Supercapacitor health state monitoring method

Abstract

The invention discloses a super capacitor health state monitoring method, which comprises the following steps of: if a preset condition is met, continuously performing online monitoring on the health state of a super capacitor in the current month, updating the health state SOH of the current month according to the most accurate health state, and synchronously calculating the health state SOH offline in an offline state by using an empirical model. When the SOH is in the corrected off-line state, the SOH is off-line, and a conversion factor is calculated; if the preset condition is not met, off-line monitoring is carried out on the health state of the supercapacitor in the current month, the SOH off-line state is calculated by using an empirical model, the SOH off-line state is corrected by using a conversion factor and a weight, and then the SOH off-line state of the supercapacitor in the current month is updated by using the corrected health state; the preset condition is set as follows: the number of times of online monitoring is greater than or equal to a first set value and the error of each health state is less than or equal to a second set value.

Description

Technical Field

[0001] The present invention belongs to the technical field of supercapacitors, and in particular relates to a method for monitoring the health status of a supercapacitor. Background Art

[0002] Supercapacitors have been gradually applied to automobile auxiliary power sources due to their extremely high power density and fast charging and discharging capabilities, and have broad market prospects. Therefore, in practical applications, accurate estimation of the SOH (State of Health) of supercapacitors is crucial to improving the efficiency and safety of battery management systems. Accurate SOH estimation can ensure that supercapacitors operate under safe and efficient conditions and reduce possible failures of supercapacitors during use.

[0003] At present, online estimation methods are often used to calculate SOH, that is, assuming that the capacity (C) of the supercapacitor is a constant value within the full voltage operating range, then C = dCap / dv, and then the dCap (capacity difference) / dv (voltage difference) between the two current and voltage collection points is used to estimate SOH. This strategy usually selects one collection point as the static voltage point at the beginning of charging and the other as the static voltage point at the end of charging. The current health status of the supercapacitor is calculated by recording the accumulated charge and discharge capacity difference (dCap) between these two points.

[0004] However, this method does not take into account the actual current and voltage acquisition errors. The larger the cumulative capacity value, the greater the impact of the current acquisition error on the calculation result, making the estimated SOH value deviate from the true value. At the same time, this method uses online estimation to update the SOH value, which requires corresponding working conditions to be met. If the conditions are not met during actual overcapacity operation, online calculations cannot be performed, and the SOH value cannot be updated in a timely and accurate manner, which will cause certain hidden dangers to the safe operation of the system. Summary of the invention

[0005] The present invention provides a supercapacitor health status monitoring method, which solves the technical problems that the existing estimation method does not consider the acquisition errors of voltage, current and cumulative capacity, causing the estimated SOH value to deviate from the true value.

[0006] The present invention can be achieved through the following technical solutions:

[0007] A supercapacitor health status monitoring method, if the preset conditions are met, the health status of the supercapacitor in the current month is continuously monitored online, and the health status SOH of the current month is updated with the most accurate health status 当在 , and simultaneously use the empirical model to calculate the health status SOH in the offline state 离线 , and then the health status SOH 当在Corrected offline health status SOH 离线 , and calculate the conversion factor;

[0008] If the preset conditions are not met, the health status of the supercapacitor in the current month is monitored offline, and the health status SOH is calculated using the empirical model. 离线 and use the conversion factor and weight to calculate the health status SOH 离线 Make corrections and then update the health status SOH of the current month with the corrected health status 当离 ;

[0009] The preset condition is set as follows: the number of online monitoring times is greater than or equal to a first set value and the error of each health status is less than or equal to a second set value.

[0010] Further, the preset conditions are set as follows:

[0011] When the vehicle is just powered on, the most recent sleep time of the supercapacitor is detected. If the sleep time is greater than the third set value, online monitoring is started to calculate the health status and error of the supercapacitor at this time. If the number of online monitoring starts in the current month is greater than or equal to the first set value, and the error of the health status each time is less than or equal to the second set value, online monitoring is continuously used;

[0012] Otherwise, sampling is performed for offline monitoring.

