Insulation resistance detection method and system of energy storage system and storage medium

Abstract

The invention provides an insulation resistance detection method and system of an energy storage system and a storage medium, and relates to the technical field of safety monitoring of the energy storage system. The method comprises the following steps: monitoring the total voltage of the energy storage system; calculating the change rate of the total voltage; comparing the change rate with a preset rate threshold; when the change rate is smaller than a preset rate threshold value, calculating a first insulation resistance value by adopting an unbalanced bridge method based on the current total voltage, and outputting the first insulation resistance value as an effective insulation resistance value; and when the change rate is greater than or equal to a preset rate threshold value, calculating a second insulation resistance value by adopting an unbalanced bridge method based on the current total voltage, comparing the second insulation resistance value with a stored reference insulation resistance value, and selecting an effective insulation resistance value output this time according to a comparison result. According to the method, the defect that the traditional BMS insulation detection function is insufficient in adaptability in the application of the super capacitor is overcome, and the anti-interference capability and the operation safety of the system are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage system safety monitoring, and in particular to an insulation resistance detection method and system for an energy storage system and a storage medium. BACKGROUND

[0002] With the rapid development of energy storage technology, energy storage systems represented by supercapacitors and lithium-ion batteries have been widely used in power peak shaving, electric vehicles, rail transit and other fields. Among them, as a typical power-type energy storage device, supercapacitors have unique advantages in scenarios requiring instantaneous high-current output due to their high power density and fast charging and discharging characteristics. As the core management component of the energy storage system, the battery management system (BMS) undertakes key functions such as state monitoring and safety protection. Insulation resistance detection is an important link to ensure the safe operation of the system. If the positive / negative electrode-to-ground insulation performance of the energy storage system is abnormal, it may cause safety accidents such as leakage, short circuit and fire. Therefore, accurate detection of insulation resistance is crucial to system reliability.

[0003] Currently, the mainstream BMS products on the market are mainly developed for lithium-ion battery systems, and their insulation detection function design is mainly adapted to the characteristics of lithium-ion batteries. As an energy-type cell, the total voltage of lithium-ion batteries changes relatively smoothly during charging and discharging. However, as a power-type cell, supercapacitors have essential differences in charging and discharging characteristics from lithium-ion batteries. For example, supercapacitors can complete high-current charging and discharging in a few seconds, resulting in a sharp fluctuation in the total voltage of the system within a short period of time. The traditional insulation detection method commonly used by BMS is mainly based on the unbalanced bridge method, which calculates the insulation resistance by detecting the unbalanced state of the bridge composed of the positive / negative electrode-to-ground resistance and the reference resistance. The total voltage of the system is a key independent variable in the calculation formula, which directly affects the result. When the supercapacitor system causes the total voltage to change rapidly due to high-current charging and discharging, the total voltage fluctuates significantly within the sampling period of the traditional unbalanced bridge method, which directly leads to a large deviation in the calculated insulation resistance or even a random jump. In severe cases, it may misreport insulation faults, affecting the normal operation of the system. SUMMARY

[0004] The present application aims to provide an insulation resistance detection method and system for an energy storage system and a storage medium to solve the technical problem of the background technology that when the total voltage of the supercapacitor system changes rapidly due to high-current charging and discharging, the total voltage fluctuates significantly within the sampling period of the traditional unbalanced bridge method, which directly leads to a large deviation in the calculated insulation resistance or even a random jump. In severe cases, it may misreport insulation faults, affecting the normal operation of the system.

[0005] To achieve the above-mentioned purpose, according to one aspect of the present application, an insulation resistance detection method for an energy storage system is provided, comprising the following steps:

[0006] monitoring a total voltage of the energy storage system;

[0007] calculating a rate of change of the total voltage;

[0008] comparing the rate of change with a preset rate threshold;

[0009] when the rate of change is less than the preset rate threshold, calculating a first insulation resistance value based on the current total voltage by using an unbalanced bridge method, and outputting the first insulation resistance value as an effective insulation resistance value;

[0010] when the rate of change is greater than or equal to the preset rate threshold, calculating a second insulation resistance value based on the current total voltage by using the unbalanced bridge method, and comparing the second insulation resistance value with a stored reference insulation resistance value, and selecting an effective insulation resistance value output this time according to a comparison result.

