Intelligent phase change immersion cooling method based on double-cavity dynamic regulation and control

Abstract

The invention relates to the technical field of battery cooling, and discloses an intelligent phase change immersion cooling method based on double-cavity dynamic regulation and control. The method comprises the following steps: firstly, executing sensor data acquisition to obtain temperature and internal resistance monitoring data of a high-internal-resistance battery cavity and a low-internal-resistance battery cavity; performing battery state analysis based on the data, and determining a high internal resistance state factor and a low internal resistance state factor; searching a target nozzle working parameter in a nozzle parameter configuration space by taking the state factor as an index; adjusting the working state of the nozzle according to the target parameters, and executing phase change immersion cooling operation; and carrying out battery capacity calibration regularly, and triggering migration judgment reminding when a calibration result meets a migration condition. According to the method, through real-time monitoring, dynamic analysis and intelligent regulation and control, differential cooling of the battery cavities with different internal resistances is realized, the method can adapt to dynamic changes of battery performance, the cooling efficiency and accuracy are improved, and the operation stability of the battery is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery cooling, in particular to an intelligent phase change immersion cooling method based on double-cavity dynamic regulation. BACKGROUND

[0002] With the rapid development of new energy technology, high-energy-density batteries are increasingly widely used in electric vehicles, energy storage systems and other fields. During the charging and discharging process of the battery, a large amount of heat is generated. If the heat cannot be dissipated in time, the temperature of the battery will rise, thereby affecting the performance and service life of the battery. In particular, in large-scale battery pack applications, the internal resistances of different battery monomers differ, and the battery with higher internal resistance generates more heat during operation, which is prone to local temperature rise and forms a risk of thermal runaway. At present, common battery cooling methods include air cooling, liquid cooling and phase change cooling. The air cooling method is simple in structure, but the cooling efficiency is low, which is difficult to meet the heat dissipation demand of high-power batteries. The liquid cooling method takes away heat through liquid circulation, and has high cooling efficiency, but the system structure is complex, an additional pump body and pipeline are needed, which increases the equipment volume and cost, and there is a risk of liquid leakage. The phase change cooling utilizes phase change materials to absorb heat for cooling, which has the advantages of high heat dissipation efficiency and stable temperature control, but the traditional phase change cooling method is passive and cannot dynamically adjust the cooling intensity according to the real-time state of the battery. When the internal resistance and temperature distribution of the battery are uneven, it is difficult to achieve precise heat dissipation, which may lead to performance degradation of some batteries due to insufficient heat dissipation. During the long-term use of the battery pack, the performance of each battery monomer will gradually degrade, and the internal resistance will also change. The battery with originally low internal resistance may gradually change into a high internal resistance battery. The existing cooling system lacks a dynamic response mechanism for the state change of the battery, and cannot timely adjust the cooling strategy, which is difficult to adapt to the dynamic change of the battery performance, thereby affecting the overall service life and safety of the battery pack. Therefore, an intelligent cooling method is needed, which can dynamically regulate the cooling intensity according to the real-time internal resistance and temperature state of the battery, and can adapt to the change of the battery performance, so as to solve the problems existing in the prior art. SUMMARY

[0003] The present application aims to provide an intelligent phase change immersion cooling method based on double-cavity dynamic regulation to solve the problems raised in the background.

[0004] To achieve the above-mentioned purpose, the present application provides an intelligent phase change immersion cooling method based on double-cavity dynamic regulation, which comprises: performing sensor data collection to obtain a battery temperature monitoring data set and an internal resistance monitoring data set of a high internal resistance battery cavity and a low internal resistance battery cavity; based on the battery temperature monitoring data set and the internal resistance monitoring data set, performing battery state analysis to determine a high internal resistance state factor and a low internal resistance state factor; performing a centralized search in a nozzle parameter configuration space indexed by the high internal resistance state factor and the low internal resistance state factor to determine a target nozzle operating parameter; based on the target nozzle operating parameter, adjusting nozzle operating states of the high internal resistance battery cavity and the low internal resistance battery cavity to perform a phase change immersion cooling operation; periodically performing battery capacity calibration to obtain a calibration result set, and triggering a migration determination reminder when the calibration result set meets a migration condition.

[0005] Preferably, the performing sensor data collection comprises: collecting battery temperature monitoring data and internal resistance monitoring data of the high internal resistance battery cavity, and battery temperature monitoring data and internal resistance monitoring data of the low internal resistance battery cavity; integrating the battery temperature monitoring data of the high internal resistance battery cavity and the battery temperature monitoring data of the low internal resistance battery cavity to generate the battery temperature monitoring data set; integrating the internal resistance monitoring data of the high internal resistance battery cavity and the internal resistance monitoring data of the low internal resistance battery cavity to generate the internal resistance monitoring data set.

[0006] Preferably, the performing battery state analysis comprises: performing data fluctuation analysis on the battery temperature monitoring data set to obtain a temperature fluctuation index; performing data stability analysis on the internal resistance monitoring data set to obtain an internal resistance stability index; based on the temperature fluctuation index and the internal resistance stability index, calculating the high internal resistance state factor and the low internal resistance state factor.

[0007] Preferably, the data fluctuation analysis comprises: extracting time series data points in the battery temperature monitoring data set; calculating the change rate between adjacent data points; summarizing the average value of all change rates as the temperature fluctuation index.

