A battery capacity verification method for a parallel intelligent DC power supply system

Abstract

The invention discloses a battery capacity verification method for a parallel intelligent direct current power supply system. The invention divides a time period based on a preset analysis time interval after reaching the preset analysis time interval, thereby obtaining divided sub-time periods, analyzes the temperature change of each group of batteries in the divided sub-time periods, thereby obtaining the temperature optimization index of each group of batteries in the divided sub-time periods, determines the charging state of the batteries according to the temperature optimization index of each group of batteries in the divided sub-time periods, and further matches the corresponding low temperature difference or high temperature difference with the corresponding preset value ranges of each group according to the determined state, and uses the matching result as the charging current adjustment value of the group of batteries in the next divided sub-time period, and ensures the accuracy of the capacity verification test by real-time monitoring and analyzing the temperature change of the batteries during the charging process and dynamically adjusting the charging strategy according to these changes.

Description

Technical Field

[0001] This invention relates to the field of battery capacity assessment technology, and in particular to a method for battery capacity assessment in a parallel intelligent DC power supply system. Background Technology

[0002] In modern power systems, batteries serve as critical energy storage devices, and their stability and reliability are essential for ensuring the continuity and security of power supply. With the development of smart grids and renewable energy technologies, the requirements for battery performance are becoming increasingly stringent; especially in parallel smart DC power systems, battery banks not only need to provide a stable DC power supply but also serve as backup power in emergencies to ensure the normal operation of critical equipment.

[0003] However, during long-term operation, batteries will gradually age due to various factors such as charge-discharge cycles, temperature changes, and self-discharge, leading to a decrease in their capacity and ultimately affecting the stability and reliability of the entire power system. Therefore, regular capacity testing of batteries is necessary to assess their health status and remaining service life.

[0004] However, existing core-capacity methods still have the following shortcomings:

[0005] Existing capacity assessment methods employ a fixed charging strategy during the capacity assessment process, without considering the actual temperature changes of the battery during charging to optimize the charging strategy. This results in a low level of intelligence and makes it impossible to guarantee the accuracy of subsequent capacity assessment.

[0006] At the same time, the method has limitations because it cannot comprehensively analyze the health status of the battery by combining the internal resistance and aging degree based on the battery's capacity results.

[0007] To address this, a method for battery capacity assessment in a parallel intelligent DC power supply system is proposed. Summary of the Invention

[0008] In view of this, the present invention provides a battery capacity assessment method for a parallel intelligent DC power supply system to solve the problems mentioned in the background art.

[0009] The objective of this invention can be achieved through the following technical solution: a battery capacity assessment method for a parallel intelligent DC power supply system, comprising:

[0010] Step 1: Number the batteries required for the parallel system, where the number is represented by k, k=1,2,...,g, and g is the total number of batteries required for the parallel system.

[0011] Step 2: Initially charge each group of batteries in the parallel system. During the charging process, based on the temperature optimization index hk of each group of batteries in the current segmented sub-period, adjust the charging current of each group of batteries in the next segmented sub-period until each group of batteries reaches the preset charging voltage.

[0012] Step 3: After each group of batteries reaches the preset charging voltage, simultaneously perform constant current discharge on each group of batteries until the preset discharge cutoff voltage is reached.

[0013] Step 4: Record the discharge time and discharge current of each battery group during the constant current discharge process, and calculate the actual capacity of each battery group based on the discharge time and discharge current.

[0014] Step 5: Calculate the difference between the actual capacity and the nominal capacity of each group of batteries to obtain the final core capacity difference value for each group of batteries;

[0015] Step Six: Based on the core capacity difference of each battery group, assess the health status of each battery group in the parallel system.

[0016] In some embodiments, during the charging process, the temperature optimization index hk for each group of batteries within the current segmented sub-period is obtained, specifically as follows:

[0017] During the charging process, based on a preset analysis time interval, the time period is divided after the preset analysis time interval is reached, thus obtaining the divided sub-time periods. The temperature change of each group of batteries within the divided sub-time periods is analyzed, thereby obtaining the temperature optimization index hk of each group of batteries within the divided sub-time periods.