[0013] Furthermore, each time the online monitoring is performed, the time point corresponding to when the vehicle is just powered on is taken as the static start point, and the time point when the vehicle is running normally and the voltage of the supercapacitor reaches the floating charge voltage is taken as the static end point.

[0014] Record the time point corresponding to the starting point of the supercapacitor standing still 起始点 , voltage V0, charging capacity Cap0, record the time point Time corresponding to the starting point of the supercapacitor standing 终止点 , voltage V1, charging capacity Cap1, use the following formula to calculate the health state SOH of the supercapacitor at this time 考虑误差在线 , Health Status SOH 不考虑误差在线 and Error;

[0015]

[0016]

[0017] Error=Abs(SOH 考虑误差在线 -SOH 不考虑误差在线 )

[0018] Among them, Cap1 and V1 represent the charging capacity and voltage of the supercapacitor at the static end point respectively; Cap0 and V0 represent the charging capacity and voltage of the supercapacitor at the static start point; (Cap1-Cap0)*1% represents the capacity gain error of the supercapacitor from the static end point to the static start point; C BOL Indicates the charging capacity of the supercapacitor when it leaves the factory; Time 终止点 Indicates the recording time point corresponding to the static end point; Time 起始点 Indicates the recording time point corresponding to the static starting point; (Time 终止点 -Time 起始点 )*10mA / 1000 represents the fixed error from the end point of the static rest to the start point of the static rest.

[0019] Furthermore, the empirical model is used to construct an offline empirical formula to iteratively calculate the corresponding health status SOH at equal time intervals. new The health status SOH calculated at the last moment of the current month new As the health status SOH for the current month 离线 , the offline empirical formula is as follows

[0020]

[0021] Among them, SOH new Indicates the SOH value updated at the current moment; SOH Last Indicates the SOH value at the last moment; LifeTime indicates the working life of the overcapacitor at the rated voltage and rated temperature; Δt indicates the time difference between the current moment and the last moment; V indicates the average voltage value between the current moment and the last moment; V rate represents the rated voltage value; α1 represents the voltage correlation coefficient; α2 represents the temperature correlation coefficient; T rate Indicates the rated temperature value; T indicates the average temperature value between the current moment and the previous moment;

[0022] Take the health status SOH of the current month 当在 Replace the health status SOH of the current month 离线 , which will be used as the starting value of the health status for the next month's iterative calculation;

[0023] Using the formula Calculate the reduction factor α T1 , α T1 Indicates the conversion factor corresponding to time point T1 in the current month.

[0024] Furthermore, when performing offline monitoring, the following formula is used to calculate the health status SOH of the current month:

[0025] SOH当离 =SOH 离 *α T1 *β T2_T1

[0026] Among them, β T2_T1 Indicates the weight coefficient corresponding to the time point T2 of the current month to the time point T1, and selects the conversion factor α corresponding to the time point T1 closest to the time point T2 of the current month T1 Health status SOH for the current month 当离 calculate.

[0027] Furthermore, the first setting value is 3, the second setting value is 5%, and the third setting value is 10 minutes.

[0028] A real-time estimation method for the health status of supercapacitors, using empirical models to iteratively calculate the health status SOH in offline state new , select the health status SOH corresponding to the last moment new As a state of health SOH 离线 If the preset conditions are met, the online health status SOH is calculated synchronously 当在 and use the health status SOH 当在 Corrected State of Health SOH 离线 , get the health status SOH of the current month 当离 , and calculate the conversion factor;

[0029] If the preset conditions are not met, the health status SOH is calculated using the conversion factor and weight. 离线 Correction is performed to obtain the health status SOH for the current month 当离 .

