A battery performance detection method and system for a battery assembly

Abstract

The present invention discloses a battery performance detection method and system for a battery assembly, which relates to the technical field of battery performance detection, monitors the process data of each battery cell production, and after the battery pack is produced, performs multiple performance tests on each battery cell, obtains the performance detection data of each battery cell, calculates the distance between the state of each battery cell and the ideal state based on the process data and the performance detection data, and then generates a relative closeness index of the battery pack in combination with the distance between the state of each battery cell and the ideal state, and adjusts the deep discharge monitoring intensity of the battery pack according to the relative closeness index of the battery pack. The detection system generates a relative closeness index of the battery pack, which reflects the differences between the battery cells, thereby improving the management and optimization capabilities of the battery pack, and dynamically analyzes the recovery ability and degradation degree of the battery cell after deep discharge through a multi-order hysteresis curve fitting method, so as to manage the battery pack in time and ensure the safe use of the battery pack.

Description

Technical Field

[0001] The present invention relates to the technical field of battery performance detection, and in particular to a battery performance detection method and system for a battery assembly. Background Art

[0002] A battery pack is usually composed of multiple battery cells, a management system (BMS), a casing, a cooling system and other accessories. It is widely used in consumer electronics, automobiles, power storage and other fields. The detection system is a key system for monitoring, evaluating and optimizing the operating status and performance of battery packs.

[0003] The prior art has the following defects:

[0004] 1. The performance and life of the battery pack are subject to the inconsistency of the status of each battery cell. Due to differences in production processes, materials, and aging rates, the health status of battery cells may vary greatly, resulting in limited performance of the entire battery pack. Most existing detection systems rely on simple averaging or only monitor a single indicator (such as voltage, resistance), which cannot fully capture the differences of each battery cell;

[0005] 2. Existing detection systems usually rely on static voltage, current and internal resistance data, and lack dynamic degradation prediction during deep discharge of the battery. As the battery is used, the hysteresis effect and internal resistance change will increase, but existing technologies cannot predict and evaluate the degradation trend of the battery in real time and in segments, which leads to late or erroneous management decisions.

[0006] Based on this, the present invention proposes a battery performance detection method and system for a battery assembly, which generates a relative closeness index for the battery pack to reflect the differences between battery cells, thereby improving the management and optimization capabilities of the battery pack. The multi-order hysteresis curve fitting method is used to dynamically analyze the recovery ability and degradation degree of the battery cells after deep discharge, so that the battery pack can be managed in time to ensure the safe use of the battery pack. Summary of the invention

[0007] The object of the present invention is to provide a battery performance detection method and system for a battery assembly to solve the deficiencies in the background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a battery performance detection method for a battery assembly, the detection method comprising the following steps:

[0009] During battery pack production, the process data of each battery cell production is monitored, and after the battery pack production is completed, multiple performance tests are performed on each battery cell to obtain the performance test data of each battery cell;

[0010] After calculating the distance between the state of each battery cell and the ideal state based on the process data and performance test data, the relative closeness index of the battery pack is generated by combining the distance between the state of each battery cell and the ideal state. The deep discharge monitoring intensity of the battery pack is adjusted according to the relative closeness index of the battery pack. During the use of each battery pack, the detection system monitors the battery pack through the adjusted deep discharge monitoring intensity;

[0011] When the monitored battery pack is in a deep discharge state, the voltage recovery curve is fitted in sections through a multi-order hysteresis curve fitting method to analyze the voltage recovery hysteresis of each battery cell in different power ranges. The voltage recovery hysteresis is combined with the internal resistance to analyze the health status of the battery cell. After comprehensively analyzing the health status of all battery cells, the overall health status of the battery pack is obtained, and corresponding management strategies are generated based on the overall health status of the battery pack.

[0012] In a preferred embodiment, after calculating the distance between the state of each battery cell and the ideal state based on the process data and the performance test data, the relative closeness index of the battery pack is generated by combining the distance between the state of each battery cell and the ideal state, including the following steps:

[0013] The decision matrix of the battery pack is constructed based on the fluctuation amplitude of the scraper pressure, the fluctuation amplitude of the substrate tension, the fluctuation amplitude of the solvent evaporation rate, the battery capacity decay rate and the self-discharge rate of the battery cell. , the rows in the matrix represent different battery cells, and the columns represent the scraper pressure fluctuation amplitude, substrate tension fluctuation amplitude, solvent evaporation rate fluctuation amplitude, battery capacity attenuation rate, and self-discharge rate indicators;

[0014] Decision Matrix Perform vector normalization to eliminate dimension effects and obtain the normalized decision matrix , the normalized decision matrix After weighted processing, the weighted decision matrix is ​​obtained ;

[0015] After constructing the scraper pressure fluctuation amplitude, substrate tension fluctuation amplitude, solvent evaporation rate fluctuation amplitude, battery capacity attenuation rate and self-discharge rate as an indicator set, based on the weighted decision matrix Generate positive ideal solutions and negative ideal solutions, and calculate the Euclidean distance between each battery cell in the battery pack and the positive ideal solutions and negative ideal solutions. The expression is:

[0016] , where For the The Euclidean distance between each battery cell and the positive ideal solution, For the The Euclidean distance between each battery cell and the negative ideal solution, is the number of indicators, Indicates The weighted The indicator value, Indicates The minimum value of the indicator, Indicates The maximum value of the indicators;

[0017] The relative closeness index of the battery pack is calculated based on the Euclidean distance between the battery cell and the positive ideal solution and the Euclidean distance between the battery cell and the negative ideal solution. The expression is: , where is the relative closeness index, For the The Euclidean distance between each battery cell and the positive ideal solution, For the The Euclidean distance between each battery cell and the negative ideal solution, is the number of battery cells.

[0018] In a preferred embodiment, the decision matrix The expression is:

[0019] , where Indicates The battery cell The indicator value, is the number of battery cells, is the number of indicators, and the decision matrix Perform vector normalization to eliminate the dimension effect, and the expression is: , where Indicates The first battery cell after vector normalization The indicator value, Indicates The battery cell The indicator value, is the number of battery cells;

[0020] Normalized decision matrix The expression is: , the normalized decision matrix Weighted processing, the expression is: , where Indicates The weighted The indicator value, Indicates The weight of the indicator value, Indicates The first battery cell after vector normalization indicator values, weighted decision matrix The expression is: , based on the weighted decision matrix Generate positive and negative ideal solutions, the expression is:

[0021] ;

[0022] ; In the formula, is a positive ideal solution, is a negative ideal solution, Represents a set of indicators, Indicates taking the minimum value, Indicates taking the maximum value.