[0018] In some embodiments, the temperature optimization index hk for each group of batteries within the segmented sub-period is obtained as follows:

[0019] Extract the temperature values ​​of each battery group at each time point within the current segmented sub-period. First, take the average temperature value at each time point as the average temperature of each battery group in the current segmented sub-period. Then, extract the highest and lowest temperature values ​​from the temperature values ​​at each time point within the current segmented sub-period.

[0020] The system presets a reference standard temperature value for the battery during the charging process; extracts the average, highest, and lowest temperature values ​​of each battery group within the current segment, and calculates the ratios of these values ​​with the preset reference standard temperature value; thus obtaining the average ratio, high temperature ratio, and low temperature ratio of each battery group within the current segment; presets the weighting coefficients corresponding to the average ratio, high temperature ratio, and low temperature ratio; multiplies the average ratio, high temperature ratio, and low temperature ratio of each battery group within the current segment by the preset weighting coefficients, and then sums them to obtain the temperature optimization index hk of each battery group within the current segment.

[0021] In some embodiments, the charging current of each battery group in the next segment is adjusted based on the temperature optimization index hk of each battery group in the current segment, specifically as follows:

[0022] The preset temperature optimization index hk is a reference normal range. The temperature optimization index hk of each battery group in the current segment is compared with the preset reference normal range. If the temperature optimization index hk of a battery group is higher than the preset reference normal range, it is determined that the battery group is in a low temperature state in the current segment. If the temperature optimization index hk of a battery group is lower than the preset reference normal range, it is determined that the battery group is in a high temperature state in the current segment.

[0023] If the battery group is determined to be in a low-temperature state during the current segment, the temperature optimization index hk of the battery group during the current segment is extracted, and the difference between it and the lowest value within the preset reference normal range is calculated. The absolute value of the calculation result is then used to obtain the low-temperature difference. If the battery group is determined to be in a high-temperature state during the current segment, the temperature optimization index hk of the battery group during the current segment is extracted, and the difference between it and the highest value within the preset reference normal range is calculated to obtain the high-temperature difference.

[0024] In some embodiments, adjusting the charging current of each battery group in the next segment based on the temperature optimization index hk of each battery group in the current segment period further includes:

[0025] Each set of value ranges for low temperature difference and high temperature difference is preset. Each set of value ranges for low temperature difference corresponds to an increase in charging current. Each set of value ranges for high temperature difference corresponds to a decrease in charging current. Based on the determination that the battery group is in a low temperature state or a high temperature state, the corresponding low temperature difference or high temperature difference is matched with the corresponding preset value ranges to obtain the increase or decrease in charging current of the battery group. The matching result is used as the charging current adjustment value of the battery group in the next segment.

[0026] In some embodiments, the health status of each group of batteries in the parallel system is assessed based on the core capacity difference of each group of batteries, specifically as follows:

[0027] The maximum allowable value of the core capacity difference of each group of batteries in the parallel system is preset. The core capacity difference of each group of batteries is extracted and compared with the preset maximum allowable value. If the core capacity difference of a certain group of batteries is greater than the preset maximum allowable value, the group of batteries is marked as a battery to be evaluated.

[0028] Based on the order of the labels, each group of batteries to be evaluated is analyzed and assessed to obtain the health assessment index Eq for each group of batteries to be evaluated.

[0029] In some embodiments, the health assessment index Eq of each group of batteries to be evaluated is obtained as follows:

[0030] Extract the actual capacity and nominal capacity of each group of batteries to be evaluated, and calculate the ratio, that is, obtain the capacity retention rate of each group of batteries to be evaluated by actual capacity / nominal capacity, denoted as y1;

[0031] The internal resistance of each group of batteries to be evaluated is measured and calculated using a battery internal resistance tester. The difference between the current measured internal resistance and the nominal internal resistance of each group of batteries to be evaluated is calculated and recorded as the internal resistance difference. The internal resistance of each group of batteries to be evaluated is extracted from historical measurements and integrated with the current measured internal resistance to form an internal resistance data package. The internal resistance of each group in the internal resistance data package is calculated using the standard deviation formula, and the result is recorded as the internal resistance change value. The internal resistance difference and the internal resistance change value are preset with weighting coefficients. The internal resistance difference and the internal resistance change value of each group of batteries to be evaluated are multiplied by the corresponding preset weighting coefficients, and then summed to obtain the internal resistance hidden danger value y2 of each group of batteries to be evaluated.