[0011] In a possible implementation, the step of selecting the effective insulation resistance value output this time according to the comparison result comprises:

[0012] when the second insulation resistance value is greater than the reference insulation resistance value, outputting the second insulation resistance value as the effective insulation resistance value output this time, and updating the stored reference insulation resistance value with the second insulation resistance value;

[0013] when the second insulation resistance value is less than the reference insulation resistance value, outputting the reference insulation resistance value as the effective insulation resistance value output this time.

[0014] In a possible implementation, after the step of monitoring the total voltage of the energy storage system, the method further comprises:

[0015] performing filtering processing on the monitored total voltage data;

[0016] the calculating of the rate of change of the total voltage and the calculating of the insulation resistance value by using the unbalanced bridge method are both based on the voltage data after the filtering processing.

[0017] In a possible implementation, the filtering processing adopts a sliding average filtering manner.

[0018] In a possible implementation, the reference insulation resistance value is an insulation resistance value calculated in a last detection period and output as an effective insulation resistance value.

[0019] In a possible implementation, the rate of change of the total voltage is obtained by calculating a voltage change amount in a unit time.

[0020] In a possible implementation, the preset rate threshold is a calibration parameter that can be adjusted according to characteristics of the energy storage system.

[0021] According to another aspect of the embodiments of the present disclosure, a battery management system is provided, which comprises:

[0022] The processing module is configured to perform the insulation resistance detection method of the energy storage system according to any of the possible implementation manners described above.

[0023] The storage module is configured to store the reference insulation resistance value and the preset rate threshold.

[0024] According to another aspect of the embodiments of the present disclosure, a super capacitor system is provided, which comprises:

[0025] A super capacitor group.

[0026] The battery management system according to the possible implementation manners described above is connected to the super capacitor group.

[0027] According to another aspect of the embodiments of the present disclosure, a computer readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the insulation resistance detection method of the energy storage system according to any of the possible implementation manners described above are implemented.

[0028] The one or more technical solutions described above in the embodiments of the present application have at least one or more of the following technical effects:

[0029] In the insulation resistance detection method of the energy storage system provided in the embodiments of the present application, the total voltage change rate is monitored in real time, and a judgment threshold is set to dynamically distinguish the system voltage state, and the insulation resistance calculation and output strategy is adaptively selected under different working conditions. When the voltage changes gently, the real-time calculation value is directly used to ensure the detection sensitivity; when the voltage fluctuates sharply, the reference resistance comparison mechanism is introduced to effectively suppress the calculation result jump and false alarm caused by voltage mutation, and the accuracy and reliability of the insulation detection result are significantly improved. The method overcomes the defects of the traditional BMS insulation detection function in the super capacitor application, enhances the system anti-interference ability and operation safety, and does not need to increase the hardware cost, is easy to integrate and implement in the existing BMS architecture, and has high engineering application value.

[0030] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the contents of the specification can be implemented, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described below. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 A flowchart of an insulation resistance detection method of an energy storage system according to an exemplary embodiment of the present disclosure is provided.

[0032] Figure 2 This is a schematic diagram of an unbalanced bridge circuit for insulation resistance detection provided according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this disclosure clearer, the embodiments of this disclosure will be described in further detail below with reference to the accompanying drawings.

[0034] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of systems and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0035] Figure 1 This is a flowchart of an insulation resistance detection method for an energy storage system according to an exemplary embodiment, as shown below. Figure 1 As shown, the method includes the following steps:

[0036] In step S100, the total voltage of the energy storage system is monitored. By monitoring the total voltage of the energy storage system, the system obtains the key parameters necessary for subsequent insulation resistance calculation and judgment. Specifically, the voltage signal between the positive and negative buses of the energy storage system, i.e., the total voltage, can be obtained in real time through the voltage detection module of the BMS.

[0037] The voltage detection module typically includes a voltage divider circuit and an analog-to-digital converter (ADC) connected between the positive and negative buses of the energy storage system. The voltage divider circuit is used to proportionally attenuate the high voltage of the energy storage system to a low voltage range suitable for measurement, and the ADC is used to convert the divided analog voltage signal into a digital signal for the system to read and process.

[0038] Those skilled in the art will understand that the sampling period of the voltage detection module can be configured according to the characteristics of the energy storage system. For example, it can be set to a predetermined fixed frequency to realize periodic monitoring of the total voltage. By reading the conversion result of the ADC and calculating based on the known voltage division ratio, the total voltage value of the energy storage system at the current moment can be obtained.

