A flow channel heat dissipation management system and method for a flow battery

Abstract

This invention discloses a flow channel heat dissipation management system and method for a flow battery, relating to the field of flow battery heat dissipation technology. This flow channel heat dissipation management system establishes a complete closed-loop process for temperature unevenness assessment, heat dissipation scenario determination, parameter matching adjustment, and dynamic optimization. This avoids the blindness of heat dissipation management, achieving precision through step-by-step diagnosis followed by control. It also adapts to dynamic operating conditions such as current fluctuations and heat load accumulation during charging. Real-time monitoring and targeted adjustments ensure temperature uniformity throughout the entire charging cycle, ultimately guaranteeing battery charging efficiency and lifespan. This solves the problem in existing technologies where it is difficult to determine electrolyte and cooling medium flow rates, as well as the initial temperatures of the electrolyte and cooling medium, leading to large temperature differences on the electrode surfaces and uneven internal temperature of the battery cell during charging.

Description

Technical Field

[0001] This invention relates to the field of flow battery heat dissipation technology, specifically to a flow battery channel heat dissipation management system and method. Background Technology

[0002] In flow battery charging, addressing the uneven internal temperature of the cell caused by large temperature differences on the electrode surfaces is crucial for ensuring battery charging efficiency and lifespan. In real-world charging scenarios, battery heat generation is dynamic and influenced by factors such as electrode structure and electrolyte circulation characteristics. Existing technologies exhibit the following shortcomings:

[0003] First, existing technologies rely on single-dimensional temperature measurement, failing to comprehensively capture the complete temperature difference distribution, leading to inaccurate determination of the location and degree of temperature unevenness. Second, existing technologies cannot differentiate between electrolyte-compatible, cooling medium-compatible, or synergistically compatible heat dissipation scenarios, resulting in a lack of specificity in determining flow rate and initial temperature, leading to parameter mismatches. Third, existing technologies do not consider the decrease in parameter adaptability caused by current fluctuations and accumulated heat load during charging, and do not develop differentiated fine-tuning strategies according to the type of temperature unevenness, making it difficult to sustain temperature uniformity effects.

[0004] Therefore, there is an urgent need for a flow battery heat dissipation management method that integrates multi-dimensional temperature monitoring, precise matching of operating conditions and scenarios, and dynamic closed-loop optimization to solve the above-mentioned technical bottlenecks and improve the accuracy and stability of temperature control under charging conditions. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a flow channel heat dissipation management system and method for flow batteries, which solves the problem that existing technologies have difficulty in determining the electrolyte flow rate and cooling medium flow rate, as well as the initial temperatures of the electrolyte and cooling medium, leading to large temperature differences on the electrode surfaces and uneven internal temperature of the battery cell during battery charging.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a flow channel heat dissipation management system for a flow battery, comprising:

[0007] The temperature uniformity judgment module is used to determine whether there is temperature unevenness inside the battery based on the temperature data of the electrode surface and the cell.

[0008] The heat dissipation scenario determination module is used to determine the heat dissipation scenario based on cell temperature data, charging current density and cooling medium outlet temperature when there is uneven temperature.

[0009] The parameter determination module is used to determine the flow rates of electrolyte and cooling medium, as well as the initial temperatures of electrolyte and cooling medium, based on electrode surface and cell temperature data, electrolyte inlet and outlet temperatures, and cooling medium inlet and outlet temperatures, for the heat dissipation scenario.

[0010] The parameter optimization module is used to monitor the internal temperature balance of the battery after determining the flow rate and initial temperature. If the temperature is uneven, the module determines the type of temperature imbalance and adjusts the flow rate and initial temperature based on the type of temperature imbalance.

[0011] A method for managing heat dissipation in the flow channel of a flow battery, comprising the following steps:

[0012] Determine whether there is temperature unevenness inside the battery based on electrode surface and cell temperature data.

[0013] If temperature unevenness exists, the heat dissipation scenario is determined based on the cell temperature data, charging current density, and cooling medium outlet temperature.

[0014] Based on the electrode surface and cell temperature data, the electrolyte inlet and outlet temperatures and the cooling medium inlet and outlet temperatures are used to determine the flow rates of the electrolyte and cooling medium corresponding to the heat dissipation scenario, as well as the initial temperatures of the electrolyte and cooling medium.