[0032] The number of charging cycles for each group of batteries to be evaluated was counted, and based on the preset conversion rules, the number of charging cycles for each group of batteries to be evaluated was converted into an aging degree value, which was recorded as y3.

[0033] The system presets the range of values ​​for each group of charging cycles, sets a value for each group of values ​​corresponding to an aging degree, and matches the number of charging cycles of each group of batteries to be evaluated with the range of values ​​for each group of values ​​to obtain the aging degree value of each group of batteries to be evaluated.

[0034] According to the formula The capacity retention rate y1, internal resistance potential value y2, and aging degree value y3 of each group of batteries to be evaluated are weighted and calculated to obtain the health assessment index Eq of each group of batteries to be evaluated; where z1 represents the preset minimum allowable capacity retention rate, z2 represents the preset maximum allowable internal resistance potential value, and z3 represents the preset maximum allowable aging degree value; f1, f2, and f3 are the influence weight factors of capacity retention rate y1, internal resistance potential value y2, and aging degree value y3, respectively.

[0035] In some embodiments, the risk level of each group of batteries to be evaluated is determined based on the health assessment index Eq of each group of batteries to be evaluated, specifically as follows:

[0036] The system presets three index ranges for the health assessment index Eq, and sets each index range to correspond to a risk level, including general risk, assessment risk, and scrap risk. The system matches the health assessment index Eq of each group of batteries to be assessed with the preset three index ranges to obtain the risk level of each group of batteries to be assessed.

[0037] Based on the risk level of each group of batteries to be evaluated, if the risk level is general risk, it means that the battery is still in good health and can continue to be used; if the risk level is assessment risk, it means that the battery's health needs to be further assessed by technicians; if the risk level is scrap risk, it means that the battery cannot continue to be used and needs to be scrapped.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] This invention is based on a preset analysis time interval. After the preset analysis time interval is reached, the time period is divided into sub-time periods. The temperature change of each group of batteries in the sub-time periods is analyzed to obtain the temperature optimization index of each group of batteries in the sub-time periods. Based on the temperature optimization index of each group of batteries in the sub-time periods, the charging status of the batteries is determined. Furthermore, based on the determined status, the corresponding low temperature difference or high temperature difference is matched with the corresponding preset value range of each group. The matching result is used as the charging current adjustment value of the group of batteries in the next sub-time period. By monitoring and analyzing the temperature change of the batteries in real time during the charging process and dynamically adjusting the charging strategy according to these changes, the accuracy of the capacity test is ensured.

[0040] This invention comprehensively analyzes the capacity difference and preset maximum allowable value of each group of batteries to obtain the batteries to be evaluated that require health assessment. It comprehensively analyzes the capacity retention rate, internal resistance potential value, and aging degree value of each group of batteries to be evaluated to obtain a health assessment index for each group of batteries to be evaluated. This solves the problem that the existing technology cannot comprehensively analyze the health status of batteries based on the capacity results and the internal resistance and aging degree, and the method has limitations.

[0041] This invention, by setting the range of values ​​for the health assessment index and the corresponding risk level, can clearly identify the health status of each group of batteries and execute corresponding steps based on the health status of each group of batteries, thereby improving the efficiency and safety of battery management. Attached Figure Description

[0042] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0043] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0044] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0045] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0046] Please see Figure 1 As shown, a method for battery capacity verification in a parallel intelligent DC power supply system includes:

[0047] Step 1: Number the batteries required for the parallel system, where the number is represented by k, k=1,2,...,g, and g is the total number of batteries required for the parallel system.

[0048] Step 2: Initially charge each group of batteries in the parallel system. During the charging process, based on the temperature optimization index hk of each group of batteries in the current segmented sub-period, adjust the charging current of each group of batteries in the next segmented sub-period until each group of batteries reaches the preset charging voltage.

[0049] Use a charging device to charge each battery until the voltage of each battery reaches a preset charging voltage. This preset voltage is usually the maximum safe charging voltage of the battery.