[0039] In step S200, the rate of change of the total voltage is calculated. Specifically, this step calculates the change in the total voltage per unit time, i.e., the rate of change, based on the total voltage data monitored in step S100. The calculation of the rate of change is based on the continuously monitored total voltage value and the corresponding sampling time. By obtaining the total voltage value at the current moment and the total voltage value at at least one historical moment, and based on these voltage values ​​and their corresponding time differences, the trend and speed of change of the total voltage are determined by calculation.

[0040] In one specific implementation, the rate of change can be obtained by calculating the difference between the total voltage value obtained in the current sampling period and the total voltage value obtained in the previous sampling period, and then dividing by the time interval between the two sampling points. Those skilled in the art will understand that this is only a simplified method for calculating the rate of change; in other optional implementations, to improve anti-interference capability or smooth data, linear fitting or moving average processing of voltage data from multiple historical sampling points can be used to obtain the slope of the changing trend, and this slope can be used as a representation of the rate of change; the sampling period can be configured according to the system's requirements for response speed and calculation accuracy. Through step S200, the system can quantify the dynamic changes in the total voltage of the energy storage system, providing a crucial decision-making basis for subsequently determining whether the system is currently in a voltage stable state or a state of violent fluctuation.

[0041] In step S300, the rate of change is compared with a preset rate threshold. This step compares the rate of change calculated in step S200 with a preset rate threshold. The preset rate threshold is a pre-set reference value stored in the system storage module. This threshold is used to define whether the change in the total voltage of the energy storage system is in a relatively stable state or a state of violent fluctuation. The specific value of the preset rate threshold can be calibrated and set by those skilled in the art based on the type of energy storage system (such as the rated parameters of a supercapacitor module), application scenario, and requirements for voltage fluctuation sensitivity, through theoretical calculations, simulations, or experimental experience. For example, for a supercapacitor system with a wide operating voltage range and extremely fast charging and discharging rate, its preset rate threshold may be set to a relatively high value to avoid frequently triggering transient processing mechanisms under normal operating conditions; conversely, for a system that is more sensitive to voltage fluctuations, a lower threshold may be set.

[0042] In step S400, when the rate of change is less than the preset rate threshold, the first insulation resistance value is calculated based on the current total voltage using the unbalanced bridge method, and the first insulation resistance value is output as the effective insulation resistance value; specifically, if the comparison result in step S300 is that the rate of change is less than the preset rate threshold, then it is determined that the energy storage system is currently in a relatively stable operating state of total voltage.

[0043] Subsequently, the insulation resistance testing process is initiated, which employs the unbalanced bridge method principle known in the art. For example, the positive-to-ground reference resistor and the negative-to-ground reference resistor are sequentially switched, and the positive-to-ground voltage and / or negative-to-ground voltage corresponding to the different reference resistors are simultaneously collected.

[0044] Subsequently, based on the currently acquired total voltage and the acquired voltage to ground, an insulation resistance value is obtained by using the built-in unbalanced bridge method calculation formula, which is defined here as the first insulation resistance value.

[0045] The specific calculation formula for the unbalanced bridge method is well known to those skilled in the art. Its basic principle is to use the known resistance of the reference resistor, the total system voltage, and the measured voltage division ratio to solve for the unknown insulation resistance. For example, please refer to... Figure 2 , Figure 2 The following is a schematic diagram of an unbalanced bridge circuit for insulation resistance detection provided according to an exemplary embodiment of this disclosure, in conjunction with... Figure 2 The principle of unbalanced bridge insulation testing is explained below. The unbalanced bridge method includes the following measurement and calculation steps:

[0046] First, disconnect the positive-to-ground reference resistor connection switch S1 and the negative-to-ground reference resistor connection switch S2, and measure the first voltage to ground V0:

[0047]

[0048] in, The total voltage of the energy storage system. Given a known reference resistor on the BMS board, this step aims to obtain a reference voltage to ground without an additional reference resistor connected.

[0049] Next, switch S1 is closed and switch S2 is opened. In this switching state, the reference resistor R4 is connected to the positive terminal to ground measurement circuit through switch S1. The second voltage to ground, V1, is measured, and its calculation formula is derived based on the voltage divider principle, as follows:

[0050]

[0051] in, The insulation resistance between the positive terminal and ground. The insulation resistance between the negative terminal and ground. This represents the parallel operation of resistors. This step, by introducing a known resistor R4, alters the resistance network from the positive terminal to ground, thus establishing the first system containing unknowns. and The equation.