[0030] Furthermore, if the current month meets the preset conditions, the health status SOH calculated online with the smallest error is selected. 当在 Alternative State of Health (SOH) 离线 , as the health status SOH for the current month 当离 ;

[0031] And using the formula Calculate the reduction factor α T1 .

[0032] If the current month does not meet the preset conditions, the following formula is used to calculate the health status SOH of the current month:

[0033] SOH 当离 =SOH 离线 *α T1 *β T2_T1

[0034] Among them, βT2_T1 Indicates the weight coefficient corresponding to the time point T2 of the current month to the time point T1, and selects the conversion factor α corresponding to the time point T1 closest to the time point T2 of the current month T1 Health status SOH for the current month 当离 calculate.

[0035] The beneficial technical effects of the present invention are as follows:

[0036] 1. By formulating preset conditions, the health status of the current month can be executed by online monitoring or offline monitoring, and there will be no situation of no monitoring, which provides protection for the safe use of supercapacitors. At the same time, the offline monitoring is initially corrected with the results of online monitoring to compensate for the errors of offline monitoring and improve the accuracy of offline monitoring. The results of offline monitoring are corrected for the second time with conversion factors and weights, which effectively reduces the errors caused by long-term iterations of offline monitoring and is more practical.

[0037] 2. Considering the actual working conditions of the supercapacitor, the health status calculated based on the conventional calculation formula will have errors. The present invention improves the calculation formula for online monitoring by adding current error and voltage error, thereby improving the calculation accuracy of online monitoring.

[0038] 3. Even if online monitoring is used in the current month, offline calculation will still be started to ensure the continuity of offline calculation during the life cycle of the supercapacitor. As long as online monitoring is used in the current month, the corresponding offline calculation result will be updated with its calculation result, which will be used as the starting value for the next month's offline iterative calculation. At the same time, the conversion factor will also be calculated synchronously for use in the next offline monitoring result correction, thereby effectively improving the accuracy of offline monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0040] Figure 2 It is a schematic diagram of the overall process of online monitoring of the present invention;

[0041] Figure 3 It is a schematic diagram of the process of online updating and offline updating of the present invention. DETAILED DESCRIPTION

[0042] The specific implementation of the present invention is described in detail below with reference to the accompanying drawings and preferred implementation examples.

[0043] Refer to the attached Figure 1-3 The present invention provides a supercapacitor health status monitoring method. If the preset conditions are met, the health status of the supercapacitor in the current month is continuously monitored online, and the health status SOH of the current month is updated with the most accurate health status.当在 , and simultaneously use the empirical model to calculate the health status SOH in the offline state 离线 , and then the health status SOH 当在 Corrected offline health status SOH 离线 , and calculate the conversion factor; if the preset conditions are not met, the health status of the supercapacitor in the current month is monitored offline, and the health status SOH is calculated using the empirical model 离线 and use the conversion factor and weight to calculate the health status SOH 离线 Make corrections and then update the health status SOH of the current month with the corrected health status 当离 ; The preset condition is set to the number of online monitoring times being greater than or equal to the first set value and the error of each health status being less than or equal to the second set value. In this way, by formulating preset conditions, the health status of the current month can be performed by online monitoring or offline monitoring, and there will be no situation of no monitoring, which provides protection for the safe use of supercapacitors. At the same time, the offline monitoring is initially corrected with the results of online monitoring to compensate for the errors of offline monitoring and improve the accuracy of offline monitoring. The results of offline monitoring are secondarily corrected with conversion factors and weights, which effectively reduces the errors caused by long-term iterations of offline monitoring and is more practical.

[0044] The details are as follows:

[0045] 1. Improvement of health status calculation for online monitoring

[0046] Existing health state SOH online estimation methods usually select the capacity and voltage data corresponding to two static sampling points, and then use the dCap / dv formula to calculate the capacity value to obtain the health state SOH value.