[0050] Specifically:

[0051] S1: During the charging process, based on the preset analysis time interval, the time period is divided after the preset analysis time interval is reached, so as to obtain the divided sub-time periods. The temperature change of each group of batteries in the divided sub-time periods is analyzed, so as to obtain the temperature optimization index hk of each group of batteries in the divided sub-time periods.

[0052] S1-102: Extract the temperature values ​​of each group of batteries at each time point within the current segmented sub-period. First, take the average temperature value at each time point as the average temperature of each group of batteries in the current segmented sub-period.

[0053] S1-103: Extract the highest and lowest temperature values ​​from the temperature values ​​at each time point within the current segmented sub-period;

[0054] S1-104: Preset reference standard temperature value for the battery during the charging process;

[0055] S1-105: Extract the average, highest, and lowest temperature values ​​of each battery group within the current segmented time period, and calculate the ratios of these values ​​with preset reference standard temperature values. Specifically, calculate the ratios of average temperature / reference standard temperature, highest temperature / reference standard temperature, and lowest temperature / reference standard temperature. This yields the average ratio, high temperature ratio, and low temperature ratio for each battery group within the current segmented time period. Preset weighting coefficients for these ratios are then multiplied by these preset weighting coefficients, and the summations are used to obtain the temperature optimization index hk for each battery group within the current segmented time period.

[0056] S1-106: The preset reference normal range of temperature optimization index hk. The temperature optimization index hk of each group of batteries in the current segmented sub-period is compared with the preset reference normal range. If the temperature optimization index hk of a certain group of batteries is higher than the preset reference normal range, it is determined that the group of batteries is in a low temperature state in the current segmented sub-period. If the temperature optimization index hk of a certain group of batteries is lower than the preset reference normal range, it is determined that the group of batteries is in a high temperature state in the current segmented sub-period.

[0057] It should be noted that, for example, if the battery is in an environment below 25°C, the charging current needs to be increased to compensate for the increase in internal resistance caused by the low temperature; conversely, in an environment above 25°C, the charging current needs to be reduced to prevent overheating.

[0058] S1-107: Based on step S1-106, if it is determined that the battery group is in a low-temperature state during the current segmented sub-period, the temperature optimization index hk of the battery group during the current segmented sub-period is extracted, and the difference between it and the lowest value within the preset reference normal range is calculated. The absolute value of the calculation result is then used to obtain the low-temperature difference. If it is determined that the battery group is in a high-temperature state during the current segmented sub-period, the temperature optimization index hk of the battery group during the current segmented sub-period is extracted, and the difference between it and the highest value within the preset reference normal range is calculated to obtain the high-temperature difference.

[0059] S1-108: Preset the value ranges for each group of low temperature difference and high temperature difference, and set each group of low temperature difference value range to correspond to an increase in charging current; each group of high temperature difference value range to correspond to a decrease in charging current; based on the determination that the battery group is in a low temperature state or a high temperature state, match the corresponding low temperature difference or high temperature difference with the corresponding preset value ranges to obtain the increase or decrease in charging current of the battery group, and use the matching result as the charging current adjustment value of the battery group in the next segment sub-period;

[0060] S1-109: Repeat steps S1-102 to S1-109 until each battery group reaches the preset charging voltage;

[0061] It should be noted that the battery temperature is continuously monitored throughout the charging process, and the charging strategy is constantly adjusted based on real-time data to ensure that the battery is charged under safe and efficient conditions until each battery group reaches the preset charging voltage.

[0062] In summary, ensuring that the battery is both safe and efficient during the charging process improves the accuracy of the capacity calculation and extends the battery's lifespan.

[0063] Step 3: After each group of batteries reaches the preset charging voltage, simultaneously perform constant current discharge on each group of batteries until the preset discharge cutoff voltage is reached.

[0064] After all batteries reach the preset charging voltage, they are simultaneously discharged at a constant current; the discharge current should remain constant until the voltage of each battery drops to the preset discharge cutoff voltage.

[0065] By discharging a battery with a constant current, the discharge conditions of the battery in actual use are simulated in order to accurately measure its capacity.

[0066] Step 4: Record the discharge time and discharge current of each battery group during the constant current discharge process, and calculate the actual capacity of each battery group based on the discharge time and discharge current; that is, calculate the actual capacity of each battery group using the formula C=(I×t) / 1000, where C is the capacity (ampere-hours), I is the discharge current (amperes), and t is the discharge time (hours).

