ripple will cause the periodic shift of the signal amplitude; the heat generated by high current charging will change the insulation impedance characteristics of the battery body; the polarization effect of the plates and the change of the degree of electrolyte ionization will cause signal phase distortion; and the fluctuation of electrolyte concentration in lead-acid batteries will also add to the interference.
[0037] Based on this, we can clarify the independent interference caused by a single factor, as well as the compound and superimposed interference caused by the simultaneous action of multiple factors such as voltage, current, and temperature.
[0038] The interference weight quantification and dynamic mapping relationship establishment adopts correlation analysis and hierarchical analysis methods to quantify the influence weight of each working condition element on the signal B disturbance and identify high-weight interference elements; with the change of working condition parameters as the independent variable and the offset of signal B as the dependent variable, a multivariate dynamic mapping relationship between working condition fluctuation and insulation detection signal offset is established to realize the quantitative correlation between working condition change and signal distortion.
[0039] By combining timing correction and distortion law mining with the charging timing characteristics of lead-acid batteries, the dynamic mapping relationship is segmented and the deviation is corrected according to each stage of the three-stage charging to eliminate the calculation error caused by timing lag. Through multiple sets of repeated charging experiments, the distortion evolution law of signal B under multi-condition coupling is explored, such as amplitude attenuation, phase shift, and noise superposition, and the trend of interference with charging time and battery state is clarified.
[0040] The model solidification process integrates and solidifies operating condition variables, interference weights, dynamic mapping relationships, and timing correction coefficients to form a directly callable charging operating condition insulation coupling model M. This model can accurately describe the coupling correlation characteristics between charging operating conditions and insulation detection signals.
[0041] Extracting operating condition interference factors: Based on the insulation coupling model M of charging conditions, a quantitative correspondence between the characteristic parameters A of charging conditions and the original signal B of ground insulation detection is established. Through wavelet decomposition algorithm, the original signal B is decomposed into the inherent attribute components of the signal itself determined by the battery insulation characteristics, and the external operating condition disturbance components caused by the charging conditions.
[0042] Based on the disturbance correlation strength of each operating condition element, core elements whose contribution to signal disturbance exceeds the threshold are screened out, and weak interference irrelevant items are eliminated. At the same time, the action mode of the operating condition disturbance is distinguished. Temperature drift and electrolyte concentration change are defined as continuous disturbances, and charging start-up and shutdown and operating condition switching impact are defined as instantaneous disturbances. Finally, the operating condition interference factor F is extracted, which provides the core basis for subsequent interference stripping.
[0043] Calibrate the compensation benchmark parameters and establish a compensation benchmark database: The standard scenario matching and benchmark parameter calibration are based on the insulation coupling model M of the charging condition, matching the standard charging scenario of lead-acid plus water battery, covering the standard operating environment of the full temperature range, full charging rate, and full charging stage; taking the output value of the original signal B of the ground insulation detection under the ideal environment without operating condition interference as the benchmark, the compensation benchmark parameters corresponding to each standard scenario are calibrated to ensure that the parameters can accurately offset the interference deviation of the corresponding operating condition.
[0044] The parameter classification and association are organized according to three operating condition attributes: temperature range, charging rate, and charging stage. The calibrated compensation benchmark parameters are hierarchically classified to clarify the unique binding relationship between each set of compensation parameters and the corresponding standard operating condition, so as to avoid parameter matching confusion.
[0045] The database generates standardized attribute identifiers for compensation benchmark parameters, including their number, applicable working conditions, correction direction, and adjustment range, and establishes parameter matching and calling logic. All categorized compensation benchmark parameters are uniformly collected and stored to generate a compensation benchmark database D, providing standardized data support for subsequent real-time working condition compensation coefficient calculations.