[0008] Preferably, the performing a centralized search in a nozzle parameter configuration space comprises: obtaining a plurality of sample high internal resistance state factors and a plurality of sample low internal resistance state factors, and a plurality of corresponding sample nozzle operating parameters; pre-constructing a two-dimensional configuration space, wherein the first dimension represents the high internal resistance state factor range and the second dimension represents the low internal resistance state factor range; mapping the plurality of sample high internal resistance state factors and the plurality of sample low internal resistance state factors into the two-dimensional configuration space to form a plurality of sample space points; The multiple sample nozzle operating parameters are used to identify the multiple sample space points, thereby generating the nozzle parameter configuration space.

[0009] Preferably, the centralized search in the nozzle parameter configuration space further includes: The high internal resistance state factor and the low internal resistance state factor are input into the two-dimensional configuration space as target points; Calculate the distance metric between the target point and each point in the sample space; Select the sample space point with the smallest distance metric as the matching point; The working parameters of the sample nozzle corresponding to the matching point are used as the working parameters of the target nozzle.

[0010] Preferably, the periodic battery capacity calibration includes: The battery capacity, internal resistance, and temperature rise sensitivity of the high internal resistance battery cavity and the low internal resistance battery cavity are measured quarterly. The calibration result set is generated by integrating the battery capacity value, the internal resistance value, and the temperature rise sensitivity value. Determine whether the calibration result set meets the migration conditions, wherein the migration conditions include a first preset percentage of battery capacity decay to the initial value of battery capacity, a second preset percentage of internal resistance increase to the initial value of internal resistance, or a temperature rise sensitivity greater than a preset threshold.

[0011] Preferably, the trigger migration determination reminder includes: When the calibration result set satisfies the migration condition, a migration signal is generated; Based on the migration signal, a migration command is output to prompt the staff to perform the battery migration and replacement operation.

[0012] Preferably, the method further includes: Based on the high internal resistance state factor and the low internal resistance state factor, predict the trend of battery thermal behavior. The operating parameters of the target nozzle are adjusted based on the battery thermal behavior trend.

[0013] Preferably, after performing the phase change immersion cooling operation, the process includes: Monitor the cooling effect data of the high internal resistance battery cavity and the low internal resistance battery cavity; Based on the cooling effect data, update the battery temperature monitoring data set and the internal resistance monitoring data set; Repeat the steps of performing battery state analysis, performing centralized search in the nozzle parameter configuration space, adjusting the nozzle working state of the high internal resistance battery cavity and the low internal resistance battery cavity based on the target nozzle working parameters, and performing phase change immersion cooling operation.

[0014] Compared with the prior art, the present application has the following advantages: The temperature and internal resistance information of the high-internal-resistance battery cavity and the low-internal-resistance battery cavity are obtained in real time through sensor data acquisition, providing accurate basis for subsequent state analysis and cooling regulation. The high-internal-resistance state factor and the low-internal-resistance state factor determined based on the monitoring data can accurately reflect the differences in the working states of the batteries in different cavities, making the cooling regulation more targeted. The target nozzle working parameters are searched in the nozzle parameter configuration space with the state factor as the index, which can realize accurate matching of the cooling parameters and avoid the blindness of parameter setting in the traditional cooling method. Adjusting the nozzle working state according to the target parameters can dynamically change the strength and range of the phase change cooling, ensuring that the high-internal-resistance battery cavity obtains stronger cooling effect, while avoiding the performance of the low-internal-resistance battery cavity from being affected due to excessive cooling, realizing differentiated cooling of different cavities. Periodically performing battery capacity calibration and triggering a reminder when the migration condition is met can timely find the changes in the battery performance, facilitating reasonable migration of the battery, so that the battery is always in a cooling environment suitable for its state. This dynamic adjustment mechanism can adapt to the performance degradation and internal resistance changes of the battery in the long-term use process, maintaining good adaptation of the cooling system to the state of the battery. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 A working principle diagram of the intelligent phase change immersion cooling method based on dual-cavity dynamic regulation according to the present application is shown in the figure. Figure 2 A flowchart of sensor data acquisition is shown in the figure. Figure 3 A flowchart of constructing a nozzle parameter configuration space is shown in the figure. Figure 4 A flowchart of battery capacity calibration is shown in the figure. Figure 5 A flowchart of post-cooling monitoring and updating is shown in the figure. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0017] Please refer to Figure 1 The present application provides an intelligent phase change immersion cooling method based on dual-cavity dynamic regulation, which comprises: The battery temperature and internal resistance data of the high internal resistance battery cavity and the low internal resistance battery cavity are collected by sensors to form a battery temperature monitoring data set and an internal resistance monitoring data set. Based on these data, the battery state is analyzed and the high internal resistance state factor and the low internal resistance state factor are calculated. Then, the state factor is used as an index to search for the matching target nozzle working parameter in the pre-constructed nozzle parameter configuration space, and the working state of the double-cavity nozzle is adjusted accordingly to perform the phase change immersion cooling operation. The system also periodically performs battery capacity calibration, and when the calibration result meets the migration condition, a migration determination reminder is triggered to prompt the staff to perform battery migration or replacement.