[0067] A data acquisition system is used to record the discharge time and discharge current of each battery during the discharge process; this data needs to be accurate to the second and ampere.

[0068] Step 5: Calculate the difference between the actual capacity and the nominal capacity of each group of batteries to obtain the final core capacity difference value for each group of batteries;

[0069] Step Six: Based on the core capacity difference of each battery group, assess the health status of each battery group in the parallel system;

[0070] Specifically:

[0071] M1: The maximum allowable value of the core capacity difference of each group of batteries in the preset parallel system is extracted and compared with the preset maximum allowable value. If the core capacity difference of a certain group of batteries is greater than the preset maximum allowable value, the group of batteries is marked as a battery to be evaluated.

[0072] M2: Based on the order of the markings, each group of batteries to be evaluated is analyzed and evaluated to obtain the health evaluation index Eq of each group of batteries to be evaluated.

[0073] M2-101: Extract the actual capacity and nominal capacity of each group of batteries to be evaluated, and calculate the ratio, that is, obtain the capacity retention rate of each group of batteries to be evaluated by actual capacity / nominal capacity, denoted as y1;

[0074] M2-102: Use a battery internal resistance tester to measure and calculate the internal resistance of each group of batteries to be evaluated; calculate the difference between the current measured internal resistance and the nominal internal resistance of each group of batteries to be evaluated, and record it as the internal resistance difference; extract the historical internal resistance measurements of each group of batteries to be evaluated and integrate them with the current measured internal resistance into an internal resistance data package; use the standard deviation formula to calculate the internal resistance of each group in the internal resistance data package, and record the calculation result as the internal resistance change value.

[0075] The internal resistance difference and internal resistance change value are preset with weighting coefficients. The internal resistance difference and internal resistance change value of each group of batteries to be evaluated are multiplied by the corresponding preset weighting coefficients, and then summed to obtain the internal resistance hidden danger value y2 of each group of batteries to be evaluated.

[0076] The internal resistance of each group of batteries to be evaluated is measured and calculated using a battery internal resistance tester. The specific calculation process is as follows: An AC signal with a known frequency and amplitude is applied to each group of batteries to be evaluated using the battery internal resistance tester; the signal is small amplitude; then the tester measures the voltage response across the battery terminals. Based on the applied AC signal and the measured voltage response, the internal resistance is calculated using V=GR, where V is the real part of the voltage response, G is the amplitude of the applied AC current, and R is the internal resistance.

[0077] Analyze the measurement results and compare them with the battery's nominal internal resistance or historical test data. If the internal resistance value increases significantly, it indicates that the battery is aging or has other problems.

[0078] M2-103: Count the number of charging cycles of each group of batteries to be evaluated, and based on the preset conversion rules, convert the number of charging cycles of each group of batteries to be evaluated into an aging degree value, and record it as y3;

[0079] The system presets the range of values ​​for each group of charging cycles, sets a value for each group of values ​​corresponding to an aging degree, and matches the number of charging cycles of each group of batteries to be evaluated with the range of values ​​for each group of values ​​to obtain the aging degree value of each group of batteries to be evaluated.

[0080] The aging level value is set in the range of 1-10. The higher the number of charging cycles of the battery, the higher the aging level value will be.

[0081] M2-104: According to the formula The capacity retention rate y1, internal resistance potential value y2, and aging degree value y3 of each group of batteries to be evaluated are weighted and calculated to obtain the health assessment index Eq of each group of batteries to be evaluated; where z1 represents the preset minimum allowable capacity retention rate, z2 represents the preset maximum allowable internal resistance potential value, and z3 represents the preset maximum allowable aging degree value; f1, f2, and f3 are the influence weighting factors of capacity retention rate y1, internal resistance potential value y2, and aging degree value y3, respectively.

[0082] M3: Determine the risk level of each group of batteries to be evaluated based on the health assessment index Eq.

[0083] M3-101: Presets three sets of index value ranges for the health assessment index Eq, and sets each set of index value ranges to correspond to a risk level, including general risk, assessment risk, and scrap risk; matches the health assessment index Eq of each group of batteries to be assessed with the preset three sets of index value ranges to obtain the risk level of each group of batteries to be assessed.

