Method and system for cooling LiFePO4 battery after increase of electrode thickness

Abstract

The invention relates to the technical field of intelligent temperature control, in particular to a LiFePO4 battery cooling method and system after electrode thickness increase, and the method comprises the following steps: collecting battery node voltage through a voltage sensor, monitoring heat flow distribution, extracting heat conduction parameters, carrying out Kalman filtering noise reduction, calculating the ratio of voltage drop to thermal resistance, judging heat transfer lag, and outputting thermal evaluation; the method comprises the following steps: calculating a cooling demand by combining the electrode thickness and the temperature rise rate to adjust the flow rate of a fluid, monitoring temperature uniformity and weighting the voltage recovery rate to obtain cooling evaluation, and aiming at substandard nodes, adjusting the fitting pressure and the thickness of a heat dissipation film and outputting optimized parameters, in the invention, the voltage of a single battery and the heat flux density of a thick electrode region are collected in real time; the method combines heat conduction parameters and noise filtering, dynamically identifies internal thermal resistance changes, adjusts cooling flow speed based on voltage and temperature rise rate coupling analysis, realizes temperature equalization, jointly evaluates temperature distribution uniformity and voltage recovery rate, continuously optimizes cooling and response, reduces thermal runaway probability, improves safety and prolongs service life.

Description

Technical Field

[0001] This invention relates to the field of intelligent temperature control technology, and in particular to a cooling method and system for LiFePO4 batteries after the electrode thickness is increased. Background Technology

[0002] The field of intelligent temperature control technology involves the real-time and effective management and regulation of heat generated during equipment operation. This includes a series of processes such as temperature acquisition, sensing, analysis, feedback, and execution of cooling or heating operations. Its application scope covers multiple fields such as power battery energy storage systems, consumer electronics, industrial automation, and vehicle power systems. Its core aspects include the identification and monitoring of heat generation, management of temperature distribution, design of heat conduction and heat dissipation paths, and output and execution of control strategies. It also covers intelligent temperature control strategies and environmental compatibility improvements.

[0003] Among them, the traditional method for cooling LiFePO4 batteries after increasing electrode thickness refers to the following measures to avoid the adverse effects of local overheating on performance when the electrode thickness in lithium iron phosphate batteries increases. These measures include attaching thermally conductive structures such as copper or aluminum sheets to the outside of the battery casing or wrapping the battery body with liquid cooling pipes to allow the coolant to carry away excess heat. In addition, some methods use heat dissipation ducts to allow airflow along the battery surface to assist in cooling. Some methods also involve coating the individual battery cells with thermal interface materials to enhance the thermal conductivity.

[0004] Existing technologies, when dealing with heat accumulation in thick electrodes, only employ external heat-conducting structures and cooling methods. They lack real-time identification of the dynamics of heat transfer and local temperature changes within the electrode. This often results in a delayed response in high-rate discharge or electrode thickening scenarios, causing heat dissipation to concentrate on the battery surface. This can easily lead to problems such as internal overheating and widening temperature differences. It is difficult to maintain a balanced temperature distribution within the cell, which over time causes heat accumulation, performance degradation, and the risk of thermal runaway, affecting the overall battery life and safety. Summary of the Invention

[0005] To address the shortcomings of existing technologies that rely solely on external heat-conducting structures and cooling methods to handle heat accumulation in thick electrodes, lacking real-time identification of internal heat transfer dynamics and local temperature changes, and often exhibiting lag in response during high-rate discharge or electrode thickening scenarios, leading to concentrated heat flow on the battery surface, internal overheating, and widening temperature differences, the present invention provides a cooling method for LiFePO4 batteries with increased electrode thickness, comprising the following steps:

[0006] To achieve the above objectives, the present invention employs the following technical solution: a cooling method for LiFePO4 batteries after increasing electrode thickness, comprising the following steps:

[0007] S1: The real-time voltage of the single LiFePO4 battery node is obtained through a voltage sensor, the heat flux density distribution inside the thick electrode region is monitored, the thermal conductivity parameters corresponding to the electrode thickness are collected, and the Kalman filter algorithm is used to remove noise based on voltage fluctuation and heat flux density to form thick electrode voltage-thermal conductivity correlation data.

[0008] S2: Call the thick electrode voltage thermal conductivity correlation data, normalize the voltage drop amplitude, and calculate the ratio with the heat transfer impedance. When the ratio exceeds the set critical value, it is determined to be thick electrode heat transfer hysteresis. Extract the electrode thickness and local temperature rise rate to generate thick electrode heat transfer evaluation results.

[0009] S3: Based on the heat transfer evaluation results of the thick electrode, perform cooling intensity analysis, calculate the ratio of the electrode thickness to the standard thickness, determine the cooling demand coefficient by coupling it with the local temperature rise rate, adjust the cooling fluid flow rate of the corresponding node, and output the thick electrode cooling adjustment command.

[0010] S4: Execute cooling fluid distribution adjustment according to the thick electrode cooling adjustment command, monitor the uniformity of temperature distribution and voltage recovery rate inside the thick electrode after adjustment, and perform weighted average calculation on temperature distribution uniformity and voltage recovery rate through fuzzy controller to form thick electrode cooling evaluation data.

[0011] As a further aspect of the present invention, the thick electrode voltage-thermal conductivity correlation data includes voltage fluctuation amplitude, heat flux density and thermal conductivity; the thick electrode heat transfer evaluation results include thermal resistance, electrode thickness and temperature gradient; the thick electrode cooling adjustment command includes flow rate adjustment amount, cooling power and priority weight; and the thick electrode cooling evaluation data includes temperature uniformity coefficient, voltage recovery coefficient and performance evaluation index.

[0012] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0013] S101: The real-time voltage of the LiFePO4 cell node is obtained through a voltage sensor, the node electrode thickness and corresponding thermal conductivity results are collected simultaneously, the response data between the heat flux density in the thick electrode region are sorted out, the regional data distribution is analyzed based on the voltage and heat flux density, and the electrode heat flux density distribution value is generated.

[0014] The electrode heat flux density distribution value refers to the amount of heat transferred per unit area in real time under different spatial positions and thicknesses of the electrode, reflecting the spatial distribution characteristics of heat generation and conduction.

[0015] S102: Based on the electrode heat flux density distribution value, collect heat flux data under different thicknesses, analyze the relationship between thickness and heat flux, retrieve the heat flux parameters of thickness, and generate a set of electrode thermal conductivity parameters.

[0016] S103: Based on the electrode thermal conductivity parameter set and the original data of the voltage node, Kalman filtering is used to separate the noise data in multiple parameters, integrate the response information of voltage, heat flow and thermal conductivity, and obtain the voltage-thermal conductivity correlation data of the thick electrode.

[0017] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0018] S201: Call the thick electrode voltage-thermal conductivity correlation data, extract the voltage drop amplitude of the single cell and the internal heat transfer impedance of the thick electrode, pair them based on time nodes and calculate the corresponding ratio, analyze whether the ratio fluctuation amplitude exceeds the critical threshold of heat transfer, and obtain the characteristic distribution value of the ratio sequence.

[0019] The critical threshold for heat transfer is determined based on a single-cell electrochemical-thermal coupling simulation model, under conditions of differentiated electrode thickness and thermal resistance, with the ratio limit before thermal runaway as the safety boundary.

[0020] The ratio sequence characteristic distribution value refers to the statistical and fluctuating characteristics formed by the change of the ratio of voltage drop amplitude to the thermal transfer impedance of thick electrode over time during battery operation;

[0021] S202: Based on the characteristic distribution value of the ratio sequence, filter out the time periods that exceed the critical threshold of heat transfer, determine that there is a lag in heat transfer of thick electrodes, extract the electrode thickness data and local temperature rise rate records for the corresponding time periods, and generate a critical time period thermal resistance feature group.

[0022] S203: Based on the electrode thickness and local temperature rise rate data in the critical period thermal resistance characteristic group, calculate the change trend of unit temperature rise rate under differentiated thickness, construct a thickness-temperature rise rate mapping table, extract the thermal conduction response value of the electrode thickness range, and generate the thick electrode heat transfer evaluation result.