[0023] In a preferred embodiment, adjusting the depth discharge monitoring intensity of the battery pack according to the relative proximity index of the battery pack includes the following steps:

[0024] The greater the relative proximity index of the battery pack, the greater the need to increase the depth of discharge monitoring of the battery pack. The depth of discharge monitoring of the battery pack is adjusted through an intelligent adjustment algorithm. The algorithm expression is: , where is the adjusted deep discharge monitoring threshold, is the depth of discharge monitoring threshold before adjustment, is a relatively close index. is the index threshold.

[0025] In a preferred embodiment, when the monitoring battery pack is in a deep discharge state, the voltage recovery hysteresis of each battery cell in different power ranges is analyzed, and the voltage recovery hysteresis is combined with the internal resistance to analyze the health status of the battery cell, including the following steps:

[0026] During the use of each battery pack, the detection system uses the adjusted deep discharge monitoring threshold. When the real-time power of the battery pack is less than the adjusted deep discharge monitoring threshold, the battery pack is judged to be in a deep discharge state.

[0027] When the battery pack enters a deep discharge state, the voltage recovery curve of each battery cell is monitored in real time. When the discharge ends and the charging phase begins, the data of the voltage change of the battery cell over time is recorded.

[0028] For each power range, the voltage recovery time constant of each battery cell in different power ranges is calculated by fitting the curve. After obtaining the recovery time constants of the low power range, the medium power range, and the high power range, the recovery time constants of the low power range, the medium power range, and the high power range are weighted to obtain the recovery time coefficient, and the internal resistance of each battery cell in different power ranges is collected;

[0029] The recovery time coefficient and internal resistance are calculated comprehensively to obtain the health factor of the battery cell, which is expressed as: , where For health factors, is the recovery time coefficient, is the internal resistance, , are the adjustment coefficients of the recovery time coefficient and the internal resistance, respectively, and , Both are greater than 0. The larger the health factor, the worse the health of the battery cell.

[0030] In a preferred embodiment, for each power range, the voltage recovery time constant of each battery cell in different power ranges is calculated by fitting the curve, and the expression is: , where is the recovery time constant, For the recovery process The voltage at the moment, To restore the starting voltage, is the voltage amplitude to be restored, obtained by subtracting the recovery starting voltage from the final voltage. For time, after obtaining the recovery time constants of the low power area, medium power area, and high power area, weighted calculation is performed on the recovery time constants of the low power area, medium power area, and high power area to obtain the recovery time coefficient, and the expression is: , where is the recovery time coefficient, The recovery time constant for the low battery area, is the recovery time constant in the medium power region, Recovery time constant for high charge region.

[0031] In a preferred embodiment, the overall health status of the battery pack is obtained after comprehensive analysis of the health status of all battery cells, including the following steps:

[0032] After obtaining the health factors of all battery cells in the battery pack, the health factors of all battery cells are summed to obtain the overall health factor of the battery pack, and the obtained overall health factor is compared with the preset health factor threshold. The health factor threshold is used to judge the overall health status of the battery pack. If the overall health factor is greater than the health factor threshold, the overall health status of the battery pack is judged to be poor. If the overall health factor is less than or equal to the health factor threshold, the overall health status of the battery pack is judged to be good.

[0033] In a preferred embodiment, after the battery pack is produced, multiple performance tests are performed on each battery cell to obtain performance test data of each battery cell, including the following steps:

[0034] Obtain performance test data of each battery cell, including battery capacity decay rate and self-discharge rate;

[0035] The calculation expression of battery capacity attenuation rate is: , where is the battery capacity decay rate, is the actual capacity, It is the standard capacity;

[0036] The calculation logic of the self-discharge rate is as follows: obtain the initial capacity and the capacity after storage of the battery cell, subtract the capacity after storage from the initial capacity to obtain the capacity difference, divide the capacity difference by the initial capacity to obtain the capacity change factor, and divide the capacity change factor by the storage time to obtain the self-discharge rate.

[0037] In a preferred embodiment, during the production of a battery pack, monitoring the process data of each battery cell production includes the following steps:

[0038] Obtaining the fluctuation amplitude of scraper pressure, substrate tension and solvent volatilization rate during the production of battery cells;

[0039] The calculation expression of scraper pressure fluctuation amplitude is: , where is the scraper pressure fluctuation amplitude, The number of time points for scraper monitoring, For the The scraper pressure at each scraper monitoring time point, is the average value of the scraper pressure, and the calculation expression of the substrate tension fluctuation amplitude is: , where is the substrate tension fluctuation amplitude, The number of time points monitored for the substrate, For the The substrate tension at each substrate monitoring time point, is the mean value of substrate tension, and the calculation expression of the fluctuation amplitude of solvent evaporation rate is: , where is the fluctuation amplitude of solvent evaporation rate, is the number of solvent monitoring time points, For the The solvent evaporation rate at each solvent monitoring time point, is the average solvent evaporation rate.

[0040] A battery performance detection system for a battery assembly, comprising a regulation module, a deep discharge monitoring module, and a management module;

[0041] Adjustment module: During the production of the battery pack, the process data of each battery cell production is monitored. After the battery pack production is completed, multiple performance tests are performed on each battery cell to obtain the performance test data of each battery cell. Based on the process data and performance test data, the distance between the state of each battery cell and the ideal state is calculated. The relative closeness index of the battery pack is generated by combining the distance between the state of each battery cell and the ideal state. The depth of discharge monitoring of the battery pack is adjusted according to the relative closeness index of the battery pack.

[0042] Deep discharge monitoring module: During the use of each battery pack, the battery pack is monitored through the adjusted deep discharge monitoring intensity;

[0043] Management module: When the monitored battery pack is in a deep discharge state, the voltage recovery curve is fitted in sections through a multi-order hysteresis curve fitting method, and the voltage recovery hysteresis of each battery cell in different power ranges is analyzed. The voltage recovery hysteresis is combined with the internal resistance to analyze the health status of the battery cell. After comprehensively analyzing the health status of all battery cells, the overall health status of the battery pack is obtained, and corresponding management strategies are generated based on the overall health status of the battery pack.

[0044] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0045] 1. The present invention monitors the process data of each battery cell during the production of the battery pack, and after the battery pack is produced, performs multiple performance tests on each battery cell to obtain the performance test data of each battery cell, calculates the distance between the state of each battery cell and the ideal state based on the process data and the performance test data, and generates the relative closeness index of the battery pack in combination with the distance between the state of each battery cell and the ideal state, and adjusts the deep discharge monitoring intensity of the battery pack according to the relative closeness index of the battery pack. The relative closeness index of the battery pack is generated to reflect the differences between the battery cells, thereby improving the management and optimization capabilities of the battery pack, and dynamically analyzes the recovery ability and degradation degree of the battery cell after deep discharge through the multi-order hysteresis curve fitting method, so that the battery pack can be managed in time to ensure the safe use of the battery pack.