[0015] After determining the flow rate and initial temperature, the internal temperature of the battery is monitored for imbalance. If the temperature is uneven, the type of imbalance is determined, and the flow rate and initial temperature are adjusted based on the type of imbalance.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] This invention establishes a complete closed-loop process for determining temperature unevenness, identifying heat dissipation scenarios, adjusting parameters, and dynamically optimizing. This avoids the blindness of heat dissipation management, achieves precision through step-by-step diagnosis and control, and adapts to dynamic operating conditions such as current fluctuations and heat load accumulation during charging. Through real-time monitoring and targeted adjustments, it ensures temperature uniformity throughout the entire charging cycle, ultimately guaranteeing battery charging efficiency and lifespan. This invention solves the problem in existing technologies where it is difficult to determine the electrolyte flow rate and cooling medium flow rate, as well as the initial temperature of the electrolyte and cooling medium, leading to large temperature differences on the electrode surfaces and uneven internal temperature of the battery cell during battery charging. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the module connection of the flow channel heat dissipation management system of the flow battery of the present invention.

[0019] Figure 2 This is a flowchart for determining the flow rate and initial temperature of the electrolyte and cooling medium in the flow channel heat dissipation management system of the flow battery of the present invention.

[0020] Figure 3 This is a flowchart illustrating how the flow channel heat dissipation management system of the flow battery of the present invention determines the type of temperature imbalance and adjusts the flow rate and initial temperature based on the type of temperature imbalance.

[0021] Figure 4 This is a flowchart of the flow channel heat dissipation management method for the flow battery of the present invention. Detailed Implementation

[0022] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. Please refer to the accompanying drawings. Figure 1 The present invention provides a technical solution:

[0023] In this specific embodiment, it should be noted that the heat dissipation scenarios in this invention include the following three heat dissipation scenarios, which are mainly divided according to the dominant factors that cause uneven temperature.

[0024] The first heat dissipation scenario is dominated by electrolyte adjustment. This refers to a situation where temperature unevenness is primarily caused by uneven electrolyte flow field distribution, leading to differences in localized heat removal efficiency. In this scenario, optimizing electrolyte flow rate and inlet temperature is the main approach.

[0025] The second heat dissipation scenario is dominated by cooling medium adjustment. This refers to temperature unevenness primarily caused by uneven distribution of contact thermal resistance between the battery cell and the cooling plate, or uneven distribution of the cooling medium flow field. In this scenario, optimizing the cooling medium parameters is more effective.

[0026] The third heat dissipation scenario is the coordinated adjustment scenario, which refers to the coupling of uneven heat generation and uneven heat dissipation factors, requiring coordinated adjustment of the electrolyte and cooling medium parameters at the same time.

[0027] A flow channel heat dissipation management system for a flow battery includes a temperature uniformity judgment module, a heat dissipation scenario determination module, an adjustment parameter determination module, and a parameter optimization module. The temperature uniformity judgment module is connected to the heat dissipation scenario determination module, the heat dissipation scenario determination module is connected to the adjustment parameter determination module, and the adjustment parameter determination module is connected to the parameter optimization module, wherein:

[0028] The temperature uniformity judgment module is used to determine whether there is temperature unevenness inside the battery based on the temperature data of the electrode surface and the cell.

[0029] To achieve comprehensive temperature monitoring across the electrode surface, cell thickness direction, and cell body surface, and to ensure the completeness and accuracy of data acquisition, the process for determining whether there is temperature unevenness inside the battery is as follows:

[0030] A thin-film temperature sensor is embedded in the flow channel surface of a bipolar plate. The temperature of the electrode surface is monitored based on the bipolar plate embedded thin-film sensor. The thin-film sensor needs to be embedded in the flow channel surface of the bipolar plate and flush with the surface to avoid interfering with the electrolyte flow.

[0031] Furthermore, a sensor mounting slot is reserved during the bipolar plate processing, with the depth consistent with the sensor thickness. After the sensor is embedded, it is fixed with high-temperature resistant sealant to ensure electrical insulation and liquid sealing with the bipolar plate. The sensors can be evenly arranged according to the electrode surface area, for example, 10-20 measuring points can be arranged on a 10cm×10cm electrode surface to cover areas prone to temperature gradients, such as the inlet, outlet, and bends of the flow channel.