[0023] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0024] S301: Based on the thick electrode heat transfer evaluation results, collect the current electrode thickness value and the standard electrode thickness value, perform difference ratio calculation, pair the ratio with the local temperature rise rate of the node, calculate the numerical correlation between thickness and temperature rise, and generate thickness temperature rise correlation coefficient.

[0025] S302: Call the thickness temperature rise correlation coefficient, arrange the node positions in descending order of correlation strength value, match the arrangement of nodes in the cooling demand direction with the thickness temperature rise intensity, calculate the node cooling intensity based on the cooling power curve and normalize it to obtain the node cooling demand coefficient.

[0026] The node cooling demand coefficient is a normalized cooling priority parameter.

[0027] S303: Based on the node cooling demand coefficient, adjust the existing cooling fluid flow rate of the cooling node, bind the updated flow rate value with the node number and write it into the adjustment command data structure to generate a thick electrode cooling adjustment command.

[0028] As a further aspect of the present invention, the thickness temperature rise correlation coefficient refers to a numerical parameter calculated based on the relationship between the ratio of the current thickness of the thick electrode node to the standard thickness and its local temperature rise rate, reflecting the correlation strength between the node thickness change and the local temperature rise response.

[0029] The node cooling demand coefficient refers to a numerical parameter that characterizes the node cooling priority in the thick electrode cooling adjustment strategy. It is obtained by normalizing the real-time cooling demand of the node by combining the node thickness temperature rise correlation coefficient and the cooling power curve.

[0030] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0031] S401: Based on the thick electrode cooling adjustment command, retrieve the cooling channel control parameters and flow rate adjustment threshold, divide the cooling area control unit, adjust the inlet pressure and the opening of the diversion valve, and obtain the cooling fluid distribution coefficient.

[0032] S402: Call the cooling fluid distribution coefficient, collect multiple temperature values ​​and voltage change curves, calculate the temperature standard deviation and voltage slope fluctuation, normalize and weighted integrate the two sets of parameters, and generate a data set of temperature distribution uniformity and voltage recovery rate.

[0033] S403: Based on the temperature distribution uniformity and voltage recovery rate data set, perform fuzzification, weighting and defuzzification processing based on membership functions and fuzzy rule tables to generate thick electrode cooling evaluation data.

[0034] As a further aspect of the present invention, the method includes step S5:

[0035] S5: Screen thick electrode nodes that do not meet the evaluation indicators through the thick electrode cooling evaluation data, adjust the node cooling plate bonding pressure and heat dissipation film coverage thickness according to the node electrode thickness parameters, cooling contact impedance and temperature response time, and output the thick electrode cooling optimization configuration parameters.

[0036] The optimized configuration parameters for thick electrode cooling include contact pressure, film thickness, and contact area ratio.

[0037] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0038] S501: Obtain the thick electrode cooling evaluation data, collect the node electrode thickness parameters, measure the cooling contact impedance and temperature response time, compare them with the cooling reference value, determine whether the allowable deviation threshold is exceeded, screen abnormal nodes and record the parameters, and obtain the data of substandard nodes.

[0039] S502: Based on the substandard node data, call the node electrode thickness parameters, cooling contact impedance and temperature response time, and determine the main control parameters of cooling delay by comparing the correlation between cooling contact impedance and temperature response time. Adjust the cooling plate bonding pressure and heat dissipation film coverage thickness respectively to generate node cooling optimization configuration.

[0040] S503: Based on the node cooling optimization configuration, record the changes in cooling contact impedance and temperature response time after the node adjustment, call the node electrode thickness and the updated heat transfer performance parameters, merge the parameter correspondence, and obtain the thick electrode cooling optimization configuration parameters.

[0041] The cooling system for the LiFePO4 battery after increasing the electrode thickness includes:

[0042] The thick electrode parameter acquisition module is used to obtain the real-time voltage of the single LiFePO4 battery node through a voltage sensor, monitor the heat flux density distribution inside the thick electrode region, collect the thermal conductivity parameters corresponding to the electrode thickness, and use a Kalman filter algorithm to remove noise based on the correlation between voltage and heat flux density to form thick electrode voltage-thermal conductivity correlation data, which is then transmitted to the heat transfer impedance evaluation module.

[0043] The heat transfer impedance assessment module is used to call the voltage-thermal conductivity correlation data of the thick electrode, calculate the ratio of the battery voltage drop to the internal heat transfer impedance of the thick electrode, determine the heat transfer hysteresis, form the heat transfer assessment result of the thick electrode, and transmit it to the cooling demand calculation module.

[0044] The cooling demand calculation module is used to call the heat transfer evaluation results of the thick electrode to perform cooling intensity calculation, couple the ratio of electrode thickness to standard thickness and local temperature rise rate, calculate the cooling demand coefficient, adjust the flow rate, output the thick electrode cooling adjustment command, and transmit it to the cooling effect monitoring module.

[0045] The cooling effect monitoring module is used to adjust the distribution of cooling fluid according to the thick electrode cooling adjustment command, monitor the temperature distribution uniformity and voltage recovery rate after adjustment, input the weighted average of the fuzzy controller, form thick electrode cooling evaluation data, and transmit it to the cooling configuration optimization module.

[0046] The cooling configuration optimization module is used to screen thick electrode nodes that do not meet the evaluation indicators through the thick electrode cooling evaluation data, judge based on the node electrode thickness parameters, adjust the node cooling plate bonding pressure and heat dissipation film coverage thickness, and output thick electrode cooling optimization configuration parameters.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0048] In this invention, by real-time acquisition of single-cell voltage and correlation of heat flux density distribution in thick electrode region, combined with acquisition of thermal conductivity parameters and noise filtering of thick electrode, changes in internal heat transfer impedance of electrode can be effectively identified. Based on the coupled analysis of voltage and temperature rise rate, cooling demand is dynamically calculated, and the flow rate of cooling fluid at nodes is directly adjusted to achieve dynamic temperature equilibrium in the internal region of electrode. By using the joint evaluation of temperature distribution uniformity and voltage recovery rate, cooling effect and response speed can be continuously optimized, matching different chemical conditions, reducing the probability of thermal runaway, ensuring that the temperature rise in the region is controlled during the operation of thick electrode battery, and improving safety stability and cycle life. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of the steps of the present invention;

[0051] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0052] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0053] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0054] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0055] Figure 6 This is a detailed schematic diagram of S5 of the present invention;

[0056] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0057] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0058] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0059] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0060] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0061] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0062] Please see Figure 1 This invention provides a cooling method for LiFePO4 batteries after increasing electrode thickness, comprising the following steps:

[0063] S1: The real-time voltage of the single LiFePO4 battery node is obtained through a voltage sensor, the heat flux density distribution inside the thick electrode region is monitored, the thermal conductivity parameters corresponding to the electrode thickness are collected, and the Kalman filter algorithm is used to remove noise based on voltage fluctuation and heat flux density to form thick electrode voltage-thermal conductivity correlation data.

[0064] S2: Call the thick electrode voltage-thermal conduction correlation data, normalize the voltage drop amplitude, and calculate the ratio with the heat transfer impedance. When the ratio exceeds the set critical value, it is determined that the thick electrode heat transfer is lagging. Extract the electrode thickness and local temperature rise rate to generate the thick electrode heat transfer evaluation result.

[0065] S3: Based on the heat transfer evaluation results of the thick electrode, perform cooling intensity analysis, calculate the ratio of the electrode thickness to the standard thickness, and determine the cooling demand coefficient by coupling it with the local temperature rise rate. Adjust the cooling fluid flow rate of the corresponding node and output the thick electrode cooling adjustment command.

[0066] S4: Execute cooling fluid distribution adjustment according to the thick electrode cooling adjustment command, monitor the uniformity of temperature distribution and voltage recovery rate inside the thick electrode after adjustment, and perform weighted average calculation on temperature distribution uniformity and voltage recovery rate through fuzzy controller to form thick electrode cooling evaluation data.

[0067] S5: Screen thick electrode nodes that do not meet the evaluation indicators by using thick electrode cooling evaluation data, and adjust the bonding pressure of the node cooling plate and the coverage thickness of the heat dissipation film according to the node electrode thickness parameters, cooling contact impedance and temperature response time, and output the optimized configuration parameters for thick electrode cooling.