[0018] Embodiment 1: refer to Figure 2 In the sensor data acquisition link, the temperature monitoring data is obtained through a distributed thermocouple network. In the high internal resistance battery cavity and the low internal resistance battery cavity, three thermocouple probes are arranged on the surface of each battery monomer, respectively located at the electrode connection, the geometric center and the edge area. The thermocouples synchronously collect temperature data with a sampling period of 100 milliseconds, and generate a raw data set with a time stamp after processing by an AD conversion module. The internal resistance monitoring adopts the alternating current injection method, and a 1 kHz test signal is applied during the intermittent period of battery charging and discharging. The voltage and current phase difference is obtained through a four-terminal measurement circuit, and the internal resistance value is calculated and marked with the corresponding time stamp.

[0019] The raw data enters the preprocessing procedure: the temperature data is subjected to median filtering to eliminate transient interference, and the internal resistance data is smoothed by the moving average method. The processed data is aligned in time sequence, and the temperature data and the internal resistance data of the high internal resistance battery cavity form ordered arrays respectively, and the low internal resistance battery cavity synchronously generates independent ordered arrays. Finally, the battery temperature monitoring data set and the internal resistance monitoring data set are integrated, and the set structure is stored in a time-value two-dimensional matrix, with the matrix row index being a millisecond-level time stamp and the column index marking the battery cavity number and parameter type.

[0020] The system extracts the time series temperature curve of each battery cavity and performs dynamic segmentation using a sliding window mechanism. The window width is set to 30 seconds, and the sliding step is 5 seconds each time. In each window, data fluctuation analysis is performed: the temperature change rate of adjacent data points in the window is calculated, and the absolute value is taken to obtain the arithmetic mean of all change rates in the window. The average values of all windows are further processed by weighted average, and the window weight is positively correlated with its time proximity. Finally, the temperature fluctuation index is output. This index reflects the thermal stability characteristics of the battery cavity in the time dimension.

[0021] For each battery cavity's internal resistance data set, divide the data block by 10 minutes as a period. Calculate the standard deviation of the internal resistance value within each data block, and at the same time, calculate the difference between the maximum and minimum values within the data block. Two values are input into the stability evaluation model, and the model outputs a stability coefficient in the 0-1 interval. The stability coefficients of all data blocks are weighted in time sequence, with recent data blocks having higher weights, and finally generating an internal resistance stability index. The two indicators of the high internal resistance battery cavity are input into a special calculation unit, which contains a dynamically adjusted weight coefficient: the initial weight of the temperature fluctuation index is set to 0.65, and the weight of the internal resistance stability index is 0.35. The weight coefficient is automatically adjusted according to the ambient temperature, and when the ambient temperature exceeds 35℃, the temperature fluctuation index weight is increased to 0.7. The calculation unit performs linear weighted fusion, and outputs a high internal resistance state factor in the 0-1 interval. The low internal resistance battery cavity uses an independent calculation unit, with the temperature fluctuation index weight fixed at 0.6 and the internal resistance stability index weight at 0.4, and generates a low internal resistance state factor through the same algorithm.

[0022] The entire analysis process adopts a pipeline architecture, and the temperature fluctuation analysis thread and the internal resistance stability analysis thread are executed in parallel. When a new data packet arrives, the system prioritizes the temperature data thread, and the internal resistance data thread is allowed to delay synchronization for a maximum of 300 milliseconds. The state factor is updated every 5 seconds, and the update result is written to the shared memory area for subsequent module calls. Historical state factor data is stored in a ring buffer in time sequence, with a buffer capacity of 1 hour of recent data, and old data is automatically overwritten.

[0023] An abnormal processing mechanism runs through the entire process: when the thermocouple data continuously samples values outside the reasonable temperature range (-20℃ to 80℃) for three times, the system automatically switches to the backup probe and marks the fault point. If the internal resistance measurement value has a step change (the difference between adjacent sampling values exceeds 20%), trigger the re-measurement mechanism and start the internal resistance calibration program. When an indicator is abnormal during the state factor calculation process, the system uses the moving average of the previous three valid values to replace the current abnormal value. All abnormal events are recorded in the system log, including timestamp, abnormal type and processing measures.

[0024] The acquisition module encapsulates the processed data into a JSON format message and pushes it to the state analysis message queue. The analysis module generates a state factor data packet after consuming the message and broadcasts it to the nozzle control module through the publish-subscribe mode. The message transmission uses TCP protocol to ensure reliability, and sets a 500 millisecond timeout retransmission mechanism. The entire process runs in a real-time operating system environment, and the key threads are given the highest priority to ensure the time determinacy of the state factor update period.

[0025] The state factor data packet contains a timestamp, a battery cavity number, a temperature fluctuation index, an internal resistance stability index, and a final state factor value. The data packet adds a CRC check code, and the receiving end can only parse and use it after verifying the check code. The historical data storage adopts a time-sharing archiving strategy, and a data snapshot is generated every minute and stored in a time series database, with a retention period of 30 days. The database establishes a joint index of the temperature fluctuation index and the internal resistance stability index, supporting fast retrieval of historical state change trends by time range.

[0026] Embodiment 2: refer to Figure 3 The construction of the nozzle parameter configuration space starts from the collection of historical operation data. The system extracts the operation records of the past six months from the time series database and screens data packets containing complete state factors and nozzle parameters. Each data packet contains a timestamp, a high internal resistance state factor value, a low internal resistance state factor value, and corresponding nozzle operating parameter records. The nozzle operating parameters specifically include three sub-items of opening duration, spraying frequency, and atomized particle diameter. The data screening conditions require that the state factor value is within the effective range and the nozzle parameter is in the normal working interval.