[0032] Miniature through-holes with a diameter of no more than 1 mm are drilled in the inactive area at the edge of the battery cell to avoid damaging the cell structure and electrolyte sealing. The through-holes extend through the thickness of the battery cell, and fiber optic grating sensors are inserted along the through-holes. The sensor measuring points are evenly distributed along the thickness direction, and the spacing between the measuring points can be set to 2-3 mm, covering the surface, middle, and inner layers of the battery cell. The gap between the sensor and the through-hole is filled with high-temperature resistant and electrolyte-resistant sealant. The fiber optic lead is fixed to the battery cell shell to avoid pulling and damaging the sensor. Temperature monitoring in the thickness direction of the battery cell can be performed based on the minimally invasive fiber optic grating sensor.

[0033] Surface mount NTC thermistors or thin film platinum resistance thermometers are selected and fixed with high-temperature resistant double-sided adhesive. The sensor surface is covered with an insulating protective film to prevent short circuits with external devices. The sensor is attached to the surface of the battery cell casing, covering the front, back, and sides of the battery cell, with a focus on covering the electrode projection area and the edge area of ​​the battery cell. The temperature of the electrode surface is monitored based on the distributed sensors on the battery cell surface.

[0034] It should be noted that the electrode surface is the main area for heat generation by electrochemical reaction during charging and is the source of temperature difference. The thickness direction of the cell is the key path for heat conduction, and the cell surface is the external manifestation of temperature distribution. The joint monitoring of these three types of sensors avoids blind spots that only measure the surface and miss the internal or only measure the external and miss the source, ensuring that no internal unevenness caused by temperature difference on the electrode surface is missed.

[0035] Before the battery starts, power all sensors for 10 minutes to preheat and ensure stable operation. The three types of sensors are connected to the data acquisition module via wired connection. The module integrates a multi-channel signal conditioning circuit to filter, amplify, and convert sensor signals to AD, and synchronously record the acquisition timestamp. It acquires electrode surface temperature data, cell thickness direction temperature data, and cell body surface temperature data according to a set cycle.

[0036] Considering that the charging process is not a steady state, the set cycle can be dynamically adjusted according to the battery's operating conditions. This allows for accurate capture of temperature changes under critical conditions, avoiding missing crucial data on temperature difference widening due to excessively long cycles, which could lead to misjudgments of temperature uniformity. For example, the steady-state data acquisition cycle is set to 10-30 seconds per acquisition; the dynamic condition data acquisition cycle is shortened to 5-10 seconds per acquisition; and the abnormal warning phase data acquisition cycle is further shortened to 2-5 seconds per acquisition.

[0037] Statistical analysis was performed on the electrode surface temperature data, cell thickness direction temperature data, and cell body surface temperature data for each cycle, and the mean square error of the electrode surface temperature, the mean square error of the cell thickness direction temperature, and the mean square error of the cell body surface temperature were calculated.

[0038] The essence of temperature non-uniformity is that the temperature distribution is discrete, rather than that the average temperature is high. The standard deviation can be used to quantify the degree of dispersion of temperature data. However, in order to predict whether the temperature non-uniformity will worsen or improve, it is also necessary to calculate the mean standard deviation gradient of the electrode surface temperature, the mean standard deviation gradient of the cell thickness direction temperature, and the mean standard deviation gradient of the cell body surface temperature.

[0039] In some cases, the mean square error may slightly exceed the threshold, but the gradient mean is negative, so there may be no need to rush to adjust it; while in other cases, the mean square error may not exceed the threshold, but the gradient mean is very large, so early intervention is needed to make the adjustment more proactive.

[0040] If any one of the following—the mean square deviation of electrode surface temperature, the mean square deviation of temperature in the cell thickness direction, and the mean square deviation of temperature on the cell body surface, or the mean gradient of the mean square deviation of electrode surface temperature, the mean gradient of the mean square deviation of temperature in the cell thickness direction, and the mean gradient of the mean square deviation of temperature on the cell body surface—is greater than the corresponding set threshold, then there is temperature unevenness inside the battery. If none of these values ​​are greater than the corresponding set threshold, then the temperature inside the battery is uniform.