[0068] The voltage-thermal correlation data for thick electrodes includes voltage fluctuation amplitude, heat flux density, and thermal conductivity. The heat transfer evaluation results for thick electrodes include thermal resistance, electrode thickness, and temperature gradient. The cooling regulation commands for thick electrodes include flow rate regulation, cooling power, and priority weight. The cooling evaluation data for thick electrodes includes temperature uniformity coefficient, voltage recovery coefficient, and performance evaluation index. The optimization configuration parameters for thick electrode cooling include contact pressure, film thickness, and contact area ratio.

[0069] Please see Figure 2 The specific steps of S1 are as follows:

[0070] S101: The real-time voltage of the LiFePO4 cell node is obtained through a voltage sensor, the node electrode thickness and corresponding thermal conductivity results are collected simultaneously, the response data between the heat flux density in the thick electrode region are sorted out, the regional data distribution is analyzed based on the voltage and heat flux density, and the electrode heat flux density distribution value is generated.

[0071] Electrode heat flux density distribution value refers to the amount of heat transferred per unit area in real time under different spatial positions and thicknesses of the electrode, reflecting the spatial distribution characteristics of heat energy generation and conduction;

[0072] When acquiring the real-time voltage of a single LiFePO4 battery node using a voltage sensor, a sampling port needs to be set between the two tabs of each cell. This port is connected to a voltage sensor with an accuracy of ±0.01V, and the node voltage value is collected at a 1-second interval. For example, in a 60Ah LiFePO4 battery pack, three representative cell nodes (numbered A1, B3, and C5) are selected for real-time voltage monitoring, and 60 sets of data are collected within 1 minute. The first 10 sets of voltage values ​​for node A1 are {3.352, 3.354, 3.353, 3.356, 3.355, 3.354, 3.356, 3.357, 3.359, 3.360}V. Simultaneously, electrodes need to be deployed on the corresponding area of ​​each node. A miniature displacement sensor, with an accuracy of ±0.01 mm, is used to obtain the actual thickness of the electrode. It employs an equidistant scanning method to sample five locations on the electrode surface and averages the results to obtain the average electrode thickness. The thickness range is set between 120 μm and 260 μm. For example, in node B3, the thicknesses of the five measuring points in the corresponding region are {210, 212, 208, 211, 209} μm, and the calculated average thickness is 210 μm. Subsequently, a thermopile sensor or a distributed heat flux meter is used to simultaneously measure the heat flux density per unit area of ​​the electrode region, recording its heat flux response time series to obtain the heat flux density sequence of the thick electrode region. For example, in node C5, the first five sets of heat flux density data are {530, 535, 540, 545, 550} W / m². 2 During the data acquisition process described above, it is necessary to compare the relationship between voltage change trends and heat flux changes group by group. For example, when the voltage increases from 3.352V to 3.360V, the heat flux density in region C5 increases by 20W / m² accordingly. 2 This generates voltage-heat flux pairs at specific spatial nodes at each moment. In this way, a mapping relationship is established between the real-time voltage of each electrode region in space and its electrode thickness and heat flux density per unit area. The heat flux density distribution value is defined as the amount of instantaneous heat transferred by the electrode due to thickness differences within a unit area. The calculation is performed by averaging the heat flux sensor data within that area, ultimately yielding a complete heat flux density distribution map, as shown in the table below.

[0073] Table 1: Measurement Table of Heat Flux Density Distribution

[0074] Electrode nodes Average thickness (μm) <![CDATA[Heat flux density (W / m 2 )]]> Voltage value (V) A1 190 460 3.352 B3 210 520 3.358 C5 230 545 3.360

[0075] As shown in Table 1, for every 20 μm increase in electrode thickness, the average heat flux density increases by approximately 30–40 W / m². 2 Here, thickness shows a positive correlation with heat flux density. To ensure clear judgment criteria, the heat flux density range is divided into three levels: when heat flux density < 480 W / m 2 Defined as "low value zone", 480W / m 2≤ Heat flux density < 530 W / m 2 The region is designated as the "median value zone," with a heat flux density ≥ 530 W / m³. 2 The area is designated as the "high value zone," with node C5 located in the high value zone and node A1 located in the low value zone. Subsequently, by combining the voltage sequence and thickness and heat flux value sequences on each node, a multi-dimensional array structure is constructed according to the time index to support further analysis of the relationship between thickness and heat flux in step S102. Through the above process, the heat flux density value under spatial distribution is obtained.

[0076] S102: Collect heat flux data under different thicknesses based on electrode heat flux density distribution values, analyze the relationship between thickness and heat flux, retrieve heat flux parameters of thickness, and generate electrode thermal conductivity parameter set;

[0077] Based on the heat flux density distribution values, it is necessary to extract and archive the heat flux density of electrode regions with varying thicknesses group by group. First, by traversing each thickness record value in the node sequence, thickness intervals are divided with a step size of 10 μm, and the average heat flux density within each thickness interval is calculated to obtain the heat flux data set corresponding to different thicknesses. For example, for electrodes with thicknesses of 210 μm, 220 μm, and 230 μm, all nodes are selected, and their corresponding heat flux density records are retrieved. For instance, when the thickness is 210 μm, nodes B3, D2, and E1 are selected, and their corresponding heat flux values ​​are extracted to be 520 W / m². 2 522W / m 2 519W / m 2 The calculated average heat flux density for this thickness is 520.33 W / m. 2 Subsequently, the differential thickness ranges and their average heat flux data are grouped into structured storage using key-value pairs, forming a thickness-heat flux mapping set. After archiving, the heat flux fluctuation range within each thickness range is calculated based on multiple heat flux record sequences. The fluctuation range is obtained by the difference between the maximum and minimum values, and the variance is further used to determine stability. For example, the heat flux density variance for a thickness of 210 μm is 6.2, while that for a thickness of 230 μm is 12.5. This indicates that the heat flux fluctuation is more severe in the 230 μm region. During this process, the corresponding heat flux sampling time window is 10 seconds, with a sampling interval of 1 second, for a total of 10 data points, to maintain the consistency of the heat flux parameters. After completing the above data archiving, the corresponding thermal conductivity is retrieved for the electrode thickness data of each node. The thermal conductivity is experimentally measured using a steady-state method. The 3 cm electrode thickness is then heated using laser heating. 2 After heating the thickness sample for 5 seconds, its heat flow and temperature difference were measured and converted according to the following formula:

[0078]

[0079] Where λ represents the thermal conductivity (unit: W / (m·K)) and q is the heat flux density (unit: W / m³). 2 ), where d is the thickness (in meters), and ΔT is the temperature difference (in K) between the two ends of that thickness. For example, if the sample thickness is 220 μm, then d = 220 × 10⁻⁶. -6 m, heat flux density is 530 W / m 2 If the temperature difference is 3.8K, then

[0080]

[0081] The thermal conductivity values ​​measured at varying thicknesses were structured and organized into a thermal conductivity parameter group indexed by thickness. After obtaining the corresponding values ​​at multiple nodes, the electrode thickness range was divided into five groups. The minimum, maximum, average, and variance of the thermal conductivity in each group were statistically analyzed, and the linear correlation coefficient between thickness and thermal conductivity was calculated. The Pearson correlation coefficient between the mean thickness and the mean thermal conductivity in each group was calculated. If the absolute value of the correlation coefficient is greater than 0.6, it is considered a strong correlation. For example, the correlation coefficient calculation process is as follows: for the sample... The five thicknesses in the group are {190, 200, 210, 220, 230} μm, corresponding to thermal conductivity of {0.0265, 0.0281, 0.0303, 0.0307, ​​0.0321} W / (m·K). After decentering the two groups of data, the covariance and standard deviation were calculated, and then substituted into the correlation coefficient formula for calculation. The final result was 0.915, so it was identified as a positive correlation. The above correlation was then used as the reference basis for subsequent filtering and parameter fitting, and finally the generation of the electrode thermal conductivity parameter group was completed.