[0052] Finally, switch S1 is opened and switch S2 is closed. In this switching state, the reference resistor R5 is connected to the negative terminal to ground measurement circuit through switch S2. The third voltage to ground is measured. The calculation formula is as follows:

[0053]

[0054] This step, by introducing a known resistor R5, alters the resistance network between the negative terminal and ground, thereby establishing a second network containing unknowns. and The equation.

[0055] Through the above three steps, three measured voltage values ​​were obtained. The system consists of three independent equations. The only unknowns in the system are... and Although there are three equations, only two (usually equations V1 and V2) are needed to solve simultaneously for the insulation resistance between the positive electrode and ground. Insulation resistance between negative electrode and ground The resistance value.

[0056] Those skilled in the art will understand that the above calculation process is typically completed automatically by the processing module (such as an MCU) within the BMS. The processing module controls the timing of switches S1 and S2, synchronously acquires the voltage signal at voltage measurement point V, and, based on the stored reference resistance value and the measured total system voltage, executes a built-in insulation detection algorithm to ultimately calculate the insulation resistance. and .

[0057] After calculating the first insulation resistance value, the system recognizes it as the current accurate and effective insulation state characterization, and directly outputs the first insulation resistance value as the effective insulation resistance value for this detection cycle. The output target can be an upper-level controller, display unit, or data storage unit, etc.

[0058] Optionally, under this stable state, the first insulation resistance value calculated this time can also be updated and stored in a designated storage area as a reliable reference insulation resistance value for data comparison and judgment under possible voltage fluctuation conditions in the future.

[0059] Through step S400, the present invention fully leverages the advantages of direct calculation using the unbalanced bridge method when the system voltage is stable, ensuring the real-time performance and accuracy of insulation detection.

[0060] In step S500, when the rate of change is greater than or equal to the preset rate threshold, a second insulation resistance value is calculated based on the current total voltage using the unbalanced bridge method. This second insulation resistance value is then compared with a stored reference insulation resistance value, and the effective insulation resistance value output this time is selected based on the comparison result. Specifically, if the comparison result in step S300 indicates that the rate of change is greater than or equal to the preset rate threshold, it is determined that the energy storage system is currently experiencing severe fluctuations in total voltage.

[0061] Under this condition, based on the currently collected total voltage, the same unbalanced bridge method principle as in step S400 is used to calculate an insulation resistance value, which is defined here as the second insulation resistance value. It should be noted that, since the total voltage is changing rapidly at this time, the reliability of the second insulation resistance value calculated based on the instantaneous voltage value may be poor, and there is a risk of inaccuracy and jumps.

[0062] To overcome this problem, the second insulation resistance value is not immediately output as a valid value. Instead, a subsequent comparison and selection logic is executed. A pre-stored reference insulation resistance value is read from the storage module. This reference insulation resistance value is typically a reliable insulation resistance value that was calculated and stored when the system was in a voltage stable state (i.e., the state described in step S400).

[0063] The calculated second insulation resistance value is compared with the read reference insulation resistance value. The purpose of the comparison is to determine the degree of deviation of the current calculated value from the historical reliable value, thereby assessing its reliability.

[0064] Based on the comparison results, the system adaptively selects the effective insulation resistance value for the final output of this testing cycle. The selection strategy is as follows:

[0065] If the deviation between the second insulation resistance value and the reference insulation resistance value is within a preset reasonable error range, then the calculated value is determined to be reliable, and the second insulation resistance value is output as the effective insulation resistance value for this calculation.

[0066] If the deviation between the second insulation resistance value and the reference insulation resistance value exceeds the preset reasonable error range, the calculated value is determined to be unreliable due to drastic voltage fluctuations. Therefore, the value is discarded, and the stored reference insulation resistance value is output as the valid insulation resistance value for this calculation.

[0067] Through step S500, the present invention introduces a verification and selection mechanism based on historical reliable data when the system voltage fluctuates drastically. This mechanism effectively filters out unreliable calculation results caused by instantaneous voltage changes, prevents abnormal jumps in insulation resistance and false alarms, and significantly improves the stability of detection results and the reliability of system operation.

[0068] By monitoring the rate of change of total voltage in real time and setting a judgment threshold, the system voltage state is dynamically distinguished, and the insulation resistance calculation and output strategy is adaptively selected under different operating conditions. When the voltage change is gradual, the real-time calculated value is directly used to ensure detection sensitivity; when the voltage fluctuates sharply, a reference resistance comparison mechanism is introduced to effectively suppress the jump in calculation results and false alarms caused by voltage abrupt changes, significantly improving the accuracy and reliability of insulation detection results. This method overcomes the shortcomings of traditional BMS insulation detection functions in supercapacitor applications, enhances the system's anti-interference capability and operational safety, and does not require additional hardware costs, making it easy to integrate and implement in existing BMS architectures, thus possessing high engineering application value.