[0046] Specifically, the process of performing feature analysis and attribute determination on real-time charging conditions is as follows: The charging condition characteristic parameters and the original ground insulation detection signal are acquired synchronously under real-time operating conditions. The inherent characteristic information of the operating conditions is compared with the charging condition insulation coupling model. The operating category to which the real-time operating conditions belong is divided, and the inherent attribute state during the operation of the operating conditions is identified. The operating condition information is classified and matched with the classification dimensions of the compensation benchmark database to lock the attribute category corresponding to the real-time operating conditions.
[0047] Specifically, the iterative calculation process for the working condition adaptation compensation coefficient is as follows: Based on the deviation characteristics of the charging condition characteristic parameters and the original signal of the ground insulation detection associated with the charging condition insulation coupling model, the initial matching coefficient is set with reference to the benchmark parameters in the compensation benchmark database; the coefficient matching deviation is continuously corrected by combining the signal offset degree of the real-time condition, the fit between the coefficient and the current condition is continuously corrected, the range of the convergence coefficient matching deviation is determined, and the condition adaptation compensation coefficient is output.
[0048] Specifically, the process of generating dynamic compensation rules through matching is as follows: Based on the benchmark constraints corresponding to the operating conditions in the compensation benchmark database, and combined with the determined operating condition adaptation compensation coefficients, the disturbance attributes of the charging operating condition characteristic parameters are associated; according to the offset performance of the original signal of the ground insulation detection, the corresponding adaptation compensation adjustment logic is used to integrate the compensation control orientation corresponding to the degree of disturbance; the compensation constraint boundary and adjustment logic are regularized under the operating condition, and the control paradigm of the matching operating condition fluctuation state is integrated to generate the dynamic compensation rule.
[0049] In this embodiment, following embodiment S1, it is applied to the same lead-acid battery energy storage and charging system. Based on the charging condition insulation coupling model M, compensation benchmark database D, charging condition characteristic parameters A, and ground insulation detection original signal B generated in S1, real-time charging condition feature analysis, condition adaptation compensation coefficient iterative calculation, and dynamic compensation rule matching generation are completed. The implementation process is as follows: Real-time charging condition feature analysis and attribute determination: In this embodiment, the charging condition characteristic parameter A and the original signal B of the ground insulation detection are simultaneously collected under the current real-time operating state of the battery system. The two sets of data are synchronously input into the charging condition insulation coupling model M to complete the benchmarking and matching of the inherent characteristic information of the operating condition.
[0050] Based on the built-in operating condition classification logic of model M, the real-time charging status is classified into operating categories to accurately identify the inherent attributes of the current operating condition, such as the charging stage, disturbance intensity, and environmental adaptation status. Then, the parsed operating condition information is matched one by one according to the three major classification dimensions of temperature, charging rate, and charging stage in the compensation benchmark database D, completing the hierarchical classification of the operating condition information. Finally, the core attribute category corresponding to the real-time operating condition is determined, providing a precise operating condition positioning basis for subsequent compensation coefficient calculations.
[0051] Iterative calculation of the working condition adaptation compensation coefficient K: Based on the insulation coupling model M of charging conditions, the deviation correlation between the real-time charging condition characteristic parameter A and the original signal B of ground insulation detection is established, and the core deviation features such as signal offset and distortion under the current conditions are extracted.
[0052] Using the standard operating condition reference parameters stored in the compensation reference database D as a reference, set the initial compensation coefficient that initially matches the current operating condition.
[0053] By combining the actual offset of the original signal B of the real-time ground insulation detection, the initial coefficients are corrected successively, continuously reducing the matching deviation between the coefficients and the current real-time operating conditions, and improving the adaptability of the coefficients to operating condition disturbances.
[0054] Through multiple rounds of iterative calculations, the coefficient matching deviation is gradually converged to a reasonable range, and finally the working condition adaptation compensation coefficient K is output to adapt to the current real-time working condition.
[0055] Matching and generating dynamic compensation rules R: Retrieve the benchmark constraints corresponding to the current real-time operating condition attribute category from the compensation benchmark database D, and associate and bind the determined operating condition adaptation compensation coefficient K with the disturbance attribute of the charging operating condition characteristic parameter A.