[0046] 2. The present invention monitors the battery pack through the adjusted deep discharge monitoring intensity. When the monitored battery pack is in a deep discharge state, the voltage recovery curve is segmentedly fitted through a multi-order hysteresis curve fitting method to analyze the voltage recovery hysteresis degree of each battery cell in different power areas, and the voltage recovery hysteresis degree is combined with the internal resistance to analyze the health status of the battery cell. After comprehensively analyzing the health status of all battery cells, the overall health status of the battery pack is obtained, and a corresponding management strategy is generated based on the overall health status of the battery pack. By dynamically analyzing the recovery ability and degradation degree of the battery cell after deep discharge through a multi-order hysteresis curve fitting method, the battery pack can be managed in a timely manner to ensure the safe use of the battery pack. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0048] Figure 1 The figure is a flow chart of the method of the present invention.

[0049] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Example 1: Please refer to Figure 1 As shown, this embodiment provides a battery performance detection method for a battery assembly, and the detection method includes the following steps:

[0052] During the production of battery packs, the process data of each battery cell production is monitored, and after the battery pack production is completed, multiple performance tests are performed on each battery cell to obtain the performance test data of each battery cell. After calculating the distance between the state of each battery cell and the ideal state based on the process data and the performance test data, the relative closeness index of the battery pack is generated in combination with the distance between the state of each battery cell and the ideal state. The deep discharge monitoring intensity of the battery pack is adjusted according to the relative closeness index of the battery pack. During the use of each battery pack, the detection system monitors the battery pack through the adjusted deep discharge monitoring intensity. When the monitored battery pack is in a deep discharge state, the voltage recovery curve is fitted in sections through a multi-order hysteresis curve fitting method, and the voltage recovery hysteresis degree of each battery cell in different power ranges is analyzed. The voltage recovery hysteresis degree is combined with the internal resistance to analyze the health status of the battery cell. After comprehensively analyzing the health status of all battery cells, the overall health status of the battery pack is obtained, and a corresponding management strategy is generated based on the overall health status of the battery pack.

[0053] During the production of the battery pack, this application monitors the process data of each battery cell, and after the battery pack is produced, performs multiple performance tests on each battery cell to obtain the performance test data of each battery cell, calculates the distance between the state of each battery cell and the ideal state based on the process data and the performance test data, and then generates the relative closeness index of the battery pack in combination with the distance between the state of each battery cell and the ideal state, and adjusts the deep discharge monitoring intensity of the battery pack according to the relative closeness index of the battery pack. The relative closeness index of the battery pack is generated to reflect the differences between the battery cells, thereby improving the management and optimization capabilities of the battery pack, and dynamically analyzes the recovery ability and degradation degree of the battery cell after deep discharge through the multi-order hysteresis curve fitting method, so that the battery pack can be managed in time to ensure the safe use of the battery pack.

[0054] This application monitors the battery pack through the adjusted deep discharge monitoring intensity. When the monitored battery pack is in a deep discharge state, the voltage recovery curve is fitted in sections through a multi-order hysteresis curve fitting method, and the voltage recovery hysteresis degree of each battery cell in different power areas is analyzed. The voltage recovery hysteresis degree is combined with the internal resistance to analyze the health status of the battery cell. After comprehensively analyzing the health status of all battery cells, the overall health status of the battery pack is obtained, and a corresponding management strategy is generated based on the overall health status of the battery pack. By dynamically analyzing the recovery ability and degradation degree of the battery cell after deep discharge through a multi-order hysteresis curve fitting method, the battery pack can be managed in a timely manner to ensure the safe use of the battery pack.

[0055] Embodiment 2: During the production of a battery pack, monitoring the process data of each battery cell production includes the following steps:

[0056] Obtaining the fluctuation amplitude of scraper pressure, substrate tension and solvent volatilization rate during the production of battery cells;

[0057] The calculation expression of scraper pressure fluctuation amplitude is: , where is the scraper pressure fluctuation amplitude, The number of time points for scraper monitoring, For the The scraper pressure at each scraper monitoring time point, is the average value of the scraper pressure, and the calculation expression of the substrate tension fluctuation amplitude is: , where is the substrate tension fluctuation amplitude, The number of time points monitored for the substrate, For the The substrate tension at each substrate monitoring time point, is the mean value of substrate tension, and the calculation expression of the fluctuation amplitude of solvent evaporation rate is: , where is the fluctuation amplitude of solvent evaporation rate, is the number of solvent monitoring time points, For the The solvent evaporation rate at each solvent monitoring time point, is the average solvent evaporation rate.

[0058] The larger the scraper pressure fluctuation amplitude is, the greater the scraper pressure fluctuation is during the production process of the battery cell, which will have the following quality impacts on the battery cell:

[0059] The fluctuation of scraper pressure will directly affect the thickness of the electrode coating. If the pressure is too high at an instant, the slurry will be scraped off excessively and the coating will become thinner; when the pressure is insufficient, the slurry will accumulate and the coating will become thicker. Uneven thickness will lead to uneven current distribution of battery electrodes in actual operation, thus affecting the overall performance and consistency of the battery.

[0060] Fluctuations in coating thickness directly affect the amount of active material in the battery. Thicker areas store more active material, resulting in higher capacity, while thinner areas have relatively less capacity. Due to differences in cell capacity, the energy output and charge and discharge performance of the entire battery pack will be affected, resulting in inconsistencies.

[0061] Uneven coating thickness results in different thicknesses of electrode materials in different areas of the battery, which in turn affects the change in local internal resistance. The thicker coating has a longer current path and greater resistance, while the thinner coating has a smaller resistance. This fluctuation in internal resistance may cause uneven heating of the battery during charging and discharging, thereby accelerating local aging.

[0062] Uneven coating thickness causes uneven current distribution, which can easily lead to overheating in local areas. Especially during high-rate charge and discharge, the current density in areas with thinner coatings is higher, and the heat generation is more significant, which may lead to accelerated degradation of the electrode material and even the risk of thermal runaway, affecting the safety of the battery.

[0063] The uneven coating thickness caused by the fluctuation of scraper pressure will lead to uneven stress distribution of electrode materials in different areas during the charge and discharge cycle. Some areas are prone to cracks or peeling due to repeated mechanical stress, and material failure is accelerated. This will lead to accelerated capacity decay of the battery and shortened cycle life.

[0064] When the scraper pressure is insufficient, the slurry may not be fully compacted, resulting in insufficient bonding between the coating and the current collector. This can cause the coating to peel off during use, affecting the long-term stability and conductivity of the battery, and further affecting the electrochemical performance of the battery.

[0065] In the case of large pressure fluctuations, the coating may be subjected to different pressures in different areas, causing the pole piece to warp or deform due to uneven stress during drying or compaction. Poor flatness of the pole piece will affect subsequent processes such as lamination or winding, and thus affect the structural integrity of the battery cell.