[0047] For supercapacitors, C=Q / U, C is the capacity F of the supercapacitor, Q is the amount of electricity As stored in the supercapacitor at the current moment, and U is the current voltage mV of the supercapacitor. Assuming that the capacity C of the supercapacitor in the entire working range is a constant value, you can arbitrarily select a supercapacitor static state as the first calculation point, record the voltage value V0 and the current charging capacity Cap0 of the supercapacitor at this time, then charge the supercapacitor to a higher voltage, and then keep it in a static state, select the voltage V1 of the second calculation point and the current charging capacity Cap1 of the supercapacitor, then the capacity C of the supercapacitor can be calculated by the following formula.

[0048] C=(Cap1-Cap0) / (V1-V0) (1)

[0049] This formula can be used to calculate the C value of the overcapacity in actual vehicle operation in real time, and then the health status SOH value can be calculated using the following formula.

[0050] SOH=C / C BOL (2)

[0051] Among them, C is the excess capacity at the current moment, C BOL It is the excess capacity when it leaves the factory. It is generally believed that when SOH < 0.8, the excess capacity cannot meet the normal driving requirements, and the excess capacity is in the EOL (End of Life) state.

[0052] Considering the actual operating conditions of supercapacitors as backup power supplies, the general supercapacitor working voltage is in the range of 0V to 3V. When the power is just turned on, if the last sleep time of the supercapacitor is greater than or equal to 10 minutes, it is often in a low voltage static state (generally considered to be around 2V), which can be used as the first static sampling point to record the current voltage value V0 and the current supercapacitor charging capacity Cap0; after the vehicle is operating normally, the 12V low-voltage power supply will charge the supercapacitor to a floating charge voltage of about 2.84V and will remain in a floating charge state. At this time, the current is small and the voltage is basically constant, that is, maintained at about 2.8V. If the current current absolute value is less than 2A and lasts for 10 minutes, it can often be considered that the supercapacitor is in a static state, which can be used as the second static sampling point to record the current voltage value V1 and the current supercapacitor charging capacity Cap1, and then use the above capacity calculation formula to obtain the current capacity value of the supercapacitor without considering the current and voltage sampling errors.

[0053] However, in actual situations, the capacity C of the supercapacitor in the entire working range is not a constant value. The capacity C calculated from charging from different static voltage points such as 1V, 1.5V, and 2V to the specified voltage (generally controlled at 2.84V) is not the same. Therefore, the online estimation algorithm needs to consider the calculation error caused by the nonlinear characteristics of the capacity in the entire working range.

[0054] Since in actual operation, the termination voltage point of overcapacitance is basically the same (generally controlled at around 2.84V), here we only distinguish different starting voltage points and calculate the capacity conversion factors corresponding to different starting voltage points, such as 1V-α cap0 , 1.5V-α cap1 , 2V-α cap2 , and then multiply the calculated current capacity value C by the capacity conversion factor to obtain the actual online calculated capacity value of the overcapacity at the current moment.

[0055] Note: If the starting voltage is between 1V and 1.5V or 1.5V and 2V, interpolation processing is generally used; if the static voltage at the starting point of overcapacity is lower than 1V, it is generally believed that the error of the current voltage sampling will exceed the set value of 5mV, and the actual calculated SOH value will deviate from the normal range and will not be calculated online.

[0056] According to the capacity calculation formula, there are two main sources of error:

[0057] 1. (Cap1-Cap0=ΔCap) is the difference in charging capacity between the two points accumulated by ampere-hour integration. The formula for calculating ΔCap by ampere-hour integration is ΔCap=∫Idt, that is, the charge and discharge capacity is calculated from the starting point until the end point, and the difference in charge and discharge capacity from the starting point to the end point is counted. The error source is mainly the current sampling error. The current sampling error is generally divided into gain error (1%) and fixed error (10mA). The gain error is generally the absolute value of the current current multiplied by 1%, while the fixed error generally has this error value regardless of the current size. The larger the current or the longer the ampere-hour integration lasts, the larger the error of the calculated ΔCap. Therefore, this part of the influence needs to be considered when calculating the error.