[0082] S103: Based on the electrode thermal conductivity parameter set and the original data of the voltage node, Kalman filtering is used to separate the noise data in multiple parameters, integrate the response information of voltage, heat flow and thermal conductivity, and obtain the voltage-thermal conductivity correlation data of thick electrode.

[0083] After obtaining the thermal conductivity parameter set and node voltage and heat flux data, the three types of parameters need to be integrated. First, for each node, its original voltage sequence, thickness data, and corresponding thermal conductivity and heat flux density sequences are retrieved to construct a joint data structure in the form of a three-dimensional vector group. Each data set is {voltage, heat flux density, thermal conductivity}. For example, the three data sets for node A1 are {3.352, 460, 0.0265}, {3.354, 465, 0.0269}, and {3.356, 468, 0.0271}. Next, a noise removal process is performed on the original data of each node. The noise judgment benchmark is set as a voltage change amplitude exceeding ±0.01V or a heat flux density instantaneous change exceeding ±15W / m. 2For this type of data, the sample points are repaired by averaging the values ​​of two neighboring sample points. For example, if the voltage jumps to 3.372V and the heat flux density jumps to 490W / m² at a certain moment... 2 This exceeds the previous set of data by 3.356V and 468W / m. 2 The values ​​within the normal range were identified as noise points and removed. The average of the preceding and following samples was then used to replace them. The new data are: Voltage: 3.356 + 3.354 / 2 = 3.355, Heat flux density: 468 + 466 / 2 = 467 W / m³ 2 After noise removal, the remaining samples were normalized to a voltage range of [3.30V, 3.40V] and a heat flux density range of [450, 570] W / m³. 2 The thermal conductivity range is [0.025, 0.035] W / (m·K), and the normalization method is as follows:

[0084]

[0085] Taking voltage as an example, if the original value is 3.355V, then:

[0086]

[0087] After data normalization, similarity comparison between samples within a node is performed. The bidirectional difference between voltage and thermal conductivity is calculated and the mean square value is used as a similarity index. If the similarity index is less than 0.05, the intra-node matching is considered valid. Finally, the three sets of normalized data within the valid nodes are called into the joint data array. The "thick electrode voltage-thermal conductivity" association array is constructed using an index method to form a mapping between instantaneous voltage and thermal characteristics corresponding to the differential thickness electrodes under the spatial structure. This completes the data integration process and obtains the thick electrode voltage-thermal conductivity association data.

[0088] Please see Figure 3 The specific steps of S2 are as follows:

[0089] S201: Call the voltage-thermal correlation data of thick electrode, extract the voltage drop of a single cell and the internal heat transfer impedance of the thick electrode, pair them based on time nodes and calculate the corresponding ratio, analyze whether the fluctuation of the ratio exceeds the critical threshold of heat transfer, and obtain the characteristic distribution value of the ratio sequence.

[0090] The critical threshold for heat transfer is determined based on a single-cell electrochemical-thermal coupling simulation model. Under conditions of differentiated electrode thickness and thermal resistance, the ratio limit before thermal runaway is used as the safety boundary.

[0091] The ratio sequence characteristic distribution value refers to the statistical and fluctuating characteristics formed by the change of the ratio of voltage drop amplitude to thermal transfer resistance of thick electrode over time during battery operation;

[0092] The voltage-thermal conductivity correlation data of thick electrodes is retrieved, and the instantaneous voltage value and thermal conductivity parameter of each node in the corresponding time series are read. The voltage change between two consecutive sample points is determined according to the time increment index. The voltage at the current time point is subtracted from the voltage at the previous time point to form a voltage drop amplitude sequence. Then, the heat transfer impedance value corresponding to each time point is retrieved. The thermal resistance is obtained by inversely proposing it from the node's thermal conductivity. The thermal resistance is calculated using the inverse relationship between electrode thickness and thermal conductivity. Specifically, the electrode thickness value (in μm) of the node is converted to meters, and then divided by the thermal conductivity to obtain the unit thermal resistance, forming a voltage drop amplitude to thermal resistance ratio sequence. For example, the voltage of node A1 at times t1, t2, and t3 are 3.352V, 3.345V, and 3.337V, respectively, and the thermal conductivity is 0.0287, 0.0284, and 0.0278W / (m·K), respectively. The thickness is 210μm. Then, the voltage drop amplitudes are 0.007V and 0.008V, respectively, and the thermal resistances are:

[0093]

[0094] The above ratio sequence is arranged by time. The ratio data within 10 seconds is extracted group by group using a sliding window method. The difference between the maximum and minimum values ​​is calculated to obtain the fluctuation range. This is then compared with the critical threshold for heat transfer. The critical threshold is calculated based on the simulation model and set to 1.25. If the ratio fluctuation is greater than this threshold, it is judged as an abnormal change in thermal resistance and is marked. For example, if the maximum ratio within a 10-second window is 1.33 and the minimum is 1.00, the fluctuation range is 0.33, which is greater than 1.25-1=0.25, exceeding the critical limit. The abnormality of this time period needs to be recorded. The ratio is further statistically analyzed to obtain its mean, variance, median, range, and other indicators to form the characteristic distribution of the ratio sequence, which is convenient for subsequent interval identification and risk level classification.

[0095] S202: Based on the characteristic distribution value of the ratio sequence, the time period exceeding the critical threshold of heat transfer is screened, it is determined that there is a lag in heat transfer of thick electrodes, the electrode thickness data and local temperature rise rate records of the corresponding time period are extracted, and the thermal resistance feature group of the critical time period is generated.

[0096] Based on the marked abnormal time periods in the ratio sequence, time points with ratio fluctuations greater than 0.25 are selected one by one, and their time indices and node positions are recorded. Electrode thickness information and local temperature rise rate data for the corresponding nodes within that time period are obtained. The temperature rise rate is obtained by sampling from the thermocouple array, which is the temperature difference measured per second over 5 consecutive seconds divided by the time interval. If the temperature of a node rises from 35.1℃ to 37.8℃ within t = 100s to 105s, then the temperature rise rate is 37.8 - 35.1 / 5 = 0.54K / s. This rate is then compared with the thickness parameter group. The thermal resistance features are combined and a critical time period feature group is constructed simultaneously. Taking nodes A1, B3, and C2 as examples, thicknesses of 210μm, 220μm, and 230μm are detected during abnormal time periods, with temperature rise rates of 0.52K / s, 0.56K / s, and 0.61K / s, respectively. These feature groups are {210, 0.52}, {220, 0.56}, and {230, 0.61}, which are then organized into a structured array for subsequent mapping relationship generation. At the same time, a corresponding time index is added to the structure to facilitate subsequent thermal response comparison and backtracking analysis.

[0097] S203: Based on the electrode thickness and local temperature rise rate data in the critical period thermal resistance characteristic group, calculate the change trend of unit temperature rise rate under different thicknesses, construct a thickness-temperature rise rate mapping table, extract the thermal conduction response value of electrode thickness range, and generate the thermal transfer evaluation result of thick electrode.

[0098] For each item in the thermal resistance characteristic group during the critical period, a thickness-temperature rise rate relationship analysis is performed. The thickness parameters and corresponding temperature rise rate values ​​in the characteristic group are called one by one. After sorting the thickness values ​​in ascending order, a differentiated thickness sequence is constructed. This sequence is then matched one-to-one with the temperature rise rate sequence, and their increment ratio is calculated. That is, the difference between the previous and subsequent thickness points is divided by the difference between the temperature rise rates of the former and latter, forming a unit temperature rise trend value. For example, in {210, 0.52}, {220, 0.56}, ... In {230, 0.61}, the thickness difference is 10 μm and 10 μm, and the temperature rise difference is 0.04 K / s and 0.05 K / s, respectively. The corresponding increment ratios are 10 / 0.04 = 250 μm / (K / s) and 10 / 0.05 = 200 μm / (K / s), which constitute a response gradient sequence. Then, the thickness range is divided into intervals, and the maximum and minimum values ​​of the temperature rise change trend within the intervals are extracted. The center value of the interval is used as the representative to generate a heat conduction response relationship table.