[0066] When the coating is too thick locally, the active materials in the inner layer may not be fully utilized during the charge and discharge process, resulting in reduced utilization of the active materials. Conversely, in areas where the coating is too thin, the material may overreact, accelerating degradation or failure, affecting the overall battery energy density and life.

[0067] The inconsistent coating thickness and density caused by pressure fluctuations affect the performance consistency between battery cells. Since each battery cell exhibits different internal resistance, capacity, and charge and discharge characteristics, the entire battery pack may exhibit uneven current distribution and working performance during use, affecting the overall stability and life of the battery pack.

[0068] Pressure fluctuations may cause uneven texture or other defects on the coating surface, such as coating ripples, pinholes, bubbles, etc. These defects not only affect the appearance of the battery, but may also provide uneven paths for electrochemical reactions, thereby affecting the performance and life of the battery.

[0069] The larger the substrate tension fluctuation amplitude is, the greater the substrate tension fluctuation is during the production process of the battery cell, which will have the following quality impacts on the battery cell:

[0070] Excessive substrate tension: When the substrate tension is too high, the substrate surface is overstretched, resulting in uneven slurry distribution during the coating process, and the coating may become too thin in some areas. This will cause local coating thickness deviations, resulting in inconsistent electrochemical reaction rates of electrode materials in different areas.

[0071] Substrate tension is too low: Too low tension will cause the substrate to wrinkle during coating, slurry to accumulate, and form areas where the coating is too thick. Uneven coating thickness will affect the consistency of battery capacity and internal resistance, and thus affect the overall battery performance.

[0072] The fluctuation of substrate tension directly affects the spreading of the slurry during coating. Tension fluctuations will cause changes in coating density. Higher tension may make the coating more sparse, resulting in lower density in local areas; lower tension may cause the coating density to be too high. Uneven coating density will affect the energy density and charge and discharge performance of the battery.

[0073] Too low tension: When the substrate tension is insufficient, the substrate is prone to wrinkles or distortions during coating or winding. These deformations may cause local cracks, blistering or surface defects in the electrode coating, thus affecting the electrochemical performance and mechanical stability of the battery.

[0074] Excessive tension fluctuations: Severe tension fluctuations can produce uneven stress on the substrate, causing warping or wrinkling in local areas. These defects can cause subsequent process problems during winding or lamination, and even lead to the risk of battery short circuits.

[0075] When the substrate tension fluctuates greatly, the electrode material may shift or overlap unevenly due to inconsistent tension during the winding or lamination process of the battery cell. Large tension fluctuations may cause uneven electrode edges, forming an irregular winding shape, resulting in unstable battery structure. Uneven arrangement between electrodes will cause increased local current density, leading to local overheating and accelerated aging.

[0076] The fluctuation of substrate tension affects the non-uniformity of coating thickness and density, which leads to differences in the conductive paths of different areas of the battery, resulting in local internal resistance fluctuations. The fluctuation of internal resistance causes uneven heating of the battery during charging and discharging, which in turn aggravates local aging and affects the cycle life and safety of the battery.

[0077] In the case of large tension fluctuations, uneven coating thickness and density will lead to uneven current distribution inside the battery, especially during high-rate charge and discharge, the current density in thinner areas may increase significantly, leading to local heating. This local heating phenomenon may accelerate the degradation of electrode materials and even cause thermal runaway of the battery, affecting the safety of the battery.

[0078] Large fluctuations in substrate tension lead to uneven coating thickness and density, which in turn leads to large differences in capacity, internal resistance and electrochemical performance between different battery cells. Inconsistent performance of individual cells in a battery pack will cause imbalance in the battery pack during operation, affecting the overall capacity output and service life of the battery pack.

[0079] Excessive tension: Excessive tension can cause the coating to not adhere firmly to the substrate, and may cause peeling during the drying or compaction process. This can cause partial shedding of the coating or increase the contact resistance between the electrode material and the current collector, affecting the conductivity and charge and discharge efficiency of the battery.

[0080] Too low tension: When the tension is insufficient, the slurry may not spread evenly on the substrate surface, resulting in poor adhesion of the coating, especially in areas with thicker coatings, where material accumulation or poor adhesion may occur.

[0081] Fluctuations in substrate tension can easily cause surface defects during the coating process, such as coating ripples, streaks, and particle accumulation. Surface defects can reduce the effective area of ​​battery electrodes, affect the uniformity of electrochemical reactions, and thus affect the capacity and consistency of the battery. In addition, these surface defects may also become a hidden danger of thermal runaway or mechanical damage to the battery.

[0082] When the substrate tension fluctuates greatly, the pole piece may be misaligned during the winding or lamination process. Inaccurate pole piece alignment will cause the distance between the positive and negative electrodes of the battery to change, increase the risk of short circuit, and affect the volume utilization efficiency and energy density of the battery.

[0083] Tension fluctuations can cause the substrate to exhibit uneven mechanical strength when stretched or compressed. The mechanical properties of the locally overstretched area may deteriorate, leading to breakage or fracture in subsequent processes, affecting the overall structural stability and life of the battery.

[0084] The larger the fluctuation amplitude of the solvent volatilization rate, the greater the fluctuation of the solvent volatilization rate during the production process of the battery cell, which will have the following quality impacts on the battery cell:

[0085] Evaporation rate is too fast: When the solvent evaporates too quickly, the active materials and conductive agents in the electrode slurry may solidify quickly without sufficient leveling, resulting in a rough coating surface and uneven internal density. This will cause the electrode material particles to accumulate, resulting in density deviation in local areas, affecting the energy density and electrochemical performance of the battery.

[0086] Evaporation rate is too slow: Evaporation that is too slow will cause the solvent in the slurry to remain for too long, resulting in uneven coating thickness or slurry sinking, and then forming uneven distribution of electrode materials, affecting the capacity and internal resistance of the battery.

[0087] Too fast evaporation: Too fast evaporation of the solvent will cause a large number of micropores and voids to form inside the electrode coating. This uneven pore structure may reduce the mechanical strength of the electrode and cause cracks or peeling of the battery during cycling, affecting the long-term stability and service life of the battery.

[0088] Evaporation too slowly: If the solvent evaporates too slowly, an uneven pore structure may form inside the coating, which will affect the permeability of the electrolyte, thereby reducing the utilization rate of the active material and affecting the charge and discharge efficiency and capacity of the battery.

[0089] Large fluctuations in evaporation rate: When the solvent evaporation rate fluctuates greatly, the curing process of the coating is uneven, which may lead to a decrease in the adhesion between the coating and the substrate (such as copper foil, aluminum foil). This poor adhesion phenomenon will cause the coating to fall off or peel off during the operation of the battery, thereby affecting the conductivity and electrochemical performance of the battery.