[0056] Based on the amplitude and phase shift of the original signal B from the ground insulation detection, the corresponding compensation adjustment logic is matched, and the compensation control direction and adjustment amplitude under different disturbance levels are integrated.
[0057] Further refine the compensation constraint boundaries under the current operating conditions, clarify the upper and lower limits and triggering conditions of compensation adjustment, and establish the correspondence between compensation constraints and adjustment logic.
[0058] Finally, a full-process control paradigm that adapts to real-time operating condition fluctuations is integrated to form a dynamic compensation rule R that can be directly used for interference stripping and solution correction, providing the core execution basis for operating condition interference separation and insulation parameter solution in step S3.
[0059] Specifically, the process of removing the interference component under the stripping condition is as follows: Based on the dynamic compensation rules and the charging condition insulation coupling model, the interference components contained in the original ground insulation detection signal are identified. By comparing the disturbance correlation characteristics of the condition interference factor, the effective detection component and the condition interference component in the original ground insulation detection signal are distinguished. Based on the condition adaptation compensation coefficient, the signal disturbance caused by the condition is offset, and the condition interference component is separated and filtered out.
[0060] Specifically, the process of real-time correction of the solution process based on the working condition adaptation compensation coefficient is as follows: Based on the pure detection signal after removing the interference components of the operating conditions, and relying on the operating condition adaptation compensation coefficient, combined with the mapping relationship of the insulation coupling model of the charging operating conditions, the residual deviations left in the insulation resistance calculation process are tracked; combined with the real-time fluctuation status of the characteristic parameters of the charging operating conditions, the correction amplitude of the calculation link is dynamically adjusted; the weak disturbance error remaining in the calculation link is offset, and the calculation logic of the insulation parameters is continuously optimized.
[0061] In this embodiment, following the previous embodiment, it is applied to the same lead-acid battery energy storage and charging system. Based on the charging condition insulation coupling model M and the condition interference factor F generated by S1, the dynamic compensation rule R and the condition adaptation compensation coefficient K output by S2, as well as the charging condition characteristic parameter A and the original signal B for ground insulation detection, the process of removing the condition interference component, real-time correction of insulation resistance calculation, and verification of the validity of the calculation results is completed. The implementation process is as follows: Disturbance components under stripping conditions: Based on the dynamic compensation rule R and combined with the insulation coupling model M under charging conditions, the original signal B for ground insulation detection is analyzed in all dimensions to accurately identify the various interference components contained in the signal.
[0062] By comparing the disturbance correlation characteristics of the operating condition interference factor F, the original signal B is decomposed and distinguished to identify the effective detection component representing the actual insulation state of the battery, as well as the operating condition interference component caused by the charging condition.
[0063] Based on the working condition adaptation compensation coefficient K, the signal amplitude shift, phase distortion and other disturbances caused by various working conditions are accurately offset. Through signal separation and filtering, the working condition interference components are completely filtered out to obtain a pure detection signal without interference, providing a reliable data basis for subsequent insulation resistance calculation.
[0064] The solution process is corrected in real time by incorporating the working condition adaptation compensation coefficient: Based on the pure detection signal after removing interference, the calculation is performed using the working condition adaptation compensation coefficient K and the working condition and signal mapping relationship built into the charging working condition insulation coupling model M. The process tracks the subtle deviations remaining in the insulation resistance calculation process.
[0065] By simultaneously combining the real-time fluctuation status of the charging condition characteristic parameter A, the correction magnitude of the calculation link is dynamically adjusted according to the change magnitude of the condition, and the slight disturbance error that is not completely eliminated in the calculation process is specifically offset.
[0066] The calculation logic for insulation parameters is continuously iterated and optimized to ensure that the calculation process is always adapted to the real-time charging conditions, so that the calculated insulation resistance value is infinitely close to the actual insulation state of the battery.
[0067] Validation of solution results: The insulation resistance calculation results output after real-time correction are verified in multiple dimensions. Through continuous comparison and stability judgment, occasional interference such as instantaneous data fluctuations and electromagnetic noise are eliminated.