[0099] Table 2: Thermal Conductivity Response of Thick Electrodes

[0100] Electrode thickness (μm) Temperature rise rate (K / s) Response gradient (μm / (K / s)) 210 0.52 250 220 0.56 200 230 0.61 —

[0101] As shown in Table 2, within the thickness range of 210 μm to 230 μm, the rate of temperature rise per unit thickness exhibits a non-linear decreasing trend with increasing thickness, and the overall response gradient decreases. Based on this data, the average thermal response gradient value for thicknesses between 200 μm and 250 μm is extracted to be 225 μm / (K / s). This gradient result is used as an important parameter for evaluating electrode heat conduction. Node mapping and matching are performed to determine whether there are local gradient out-of-tolerance conditions. If a region exhibits a temperature rise rate of 0.64 K / s at 220 μm, the corresponding response gradient is:

[0102]

[0103] Significantly lower than the mean of 225, it was marked as an abnormal response node, thus forming a set of thick electrode thermal conduction evaluation results dataset, covering all detection nodes and can be used for the next stage.

[0104] Please see Figure 4 The specific steps of S3 are as follows:

[0105] S301: Based on the thick electrode heat transfer evaluation results, collect the current electrode thickness value and the standard electrode thickness value, perform difference ratio calculation, pair the ratio with the local temperature rise rate of the node, calculate the numerical correlation between thickness and temperature rise, and generate thickness temperature rise correlation coefficient.

[0106] Based on the thick electrode heat transfer evaluation results, the electrode thickness must first be measured from the thick electrode heat conduction simulation system or sensor. The current electrode thickness value can be obtained by scanning optical profilometer or laser thickness measurement device. The standard electrode thickness is set to 200μm. The actual thicknesses measured at the current node are 220μm, 240μm, 210μm, 180μm, and 170μm, respectively. The corresponding node numbers are 1, 2, 3, 4, and 5. The thickness difference ratio is calculated by dividing the current thickness by the standard thickness. For example, the ratio for node 1 is 220 / 200 = 1.10. The ratio for each node is obtained in this way. The difference ratio table is shown in Table 3 below.

[0107] Next, the local temperature rise rate at the current thickness is extracted from the local temperature rise sensor or from thermal simulation data. The temperature rise rates of nodes 1 to 5 are 0.65, 0.82, 0.60, 0.40, and 0.35 K / s, respectively. Each thickness ratio is paired with its corresponding temperature rise rate, and polynomial least squares fitting is performed on the paired data to obtain the linear correlation trend. The slope of the fitted curve is calculated as the thickness-temperature rise correlation coefficient k. i A first-order linear regression model was used for fitting, and the fitting points are as follows:

[0108] (1.10, 0.65), (1.20, 0.82), (1.05, 0.60), (0.90, 0.40), (0.85, 0.35), substituting the points into the least squares method, we can calculate the fitting slope k≈1.2, the intercept b≈-0.67, and the thickness temperature rise correlation coefficient is 1.2;

[0109] During this process, the paired data needs to be sorted and outlier ratios are filtered out. If the difference ratio exceeds 1.5 or is lower than 0.5, it is removed. In this example, all ratios are within a reasonable range (0.85~1.20), so there is no need to remove them. Based on the thickness distribution of the nodes and the temperature rise response intensity, the corresponding relationship is established one by one and the correlation value is output and stored in the array k = [1.2, 1.5, 1.1, 0.8, 0.7]. The thickness temperature rise correlation coefficient is established through the above process.

[0110] Table 3: Relationship between thickness and temperature rise rate

[0111] Node number Current thickness (μm) Difference ratio Local temperature rise rate (K / s) 1 220 1.10 0.65 2 240 1.20 0.82 3 210 1.05 0.60 4 180 0.90 0.40 5 170 0.85 0.35

[0112] As shown in Table 3, the difference ratio is obtained by comparing the current thickness with the standard thickness, and the thickness temperature rise correlation coefficient is fitted and established by combining the actual measured local temperature rise rate.

[0113] S302: Call the thickness temperature rise correlation coefficient, arrange the node positions in descending order of correlation strength value, match the arrangement of nodes in the cooling demand direction with the thickness temperature rise intensity, calculate the node cooling intensity based on the cooling power curve and normalize it to obtain the node cooling demand coefficient.

[0114] The thickness temperature rise correlation coefficient array k = [1.2, 1.5, 1.1, 0.8, 0.7] is called, and the nodes are sorted in descending order according to their correlation values. The sorted result is node 2 (1.5), node 1 (1.2), node 3 (1.1), node 4 (0.8), and node 5 (0.7). After obtaining the sorted result, the cooling demand direction needs to be determined. Assuming that the cooling direction extends from node number 1 to 5, this direction is matched with the strength of the descending nodes. Nodes with high correlation coefficients are placed first on the cooling inlet side, and nodes with low correlation coefficients are gradually arranged towards the end, forming a cooling matching sequence [2, 1, 3, 4, 5]. Based on this, the cooling power is calculated, and the cooling power curve is P. i =P max ·e -αi Set the maximum cooling power P max =50W, attenuation coefficient α=0.2, the cooling intensity of the i-th node in the cooling sequence is calculated as follows:

[0115] If node 2 is the first position, then P = 50·e -0.2·1≈40.94W, node 1 is the second node, P≈33.52W, and so on for the remaining nodes. The cooling intensity is normalized, and the normalization method is assumed to be linear normalization.

[0116]

[0117] The maximum value is 50 and the minimum value is 24.83, so the normalized result is [1.00, 0.67, 0.43, 0.25, 0.00];

[0118] This cooling intensity is mapped to the node number, ultimately forming the node cooling demand coefficient C. j This value is used to guide subsequent cooling flow rate adjustments and to calculate the node cooling demand coefficient using the formula:

[0119]

[0120] Among them, C j k represents the cooling demand coefficient of the j-th node. i T represents the thickness-temperature rise correlation coefficient corresponding to the i-th node. i,j This represents the local temperature rise rate of the i-th node in the j-th permutation. T represents the arithmetic mean of the local temperature rise rate of the node under the j-th permutation. j,max T represents the maximum local temperature rise rate of the node under the j-th permutation. j,ref denoted by , where represents the local temperature rise rate at the standard electrode thickness of node j, n represents the total number of nodes involved in the calculation, i represents the node index, and j represents the permutation node index.

[0121] Taking node j=2 as an example, the node is the first one in the arrangement, and the corresponding temperature rise rate is as described above [T=0.82, 0.65, 0.60, 0.40, 0.35], with an average temperature rise of:

[0122]

[0123] The maximum temperature rise is T 2,max =0.82K / s, assuming a reference temperature rise T 2,ref =0.55K / s, then substituting into the formula:

[0124] Molecular part:

[0125]

[0126] Denominator part:

[0127]

[0128] Therefore, the cooling demand coefficient is:

[0129]

[0130] The result shows that the cooling demand coefficient of node 2 in the current arrangement is 0.804, which is a relatively high demand node. A value above 0.8 indicates a node with high demand intensity (the judgment criteria are set as 0.8-1.0 as high, 0.5-0.8 as medium, and <0.5 as low).

[0131] S303: Based on the node cooling demand coefficient, adjust the existing cooling fluid flow rate of the cooling node, bind the updated flow rate value with the node number and write it into the adjustment command data structure to generate a thick electrode cooling adjustment command.

[0132] Based on the cooling demand coefficient C obtained in the previous step j The demand coefficient of each node is matched with the existing cooling fluid velocity. Assuming the original velocity distribution is [0.5, 0.5, 0.5, 0.5, 0.5] L / min, and the demand coefficient is [0.804, 0.650, 0.610, 0.420, 0.315], with a maximum allowable velocity of 1.0 L / min and a minimum of 0.2 L / min, the target velocity value of each node is calculated using linear interpolation. The calculation method is as follows:

[0133] v i =0.2+C j • (1.0-0.2);

[0134] Node 1 is calculated as 0.2 + 0.804 * 0.8 = 0.843 L / min. The target flow rates for the remaining nodes are calculated accordingly [0.843, 0.720, 0.688, 0.536, 0.452]. Next, the original flow rate values ​​are compared, and the adjustment range for each node is calculated, defining the adjustment range Δv. i The difference between the target flow velocity and the original flow velocity is calculated as follows:

[0135] Δv i =v i -0.5;

[0136] The adjustment range was obtained as [+0.343, +0.220, +0.188, +0.036, -0.048] L / min;

[0137] The above target flow rate values ​​are bound to node numbers to construct an adjustment data structure. An example of the structure is as follows:

[0138] Node 1: 0.843 L / min;

[0139] Node 2: 0.720 L / min;

[0140] Node 3: 0.688 L / min;

[0141] Node 4: 0.536 L / min;

[0142] Node 5: 0.452 L / min;

[0143] Finally, a thick electrode cooling adjustment command is generated. The command content can be written to the controller data channel in text structure or byte stream to drive the cooling system to perform flow rate adjustment.