[0090] Uneven coating thickness and density caused by fluctuations in solvent evaporation rates can lead to uneven current paths within the battery. Thicker or denser coating areas can increase the battery's internal resistance, especially during high-rate charge and discharge. Internal resistance fluctuations can cause local heating, affecting battery performance and safety.

[0091] Increased surface roughness: Rapid evaporation of solvents can lead to irregular textures and defects on the surface of the electrode coating, such as particle aggregation, pinholes, bubbles, etc. Surface defects can reduce the active area of ​​the battery, lead to uneven electrode reactions, and affect the battery's charge and discharge efficiency and capacity.

[0092] Poor surface leveling: If the volatilization rate is too slow, the slurry may have poor leveling problems, resulting in ripples or uneven distribution on the coating surface, affecting the subsequent electrode lamination or winding process.

[0093] When the volatilization rate fluctuates greatly, the solvent may not be completely volatilized, resulting in residual solvent in the coating. The residual solvent may react with the electrolyte during the battery charging and discharging process to produce side reactions, resulting in battery capacity decay, increased internal resistance, and even safety hazards such as gas expansion and bulging.

[0094] When the solvent evaporation rate is unstable, the spreadability of the slurry on the substrate surface will be affected, resulting in uncontrollable coating thickness and uniformity. This will not only affect the consistency of the battery cell, but also cause uneven current distribution in the local electrode, affecting the overall capacity and life of the battery pack.

[0095] Uneven solvent evaporation rate will lead to uneven distribution of active materials, conductive agents and binders in the electrode. Too fast evaporation rate may cause active materials to float up, forming surface material accumulation, while conductive agents settle; too slow evaporation rate may cause slurry stratification, affecting the uniformity of electrode materials, thereby affecting the conductivity and charge and discharge performance of the battery.

[0096] Due to the variation in coating thickness, density and uniformity caused by fluctuations in solvent evaporation rate, the electrochemical performance differences between different battery cells may increase. This will lead to increased inconsistency of single cells within the battery pack, affecting the overall performance and life of the battery pack.

[0097] Too fast solvent evaporation may cause excessive stress in the coating, affecting the mechanical strength and durability of the coating, and causing problems such as cracks and shedding of the coating during the charge and discharge cycle. Especially under repeated expansion and contraction cycles, this mechanical damage will accelerate battery aging and reduce cycle life.

[0098] When the volatilization rate fluctuates greatly, the particles of the electrode material may not be evenly distributed, resulting in local accumulation of active materials or excessive voids, affecting the electrochemical reaction rate and consistency of the battery, and ultimately affecting the charge and discharge performance and capacity.

[0099] If uneven volatilization leads to uneven coating or defects, these defects may become hidden dangers of thermal runaway during battery use. The probability of local heating, short circuit and other problems will increase, thus affecting the safety of the battery.

[0100] After the battery pack is produced, multiple performance tests are performed on each battery cell to obtain performance test data of each battery cell, including the following steps:

[0101] Obtain performance test data of each battery cell, including battery capacity decay rate and self-discharge rate;

[0102] The calculation expression of battery capacity attenuation rate is: , where is the battery capacity decay rate, is the actual capacity, This is the standard capacity.

[0103] The calculation logic of the self-discharge rate is as follows: obtain the initial capacity and the capacity after storage of the battery cell, subtract the capacity after storage from the initial capacity to obtain the capacity difference, divide the capacity difference by the initial capacity to obtain the capacity change factor, divide the capacity change factor by the storage time to obtain the self-discharge rate, and the capacity after storage is the capacity measured after the battery has been stored for a period of time.

[0104] The greater the battery capacity attenuation rate obtained after electromagnetic unit detection, the following quality impacts will be brought to the battery unit:

[0105] The attenuation of battery capacity means that its ability to store energy decreases, which directly leads to a shortened device life. For devices that need to run for a long time, such as electric vehicles or mobile devices, the battery life will be greatly reduced, and users will need to charge more frequently. The device's user experience is reduced, user satisfaction is reduced, and the device's operating time may not meet expectations.

[0106] As the capacity decays, the voltage stability of the battery during discharge will deteriorate, especially during deep discharge (low SOC), when the voltage is prone to drop rapidly. The unstable voltage of the battery at low power may cause the device to lose power or shut down unexpectedly at critical moments. The reliability of the device is reduced, and the battery may be at risk of over-discharge under high load or low power conditions, affecting the normal operation of the system.

[0107] Battery capacity decay often means that its charge and discharge cycle life is approaching the end. A large capacity decay rate indicates that the battery's effective cycle times are reduced, it can no longer withstand long-term use loads, and the battery cells may need to be replaced in a short period of time. As the battery life is shortened, users may need to replace the battery more frequently, increasing maintenance costs and usage costs.

[0108] Capacity decay is usually accompanied by an increase in internal resistance. The increase in battery internal resistance will lead to increased battery heating and higher energy loss during the charging and discharging process. Especially when charging and discharging at high currents, excessive internal resistance will affect the battery's power output capacity, resulting in the battery cells being unable to provide the required peak power. The battery overheating phenomenon is aggravated, reducing the safety of the battery, and may have a negative impact on its lifespan, and even cause the risk of thermal runaway.

[0109] Batteries with high capacity decay rates tend to exhibit higher self-discharge rates when stored. This means that the battery will quickly deplete its power even when not in use, resulting in power loss during storage and inability to maintain long-term standby. Devices need to be charged frequently after being unused for a long time, which cannot meet the needs of long-term storage or backup power, reducing efficiency.

[0110] An increase in the capacity fade rate means that the total energy storage capacity of the battery cells decreases, resulting in a decrease in their ability to withstand high power loads. Under high loads, the battery may not be able to provide enough power, affecting device performance, such as the acceleration ability of electric vehicles or the processing speed of electronic devices. Devices perform poorly when working under high loads and may even be unable to complete high-power tasks, such as vehicle acceleration or the operation of high-power applications.

[0111] The greater the self-discharge rate obtained after the electromagnetic unit test, the following quality impacts will be brought to the battery unit:

[0112] Batteries with a high self-discharge rate consume power faster when not in use, resulting in a significant reduction in the battery's available time in standby mode. For devices that require long standby periods or are infrequently used (such as backup power supplies, security equipment, etc.), increased self-discharge rates will cause the battery to lose its long-term standby advantage. The device cannot remain in standby mode for a long time, and users need to charge or replace the battery more frequently, affecting the device's long-term performance.

[0113] Due to the high self-discharge rate, the battery continues to lose power during long periods of storage or non-use, which causes the effective capacity of the battery to decrease. Even if the battery has a normal nominal capacity, a battery with a high self-discharge rate will be insufficient in actual use and will have difficulty achieving the expected output of its rated capacity. The available battery life of the device is shortened when in use, and the user experience is reduced, especially in applications that require long-term use, such as electric vehicles, power tools, or mobile devices.