[0068] Verify whether the calculation results are within a reasonable range and whether they match the insulation change pattern under the current charging conditions. Only retain valid calculation results that meet the verification rules, and eliminate abnormal and distorted data to ensure that the insulation parameters used for fault determination are true and reliable.
[0069] Specifically, the process of comparing the insulation fault state with the preset safety threshold is as follows: The actual insulation resistance parameters after calibration are retrieved and matched with a preset safety threshold; the deviation between the actual insulation resistance parameters and the safety threshold is compared, and occasional data fluctuation interference is eliminated by combining charging condition characteristic parameters; different insulation judgment states are divided according to the degree of parameter deviation, normal insulation state and abnormal fault state are identified, and the current insulation fault judgment result of the battery system is determined.
[0070] Specifically, the process of implementing adaptive control in the linked charging system is as follows: Based on the identified insulation fault state, the corresponding criteria for matching and adapting the control are matched with the characteristic parameters of the charging condition; the corresponding operation control orientation is matched according to the difference level of the insulation state, and the current operation adaptation logic of the charging system is adjusted; the control adaptation scale is matched synchronously with the real-time changes of the operating condition to match the system operation mode that is adapted to the current insulation state.
[0071] Specifically, the process of performing closed-loop safety management based on fault status is as follows: The system continuously acquires the charging condition characteristic parameters and the original signal of the ground insulation detection, and tracks the dynamic change trend of the insulation fault state in real time; it updates the control execution logic synchronously according to the changes in the fault state to maintain the coordinated adaptation between the charging system operation and insulation protection; it retains the correlation information between the state evolution and the control execution throughout the process, and continuously corrects the control execution logic based on real-time feedback.
[0072] In this embodiment, following the entire aforementioned implementation process, it is applied to the same lead-acid battery energy storage and charging system. Based on the acquired charging condition characteristic parameters A, the original ground insulation detection signal B, the charging condition insulation coupling model M, the condition adaptation compensation coefficient K, the dynamic compensation rule R, and the calibrated real insulation resistance data, the insulation parameter output, fault status determination, charging system linkage control, and closed-loop safety management are completed. The implementation process is as follows: Insulation fault status determination: The actual insulation resistance parameter Z, after interference stripping, calculation correction, and validity verification, is retrieved. The parameter Z is then precisely matched and compared with the system's preset safety threshold Y, and the deviation between the two values is calculated.
[0073] By combining the real-time status of the charging condition characteristic parameter A, interference screening is performed on the deviation data to eliminate occasional data fluctuations caused by charging start-up and shutdown, instantaneous electromagnetic interference, and switching of operating conditions, and invalid abnormal deviations are eliminated.
[0074] Based on the degree of deviation between parameter Z and safety threshold Y, the insulation status of the battery system is divided into three categories: normal, warning, and fault. This accurately identifies the current insulation working status of the battery system and finally outputs the insulation fault judgment result G, providing a clear basis for subsequent charging regulation.
[0075] The charging system implements adaptive control: Using the insulation fault determination result G as the core instruction, and combining the real-time attributes of the charging condition characteristic parameter A, the system's built-in control criterion library is retrieved to match the control rules that correspond one-to-one with the current fault level and operating condition.
[0076] Based on different insulation status levels—normal, warning, and fault—corresponding charging operation control orientations are matched. Under normal insulation conditions, standard charging logic is maintained; under insulation warning conditions, charging operation parameters are adjusted appropriately; and under insulation fault conditions, a safety limiting mechanism is activated. The system synchronously tracks the real-time fluctuations of the charging condition characteristic parameter A, dynamically adjusting the adaptation scale of control execution to ultimately determine a charging system operation mode that perfectly matches the current insulation status and charging conditions, achieving real-time linkage between insulation detection and charging control.