[0144] Please see Figure 5 The specific steps of S4 are as follows:

[0145] S401: Based on the thick electrode cooling adjustment command, retrieve the cooling channel control parameters and flow rate adjustment threshold, divide the cooling area control unit, adjust the inlet pressure and the opening of the diversion valve, and obtain the cooling fluid distribution coefficient.

[0146] Based on the thick electrode cooling adjustment command, the target flow rate value set by the node in the previous stage is read first. The target flow rate is compared with the current channel distribution item by item. During the comparison process, the node number in the control register area and its corresponding adjustment value are called in sequence. Combined with the total number of nodes and the number of diversion channels set in the cooling system, the matching channel of the adjustment command is determined. In actual operation, taking 5 nodes and 3 cooling branches as an example, the corresponding adjustment flow rate values ​​are 0.843, 0.720, 0.688, 0.536, 0.452 L / min. When it is distributed to the three branches, it is initially divided into regions according to the odd or even number and the load position. The division method can be set as nodes 1 and 2 belonging to region A, node 3 belonging to region B, and nodes 4 and 5 belonging to region C. Then, the initial inlet pressure value [P0] corresponding to each channel is called from the control parameters. In the example, the initial inlet pressures of regions A, B, and C are [0.18, 0.16, 0.20] MPa, respectively. Based on the target flow velocity and pressure ratio rule, a pressure adjustment target value is set for each region. Inlet pressure adjustment is achieved by controlling the total channel pressure via an electrically controlled pump, and then fine-tuning is performed by comparing the adjustment command with the actual flow velocity output using the diversion valve control unit. The judgment logic is as follows: if the target flow velocity is higher than the actual output, the valve opening is increased by 2% until the absolute value of the deviation is less than 0.01 L / min; otherwise, it is decreased by 3%. This process executes a maximum of 5 iterations; an alarm is triggered if the limit is exceeded. By comparing the diversion valve opening value obtained after each adjustment with the opening value before adjustment, the change amplitude is recorded, and the pressure response offset value is recorded simultaneously. Finally, the flow velocity monitor is called to return the actual output flow velocity of each channel. The cooling fluid distribution coefficient is obtained by calculating the channel output ratio, defined as:

[0147]

[0148] Among them, Q i Let ∑Q be the actual flow velocity of the i-th channel. jLet L be the total flow velocity across all channels. If the actual flow velocities of the three channels are [1.32, 0.68, 1.17] L / min, then the corresponding cooling distribution coefficients are:

[0149]

[0150] Table 4: Initial Pressure and Fluid Distribution in the Cooling Zone

[0151]

[0152] Table 4 shows the specific results of node partitioning, initial pressure and cooling distribution coefficient. The results indicate that region A bears the main cooling load, region B has the smallest flow rate and the fluid distribution is relatively uneven, requiring subsequent allocation optimization as a reference for feedback adjustment.

[0153] S402: Call the cooling fluid distribution coefficient, collect multiple temperature values ​​and voltage change curves, calculate the temperature standard deviation and voltage slope fluctuation, normalize and weight the two sets of parameters, and generate a data set of temperature distribution uniformity and voltage recovery rate.

[0154] Call the cooling fluid distribution coefficient α i Temperature and voltage acquisition modules were loaded onto the monitoring nodes respectively. Real-time temperature change data and voltage recovery curves were collected from each of the five nodes over a 10-second period. Ten frames of temperature data were collected for each node. Voltage data were obtained from 100 consecutive data points recorded within each 1-second frame. The temperature standard deviation was calculated by taking the average temperature from the 10 frames at each node, averaging the squared differences between the temperature values ​​and the average value, and then taking the square root. For example, the 10-frame temperature values ​​for node 3 were:

[0155] [32.1, 32.3, 32.2, 31.9, 32.4, 32.5, 32.1, 32.0, 32.2, 32.3]℃;

[0156] Its mean is 32.2, and the standard deviation is calculated as follows:

[0157]

[0158] The standard deviation calculation is performed on each node. The voltage recovery rate is based on the voltage rise gradient per second. The average slope change of the voltage curve in the 1-3 second interval of each node's voltage sampling points is taken as the rate value. The voltage recovery rate fluctuation is defined as the variance of the slope change per second. The normalization process adopts a maximum-minimum normalization strategy. The maximum temperature standard deviation in the node is set to 1, and the minimum is set to 0. For node 3, the standard deviation is 0.17. If the maximum standard deviation of all nodes is 0.45 and the minimum is 0.12, then the normalized value is:

[0159]

[0160] After normalization, the temperature and voltage parameters are assigned weights separately, with the temperature standard deviation weighted at 0.6 and the voltage fluctuation weighted at 0.4, resulting in the integrated parameters:

[0161] U i =0.6·T norm +0.4·V norm ;

[0162] If the normalized voltage fluctuation of node 3 is 0.33, then the final consolidated value is:

[0163] U3=0.6·0.152+0.4·0.33=0.0912+0.132=0.2232;

[0164] In this way, an integrated data set of temperature uniformity and voltage recovery rate of all nodes is generated for subsequent fuzzy evaluation calculation of cooling performance.

[0165] S403: Based on the temperature distribution uniformity and voltage recovery rate data set, fuzzification, weighting and defuzzification processing are performed based on membership functions and fuzzy rule tables to generate thick electrode cooling evaluation data;

[0166] Based on the node integrated value data set, and in accordance with the fuzzy evaluation rules, a four-level evaluation division interval is set, with the integrated value U... i The evaluation level is set between 0 and 1 as "Excellent" (0.0-0.25), "Good" (0.25-0.5), "Average" (0.5-0.75), and "Poor" (0.75-1.0). The triangular membership function is used to divide the membership degree. Specifically, taking node 3 with an integrated value of 0.2232 as an example, this value falls within the "Excellent" evaluation range. The membership degree is obtained by calculating its distance from the center of the "Excellent" membership function (0.125).

[0167]

[0168] Similarly,

[0169] For all other levels, membership degrees less than zero are considered 0. The fuzzy rule matrix is ​​then used to multiply and weight the level weight factors. Assuming the weight vector is [1.0, 0.7, 0.4, 0.1], the final unfuzzy value is:

[0170]

[0171] The results are output separately according to the partition nodes to form the evaluation results of the thick electrode cooling performance. Finally, a set of section cooling state distribution data is formed to provide a reference for subsequent cooling uniformity adjustment. The closer the evaluation value is to 1, the better the grade.

[0172] Please see Figure 6 The specific steps of S5 are as follows:

[0173] S501: Acquire thick electrode cooling evaluation data, collect node electrode thickness parameters, measure cooling contact impedance and temperature response time, compare with cooling reference values ​​respectively, determine whether the allowable deviation threshold is exceeded, screen abnormal nodes and record parameters, and obtain substandard node data.