[0069] This patent provides an insulation resistance detection scheme for energy storage systems, primarily addressing the unique problems encountered during the operation of energy storage systems composed of supercapacitors. When an external load connected to the supercapacitor system undergoes high-current charging and discharging, the total system voltage changes drastically within a very short time. This causes a significant deviation between the insulation resistance value calculated by the battery management system using the traditional unbalanced bridge method and the actual value, and may even trigger false alarms.

[0070] This solution dynamically identifies whether the system is in a state of rapid voltage fluctuation by monitoring the rate of change of the total system voltage in real time. Based on this, it adaptively adjusts the calculation and output strategy of insulation resistance. When the voltage change is gradual, real-time calculated values ​​are used to ensure detection sensitivity. When the voltage fluctuates drastically, a comparison mechanism based on historical reliable data is introduced to effectively suppress jumps in calculation results, avoid false alarms, and significantly improve the accuracy of detection results and the reliability of system operation.

[0071] Although this method is also applicable to other types of energy storage systems such as lithium-ion batteries, the advantages of this patented technology are particularly prominent in this application scenario because the voltage of supercapacitor systems changes more rapidly and frequently.

[0072] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0073] Preferably, the step of selecting the effective insulation resistance value for this output based on the comparison result includes:

[0074] When the second insulation resistance value is greater than the reference insulation resistance value, the second insulation resistance value is output as the valid insulation resistance value for this operation, and the stored reference insulation resistance value is updated with this second insulation resistance value. Specifically, if the second insulation resistance value is greater than the reference insulation resistance value, this indicates that the currently calculated insulation resistance shows an increasing trend, which usually means that the insulation state of the system has not deteriorated or is in the process of recovery. In this case, the second insulation resistance value is determined to be reliable and better reflects the current good insulation state of the system. Therefore, the second insulation resistance value is output as the valid insulation resistance value for this operation. At the same time, in order to ensure that the reference value used for subsequent comparisons can track the good changes in the insulation state of the system, this updated and larger insulation resistance value is also written to the storage module, updating and replacing the previously stored reference insulation resistance value.

[0075] When the second insulation resistance value is less than the reference insulation resistance value, the reference insulation resistance value is used as the valid insulation resistance value output this time. Specifically, if the second insulation resistance value is less than the reference insulation resistance value, this indicates that the currently calculated insulation resistance shows a decreasing trend. Given that the system is operating under conditions of drastic voltage fluctuations, this decreasing trend is most likely due to calculation inaccuracies caused by rapid changes in the total voltage, rather than a true decrease in insulation performance. To avoid outputting this unreliable low value and causing false alarms, the second insulation resistance value will be discarded. Instead, the stored, historically reliable reference insulation resistance value will be used as the valid insulation resistance value output this time. In this case, the reference insulation resistance value remains unchanged and is not updated.

[0076] Through the above judgment rules, the present invention can intelligently identify the reliability of calculation results during voltage fluctuation periods: only calculation results indicating improved or stable insulation status are adopted and the reference benchmark is updated; while calculation results suspected of abnormally decreasing are discarded, maintaining the original reliable data output, thereby greatly enhancing the anti-interference ability and output stability of the insulation detection algorithm.

[0077] In an exemplary embodiment, after the step of monitoring the total voltage of the energy storage system, the method further includes:

[0078] The monitored total voltage data is filtered; specifically, this step performs a digital filtering algorithm on the raw total voltage data obtained by the voltage detection module to obtain more stable and reliable total voltage data.

[0079] The filtering process aims to suppress or eliminate high-frequency noise, transient interference, or sampling value jitter that may be mixed in the sampled signal, thereby improving the stability and reliability of the data and providing a high-quality data foundation for subsequent calculations. The digital filtering algorithm can be implemented using various software methods known to those skilled in the art, such as, but not limited to, moving average filtering and first-order low-pass filtering. The specific type of the filtering algorithm and its parameters can be configured and calibrated according to the characteristics and application requirements of the energy storage system.

[0080] The calculation of the rate of change of the total voltage and the calculation of the insulation resistance value using the unbalanced bridge method are both based on the filtered voltage data. In subsequent steps S200, S400, and S500, the total voltage used is the voltage data after the filtering process described in this step.