[0058] 2. (V1-V0) is the voltage value at the end point minus the voltage value at the start point. It mainly considers the voltage sampling error. For single-point voltage sampling, the general error is 5mV. Therefore, the error sampling of two points is generally 10mV. The impact of this part should also be considered when calculating the error.

[0059] According to the above formula analysis, considering the maximum error, that is, the current sampling error is calculated as 1% ± 10mA at the maximum, and the voltage sampling error is calculated as 10mV at the maximum, the SOH value calculation formula considering the error is as follows:

[0060]

[0061] Among them, Cap1 and V1 represent the charging capacity and voltage of the supercapacitor at the static end point respectively; Cap0 and V0 represent the charging capacity and voltage of the supercapacitor at the static start point; (Cap1-Cap0)*1% represents the capacity gain error of the supercapacitor from the static end point to the static start point; C BOL Indicates the charging capacity of the supercapacitor when it leaves the factory; Time 终止点 Indicates the recording time point corresponding to the static end point; Time 起始点 Indicates the recording time point corresponding to the static starting point; (Time 终止点 -Time 起始点 )*10mA / 1000 represents the fixed error from the end point of the static rest to the start point of the static rest.

[0062] 2. Offline empirical formula construction

[0063] Use the empirical model to construct an offline empirical formula, as follows: The corresponding health status SOH is obtained by continuous iterative calculation at equal time intervals new .

[0064]

[0065] Among them, SOH newIndicates the SOH value updated at the current moment; SOH Last Indicates the SOH value at the last moment; LifeTime indicates the working life of the overcapacitor at the rated voltage and rated temperature; Δt indicates the time difference between the current moment and the last moment; V indicates the average voltage value between the current moment and the last moment; V rate Indicates the rated voltage value; α volt represents the voltage correlation coefficient; α temp represents the temperature correlation coefficient; T rate represents the rated temperature value; T represents the average temperature value between the current moment and the previous moment.

[0066] 3. Online and offline real-time monitoring

[0067] Usually, the health status SOH of the supercapacitor will be updated once a month. The supercapacitor management system will have multiple power-on and power-off processes within a month. Each time if the specified charging conditions are met, it can calculate an online monitoring value, and at the same time, the error corresponding to the online monitoring value can be calculated. Finally, each online monitoring value and the corresponding error are recorded in the online calculation array of each month and the error array corresponding to the online calculation. Therefore, we can use these two arrays to set the preset conditions in advance, that is, the number of online monitoring is greater than or equal to the first set value, such as three times, and the error of each health status is less than or equal to the second set value, such as 5%. If the preset conditions are met, online monitoring will continue to be used, otherwise offline monitoring will be used.

[0068] Specifically, each time the vehicle is powered on, the most recent sleep time of the supercapacitor is detected. If the sleep time is greater than a third set value, such as ten minutes, online monitoring is started to calculate the health status and error of the supercapacitor at this time. If the number of online monitoring starts in the current month is greater than or equal to the first set value, and the error of the health status each time is less than or equal to the second set value, online monitoring is continuously used;

[0069] Otherwise, offline monitoring is adopted.

[0070] When judging the preset conditions, the error Error of the health status is calculated according to the following formula:

[0071] Error=Abs(SOH 考虑误差在线 -SOH 不考虑误差在线 ) (5)

[0072] Among them, SOH 考虑误差在线 According to formula (3), SOH 不考虑误差在线 Calculate according to formula (1)(2).

[0073] When performing online monitoring, in addition to calculating the corresponding health status SOH according to formula (3), 在, and select the most accurate health status SOH 在 Update the health status SOH for the current month 当在 In addition, it is also necessary to iteratively calculate the health status SOH in the offline state according to formula (4) new , and in a healthy state SOH 当在 Correct the health status SOH in offline state for the current month 离线 , we can select the last moment corresponding to the health state SOH new As the health status SOH in the offline state of the current month 离线 Since the online calculation results are much more accurate than the offline calculation results, we use the healthy state SOH 当在 Replace the last moment corresponding to the health status SOH new , which is used as the starting value of the health status for the next month's iterative calculation, that is, the health status SOH in the offline state of the current month 离线 That is the state of health SOH 当在 , so as to correct the offline calculation results.