[0174] In the process of acquiring thick electrode cooling evaluation data, the electrode thickness parameters of the monitoring nodes are first collected. Displacement sensors are used to measure the positive and negative electrode material layers at each node location. The thickness unit is micrometers (μm), and the accuracy of each electrode thickness measurement point is controlled within ±1μm. If 30 monitoring nodes are selected, the electrode thickness sequence at each node is [205, 208, 213, 199, 215, ...] (unit: μm). Then, thermocouples are set at the corresponding positions of each node, and a steady-state heating source is used to trigger the cooling process. The cooling contact impedance at multiple points is measured by measuring the temperature rise time and heat flow. The heat flow is set to 10W. If the temperature rise of node 1 is 2.5℃, the contact impedance per unit area is calculated as follows:

[0175]

[0176] The corresponding temperature response time is recorded synchronously, i.e., the time required from the start of heating to the temperature stabilizing and dropping to the target temperature, in seconds. The response time for node 1 is 7.2 seconds, and for node 2 it is 9.5 seconds. Then, the set cooling reference value sequence is invoked. The electrode thickness reference is set to 210 μm with an allowable deviation threshold of ±5 μm; the cooling contact impedance reference is 0.22 K / W with a deviation threshold of ±0.03 K / W; and the temperature response time reference is 8 seconds with a deviation of ±1 second. The sampled values ​​are compared item by item with the above reference values. Values ​​with an absolute deviation in electrode thickness greater than 5 μm or a deviation in cooling contact impedance exceeding 0.03 K / W are considered. Nodes with K / W or response time deviations exceeding 1 second are considered abnormal nodes. For example, if node 1 has an electrode thickness of 205μm with a deviation of 5μm that meets the critical value, a contact impedance of 0.25K / W with a deviation of 0.03K / W that still meets the critical value, and a response time of 7.2 seconds with a deviation of 0.8 seconds, then this node is not marked as abnormal. If node 4 has an electrode thickness of 199μm with a deviation of 11μm, a contact impedance of 0.28K / W with a deviation of 0.06K / W, and a response time of 9.5 seconds with a deviation of 1.5 seconds, then it is marked as an abnormal node and its parameters are recorded in the abnormal node database to complete the data filtering and obtain the data of non-compliant nodes.

[0177] S502: Based on the data of substandard nodes, call the node electrode thickness parameters, cooling contact impedance and temperature response time. By comparing the correlation between cooling contact impedance and temperature response time, determine the main control parameters of cooling delay, and adjust the cooling plate bonding pressure and heat dissipation film coverage thickness respectively to generate the node cooling optimization configuration.

[0178] Based on the electrode thickness parameters, cooling contact impedance, and temperature response time extracted from the data of substandard nodes, the main control parameters of cooling delay are determined sequentially. The correlation between temperature response time and contact impedance is examined first. If the temperature response time of a node is significantly larger than the baseline, its corresponding contact impedance is further checked. If both are larger, it is initially judged that insufficient bonding pressure leads to heat transfer lag. If the contact impedance is normal but the response time is larger, it is due to electrode thickness deviation or heat capacity variation caused by heat dissipation film material configuration. Judgment conditions are set: if the electrode thickness deviation is >8μm, it is recorded as thickness-controlled; if the contact impedance deviation is >0.05K / W, it is recorded as contact impedance-controlled. For example, node 4 has an electrode thickness of 199μm and a deviation of 11μm, with a contact impedance... With a resistance of 0.28 K / W and a deviation of 0.06 K / W, it was determined to be a dual-master control type node. Subsequently, the bonding pressure of the cooling plate was adjusted, and the pressure was gradually applied using a mechanical clamp, increasing by 0.05 MPa each time. The change in contact resistance was recorded. The initial pressure was set at 0.20 MPa, and the final optimized pressure for node 4 was set at 0.35 MPa. When the pressure was reduced to this value, the node contact resistance decreased to 0.22 K / W, close to the reference value. At the same time, the thickness of the heat dissipation film was adjusted from the original 15 μm to 18 μm, and its effect on the temperature response was observed. The node response time decreased from 9.5 seconds to 7.8 seconds after the adjustment. Thus, the optimized cooling configuration for node 4 was established as follows: electrode thickness 199 μm, bonding pressure 0.35 MPa, and heat dissipation film thickness 18 μm.

[0179] S503: Based on the node cooling optimization configuration, record the changes in cooling contact impedance and temperature response time after the node adjustment, call the node electrode thickness and the updated heat transfer performance parameters, merge the parameter correspondence, and obtain the thick electrode cooling optimization configuration parameters.

[0180] Based on the node cooling optimization configuration results, the changes in contact impedance and temperature response time after optimization were recorded for each node. In node 4, the contact impedance decreased from 0.28 K / W to 0.22 K / W, a change of -0.06 K / W, and the temperature response time decreased from 9.5 seconds to 7.8 seconds, a change of -1.7 seconds. The adjusted node parameters were merged with the original electrode thickness parameters to establish a mapping relationship, forming a six-tuple mapping of node number - electrode thickness - bonding pressure - heat dissipation film thickness - contact impedance - temperature response time. The merged parameter relationship of the optimized nodes is recorded in the table below:

[0181] Table 5: Node Cooling Optimization Configuration Table

[0182]

[0183] Table 5 shows the final configuration results of the two optimization nodes. The configuration can be called in the subsequent system by merging the parameter tables, thus forming a set of optimization configuration parameters for thick electrode cooling.

[0184] Please see Figure 7 The LiFePO4 battery cooling system, after the electrode thickness is increased, includes:

[0185] The thick electrode parameter acquisition module is used to obtain the real-time voltage of the single LiFePO4 battery node through a voltage sensor, monitor the heat flux density distribution inside the thick electrode region, collect the thermal conductivity parameters corresponding to the electrode thickness, and use a Kalman filter algorithm to remove noise based on the correlation between voltage and heat flux density to form thick electrode voltage-thermal conductivity correlation data, which is then transmitted to the heat transfer impedance evaluation module.

[0186] The thermal transfer impedance assessment module is used to call the voltage-thermal conduction correlation data of the thick electrode, calculate the ratio of the battery voltage drop to the internal thermal transfer impedance of the thick electrode, determine the thermal transfer hysteresis, form the thermal transfer assessment result of the thick electrode, and transmit it to the cooling demand calculation module.

[0187] The cooling demand calculation module is used to call the heat transfer evaluation results of the thick electrode to perform cooling intensity calculation. It couples the ratio of electrode thickness to standard thickness and local temperature rise rate to calculate the cooling demand coefficient, adjusts the flow rate, outputs the thick electrode cooling adjustment command, and transmits it to the cooling effect monitoring module.

[0188] The cooling effect monitoring module is used to adjust the distribution of cooling fluid according to the thick electrode cooling adjustment command, monitor the temperature distribution uniformity and voltage recovery rate after adjustment, input the weighted average of the fuzzy controller, form thick electrode cooling evaluation data, and transmit it to the cooling configuration optimization module.

[0189] The cooling configuration optimization module is used to screen thick electrode nodes that do not meet the evaluation indicators through thick electrode cooling evaluation data, judge based on the node electrode thickness parameters, adjust the node cooling plate bonding pressure and heat dissipation film coverage thickness, and output thick electrode cooling optimization configuration parameters.

[0190] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for cooling a LiFePO4 battery after increasing electrode thickness, characterized in that, Includes the following steps: S1: The real-time voltage of the single LiFePO4 battery node is obtained through a voltage sensor, the heat flux density distribution inside the thick electrode region is monitored, the thermal conductivity parameters corresponding to the electrode thickness are collected, and the Kalman filter algorithm is used to remove noise based on voltage fluctuation and heat flux density to form thick electrode voltage-thermal conductivity correlation data. S2: Call the thick electrode voltage thermal conductivity correlation data, normalize the voltage drop amplitude, and calculate the ratio with the heat transfer impedance. When the ratio exceeds the set critical value, it is determined to be thick electrode heat transfer hysteresis. Extract the electrode thickness and local temperature rise rate to generate thick electrode heat transfer evaluation results. S3: Based on the heat transfer evaluation results of the thick electrode, perform cooling intensity analysis, calculate the ratio of the electrode thickness to the standard thickness, determine the cooling demand coefficient by coupling it with the local temperature rise rate, adjust the cooling fluid flow rate of the corresponding node, and output the thick electrode cooling adjustment command. S4: Execute cooling fluid distribution adjustment according to the thick electrode cooling adjustment command, monitor the uniformity of temperature distribution and voltage recovery rate inside the thick electrode after adjustment, and perform weighted average calculation on temperature distribution uniformity and voltage recovery rate through fuzzy controller to form thick electrode cooling evaluation data.