[0027] The original value range of the high internal resistance state factor is 0-1.2, which is mapped to the standard range of 0-1.0 through linear transformation. The original value range of the low internal resistance state factor is 0-0.9, which is simultaneously mapped to the range of 0-0.8. The opening duration in the nozzle parameter is normalized to the 0-1 interval in seconds, the spraying frequency is converted to a percentage representation in hertz units, and the atomized particle diameter is scaled according to the range of 50-200 microns. All normalization operations preserve the conversion relationship between the original value and the standard value, and a bidirectional mapping table is established and stored in the configuration file.

[0028] The horizontal axis is defined as the high internal resistance state factor dimension, with a scale interval of 0.01, and the vertical axis is the low internal resistance state factor dimension, with a scale interval of 0.008. The coordinate system covers the possible distribution area of all sample data, with a 5% expansion margin set for the boundary area. Each historical data packet generates a sample space point, and the coordinates of the sample space point are determined by the normalized state factor values. The sample point carries a nozzle parameter label, and the label data structure includes three fields: opening duration encoding, frequency encoding, and atomized particle encoding, each field storing the normalized parameter value.

[0029] The system detects coordinate overlapping sample points, and when the distance between two points is less than 0.005, it is considered as an overlapping point. The nozzle parameters of the overlapping point set are taken as the arithmetic mean, and are merged into a single representative point. The sparse point set in the edge area of the space is supplemented by virtual points through an interpolation algorithm. The interpolation is generated according to the parameter gradient change trend of the adjacent area. The final nozzle parameter configuration space contains about 1200 effective sample points, each point has a unique coordinate and parameter label, and the space data is stored in a KD tree structure to improve the retrieval efficiency.

[0030] The system obtains the original values of the current high-resistance state factor and the low-resistance state factor, converts them into standard coordinate values through the mapping relationship in the preprocessing stage, and forms the target point coordinate. Range search is performed in the KD tree structure: the target point is taken as the center, and the initial search radius is set to 0.05. If there are sample points within this radius, the Euclidean distance between the target point and each sample point is calculated; if there are no sample points, the search radius is increased by 0.01 steps until an effective point is found.

[0031] In addition to the difference in state factor coordinates, a time dimension weight is introduced: recent sample points are given an advantage in distance calculation, and the time decay coefficient is set according to an exponential function. The final distance value is the product of the spatial distance and the time adjustment factor. The system selects the three sample points with the smallest distance as the candidate point set, and when the distance difference between the candidate points is less than 0.02, the weighted average value of the nozzle parameters of the three points is taken, with the weight being inversely proportional to the distance value. If the distance difference between the candidate points is large, the parameter of the nearest neighbor single point is selected.

[0032] The system detects the rationality of the candidate parameters: whether the on duration is within the allowed range of 1-15 seconds, whether the spray frequency is within the safe range of 20-100 Hz, and whether the atomized particle diameter meets the process requirement of 60-180 microns. If any parameter exceeds the threshold, the system switches to the backup strategy: expands the search along the state factor gradient direction in the configuration space until the compliant parameter is obtained. The final output target nozzle operating parameters include three sub-items in the original value format, and are accompanied by a parameter confidence score. The system compares the difference between the new parameters and the current operating parameters, and when the on duration changes by more than 2 seconds, the frequency changes by more than 15 Hz, or the particle diameter changes by more than 30 microns, the step-by-step adjustment program is started. The adjustment amplitude does not exceed 20% of the original value each time, and the adjustment interval is set to 10 seconds. The battery temperature change rate is continuously monitored during the adjustment process, and if the change rate exceeds 3°C / s, the adjustment is paused. After the parameters are stable, the actual running effect is recorded, and a new sample point is added to the configuration space.

[0033] The configuration space is checked for integrity every week: the sample point distribution density is detected, labeled points are added to areas with a density of less than 0.5 points per unit area; expired sample points are scanned, and points that exceed three months are moved to the archive; parameter label consistency is analyzed, and points with a parameter difference of more than 20% in the same coordinate area are clustered and reorganized. Maintenance operations are performed during idle periods of the system, and the time consumption is controlled within 5 minutes, during which the cache space is used to maintain normal operation of the search function.

[0034] When a target point falls into a blank area with no sample points, the system generates virtual parameters based on the parameter gradients of surrounding points and triggers a sample collection command. When candidate parameters conflict, conflict details are recorded and a manual review process is initiated. All search operations generate logs containing target point coordinates, candidate point information, output parameters, and adjustment process data; logs are retained for three months. The main storage uses memory mirroring technology, synchronizing to the solid-state drive every two hours. Incremental backups are performed daily, and full backups are generated and uploaded to cloud storage weekly. Data version management records every spatial structure adjustment, supporting rollback to any version from the last seven days. Access control mechanisms restrict modification permissions in the configuration space, allowing only authorized accounts to operate after two-factor authentication.

[0035] The search interface receives JSON requests containing two state factors and returns a JSON response containing three nozzle parameters. The interface is configured with flow control, limiting the maximum number of requests processed per second to no more than 50. The service health monitoring module tracks response latency in real time and automatically scales up service instances when the average latency exceeds 200 milliseconds. The service status dashboard displays real-time operational metrics such as search hit rate and parameter adjustment frequency to assist in operational decision-making.

[0036] After each successful application of new parameters, the system records actual operational data, including temperature drop and internal resistance changes within 5 minutes of cooling startup. When the same set of parameters accumulates ten valid operational records, the system evaluates the parameter performance stability. Data packets that meet the performance evaluation criteria will be used as high-quality sample points and given priority in subsequent search and matching processes. Low-quality sample points will have their search weight reduced until they are moved to the isolation observation area.