[0114] Batteries with high self-discharge rates lose more power during storage, which is especially noticeable during long-term storage or transportation. Especially in scenarios where backup or emergency use is required (such as UPS power supplies or backup batteries), this power loss will greatly reduce the storage efficiency of the battery. The battery cannot maintain the power demand after long-term storage, and may not be able to perform its reserve function in an emergency or long-term non-use, causing the equipment to suddenly fail.

[0115] Batteries with high self-discharge rates are prone to enter a deep discharge state due to power loss when not used for a long time. This deep discharge may cause the battery voltage to be too low, trigger chemical instability inside the battery, and accelerate battery degradation. Battery life is shortened, performance deteriorates, and the battery pack may need to be replaced or maintained in advance, increasing the cost of use and the complexity of maintenance.

[0116] After calculating the distance between the state of each battery cell and the ideal state based on the process data and the performance test data, the relative closeness index of the battery pack is generated by combining the distance between the state of each battery cell and the ideal state, including the following steps:

[0117] The process data include the fluctuation amplitude of scraper pressure, substrate tension and solvent volatilization rate; the performance test data include battery capacity attenuation rate and self-discharge rate;

[0118] A decision matrix for the battery pack is constructed based on the scraper pressure fluctuation amplitude, substrate tension fluctuation amplitude, solvent volatilization rate fluctuation amplitude, battery capacity decay rate, and self-discharge rate of the battery cells. The rows in the matrix represent different battery cells, and the columns represent indicators such as scraper pressure fluctuation amplitude, substrate tension fluctuation amplitude, solvent volatilization rate fluctuation amplitude, battery capacity decay rate, and self-discharge rate. The decision matrix The expression is as follows:

[0119] , where Indicates The battery cell The indicator value, is the number of battery cells, is the number of indicators. In this application, ;

[0120] Decision Matrix Perform vector normalization to eliminate the dimension effect, and the expression is:

[0121] , where Indicates The first battery cell after vector normalization The indicator value, Indicates The battery cell The indicator value, is the number of battery cells;

[0122] Normalized decision matrix The expression is: , the normalized decision matrix Weighted processing, the expression is: , where Indicates The weighted The indicator value, Indicates The weight of the indicator value, Indicates The first battery cell after vector normalization indicator values, weighted decision matrix The expression is: .

[0123] After constructing the scraper pressure fluctuation amplitude, substrate tension fluctuation amplitude, solvent evaporation rate fluctuation amplitude, battery capacity attenuation rate and self-discharge rate as an indicator set, based on the weighted decision matrix Generate positive and negative ideal solutions, the expression is:

[0124] ;

[0125] ; In the formula, is a positive ideal solution, is a negative ideal solution, Represents a set of indicators, Indicates taking the minimum value, Indicates taking the maximum value. In this application, the smaller the scraper pressure fluctuation amplitude, substrate tension fluctuation amplitude, solvent volatilization rate fluctuation amplitude, battery capacity decay rate and self-discharge rate, the better the overall state of the battery cell. Therefore, the positive ideal solution is the minimized scraper pressure fluctuation amplitude, substrate tension fluctuation amplitude, solvent volatilization rate fluctuation amplitude, battery capacity decay rate and self-discharge rate, and the negative ideal solution is the maximized scraper pressure fluctuation amplitude, substrate tension fluctuation amplitude, solvent volatilization rate fluctuation amplitude, battery capacity decay rate and self-discharge rate.

[0126] Calculate the Euclidean distance between each battery cell in the battery pack and the positive ideal solution and the negative ideal solution. The expression is:

[0127] , where For the The Euclidean distance between each battery cell and the positive ideal solution, For the The Euclidean distance between each battery cell and the negative ideal solution, is the number of indicators, Indicates The weighted The indicator value, Indicates The minimum value of the indicator, Indicates The maximum value of the indicators;

[0128] The relative closeness index of the battery pack is calculated based on the Euclidean distance between the battery cell and the positive ideal solution and the Euclidean distance between the battery cell and the negative ideal solution. The expression is: , where is the relative closeness index, For the The Euclidean distance between each battery cell and the positive ideal solution, For the The Euclidean distance between each battery cell and the negative ideal solution, is the number of battery cells.

[0129] Adjusting the depth of discharge monitoring of the battery pack according to the relative proximity index of the battery pack includes the following steps:

[0130] The greater the relative proximity index of the battery pack, the greater the need to increase the depth of discharge monitoring of the battery pack. The depth of discharge monitoring of the battery pack is adjusted through an intelligent adjustment algorithm. The algorithm expression is: , where is the adjusted deep discharge monitoring threshold, is the depth of discharge monitoring threshold before adjustment, is a relatively close index. is the index threshold.

[0131] During the use of each battery pack, the detection system monitors the battery pack through the adjusted deep discharge monitoring intensity. When the monitored battery pack is in a deep discharge state, the voltage recovery curve is fitted in sections through a multi-order hysteresis curve fitting method to analyze the voltage recovery hysteresis of each battery cell in different power ranges. The voltage recovery hysteresis is combined with the internal resistance to analyze the health status of the battery cell, including the following steps:

[0132] During the use of each battery pack, the detection system uses the adjusted deep discharge monitoring threshold. When the real-time power of the battery pack is less than the adjusted deep discharge monitoring threshold, the battery pack is judged to be in a deep discharge state.

[0133] When the battery pack enters a deep discharge state, the voltage recovery curve of each battery cell is monitored in real time. When the discharge ends and the charging phase begins, the data of the voltage change over time of the battery cell is recorded, with special attention paid to the time period of voltage recovery.

[0134] The voltage recovery process is divided into several different power areas for analysis, such as:

[0135] Low battery area (battery level < 20%);

[0136] Medium power area (power 20%-80%);

[0137] High power area (power>80%);

[0138] For each power region,

[0139] The voltage recovery time constant of each battery cell in different power ranges is calculated by fitting the curve, and the expression is: , where is the recovery time constant, For the recovery process The voltage at the moment, To restore the starting voltage, is the voltage amplitude to be restored, obtained by subtracting the recovery starting voltage from the final voltage. The larger the hysteresis time constant is, the slower the voltage recovery is and the more obvious the hysteresis effect is. The hysteresis effects in different power ranges are compared, and special attention is paid to the hysteresis degree in the low power range and the high power range, because the voltage recovery in these areas can reflect the recovery ability of the battery after deep discharge. After obtaining the recovery time constants of the low power range, the medium power range and the high power range, the recovery time coefficient is obtained by weighted calculation of the recovery time constants of the low power range, the medium power range and the high power range. The expression is:

[0140] , where is the recovery time coefficient, The recovery time constant for the low battery area, is the recovery time constant in the medium power region, Recover time constant for high charge area;

[0141] Collect the internal resistance of each battery cell in different power ranges. Usually, the internal resistance can be obtained through EIS (electrochemical impedance spectroscopy) or dynamic measurement during charging and discharging;

[0142] The recovery time coefficient and internal resistance are calculated comprehensively to obtain the health factor of the battery cell, which is expressed as: , where For health factors, is the recovery time coefficient, is the internal resistance, , are the adjustment coefficients of the recovery time coefficient and the internal resistance, respectively, and , Both are greater than 0. The larger the health factor, the worse the health of the battery cell.