[0041] It should be noted that the set threshold corresponding to the mean square error is the mean square error threshold σth, and the set threshold corresponding to the mean mean square error gradient is the mean mean square error gradient threshold Gth. These two set thresholds can be determined through battery bench testing. Temperature data are collected under different discharge conditions (current density 0.1-1 A / cm²) and different ambient temperatures (-10℃-40℃). The maximum value of the mean square error σmax and the maximum value of the mean mean gradient Gmax of each dimension during normal operation are statistically analyzed and set as follows:

[0042] The mean squared error threshold σth = 1.2 × σmax, with a 20% safety margin.

[0043] The mean value threshold of the root mean deviation gradient, Gth = 1.5 × Gmax, is used to sensitively capture the trend of increasing temperature unevenness.

[0044] The heat dissipation scenario determination module is used to determine the heat dissipation scenario based on cell temperature data, charging current density and cooling medium outlet temperature when there is uneven temperature.

[0045] In order to determine the heat dissipation scenario, it is necessary to first establish a standard and process for determining the heat dissipation scenario. Therefore, the process for determining the heat dissipation scenario is as follows:

[0046] First, extract all operational samples in the battery historical operation database that have been identified as having uneven temperature. Then, for each operational sample that has been identified as having uneven temperature, calculate the difference based on the maximum and minimum values ​​of the temperature at each monitoring point in the cell thickness direction in the corresponding historical cell temperature data. Then, calculate the mean of the difference to obtain the temperature difference in the cell thickness direction. At the same time, calculate the standard deviation of the surface temperature based on the temperature at each monitoring point on the cell surface.

[0047] The electrode surface temperature range is obtained by taking the mean of the differences between the maximum and minimum temperatures at each monitoring point on the historical electrode surface.

[0048] It should be noted that the maximum and minimum values ​​mentioned above refer to the maximum and minimum values ​​of the temperature monitored multiple times at the same monitoring point, and the difference is the difference between the maximum and minimum temperature values ​​at the same monitoring point.

[0049] By using three dimensions—temperature difference in the thickness direction, standard deviation of the body surface, and range of the electrode surface—the heterogeneity of historical samples is characterized, providing a unified standard for subsequent screening of effective samples and ensuring that the screened samples can cover scenarios related to core pain points.

[0050] Historical battery operation sample data that meets the requirements are selected based on the temperature difference along the cell thickness, the standard deviation of the body surface temperature, and the range of the electrode surface temperature. The specific process is as follows:

[0051] Arrange all data for the same parameter in ascending order, and calculate the first quartile, third quartile, and interquartile range of the temperature difference in the cell thickness direction, the standard deviation of the body surface temperature, and the range of the electrode surface temperature.

[0052] If the cell thickness temperature difference, body surface temperature standard deviation, and electrode surface temperature range of a certain historical battery operation sample data are greater than or equal to the corresponding third quartile and less than the third quartile plus 1.5 times the interquartile range, then it is considered a qualified historical battery operation sample data.

[0053] The above screening process can eliminate samples with excessively low or high unevenness, retaining only representative samples with moderate unevenness. This ensures that the results of subsequent simulation adjustments can be adapted to the vast majority of normal operating scenarios, and that the degree of unevenness in the effective sample set is similar, avoiding misjudgments of the adjustment mode caused by extreme samples.

[0054] It should be noted that if no historical battery operation sample data meets the requirements after screening, the screening range will be expanded to be greater than or equal to the first quartile and less than the third quartile + 1.5 times the interquartile range. The samples in the expanded range will be weighted and adjusted, with the weight configured as follows: the sample weight in the range of the third quartile to the third quartile + 1.5 times the interquartile range is 1.0, and the sample weight in the range of the first quartile to the third quartile is 0.6, to ensure the representativeness of the samples.

[0055] Each operating sample in the simulated historical battery operating sample data was adjusted separately for electrolyte, cooling medium and synergistic adjustment, and the degree of improvement in battery temperature after adjustment was evaluated.

[0056] It should be noted that single-variable adjustment of the electrolyte refers to adjusting only the flow rate and initial temperature of the electrolyte; single-variable adjustment of the cooling medium refers to adjusting only the flow rate and initial temperature of the cooling medium; and coordinated adjustment refers to adjusting the flow rate and initial temperature of both the electrolyte and the cooling medium. Each adjustment is made according to a set range, for example, the flow rate is adjusted by 20% and the initial temperature is adjusted by 5°C each time.