[0037] The algorithm analyzes the sample point density and parameter dispersion in each region of the configuration space, increasing the sample point collection frequency in regions with high dispersion. It identifies regions sensitive to parameter changes and increases the sample point density by 50% in regions where the state factor gradient is greater than 0.3. The optimized spatial structure generates an evaluation report, showcasing the trends in key indicators such as spatial coverage and parameter consistency.

[0038] Example 3: See Figure 4 The battery capacity calibration operation starts automatically according to a preset cycle. Before execution, the system pauses the phase change immersion cooling process to ensure the battery is in a static state. The calibration trigger signal is generated by the time management module, which synchronously coordinates the calibration sequence of multiple battery chambers to avoid power load fluctuations caused by simultaneous measurements. During the calibration preparation stage, the system disconnects the load connection of the battery under test and connects to a dedicated measurement circuit, which includes a constant current discharge unit, a voltage sampling circuit, and a temperature compensation module.

[0039] When measuring the battery capacity by constant current discharge method, the discharge current is set to 0.2 times of the rated capacity of the battery, and the cut-off voltage is automatically matched according to the battery chemical type. During the measurement process, the discharge time-voltage curve is recorded, and the discharge is stopped when the voltage drops to the cut-off point. The actual capacity value is obtained by integral calculation. The integral formula is as follows:

[0040] Wherein: is the calculated battery capacity value, is the constant current discharge current, and represent the start and end times of discharge, respectively, is the temperature compensation coefficient, which is obtained from the compensation curve according to the real-time measured battery surface temperature.

[0041] The test signal is a 1 kHz sine wave, and the current amplitude is controlled within 5% of the rated capacity of the battery. The measurement system synchronously collects the voltage response signal, extracts the same frequency component through a digital lock-in amplifier, and calculates the modulus of the complex impedance as the internal resistance value. Each battery cavity is measured three times, and the median value is taken as the final internal resistance value. The measurement interval time is not less than 30 seconds to avoid the influence of battery polarization. The system controls the environmental temperature to linearly rise at a rate of 5℃ / min, while continuously measuring the battery internal resistance at intervals of 10 seconds. The least square fitting slope of the temperature-internal resistance curve is the temperature rise sensitivity value. A safety protection mechanism is set during the measurement process, which immediately terminates the test when the internal resistance suddenly increases by more than 15% or the temperature reaches 60℃.

[0042] When the capacity value differs from the last calibration result by more than 20%, retest is triggered; when the internal resistance value exceeds the battery technical specification range, calibration program is started; when the temperature rise sensitivity value is negative, check the temperature sensor state. The data that pass the verification are stored according to the battery cavity number, and the calibration result set containing the time stamp, measurement value and verification flag is generated.

[0043] Battery capacity decay judgment sets double standards: record a warning when the current capacity is lower than 85% of the initial value, and determine that the migration condition is met when it is lower than 80%. The time-weighted algorithm is introduced to judge the growth of internal resistance, and the recent growth data has a higher weight. If the internal resistance growth value within three months reaches 50% of the initial value, the determination is triggered. The temperature rise sensitivity threshold is dynamically adjusted according to the battery type, and is set to 8Ω / ℃ for lithium iron phosphate batteries and 12Ω / ℃ for ternary lithium batteries. The capacity decay condition has the highest priority, and a migration signal is generated immediately once it is met; the internal resistance growth condition requires two consecutive calibrations to exceed the threshold; the temperature rise sensitivity condition requires comprehensive judgment combined with environmental temperature, and the threshold is appropriately relaxed in high-temperature environments. When multiple conditions are met at the same time, a composite warning is generated, and the specific values of all parameters that exceed the standard are marked. The original measurement data is retained in the cache area for 30 days for real-time analysis; the calibration result set is stored in the time series database, and a joint index is established according to the battery number and calibration time; the migration determination record is written into a special event log, which is linked with the operation order system. The database implements daily incremental backup, and retains historical data for trend analysis for the past five years.

[0044] When power fluctuations occur during measurement, automatic retry; when data verification fails, switch to the backup measurement scheme; when threshold determination has boundary condition disputes, submit manual review. All abnormal events record detailed context information, including system state, environmental parameters and previous operation records when the abnormality occurs.

[0045] The migration warning generation module starts when the determination condition is met, and the warning information includes the battery cavity position number, the list of parameters that exceed the standard, the percentage of the extent of exceeding the standard, and the recommended processing time limit. The warning level is divided into three levels according to the exceeding amplitude: the first level warning requires processing within 72 hours, the second level warning requires processing within 168 hours, and the third level warning is only for observation record. Warning information is pushed through multiple channels, including system monitoring interface pop-up window, email notification and mobile APP reminder. The system analyzes the trend of historical calibration data, and automatically shortens the calibration interval for batteries with faster performance decay. The initial default period is 90 days, and the shortest can be adjusted to 15 days. The period adjustment algorithm considers factors such as battery usage frequency, environmental temperature fluctuation range and historical migration records, and the adjustment amplitude is not more than 30% of the original period each time. The constant current source automatically checks the current accuracy every month, and triggers the calibration program when the deviation exceeds 0.5%; the voltage measurement channel uses a reference voltage source for regular self-checking; the temperature sensor is compared and calibrated by a platinum resistance standard. Calibration data is recorded in the device file, and calibration status is a prerequisite for the validity of measurement data. The calibration tasks of multiple battery cavities are queued according to the power load capacity; large capacity battery measurement is arranged in the low electricity consumption period; the measurement gap automatically switches to low power consumption mode. Energy consumption monitoring displays the total power consumption of the current calibration task in real time, and automatically pauses subsequent tasks when it exceeds the preset limit.