[0077] Implement closed-loop safety management based on fault status: During charging operation, characteristic parameters A of the charging condition and the original signal B of the ground insulation detection are continuously collected. Based on the aforementioned model and rules, the insulation detection results are updated in real time, and the dynamic trend of the insulation fault judgment result G is tracked throughout the process. According to the escalation, mitigation or elimination of the fault state, the control execution logic of the charging system is updated synchronously to always maintain a high degree of coordination and adaptation between the charging operation strategy and the insulation protection requirements.
[0078] The system automatically retains data on insulation state evolution, charging control execution records, and changes in operating parameters throughout the entire process, forming a traceable operation log. It also uses real-time collected data as feedback to continuously correct the control execution logic, constantly optimize control accuracy and response speed, and build a closed-loop safety control system of "detection-judgment-control-feedback-correction" to ensure insulation safety throughout the entire battery charging process.
[0079] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. A circuit and method for dynamic detection of insulation resistance based on adaptive compensation under charging conditions, characterized in that, include: S1: Obtain the characteristic parameters of the battery system charging condition and the original signal of the ground insulation detection, construct the charging condition insulation coupling model, extract the condition interference factor, calibrate the compensation benchmark parameters and establish a compensation benchmark database. S2: Based on the charging condition insulation coupling model and the compensation benchmark database, perform feature analysis and attribute determination on the real-time charging condition, iteratively calculate the condition adaptation compensation coefficient, and generate dynamic compensation rules. S3: Based on the dynamic compensation rules, the working condition interference components are stripped off, and the working condition adaptation compensation coefficient is combined to correct the solution process in real time and verify the validity of the solution results. S4: Output the calibrated actual insulation resistance parameters, compare them with the preset safety threshold to determine the insulation fault status, link the charging system to implement adaptive control, and perform closed-loop safety management according to the fault status.
2. The method according to claim 1, characterized in that, The construction process of the charging condition insulation coupling model is as follows: The charging condition characteristic parameters and the original ground insulation detection signal are preprocessed; the causes of disturbances to the original ground insulation detection signal by the charging condition elements during operation are analyzed, and the effects of independent interference and compound superposition interference are distinguished; the interference influence weights corresponding to the condition elements are quantified, and a dynamic mapping relationship between the condition fluctuation and the offset of the original ground insulation detection signal is established. By combining the temporal variation characteristics of the charging process to correct the correlation mapping deviation, the signal distortion evolution law under multi-condition coupling is explored; the correlation characteristics between the condition variables and the insulation detection signal are solidified to generate the charging condition insulation coupling model.
3. The method according to claim 2, characterized in that, The specific process for extracting the operating condition interference factor is as follows: Based on the aforementioned charging condition insulation coupling model, the corresponding correlation between the charging condition characteristic parameters and the original signal of the ground insulation detection is established; the inherent causes of signal deviation caused by the condition elements are analyzed, and the inherent attribute components of the signal itself and the external condition disturbance components are separated; the influencing factors of the condition are screened according to the strength of the disturbance correlation, and the continuous and instantaneous effects of the condition disturbance are distinguished.
4. The method according to claim 2, characterized in that, The specific process of calibrating the compensation benchmark parameters and establishing the compensation benchmark database is as follows: Based on the aforementioned insulation coupling model for charging conditions, a standard charging scenario under operating conditions is matched; referring to the original ground insulation detection benchmark performance under the standard scenario, the corresponding compensation benchmark parameters under the calibration scenario are matched. The calibrated compensation benchmark parameters are categorized according to the working condition attribute category, and the binding relationship between the parameters and the corresponding working conditions is sorted out. The attribute identifiers and matching logic of the regularization parameters are used to uniformly collect and summarize the classified compensation benchmark parameters to generate the compensation benchmark database.
5. The method according to claim 1, characterized in that, The specific process for performing feature analysis and attribute determination on real-time charging conditions is as follows: The charging condition characteristic parameters and the original ground insulation detection signal are acquired synchronously under real-time operating conditions, and the inherent characteristic information of the charging condition is compared with the charging condition insulation coupling model. Classify the real-time operating conditions into their respective operating categories and identify the inherent attribute states during the operating process; By classifying and matching the working condition information according to the classification dimensions of the compensation benchmark database, the attribute category corresponding to the real-time working condition is determined.