[0081] By introducing this filtering preprocessing step, the present invention can effectively improve the quality of the original sampling data, so that the calculated voltage change rate can more realistically reflect the overall change trend of the system voltage, rather than instantaneous interference; at the same time, it also provides a more stable and accurate total voltage input value for the calculation of the unbalanced bridge method, thereby further enhancing the anti-interference ability and result accuracy of the entire insulation resistance detection method from the source.

[0082] Preferably, the filtering process employs a moving average filtering method. Specifically, the moving average filtering is implemented as follows: the system maintains a sampling data queue of fixed length N to store N consecutively acquired raw total voltage data. Whenever a new total voltage sampling data is acquired, it is stored at the end of the queue, while the oldest historical data in the queue is removed. Subsequently, the arithmetic mean of all N data in the current queue is calculated, and this average is output as the effective total voltage value after this filtering process for subsequent calculations.

[0083] The queue length N (i.e. the size of the sliding window) is a configurable integer value, such as N=5; this means that the main control unit will always perform subsequent calculations based on the average of the latest N total voltage sampling data.

[0084] The moving average filtering method can effectively smooth out random high-frequency noise and instantaneous spike interference in the sampled data, providing stable data that reflects the overall trend of voltage change. At the same time, the algorithm is simple to implement and has low computational load, making it very suitable for real-time operation in embedded systems. This lays a reliable data foundation for further accurate calculation of voltage change rate and insulation resistance.

[0085] Preferably, the reference insulation resistance value is the insulation resistance value calculated in the previous detection cycle and output as the effective insulation resistance value. Specifically, after completing a full insulation detection cycle, the final determined and output effective insulation resistance value is stored in the storage module. In the next detection cycle, if step S500 needs to be executed, the effective insulation resistance value stored in the previous detection cycle will be read from the storage module and used as the current reference insulation resistance value for comparison with the second insulation resistance value calculated in the new round.

[0086] This configuration ensures that the reference insulation resistance value is always a verified and reliable benchmark, representing the insulation state confirmed by the system at the most recent normal operation moment, thus providing a solid and real-time updated basis for comparison to determine the reliability of calculation results during voltage fluctuations.

[0087] Preferably, the rate of change of the total voltage is obtained by calculating the voltage change per unit time. In a specific implementation, the main control unit obtains the filtered total voltage value at the current sampling time and the filtered total voltage value at the previous sampling time. The rate of change is obtained by calculating the ratio of the voltage difference between two adjacent sampling points to the corresponding time interval, i.e.: in, The voltage difference between two adjacent sampling points. The sampling time interval is the preset sampling period.

[0088] Through the above methods, the system can quantify the rate and trend of total voltage changes in real time with simple and efficient calculations, providing a direct and effective basis for subsequent judgment on whether the system is in a state of violent voltage fluctuations.

[0089] Preferably, the preset rate threshold is a calibration parameter that can be adjusted according to the characteristics of the energy storage system. Specifically, the preset rate threshold is not a fixed constant, but a configurable variable stored in the non-volatile storage module of the system. Its value can be externally calibrated and set according to the specific type of the energy storage system, rated voltage, maximum charge and discharge current capability, and specific requirements for voltage fluctuation sensitivity in the application scenario, using professional debugging tools or host computer software.

[0090] For example, for a supercapacitor system with extremely high power characteristics and drastic voltage fluctuations during charging and discharging, the preset rate threshold can be calibrated to a relatively large value to avoid frequent triggering of transient processing logic under normal high-power conditions. Conversely, for a system that is more sensitive to voltage fluctuations and operates more smoothly, a smaller threshold can be set to improve the sensitivity of voltage change monitoring.

[0091] This design, which sets the key judgment threshold as a calibrable parameter, greatly enhances the adaptability and flexibility of the insulation detection method. It enables the same hardware and algorithm framework to be flexibly applied to various types of energy storage systems through different parameter configurations, thereby improving the method's versatility and engineering application value.

[0092] To more clearly illustrate the working principle and effect of the above output selection strategy, specific numerical examples are provided below. It should be noted that the insulation resistance values ​​(unit: kiloohms) and variation sequences in the following examples are only set to clearly demonstrate the logic of this patent's algorithm and are not intended to limit the scope of protection of this patent.