[0046] The trigger flow of the migration judgment reminder in actual operation is a multi-level cascading alarm response mechanism. When any parameter in the calibration result set meets the migration condition, the system first generates a structured record in the event log. Taking the high internal resistance battery cavity numbered B7-12 as an example, the calibration data shows that the current capacity is 78% of the initial value, the internal resistance increases to 158% of the initial value, the temperature rise sensitivity reaches 9.3Ω / ℃, and the capacity attenuation and internal resistance growth are triggered. The system automatically marks the battery cavity as a state to be migrated, and records the detailed values of the over-standard parameters and the triggering time in the event log.

[0047] The migration signal generation module generates differentiated signal content according to the combination of over-standard parameters. The signal data structure includes four fields: battery position identification, migration urgency rating, main over-standard parameter, and secondary over-standard parameter. The urgency rating is divided into five levels according to the over-standard amplitude and parameter type. The rating standards for different over-standard situations are shown in Table 1.

[0048] Table 1: Migration signal urgency rating standard table

[0049] For the case of B7-12 battery cavity, the system generates a level 1 emergency signal, including the complete position path (area B-7th row-12th slot), the main over-standard parameter (capacity 78% / internal resistance 158%) and the associated temperature rise sensitivity data. After the signal is generated, the multi-channel notification system is activated: a red-framed warning box pops up on the large screen display in the central control room, a high-priority alarm message is pushed to the mobile terminal of the operation and maintenance personnel, and a spare part query request is sent to the warehouse management system.

[0050] The instruction output link adapts different formats according to the device type. The instruction sent to the intelligent inspection robot uses binary encoding, including target position coordinates and operation step sequence; the instruction displayed on the engineer's tablet shows the visual navigation path, marking the specific position of the battery cavity in the rack; the request message transmitted to the warehouse management system contains the battery model code and serial number verification information. All instructions are accompanied by a timestamp and issuer identification, forming a complete operation traceability chain.

[0051] The battery thermal behavior trend prediction in actual application is a dynamically updated thermal map. The system continuously records the time series changes of high internal resistance state factors and low internal resistance state factors, and calculates the moving average with a 30-minute window. When the state factors of B7-12 battery cavity show a monotonic upward trend for three consecutive windows, and the upward slope exceeds the threshold, the prediction model determines that the battery has a thermal runaway risk. The model output includes a 15-minute temperature rise prediction curve, which is superimposed with real-time monitoring data on the warning interface.

[0052] For the prediction results of the B7-12 battery cavity, the system first gradually reduces the spraying frequency from 80 Hz to 65 Hz, with each adjustment not exceeding 5 Hz; then increases the atomized particle diameter from 120 microns to 150 microns, with three adjustments; and finally extends the opening time by 2 seconds. After each adjustment, the rate of change of the state factor is monitored, and if the rate of change has not slowed down, a backup strategy is started, and collaborative cooling is implemented on adjacent nozzle groups.

[0053] When the system detects that a certain battery cavity meets three migration conditions at the same time, it automatically generates a task for review. The terminal device of the operation and maintenance supervisor receives a review request containing complete calibration data, and the interface displays a historical calibration data comparison curve and a current parameter deviation analysis. The supervisor can magnify the key data segment through gesture operation, view detailed measurement logs, and finally choose to confirm the migration or mark it as a false alarm. The review operation records the operation time and decision basis throughout the process and is included in the quality audit archives.

[0054] When the migration signal reaches level 3 and above, the system automatically searches the inventory database to lock the matching backup battery unit. Taking the B7-12 battery cavity as an example, the system locates the nearest replaceable unit in the warehouse three-dimensional map according to its model identifier LFP-120Ah, and generates a work order containing the picking path and quality inspection points. After receiving the work order, the warehouse robot performs a quick battery performance test to ensure that the backup unit has a capacity of not less than 95% of the nominal value and an internal resistance deviation of within 5%.

[0055] After the installation of the new battery is completed, the system performs three verification tests: the first test measures the open-circuit voltage and internal resistance reference value; the second test detects the voltage drop under a 5A load current; and the third test records the self-discharge rate for 8 hours in a static state. All test data are compared with the factory standard values, and if the deviation exceeds the allowed range, a reinstallation process is triggered. After verification, the new battery information is written into the system database, the old battery is marked as a state to be recycled, and enters the life analysis queue. The self-optimization of the trend prediction model is achieved through online learning. The deviation of each prediction result from the actual monitoring data is recorded as a learning sample, and when the sample accumulation reaches 100 groups, the model parameter update is triggered. The update process uses a sliding window mechanism to retain the effective samples of the last three months and gradually eliminate early data. The model version management records the performance indicator changes of each update, supporting rollback to the previous two stable versions.

[0056] This embodiment ensures the accuracy of the migration decision through a structured signal generation mechanism, and realizes reasonable allocation of resources through a multi-level emergency degree system. The closed-loop operation of the prediction model and real-time regulation effectively prevents the risk of thermal runaway, and the review mechanism of man-machine cooperation balances the efficiency of automation and the reliability of decision-making. The entire migration process seamlessly connects with the spare parts management system, forming a complete operation and maintenance chain from detection to replacement.