[0074] Considering that the next month may not be monitored offline, when online monitoring is used in the current month, we will also use the formula Calculate the reduction factor α T1 , α T1 Indicates the conversion factor corresponding to the T1 time point of the current month, SOH 离线 This is the monitoring result before correction.

[0075] When using offline monitoring, the health status SOH is calculated using empirical models 离线 , perform iterative calculation according to formula (4), and select the health state SOH corresponding to the last moment new As the health status SOH for the current month 离线 , and then use the conversion factor and weight to calculate the health status SOH 离线 Make corrections and update the health status SOH for the current month with the corrected health status 当离 .

[0076] Use the following formula to calculate the health status SOH for the current month 当离 ,

[0077] SOH 当离 =SOH 离线 *α T1 *β T2_T1 (6)

[0078] Among them, β T2_T1Indicates the weight coefficient corresponding to the time point T2 of the current month to the time point T1, and selects the conversion factor α corresponding to the time point T1 closest to the time point T2 of the current month T1 Health status SOH for the current month 当离 calculate.

[0079] It should be noted that the reduction factor α T1 It is the conversion factor closest to the current month. For example, if offline monitoring is used in the current month, and the previous month is also offline monitoring (the conversion factor will not be updated in this month), and online monitoring is used in the month before that, the conversion factor calculated in that month will be used to correct the offline monitoring results of the current month.

[0080] Although specific embodiments of the present invention are described above, those skilled in the art should understand that these are merely examples and that various changes or modifications may be made to these embodiments without departing from the principles and essence of the present invention. Therefore, the scope of protection of the present invention is limited by the appended claims.

Claims

1. A method for monitoring the health status of a supercapacitor, characterized in that: If the preset conditions are met, the health status of the supercapacitor in the current month will continue to be monitored online, and the health status SOH of the current month will be updated with the most accurate health status 当在 , and simultaneously use the empirical model to calculate the health status SOH in the offline state 离线 , and then the health status SOH 当在 Corrected offline health status SOH 离线 , and calculate the conversion factor; If the preset conditions are not met, the health status of the supercapacitor in the current month is monitored offline, and the health status SOH is calculated using the empirical model. 离线 , and use the conversion factor and weight to calculate the health status SOH 离线 Make corrections and then update the health status SOH of the current month with the corrected health status 当离 ; The preset condition is set as follows: the number of online monitoring times is greater than or equal to a first set value and the error of each health status is less than or equal to a second set value.

2. The supercapacitor health status monitoring method according to claim 1, characterized in that: The preset conditions are set as follows: When the vehicle is just powered on, the most recent sleep time of the supercapacitor is detected. If the sleep time is greater than the third set value, online monitoring is started to calculate the health status and error of the supercapacitor at this time. If the number of online monitoring starts in the current month is greater than or equal to the first set value, and the error of the health status each time is less than or equal to the second set value, online monitoring is continuously used; Otherwise, sampling is performed for offline monitoring.