[0084] M3-102: Based on the risk level of each group of batteries to be evaluated, if the risk level is general risk, it means that the battery is still in good health and can continue to be used. If the risk level is assessment risk, it means that the battery's health needs to be further assessed by technicians. The technicians can be reminded to conduct the assessment by sending an assessment signal to their mobile terminal. If the risk level is scrap risk, it means that the battery cannot continue to be used and needs to be scrapped.

[0085] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A battery capacity verification method for a parallel intelligent DC power supply system, characterized in that: include: Step 1: Number the batteries of required core capacity in the parallel system, where the number is represented by k, k=1, 2, ..., g, g is the total number of required core capacity batteries; Step 2: Initially charge each battery group in the parallel system. During the charging process, based on the temperature optimization index hk of each battery group in the current split sub-period, adjust the charging current of each battery group in the next split sub-period until each battery group reaches a preset charging voltage; Step 3: After each battery group reaches a preset charging voltage, each battery group is simultaneously discharged at a constant current until a preset discharge cut-off voltage is reached; Step 4: Record the discharge time and discharge current of each battery group during constant current discharge, and calculate the actual capacity of each battery group based on the discharge time and discharge current; Step 5: Calculate the difference between the calculated actual capacity and the nominal capacity of each battery group to obtain the final core tolerance value of each battery group; Step 6: Based on the core tolerance value of each battery group, evaluate the health status of each battery group in the parallel system; Based on the core tolerance value of each battery group, the health status of each battery group in the parallel system is evaluated, specifically: Preset the maximum permissible value of the core tolerance value of each battery group in the parallel system, extract the core tolerance value of each battery group and compare it with the preset maximum permissible value respectively, if the core tolerance value of a battery group is greater than the preset maximum permissible value, mark the battery group as a battery to be evaluated; Based on the order of marking, each group of batteries to be evaluated is analyzed and evaluated, thereby obtaining the health assessment index Eq of each group of batteries to be evaluated; The health assessment index Eq of each group of batteries to be assessed is obtained, specifically: Extract the actual capacity and nominal capacity of each group of batteries to be evaluated, and calculate the ratio, that is, by dividing the actual capacity by the nominal capacity, to obtain the capacity retention rate of each group of batteries to be evaluated, recorded as y1; Use a battery internal resistance tester to measure and calculate the internal resistance of each group of batteries to be evaluated; Calculate the difference between the currently measured internal resistance and the nominal internal resistance of each group of batteries to be evaluated, and record it as the internal resistance difference; Extract the historical measured internal resistance of each group of batteries to be evaluated and integrate them with the current measured internal resistance into an internal resistance data packet, calculate the internal resistance of each group in the internal resistance data packet using the standard deviation formula, and record the calculated result as the internal resistance change value; preset the weight coefficients of the internal resistance difference and the internal resistance change value, multiply the internal resistance difference and the internal resistance change value of each group of batteries to be evaluated by the corresponding preset weight coefficients, and then sum them to obtain the internal resistance hidden danger value y2 of each group of batteries to be evaluated; Counting the number of charging cycles of each group of batteries to be evaluated, and based on a preset conversion rule, converting the number of charging cycles of each group of batteries to be evaluated into an aging degree value, which is recorded as y3; Preset the range of times corresponding to the number of charging cycles, set each range of times to correspond to an aging degree value, match the number of charging cycles of each group of batteries to be evaluated with the range of times, so as to obtain the aging degree value of each group of batteries to be evaluated; According to the formula , weighted calculation is performed on the capacity retention rate y1, internal resistance hidden danger value y2 and aging degree value y3 of each group of batteries to be evaluated, so as to obtain the health assessment index Eq of each group of batteries to be evaluated; wherein z1 represents the preset minimum allowable capacity retention rate, z2 represents the preset maximum allowable internal resistance hidden danger value, and z3 represents the preset maximum allowable aging degree value; f1, f2 and f3 are the influencing weight factors of the capacity retention rate y1, the internal resistance hidden danger value y2 and the aging degree value y3 respectively; According to the health assessment index Eq of each group of batteries to be evaluated, the risk level of each group of batteries to be evaluated is determined, specifically: Three groups of index value ranges of the health assessment index Eq are preset, and each group of index value ranges is set to correspond to a risk level, and the risk level includes general risk, assessment risk and scrap risk; the health assessment index Eq of each group of batteries to be evaluated is matched with the preset three groups of index value ranges, so as to obtain the risk level of each group of batteries to be evaluated; Based on the risk level of each group of batteries to be evaluated, if the risk level is general risk, it means that the battery can continue to be put into use in good health; if the risk level is assessment risk, it means that the battery's health status requires further evaluation by technicians; if the risk level is scrap risk, it means that the battery cannot continue to be put into use and needs to be scrapped.