[0143] After comprehensively analyzing the health status of all battery cells, the overall health status of the battery pack is obtained, and a corresponding management strategy is generated based on the overall health status of the battery pack, including the following steps:

[0144] After obtaining the health factors of all battery cells in the battery pack, sum up the health factors of all battery cells to obtain the overall health factor of the battery pack. The larger the overall health factor, the worse the overall health of the battery pack. Compare the obtained overall health factor with the preset health factor threshold. The health factor threshold is used to judge the overall health of the battery pack. If the overall health factor is greater than the health factor threshold, the overall health of the battery pack is judged to be poor. If the overall health factor is less than or equal to the health factor threshold, the overall health of the battery pack is judged to be good.

[0145] If the overall health of the battery pack is judged to be good, continue to use the battery pack according to the original plan and charging strategy. If the overall health of the battery pack is judged to be poor, it is recommended that the user immediately stop using the battery pack to prevent safety hazards caused by failures and shorten the maintenance cycle of the maintenance and servicing plan to increase the service life of the battery pack.

[0146] Example 3: Please refer to Figure 2 As shown, the present embodiment provides a battery performance detection system for a battery assembly, including a regulation module, a deep discharge monitoring module, and a management module;

[0147] Adjustment module: During the production of the battery pack, the process data of each battery cell production is monitored. After the battery pack production is completed, multiple performance tests are performed on each battery cell to obtain the performance test data of each battery cell. After calculating the distance between the state of each battery cell and the ideal state based on the process data and the performance test data, the relative closeness index of the battery pack is generated by combining the distance between the state of each battery cell and the ideal state. The deep discharge monitoring intensity of the battery pack is adjusted according to the relative closeness index of the battery pack, and the adjusted deep discharge monitoring intensity is sent to the deep discharge monitoring module;

[0148] Deep discharge monitoring module: During the use of each battery pack, the battery pack is monitored through the adjusted deep discharge monitoring intensity, and the deep discharge monitoring results are sent to the management module;

[0149] Management module: When the monitored battery pack is in a deep discharge state, the voltage recovery curve is fitted in sections through a multi-order hysteresis curve fitting method, and the voltage recovery hysteresis of each battery cell in different power ranges is analyzed. The voltage recovery hysteresis is combined with the internal resistance to analyze the health status of the battery cell. After comprehensively analyzing the health status of all battery cells, the overall health status of the battery pack is obtained, and corresponding management strategies are generated based on the overall health status of the battery pack.

[0150] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0151] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0152] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well.

Claims

1. A method for detecting battery performance of a battery assembly, characterized in that: The detection method comprises the following steps: During battery pack production, the process data of each battery cell production is monitored, and after the battery pack production is completed, multiple performance tests are performed on each battery cell to obtain the performance test data of each battery cell; After calculating the distance between the state of each battery cell and the ideal state based on the process data and performance test data, the relative closeness index of the battery pack is generated by combining the distance between the state of each battery cell and the ideal state. The deep discharge monitoring intensity of the battery pack is adjusted according to the relative closeness index of the battery pack. The deep discharge monitoring intensity of the battery pack is adjusted through the intelligent adjustment algorithm. The algorithm expression is: In the formula, SOC new is the adjusted deep discharge monitoring threshold, SOC old is the depth discharge monitoring threshold before adjustment, DZ s To be relatively close to the index, ΔZ is the index threshold. During the use of each battery pack, the detection system monitors the battery pack through the adjusted deep discharge monitoring intensity; When the monitored battery pack is in a deep discharge state, the voltage recovery curve is fitted in sections through a multi-order hysteresis curve fitting method to analyze the voltage recovery hysteresis of each battery cell in different power ranges. The voltage recovery hysteresis is combined with the internal resistance to analyze the health status of the battery cell. After comprehensively analyzing the health status of all battery cells, the overall health status of the battery pack is obtained, and corresponding management strategies are generated based on the overall health status of the battery pack.

2. The battery performance detection method of a battery assembly according to claim 1, characterized in that: After calculating the distance between the state of each battery cell and the ideal state based on the process data and the performance test data, the relative closeness index of the battery pack is generated by combining the distance between the state of each battery cell and the ideal state, including the following steps: A decision matrix D of a battery pack is constructed based on the scraper pressure fluctuation amplitude, substrate tension fluctuation amplitude, solvent volatilization rate fluctuation amplitude, battery capacity decay rate and self-discharge rate of the battery cells. The rows in the matrix represent different battery cells, and the columns represent scraper pressure fluctuation amplitude, substrate tension fluctuation amplitude, solvent volatilization rate fluctuation amplitude, battery capacity decay rate and self-discharge rate indicators. Performing vector normalization processing on the decision matrix D to obtain a normalized decision matrix R, and performing weighted processing on the normalized decision matrix R to obtain a weighted decision matrix V; After constructing the scraper pressure fluctuation amplitude, substrate tension fluctuation amplitude, solvent volatilization rate fluctuation amplitude, battery capacity decay rate and self-discharge rate as an indicator set, the positive ideal solution and negative ideal solution are generated based on the weighted decision matrix W, and the expression is: Where P + is a positive ideal solution, P - is the negative ideal solution, N represents the indicator set, min(*) represents the minimum value, and max(*) represents the maximum value. The Euclidean distance between each battery cell in the battery pack and the positive ideal solution and the negative ideal solution is calculated as follows: In the formula, is the Euclidean distance between the ith battery cell and the positive ideal solution, is the Euclidean distance between the ith battery cell and the negative ideal solution, n is the number of indicators, v ij represents the jth index value after weighting of the i-th battery cell, represents the minimum value of the jth index, represents the maximum value of the jth index; The relative closeness index of the battery pack is calculated based on the Euclidean distance between the battery cell and the positive ideal solution and the Euclidean distance between the battery cell and the negative ideal solution. The expression is: Where DZ s is the relative closeness index, and m is the number of battery cells.