[0057] Example 5: refer toFigure 5 The cooling effect monitoring is initiated immediately after the phase change immersion cooling operation is completed. The array of infrared thermographic cameras performs a full-scan of the high-internal resistance battery cavities and the low-internal resistance battery cavities, with a scan frequency set to twice per second. The monitoring area of each battery cavity is divided into grid cells, with each cell having a size of one percent of the surface of the battery monomer. The thermographic cameras collect temperature data for each cell, generating a temperature distribution matrix. The cooling effect data includes three core indicators: the highest temperature drop value is obtained by comparing the values of the highest temperature points before and after cooling; the temperature uniformity coefficient is calculated as the inverse of the standard deviation of the surface temperature after cooling; and the cooling rate is the average temperature change slope during the cooling start to end period.

[0058] The newly collected cooling effect data enters the preprocessing channel: first, sensor bias correction is performed, and each thermographic camera probe compensates for measurement errors according to the calibration parameter table; then, data alignment is performed, matching the temperature matrix with the battery position mapping table; finally, time stamp synchronization is performed to ensure consistency with the time axis of the internal resistance monitoring data. The processed temperature data replaces the oldest twenty percent of the data in the battery temperature monitoring data set, and the internal resistance monitoring data set is supplemented with three measurement values after cooling, which are set at the 30th second, the 2nd minute, and the 5th minute after cooling.

[0059] When the amount of temperature data updated in the new data set exceeds fifteen percent of the total capacity, or the number of newly added internal resistance data records reaches three, the system activates the battery state analysis module. The analysis process uses the same algorithm flow as the initial calculation, but introduces incremental calculation optimization: only the time window involved in the updated data is reprocessed, and the historical window data reuses the cache results. The newly generated high-internal resistance state factor and low-internal resistance state factor are compared with the previous version, and the change amount and direction are recorded.

[0060] The system determines the search area according to the change direction of the state factor: if the state factor increases by more than five percent, the search area is shifted to the positive gradient direction of the configuration space; if it decreases by more than five percent, it is expanded to the negative gradient direction. The search range radius is dynamically adjusted according to the change amplitude, and the radius is expanded by zero point zero one unit for every two percent increase in the change amplitude. Within the locked area, the system only retrieves newly added sample points in the last three months, and preferentially selects labeled data matching the current battery aging degree.

[0061] The initial retrieval results are input into the parameter change evaluator, which calculates the difference value between the new parameters and the current operating parameters. Parameter sets with a difference value below ten percent are directly ignored; parameter sets with a difference value between ten percent and twenty percent enter the safety verification link; and parameter sets with a difference value exceeding twenty percent need to be submitted for manual review. The safety verification link checks the process feasibility of the parameter combination, including the matching degree of atomized particle diameter and spraying frequency, the coordination of opening duration and coolant flow, etc.

[0062] The parameter adjustment scheme passed the security check is disassembled into multiple sub-steps, each modifying only a single parameter dimension. The on duration adjustment is performed in steps of seconds, with each increment or decrement not exceeding ten percent of the current value; the spray frequency adjustment is performed in steps of hertz, with a single change amplitude controlled within five hertz; the atomized particle diameter adjustment is implemented in three levels through a piezoelectric controller. After completing each sub-step, the system pauses for two minutes to collect temperature response data, and if the temperature change rate exceeds three degrees Celsius per second, it reverts to the previous step.

[0063] The system continuously compares the deviation of the current battery temperature from the target temperature interval, and when all monitored point temperatures are maintained within the target interval for five consecutive minutes, it determines that the stable state is reached. If six rounds of parameter adjustment still do not achieve stability, the system starts the abnormal handling program: freezes the current parameter configuration, generates a diagnostic report, and switches to the basic cooling mode. The basic cooling mode uses a fixed parameter combination, which is selected from a pre-set scheme library according to the ambient temperature.

[0064] The complete record of each closed-loop control includes fields such as initial state factors, retrieved parameters, adjustment steps, final parameters, and stable time. These records are indexed by battery cavity number and date, with raw monitoring data retained for thirty days, analysis result data retained for ninety days, and operation logs permanently stored. Archival data is used to generate performance reports regularly, showing the correlation trend between nozzle parameter adjustment frequency and cooling effect. Self-test content includes infrared thermal imager lens cleanliness detection, which analyzes the background thermal image to determine whether cleaning is needed; nozzle clogging detection, which compares the difference between commanded flow and actual flow; and communication link integrity testing, which sends test messages to verify node responses. Problems found by self-test are queued for processing according to priority, and minor problems that do not affect the normal control process are delayed for repair during the maintenance window.

[0065] This implementation realizes quantitative evaluation of cooling effect through high-resolution temperature monitoring, and improves parameter adjustment efficiency through incremental search mechanism. The step-by-step loading strategy avoids the risk of parameter mutation, and the dynamic termination condition balances the control effect and energy cost. The entire closed-loop system forms a continuous optimization feedback mechanism, adapting to the dynamic changes of battery operating state.

[0066] It is to be understood that the terminology used herein such as first and second, and the like, is only used to distinguish one entity or action from another entity or action, and does not necessarily require or imply any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0067] While embodiments of the present application have been shown and described with reference to particular embodiments thereof, it will be understood by those skilled in the art that various changes in form and details can be made therein without departing from the spirit and scope of the application. The scope of the application is defined by the appended claims and their equivalents.