3. The supercapacitor health status monitoring method according to claim 2, characterized in that: Each time online monitoring is performed, the time point corresponding to when the vehicle is just powered on is taken as the static start point, and the time point when the vehicle is running normally and the voltage of the supercapacitor reaches the floating charge voltage is taken as the static end point. Record the time point corresponding to the starting point of the supercapacitor standing still 起始点 , voltage V0, charging capacity Cap0, record the time point Time corresponding to the starting point of the supercapacitor standing 终止点 , voltage V1, charging capacity Cap1, use the following formula to calculate the health state SOH of the supercapacitor at this time 考虑误差在线 , Health Status SOH 不考虑误差在线 and Error; Error=Abs(SOH 考虑误差在线 -SOH 不考虑误差在线 ) Among them, Cap1 and V1 represent the charging capacity and voltage of the supercapacitor at the static end point respectively; Cap0 and V0 represent the charging capacity and voltage of the supercapacitor at the static start point; (Cap1-Cap0)*1% represents the capacity gain error of the supercapacitor from the static end point to the static start point; C BOL Indicates the charging capacity of the supercapacitor when it leaves the factory; Time 终止点 Indicates the recording time point corresponding to the static end point; Time 起始点 Indicates the recording time point corresponding to the static starting point; (Time 终止点 -Time 起始点 )*10mA / 1000 represents the fixed error from the end point of the static rest to the start point of the static rest.

4. The supercapacitor health status monitoring method according to claim 3, characterized in that: Use the empirical model to construct an offline empirical formula to iteratively calculate the corresponding health status SOH at equal time intervals new The health status SOH calculated at the last moment of the current month new As the health status SOH for the current month 离线 , the offline empirical formula is as follows Among them, SOH new Indicates the SOH value updated at the current moment; SOH Last Indicates the SOH value at the last moment; LifeTime indicates the working life of the overcapacitor at the rated voltage and rated temperature; Δt indicates the time difference between the current moment and the last moment; V indicates the average voltage value between the current moment and the last moment; V rate represents the rated voltage value; α1 represents the voltage correlation coefficient; α2 represents the temperature correlation coefficient; T rate Indicates the rated temperature value; T indicates the average temperature value between the current moment and the previous moment; Take the health status SOH of the current month 当在 Replace the health status SOH of the current month 离线 , which will be used as the starting value of the health status for the next month's iterative calculation; Using the formula Calculate the reduction factor α T1 , α T1 Indicates the conversion factor corresponding to time point T1 in the current month.

5. The supercapacitor health status monitoring method according to claim 4, characterized in that: When performing offline monitoring, use the following formula to calculate the health status SOH for the current month. 当离 , SOH 当离 =SOH 离线 *α T1 *β T2_T1 Among them, β T2_T1 Indicates the weight coefficient corresponding to the time point T2 of the current month to the time point T1, and selects the conversion factor α corresponding to the time point T1 closest to the time point T2 of the current month T1 Health status SOH for the current month 当离 calculate.

6. The supercapacitor health status monitoring method according to claim 2, characterized in that: The first setting value is 3, the second setting value is 5%, and the third setting value is 10 minutes.

7. A real-time estimation method for the health status of a supercapacitor, characterized in that: Use the empirical model to iteratively calculate the health status SOH in offline state new , select the health status SOH corresponding to the last moment new As a state of health SOH 离线 If the preset conditions are met, the online health status SOH is calculated synchronously 当在 and use the health status SOH 当在 Corrected State of Health SOH 离线 , get the health status SOH of the current month 当离 , and calculate the conversion factor; If the preset conditions are not met, the health status SOH is calculated using the conversion factor and weight. 离线 Correction is performed to obtain the health status SOH for the current month 当离 .

8. The method for real-time estimation of the health status of a supercapacitor according to claim 7, characterized in that: If the current month meets the preset conditions, the health status SOH calculated online with the smallest error is selected 当在 Alternative State of Health (SOH) 离线 , as the health status SOH for the current month 当离 ; And using the formula Calculate the reduction factor α T1 . If the current month does not meet the preset conditions, the following formula is used to calculate the health status SOH of the current month 当离 , SOH 当离 =SOH 离线 *α T1 *β T2_T1 Among them, β T2_T1 Indicates the weight coefficient corresponding to the time point T2 of the current month to the time point T1, and selects the conversion factor α corresponding to the time point T1 closest to the time point T2 of the current month T1 Health status SOH for the current month 当离 calculate.

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