[0093] Example 1: Filtering abnormal fluctuation values ​​(preventing jumps)

[0094] Assume that a sudden anomaly occurs in the insulation test calculation due to a drastic fluctuation in the total voltage. The insulation resistance values ​​calculated by the BMS for the next three consecutive test cycles are as follows: , , The processing logic and output results are as follows:

[0095] Initially, the stored reference insulation resistance value (i.e., the output value of the previous cycle) is 900 kΩ.

[0096] First moment: The new resistance value is calculated. .because Therefore, the value is deemed reliable, and the effective insulation resistance value for this test is output as follows: This value is used to update the stored reference insulation resistance value.

[0097] Second moment: Calculate the new resistance value. .because (Comparison of current value and latest reference value) Therefore, this value is determined to be unreliable and is abandoned. The effective insulation resistance value for this time is still output as [value]. The stored reference insulation resistance value remains unchanged.

[0098] Third moment: The new resistance value is calculated. .because (But compared with the current reference value) Comparison According to the rules, if the new value is greater than the previous calculated value... But less than the reference value Usually, it will still be due to Output reference value However, to more accurately simulate the logic of the disclosure document (i.e., comparing with the "calculated value at the previous moment" rather than the "reference value"), the comparison object needs to be clearly defined. The logic in the disclosure document is actually: comparing the current calculated value with the calculated value (not the output value) of the previous calculation cycle. To accurately reflect this logic, the rules should be more precisely defined. Preferably, the "insulation resistance value calculated at the previous moment" refers to the original resistance value calculated by the unbalanced bridge method in the previous detection cycle, rather than the effective resistance value output after selection logic. The comparison is a direct comparison between the current calculated value and the calculated value of the previous cycle.

[0099] According to this precise logic: the calculated value at the third time step Comparison with the calculated value at the second time point Comparison, because Therefore, the value is deemed reliable, and the current valid insulation resistance value output is updated to [value]. This value is used to update the stored reference insulation resistance value.

[0100] Through the above process, outliers Effectively filtered, output values ​​from Smooth change to This avoids the insulation resistance value jumping due to voltage fluctuations.

[0101] Example 2: Delayed reporting of actual insulation faults

[0102] Assume the system begins to experience a genuine decline in insulation performance. The insulation resistance values ​​calculated by the BMS over the next four consecutive monitoring cycles show a decreasing trend: The processing logic and output results are as follows:

[0103] Initially, the stored reference insulation resistance value (i.e., the output value of the previous cycle) is: .

[0104] First moment: Calculated value Output Update the reference values.

[0105] Second moment: Calculated value Output maintained The reference value remains unchanged.

[0106] Third moment: Calculated value (Compared with the calculated value from the previous time step), the output is maintained. The reference value remains unchanged. (Note: The output value here is not updated; the actual fault status is reported with a one-cycle delay.)

[0107] Fourth time step: Calculated value (Compared with the previous calculated value), the output is updated to... And update the reference values.

[0108] Therefore, when a real insulation fault occurs in the system, this method may delay reporting the final drop value by about 1-2 detection cycles, but it will never miss a report. This short delay is to obtain extremely high stability and false alarm prevention capability under conditions of drastic voltage fluctuations. For systems such as supercapacitors, this trade-off is necessary and beneficial.

[0109] This disclosure also provides a battery management system, including:

[0110] The processing module is used to execute an insulation resistance detection method for an energy storage system as described in any of the above embodiments;

[0111] The storage module is used to store the reference insulation resistance value and the preset rate threshold.

[0112] The processing module, as the core computing and control unit of the system, is typically implemented by a microcontroller unit (MCU), a digital signal processor (DSP), or a combination thereof. It is responsible for executing all algorithm steps of the insulation resistance detection method, including but not limited to: controlling voltage sampling, calculating the total voltage change rate, performing threshold comparison, performing unbalanced bridge calculation, and selecting and outputting the effective insulation resistance value based on the comparison result.

[0113] The storage module provides necessary data storage support for the processing module. Its stored content mainly includes:

[0114] The reference insulation resistance value: This value is calculated and written by the processing module when the system is in a stable state, or updated or maintained according to the strategy during transient processes, providing a benchmark for comparison and judgment under transient conditions.

[0115] The preset rate threshold: This threshold serves as a benchmark for determining whether the system has entered a state of severe voltage fluctuation, and is a calibrable parameter.

[0116] The storage module can be persistently stored using non-volatile memory to ensure that key parameters are not lost after power failure; at the same time, volatile memory can also be used to store temporary calculation data and intermediate variables.