Claims

1. A smart phase change immersion cooling method based on dual-cavity dynamic control, characterized in that, The method includes: Perform sensor data acquisition to obtain battery temperature monitoring data sets and internal resistance monitoring data sets for high internal resistance battery chambers and low internal resistance battery chambers; Based on the battery temperature monitoring data set and the internal resistance monitoring data set, battery state analysis is performed to determine the high internal resistance state factor and the low internal resistance state factor. Using the high internal resistance state factor and the low internal resistance state factor as indexes, a centralized search is performed in the nozzle parameter configuration space to determine the target nozzle operating parameters. Based on the target nozzle operating parameters, adjust the nozzle operating states of the high internal resistance battery cavity and the low internal resistance battery cavity to perform phase change immersion cooling operation; Periodically perform battery capacity calibration to obtain a calibration result set. When the calibration result set meets the migration conditions, trigger a migration judgment reminder. The battery state analysis includes: Data fluctuation analysis was performed on the battery temperature monitoring data set to obtain temperature fluctuation indicators; Data stability analysis is performed on the internal resistance monitoring data set to obtain internal resistance stability index; Based on the temperature fluctuation index and the internal resistance stability index, the high internal resistance state factor and the low internal resistance state factor are calculated.

2. The intelligent phase change immersion cooling method based on dual-cavity dynamic control as described in claim 1, characterized in that, The sensor data acquisition process includes: Collect battery temperature monitoring data and internal resistance monitoring data of the high internal resistance battery cavity, and battery temperature monitoring data and internal resistance monitoring data of the low internal resistance battery cavity. The battery temperature monitoring data of the high internal resistance battery cavity and the battery temperature monitoring data of the low internal resistance battery cavity are integrated to generate the battery temperature monitoring data set. The internal resistance monitoring data of the high internal resistance battery cavity and the internal resistance monitoring data of the low internal resistance battery cavity are integrated to generate the internal resistance monitoring data set.

3. The intelligent phase change immersion cooling method based on dual-cavity dynamic control as described in claim 1, characterized in that, The data fluctuation analysis includes: Extract time-series data points from the battery temperature monitoring dataset; Calculate the rate of change between adjacent data points; The average of all rates of change is used as the temperature fluctuation index.

4. The intelligent phase change immersion cooling method based on dual-cavity dynamic control as described in claim 1, characterized in that, The centralized search in the nozzle parameter configuration space includes: Obtain the high internal resistance state factors and low internal resistance state factors of multiple samples, as well as the corresponding nozzle operating parameters of multiple samples. A two-dimensional configuration space is pre-constructed, where the first dimension represents the range of high internal resistance state factors and the second dimension represents the range of low internal resistance state factors. The multiple high internal resistance state factors and the multiple low internal resistance state factors of the samples are mapped to the two-dimensional configuration space to form multiple sample space points; The multiple sample nozzle operating parameters are used to identify the multiple sample space points, thereby generating the nozzle parameter configuration space.

5. The intelligent phase change immersion cooling method based on dual-cavity dynamic control as described in claim 4, characterized in that, The centralized search in the nozzle parameter configuration space also includes: The high internal resistance state factor and the low internal resistance state factor are input into the two-dimensional configuration space as target points; Calculate the distance metric between the target point and each point in the sample space; Select the sample space point with the smallest distance metric as the matching point; The working parameters of the sample nozzle corresponding to the matching point are used as the working parameters of the target nozzle.

6. The intelligent phase change immersion cooling method based on dual-cavity dynamic control as described in claim 1, characterized in that, The periodic battery capacity calibration includes: The battery capacity, internal resistance, and temperature rise sensitivity of the high internal resistance battery cavity and the low internal resistance battery cavity are measured quarterly. The calibration result set is generated by integrating the battery capacity value, the internal resistance value, and the temperature rise sensitivity value. Determine whether the calibration result set meets the migration conditions, wherein the migration conditions include a first preset percentage of battery capacity decay to the initial value of battery capacity, a second preset percentage of internal resistance increase to the initial value of internal resistance, or a temperature rise sensitivity greater than a preset threshold.

7. The intelligent phase change immersion cooling method based on dual-cavity dynamic control as described in claim 6, characterized in that, The trigger migration determination reminder includes: When the calibration result set satisfies the migration condition, a migration signal is generated; Based on the migration signal, a migration command is output to prompt the staff to perform the battery migration and replacement operation.

8. The intelligent phase change immersion cooling method based on dual-cavity dynamic control as described in claim 1, characterized in that, The method further includes: Based on the high internal resistance state factor and the low internal resistance state factor, predict the trend of battery thermal behavior. The operating parameters of the target nozzle are adjusted based on the battery thermal behavior trend.

9. The intelligent phase change immersion cooling method based on dual-cavity dynamic control as described in claim 1, characterized in that, After performing the phase change immersion cooling operation, the following is included: Monitor the cooling effect data of the high internal resistance battery cavity and the low internal resistance battery cavity; Based on the cooling effect data, update the battery temperature monitoring data set and the internal resistance monitoring data set; Repeat the steps of performing battery state analysis, performing a centralized search in the nozzle parameter configuration space, adjusting the nozzle operating state of the high internal resistance battery cavity and the low internal resistance battery cavity based on the target nozzle operating parameters, and performing phase change immersion cooling operation.

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