3. The battery performance detection method of a battery assembly according to claim 2, characterized in that: The expression of the decision matrix D is: In the formula, x ij represents the jth indicator value of the ith battery cell, m is the number of battery cells, n is the number of indicators, and the decision matrix D is vector normalized to eliminate the dimension effect. The expression is: In the formula, r ij represents the jth index value of the i-th battery cell after vector normalization processing; The normalized decision matrix R is expressed as: The normalized decision matrix R is weighted and the expression is: ij =w j ·r ij , where v ij represents the jth index value after weighting of the i-th battery cell, w j represents the weight of the jth indicator value, r ij It represents the jth index value of the i-th battery cell after vector normalization. The weighted decision matrix V is expressed as:

4. The battery performance detection method of a battery assembly according to claim 3, characterized in that: When the monitored battery pack is in a deep discharge state, the voltage recovery curve is fitted in sections by a multi-order hysteresis curve fitting method to analyze the voltage recovery hysteresis of each battery cell in different power ranges. The voltage recovery hysteresis is combined with the internal resistance to analyze the health status of the battery cell, including the following steps: During the use of each battery pack, the detection system uses the adjusted deep discharge monitoring threshold. When the real-time power of the battery pack is less than the adjusted deep discharge monitoring threshold, the battery pack is judged to be in a deep discharge state. When the battery pack enters a deep discharge state, the voltage recovery curve of each battery cell is monitored in real time. When the discharge ends and the charging phase begins, the data on the voltage change of the battery cell over time is recorded. For each power range, the voltage recovery time constant of each battery cell in different power ranges is calculated by fitting the curve. After obtaining the recovery time constants of the low power range, the medium power range, and the high power range, the recovery time coefficient is obtained by weighted calculation of the recovery time constants of the low power range, the medium power range, and the high power range, and the internal resistance of each battery cell in different power ranges is collected; The recovery time coefficient and internal resistance are calculated comprehensively to obtain the health factor of the battery cell, which is expressed as: jk = α·hf z +β·θ, where jk is the health factor, hf z is the recovery time coefficient, θ is the internal resistance, α and β are the adjustment coefficients of the recovery time coefficient and the internal resistance respectively, and both α and β are greater than 0.

5. The battery performance detection method of the battery assembly according to claim 4, characterized in that: For each power range, the voltage recovery time constant of each battery cell in different power ranges is calculated by fitting the curve, and the expression is: Wherein, τ is the recovery time constant, V(t) is the voltage at time t during the recovery process, V0 is the recovery starting point voltage, A is the voltage amplitude to be recovered, which is obtained by subtracting the recovery starting point voltage from the final voltage, and t is the time. After obtaining the recovery time constants of the low power area, medium power area, and high power area, the recovery time coefficient is obtained by weighted calculation of the recovery time constants of the low power area, medium power area, and high power area. The expression is: hf z =0.4*τ Low +0.2*τ medium +0.4*τ high , where hf z is the recovery time coefficient, τ Low is the low power area recovery time constant, τ medium is the recovery time constant in the medium power region, τ high Recovery time constant for high charge region.

6. The battery performance detection method of a battery assembly according to claim 5, characterized in that: The overall health status of the battery pack is obtained by comprehensively analyzing the health status of all battery cells, including the following steps: After obtaining the health factors of all battery cells in the battery pack, the health factors of all battery cells are summed to obtain the overall health factor of the battery pack, and the obtained overall health factor is compared with the preset health factor threshold. The health factor threshold is used to judge the overall health status of the battery pack. If the overall health factor is greater than the health factor threshold, the overall health status of the battery pack is judged to be poor. If the overall health factor is less than or equal to the health factor threshold, the overall health status of the battery pack is judged to be good.

7. The battery performance detection method of a battery assembly according to claim 6, characterized in that: After the battery pack is produced, multiple performance tests are performed on each battery cell to obtain performance test data of each battery cell, including the following steps: Obtain performance test data of each battery cell, including battery capacity decay rate and self-discharge rate; The calculation expression of battery capacity attenuation rate is: Where, L s is the battery capacity decay rate, rl s is the actual capacity, rl z It is the standard capacity; The calculation logic of the self-discharge rate is: obtain the initial capacity and the capacity after storage of the battery cell, subtract the capacity after storage from the initial capacity to obtain the capacity difference, divide the capacity difference by the initial capacity to obtain the capacity change factor, and divide the capacity change factor by the storage time to obtain the self-discharge rate.

8. The battery performance detection method of a battery assembly according to claim 7, characterized in that: During battery pack production, the process data of each battery cell production is monitored, including the following steps: Obtaining the fluctuation amplitude of scraper pressure, substrate tension and solvent volatilization rate during the production of battery cells; The calculation expression of scraper pressure fluctuation amplitude is: In the formula, B d is the scraper pressure fluctuation amplitude, a is the number of scraper monitoring time points, d i is the scraper pressure at the i-th scraper monitoring time point, d avg is the average value of the scraper pressure, and the calculation expression of the substrate tension fluctuation amplitude is: In the formula, B g is the substrate tension fluctuation amplitude, b is the number of substrate monitoring time points, g is i is the substrate tension at the i-th substrate monitoring time point, g avg is the mean value of substrate tension, and the calculation expression of the fluctuation amplitude of solvent evaporation rate is: In the formula, B r is the fluctuation amplitude of solvent evaporation rate, c is the number of solvent monitoring time points, r i is the solvent evaporation rate at the i-th solvent monitoring time point, r avg is the average solvent evaporation rate.

9. A battery performance detection system for a battery assembly, used to implement the detection method according to any one of claims 1 to 8, characterized in that: Including regulation module, deep discharge monitoring module and management module; Adjustment module: During the production of the battery pack, the process data of each battery cell is monitored. After the battery pack is produced, multiple performance tests are performed on each battery cell to obtain the performance test data of each battery cell. The distance between the state of each battery cell and the ideal state is calculated based on the process data and the performance test data. The relative closeness index of the battery pack is generated by combining the distance between the state of each battery cell and the ideal state. The depth of discharge monitoring of the battery pack is adjusted according to the relative closeness index of the battery pack. The expression is: In the formula, SOC new is the adjusted deep discharge monitoring threshold, SOC old is the depth discharge monitoring threshold before adjustment, DZ s is the relative closeness index, ΔZ is the index threshold; Deep discharge monitoring module: During the use of each battery pack, the battery pack is monitored through the adjusted deep discharge monitoring intensity; Management module: When the monitored battery pack is in a deep discharge state, the voltage recovery curve is fitted in sections through a multi-order hysteresis curve fitting method, and the voltage recovery hysteresis of each battery cell in different power ranges is analyzed. The voltage recovery hysteresis is combined with the internal resistance to analyze the health status of the battery cell. After comprehensively analyzing the health status of all battery cells, the overall health status of the battery pack is obtained, and corresponding management strategies are generated based on the overall health status of the battery pack.

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