2. The method for evaluating the battery capacity of a parallel intelligent DC power supply system according to claim 1, characterized in that: During the charging process, the temperature optimization index hk of each battery group in the current segmentation sub-period is obtained, specifically: During the charging process, based on a preset analysis time interval, the time period is divided after reaching the preset analysis time interval to obtain divided sub-periods, and the temperature changes of each group of batteries in the divided sub-periods are analyzed to obtain the temperature optimization index hk of each group of batteries in the divided sub-periods.

3. The method for evaluating the battery capacity of a parallel intelligent DC power supply system according to claim 2, characterized in that: The temperature optimization index hk of each battery group in the divided sub-period is obtained, specifically: Extract the temperature value of each battery group at each time point in the current segmentation sub-period. First, take the average of the temperature values ​​at each time point as the average temperature value of each battery group in the current segmentation sub-period. Extract the highest temperature value and the lowest temperature value from the temperature values ​​at each time point in the current segmentation sub-period. Preset the reference standard temperature value of the battery during charging; Extract the temperature average, maximum temperature and minimum temperature of each battery group in the current segmentation sub-period, and calculate the ratio with the preset reference standard temperature value respectively; Thus, the mean ratio, high temperature ratio and low temperature ratio of each group of batteries in the current divided sub-period are obtained; the weight coefficients corresponding to the preset mean ratio, high temperature ratio and low temperature ratio are multiplied by the preset weight coefficients respectively, and then the sum is obtained to obtain the temperature optimization index hk of each group of batteries in the current divided sub-period.

4. The method for evaluating the battery capacity of a parallel intelligent DC power supply system according to claim 3, characterized in that: Based on the temperature optimization index hk of each battery group in the current split sub-period, the charging current of each battery group in the next split sub-period is adjusted, specifically: A reference normal range of the temperature optimization index hk is preset, and the temperature optimization index hk of each battery group in the current segmentation sub-period is compared with the preset reference normal range. If the temperature optimization index hk of a battery group is higher than the preset reference normal range, it is determined that the battery group is in a low temperature state in the current segmentation sub-period. If the temperature optimization index hk of a battery group is lower than the preset reference normal range, it is determined that the battery group is in a high temperature state in the current segmentation sub-period. If it is determined that the group of batteries is in a low temperature state during the current segmentation sub-period, the temperature optimization index hk of the group of batteries in the current segmentation sub-period is extracted, and the difference is calculated with the lowest value within the preset reference normal range, and the absolute value of the calculation result is taken to obtain the low temperature difference; if it is determined that the group of batteries is in a high temperature state during the current segmentation sub-period, the temperature optimization index hk of the group of batteries in the current segmentation sub-period is extracted, and the difference is calculated with the highest value within the preset reference normal range to obtain the high temperature difference.

5. The method for evaluating the battery capacity of a parallel intelligent DC power supply system according to claim 4, characterized in that: Based on the temperature optimization index hk of each group of storage batteries in the current segmentation sub-period, the charging current of each group of storage batteries in the next segmentation sub-period is adjusted, and further comprising: The value ranges of low temperature difference and high temperature difference are preset respectively, and the value range corresponding to each group of low temperature difference is set to correspond to an increase value of charging current; the value range corresponding to each group of high temperature difference corresponds to a decrease value of charging current; based on determining whether the group of batteries is in a low temperature state or a high temperature state, the corresponding low temperature difference or high temperature difference is matched with the corresponding preset value ranges, so as to obtain an increase value or decrease value of the charging current of the group of batteries, and the matching result is used as the charging current adjustment value of the group of batteries in the next divided sub-time period.

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