[0117] The battery management system, through the collaborative operation of the processing module and the storage module, implements the above-mentioned insulation resistance detection method, thereby adaptively responding to the operating conditions of drastic voltage fluctuations in energy storage systems such as supercapacitors, providing accurate and reliable insulation status monitoring, and effectively preventing false alarms.

[0118] This disclosure also provides a supercapacitor system, including:

[0119] Supercapacitor bank;

[0120] The battery management system described in the above embodiment is connected to the supercapacitor bank. The battery management system is connected to the positive and negative busbars of the supercapacitor bank via its sampling lines to monitor its total voltage; simultaneously, the insulation detection execution module of the battery management system is connected to the system ground via a detection point, thereby forming an unbalanced bridge circuit for calculating the insulation resistance of the positive and negative terminals to ground.

[0121] By integrating the optimized battery management system, this supercapacitor system can effectively solve the problems of false alarms and voltage jumps in insulation detection caused by the drastic fluctuations in total voltage due to the high current charging and discharging of the supercapacitor bank, thus significantly improving the safety and reliability of the system operation.

[0122] In an exemplary embodiment, a computer-readable storage medium is also provided, on which a computer program is stored. When executed by a processor, the computer program implements the steps of an insulation resistance detection method for an energy storage system as described in any of the above embodiments. Optionally, the storage medium is a non-transitory computer-readable storage medium, such as a ROM (Read-Only Memory), RAM (Random Access Memory), CD-ROM (Compact Disc Read-Only Memory), magnetic tape, floppy disk, and optical data storage device.

[0123] In an exemplary embodiment, a computer program product is also provided, which includes computer program code stored in a computer-readable storage medium. A processor of a computer device reads the computer program code from the computer-readable storage medium and executes the computer program code, causing the computer device to perform the operations performed in the above-described method for detecting the insulation resistance of an energy storage system.

[0124] Any aspects of this invention not described in detail are well-known to those skilled in the art.

[0125] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for detecting the insulation resistance of an energy storage system, characterized in that, Includes the following steps: Monitor the total voltage of the energy storage system; Calculate the rate of change of the total voltage; The rate of change is compared with a preset rate threshold. When the rate of change is less than the preset rate threshold, the first insulation resistance value is calculated based on the current total voltage using the unbalanced bridge method, and the first insulation resistance value is output as the effective insulation resistance value. When the rate of change is greater than or equal to the preset rate threshold, the second insulation resistance value is calculated based on the current total voltage using the unbalanced bridge method, and the second insulation resistance value is compared with the stored reference insulation resistance value. Based on the comparison result, the effective insulation resistance value for this output is selected.

2. The insulation resistance detection method for an energy storage system according to claim 1, characterized in that, The step of selecting the effective insulation resistance value for this output based on the comparison result includes: When the second insulation resistance value is greater than the reference insulation resistance value, the second insulation resistance value is output as the effective insulation resistance value for this output, and the stored reference insulation resistance value is updated with the second insulation resistance value. When the second insulation resistance value is less than the reference insulation resistance value, the reference insulation resistance value is taken as the effective insulation resistance value of this output.

3. The insulation resistance detection method for an energy storage system according to claim 1, characterized in that, Following the step of monitoring the total voltage of the energy storage system, the following is also included: The monitored total voltage data is filtered. The calculation of the rate of change of the total voltage and the calculation of the insulation resistance value by the unbalanced bridge method are both based on the filtered voltage data.

4. The insulation resistance detection method for an energy storage system according to claim 3, characterized in that, The filtering process employs a moving average filtering method.

5. The insulation resistance detection method for an energy storage system according to claim 1, characterized in that, The reference insulation resistance value is the insulation resistance value calculated in the previous detection cycle and output as the effective insulation resistance value.

6. The method for detecting the insulation resistance of an energy storage system according to claim 1, characterized in that, The rate of change of the total voltage is obtained by calculating the voltage change per unit time.

7. The insulation resistance detection method for an energy storage system according to claim 1, characterized in that, The preset rate threshold is a calibration parameter that can be adjusted according to the characteristics of the energy storage system.

8. A battery management system, characterized in that, include: The processing module is used to execute the insulation resistance detection method of an energy storage system as described in any one of claims 1 to 7; The storage module is used to store the reference insulation resistance value and the preset rate threshold.

9. A supercapacitor system, characterized in that, include: Supercapacitor bank; The battery management system as described in claim 8 is connected to the supercapacitor bank.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the insulation resistance detection method for an energy storage system as described in any one of claims 1 to 7.

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