2. The method for cooling a LiFePO4 battery after increasing electrode thickness according to claim 1, characterized in that, The thick electrode voltage-thermal correlation data includes voltage fluctuation amplitude, heat flux density, and thermal conductivity; the thick electrode heat transfer evaluation results include thermal resistance, electrode thickness, and temperature gradient; the thick electrode cooling regulation commands include flow rate regulation amount, cooling power, and priority weight; and the thick electrode cooling evaluation data includes temperature uniformity coefficient, voltage recovery coefficient, and performance evaluation index.

3. The method for cooling a LiFePO4 battery after increasing electrode thickness according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: The real-time voltage of the LiFePO4 cell node is obtained through a voltage sensor, the node electrode thickness and corresponding thermal conductivity results are collected simultaneously, the response data between the heat flux density in the thick electrode region are sorted out, the regional data distribution is analyzed based on the voltage and heat flux density, and the electrode heat flux density distribution value is generated. S102: Based on the electrode heat flux density distribution value, collect heat flux data under different thicknesses, analyze the relationship between thickness and heat flux, retrieve the heat flux parameters of thickness, and generate a set of electrode thermal conductivity parameters. S103: Based on the electrode thermal conductivity parameter set and the original data of the voltage node, Kalman filtering is used to separate the noise data in multiple parameters, integrate the response information of voltage, heat flow and thermal conductivity, and obtain the voltage-thermal conductivity correlation data of the thick electrode.

4. The method for cooling a LiFePO4 battery after increasing electrode thickness according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Call the thick electrode voltage-thermal conductivity correlation data, extract the voltage drop amplitude of the single cell and the internal heat transfer impedance of the thick electrode, pair them based on time nodes and calculate the corresponding ratio, analyze whether the ratio fluctuation amplitude exceeds the critical threshold of heat transfer, and obtain the characteristic distribution value of the ratio sequence. S202: Based on the characteristic distribution value of the ratio sequence, filter out the time periods that exceed the critical threshold of heat transfer, determine that there is a lag in heat transfer of thick electrodes, extract the electrode thickness data and local temperature rise rate records for the corresponding time periods, and generate a critical time period thermal resistance feature group. S203: Based on the electrode thickness and local temperature rise rate data in the critical period thermal resistance characteristic group, calculate the change trend of unit temperature rise rate under differentiated thickness, construct a thickness-temperature rise rate mapping table, extract the thermal conduction response value of the electrode thickness range, and generate the thick electrode heat transfer evaluation result.

5. The method for cooling a LiFePO4 battery after increasing electrode thickness according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: Based on the thick electrode heat transfer evaluation results, collect the current electrode thickness value and the standard electrode thickness value, perform difference ratio calculation, pair the ratio with the local temperature rise rate of the node, calculate the numerical correlation between thickness and temperature rise, and generate thickness temperature rise correlation coefficient. S302: Call the thickness temperature rise correlation coefficient, arrange the node positions in descending order of correlation strength value, match the arrangement of nodes in the cooling demand direction with the thickness temperature rise intensity, calculate the node cooling intensity based on the cooling power curve and normalize it to obtain the node cooling demand coefficient. S303: Based on the node cooling demand coefficient, adjust the existing cooling fluid flow rate of the cooling node, bind the updated flow rate value with the node number and write it into the adjustment command data structure to generate a thick electrode cooling adjustment command.

6. The method for cooling a LiFePO4 battery after increasing electrode thickness according to claim 5, characterized in that, The thickness temperature rise correlation coefficient refers to a numerical parameter calculated based on the relationship between the ratio of the current thickness of the thick electrode node to the standard thickness and its local temperature rise rate. The node cooling demand coefficient refers to a numerical parameter that characterizes the node cooling priority in the thick electrode cooling adjustment strategy. It is obtained by normalizing the real-time cooling demand of the node by combining the node thickness temperature rise correlation coefficient and the cooling power curve.

7. The method for cooling a LiFePO4 battery after increasing electrode thickness according to claim 5, characterized in that, The specific steps of S4 are as follows: S401: Based on the thick electrode cooling adjustment command, retrieve the cooling channel control parameters and flow rate adjustment threshold, divide the cooling area control unit, adjust the inlet pressure and the opening of the diversion valve, and obtain the cooling fluid distribution coefficient. S402: Call the cooling fluid distribution coefficient, collect multiple temperature values ​​and voltage change curves, calculate the temperature standard deviation and voltage slope fluctuation, normalize and weighted integrate the two sets of parameters, and generate a data set of temperature distribution uniformity and voltage recovery rate. S403: Based on the temperature distribution uniformity and voltage recovery rate data set, perform fuzzification, weighting and defuzzification processing based on membership functions and fuzzy rule tables to generate thick electrode cooling evaluation data.

8. The method for cooling a LiFePO4 battery after increasing electrode thickness according to claim 1, characterized in that, The method also includes step S5: S5: Screen thick electrode nodes that do not meet the evaluation indicators through the thick electrode cooling evaluation data, adjust the node cooling plate bonding pressure and heat dissipation film coverage thickness according to the node electrode thickness parameters, cooling contact impedance and temperature response time, and output the thick electrode cooling optimization configuration parameters. The optimized configuration parameters for thick electrode cooling include contact pressure, film thickness, and contact area ratio.

9. The method for cooling a LiFePO4 battery after increasing electrode thickness according to claim 8, characterized in that, The specific steps of S5 are as follows: S501: Obtain the thick electrode cooling evaluation data, collect the node electrode thickness parameters, measure the cooling contact impedance and temperature response time, compare them with the cooling reference value, determine whether the allowable deviation threshold is exceeded, screen abnormal nodes and record the parameters, and obtain the data of substandard nodes. S502: Based on the substandard node data, call the node electrode thickness parameters, cooling contact impedance and temperature response time, and determine the main control parameters of cooling delay by comparing the correlation between cooling contact impedance and temperature response time. Adjust the cooling plate bonding pressure and heat dissipation film coverage thickness respectively to generate node cooling optimization configuration. S503: Based on the node cooling optimization configuration, record the changes in cooling contact impedance and temperature response time after the node adjustment, call the node electrode thickness and the updated heat transfer performance parameters, merge the parameter correspondence, and obtain the thick electrode cooling optimization configuration parameters.

10. A cooling system for a LiFePO4 battery after increasing electrode thickness, characterized in that, The system is used to implement the LiFePO4 battery cooling method after increasing electrode thickness as described in any one of claims 1-9, the system comprising: The thick electrode parameter acquisition module is used to obtain the real-time voltage of the single LiFePO4 battery node through a voltage sensor, monitor the heat flux density distribution inside the thick electrode region, collect the thermal conductivity parameters corresponding to the electrode thickness, and use a Kalman filter algorithm to remove noise based on the correlation between voltage and heat flux density to form thick electrode voltage-thermal conductivity correlation data, which is then transmitted to the heat transfer impedance evaluation module. The heat transfer impedance assessment module is used to call the voltage-thermal conductivity correlation data of the thick electrode, calculate the ratio of the battery voltage drop to the internal heat transfer impedance of the thick electrode, determine the heat transfer hysteresis, form the heat transfer assessment result of the thick electrode, and transmit it to the cooling demand calculation module. The cooling demand calculation module is used to call the heat transfer evaluation results of the thick electrode to perform cooling intensity calculation, couple the ratio of electrode thickness to standard thickness and local temperature rise rate, calculate the cooling demand coefficient, adjust the flow rate, output the thick electrode cooling adjustment command, and transmit it to the cooling effect monitoring module. The cooling effect monitoring module is used to adjust the distribution of cooling fluid according to the thick electrode cooling adjustment command, monitor the temperature distribution uniformity and voltage recovery rate after adjustment, input the weighted average of the fuzzy controller, form thick electrode cooling evaluation data, and transmit it to the cooling configuration optimization module. The cooling configuration optimization module is used to screen thick electrode nodes that do not meet the evaluation indicators through the thick electrode cooling evaluation data, judge based on the node electrode thickness parameters, adjust the node cooling plate bonding pressure and heat dissipation film coverage thickness, and output thick electrode cooling optimization configuration parameters.

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