6. The method according to claim 1, characterized in that, The specific process for iteratively calculating the working condition adaptation compensation coefficient is as follows: Based on the deviation characteristics of the charging condition characteristic parameters and the original signal of the ground insulation detection associated with the charging condition insulation coupling model, the initial matching coefficient is set with reference to the benchmark parameters in the compensation benchmark database; the coefficient matching deviation is continuously corrected by combining the signal offset degree of the real-time condition, the fit between the coefficient and the current condition is continuously corrected, the range of the convergence coefficient matching deviation is determined, and the condition adaptation compensation coefficient is output.
7. The method according to claim 1, characterized in that, The specific process of generating dynamic compensation rules through matching is as follows: Based on the benchmark constraints corresponding to the operating conditions in the compensation benchmark database, and combined with the determined operating condition adaptation compensation coefficients, the disturbance attributes of the charging operating condition characteristic parameters are associated; according to the offset performance of the original signal of the ground insulation detection, the corresponding adaptation compensation adjustment logic is used to integrate the compensation control orientation corresponding to the degree of disturbance; the compensation constraint boundary and adjustment logic are regularized under the operating condition, and the control paradigm of the matching operating condition fluctuation state is integrated to generate the dynamic compensation rule.
8. The method according to claim 1, characterized in that, The specific process of the stripping condition interference component is as follows: Based on the dynamic compensation rules and the charging condition insulation coupling model, the interference components contained in the original ground insulation detection signal are identified. By comparing the disturbance correlation characteristics of the condition interference factor, the effective detection component and the condition interference component in the original ground insulation detection signal are distinguished. Based on the condition adaptation compensation coefficient, the signal disturbance caused by the condition is offset, and the condition interference component is separated and filtered out.
9. The method according to claim 1, characterized in that, The specific process of real-time correction of the solution process based on the working condition adaptation compensation coefficient is as follows: Based on the pure detection signal after removing the interference components of the operating conditions, and relying on the operating condition adaptation compensation coefficient, combined with the mapping relationship of the insulation coupling model of the charging operating conditions, the residual deviations left in the insulation resistance calculation process are tracked; combined with the real-time fluctuation status of the characteristic parameters of the charging operating conditions, the correction amplitude of the calculation link is dynamically adjusted; the weak disturbance error remaining in the calculation link is offset, and the calculation logic of the insulation parameters is continuously optimized.
10. The method according to claim 1, characterized in that, The specific process for determining the insulation fault state by comparing it with the preset safety threshold is as follows: Retrieve the actual insulation resistance parameters after calibration and match them with the preset safety threshold; compare the deviation of the actual insulation resistance parameters from the safety threshold, and combine the charging condition characteristic parameters to eliminate occasional data fluctuation interference. Different insulation judgment states are classified according to the degree of parameter deviation, the normal insulation state and the abnormal fault state are distinguished, and the current insulation fault judgment result of the battery system is clarified.
11. The method according to claim 1, characterized in that, The specific process of implementing adaptive control in the linked charging system is as follows: Based on the identified insulation fault state, the corresponding criteria for matching and adapting the control are matched with the characteristic parameters of the charging condition; the corresponding operation control orientation is matched according to the difference level of the insulation state, and the current operation adaptation logic of the charging system is adjusted; the control adaptation scale is matched synchronously with the real-time changes of the operating condition to match the system operation mode that is adapted to the current insulation state.
12. The method according to claim 1, characterized in that, The specific process of performing closed-loop safety management based on fault status is as follows: The system continuously acquires the charging condition characteristic parameters and the original signal of the ground insulation detection, and tracks the dynamic change trend of the insulation fault state in real time; it updates the control execution logic synchronously according to the changes in the fault state to maintain the coordinated adaptation between the charging system operation and insulation protection; it retains the correlation information between the state evolution and the control execution throughout the process, and continuously corrects the control execution logic based on real-time feedback.