[0057] Furthermore, the calculation process for the battery temperature improvement is as follows:

[0058] For a single operating sample under a specific adjustment mode, calculate the mean square error of electrode surface temperature before and after adjustment, the mean square error of cell thickness temperature, and the mean square error of cell surface temperature.

[0059] The weighted summation of the mean square error of electrode surface temperature, mean square error of cell thickness temperature, and mean square error of cell surface temperature before and after adjustment is used to obtain the comprehensive mean square error before and after adjustment.

[0060] For example, the weights of the mean square error of electrode surface temperature, the mean square error of cell thickness temperature, and the mean square error of cell body surface temperature are 0.4, 0.3, and 0.3, respectively.

[0061] The difference between the adjusted overall mean squared error and the original overall mean squared error is used as the degree of improvement in battery temperature.

[0062] Specifically, the difference between the original and adjusted mean squared error is calculated. The larger the difference, the better the adjustment method improves the temperature of the sample.

[0063] Based on the improvement in battery temperature, the operating samples are classified into the electrolyte adjustment group, the cooling medium adjustment group, or the synergistic adjustment group. The samples are divided into the electrolyte adjustment group, the cooling medium adjustment group, and the synergistic adjustment group, which directly provides adjustment mode labels for subsequent scenario determination.

[0064] It should be noted that the process of relocation is as follows:

[0065] For single-variable adjustment of electrolyte and single-variable adjustment of cooling medium, if the corresponding improvement in battery temperature is greater than K times the improvement in battery temperature corresponding to other adjustment modes, the corresponding battery operating state will be assigned to the electrolyte adjustment group or the cooling medium adjustment group; otherwise, it will be assigned to the coordinated adjustment group.

[0066] The K value was determined through bench experiments. The K value is a quantitative threshold for defining whether the single-variable adjustment of the electrolyte and the single-variable adjustment of the cooling medium is the absolutely optimal heat dissipation adjustment mode. Its value range is 1.2-1.5. It was obtained by calibration through 120 groups of moderate temperature non-uniformity samples with three types of temperature non-uniformity: electrode surface dominant, thickness direction dominant, and composite, charging current density of 0.1-1.0 A / cm², and cooling medium outlet temperature of 25-40℃. Specifically, the three adjustment modes were executed for each group of samples and the temperature improvement was calculated. The K values ​​within this range were traversed and the scene matching accuracy was statistically analyzed. Finally, the value with the highest scene matching accuracy and the lowest misjudgment rate was selected as the K value.

[0067] In order to select the most representative core cluster from each adjustment group and extract the typical operating conditions of the group, the maximum cell temperature and the cell temperature range are calculated using the cell temperature data in the operating samples. The operating samples in the electrolyte adjustment group, cooling medium adjustment group or co-adjustment group are clustered using the maximum cell temperature, cell temperature range, charging current density and cooling medium outlet temperature as clustering indicators.

[0068] In this embodiment, the K-means clustering algorithm can be used for clustering, with the number of clusters ranging from 2 to 8. By calculating the sum of squared errors (SSE) corresponding to different numbers of clusters, the optimal number of clusters is determined when the rate of decrease of SSE is no greater than 10%. By clustering, samples with similar working conditions in the same adjustment group are aggregated to avoid the blurring of scene features caused by scattered samples. After the clustering is completed, the number of samples in each cluster is counted for each adjustment group.

[0069] The clusters of the most frequently used samples corresponding to the electrolyte adjustment group, cooling medium adjustment group, or synergistic adjustment group are identified and statistically analyzed. The mean of the highest temperature, the mean of the temperature range, the mean of the charging current density, and the mean of the cooling medium outlet temperature are used as the benchmark values ​​for scenario matching. The benchmark values ​​are the mean values ​​of the indicators of the core clusters of each adjustment group, representing the typical characteristics of the scenario. The mean is a core statistic describing the central trend of the data, which can smooth the random fluctuations of individual samples, ensure the stability and representativeness of the benchmark values, and avoid the impact of abnormal data from individual samples on the accuracy of scenario matching.

[0070] The cell temperature data, charging current density, and cooling medium outlet temperature are first standardized with the matching benchmark values ​​for each scenario. Then, Euclidean distance is calculated, and the corresponding heat dissipation scenario is determined based on the minimum Euclidean distance. The minimum distance corresponds to the optimal scenario, avoiding subjective errors in judging scenarios based on experience and ensuring that the scenario judgment standards are consistent under different operating conditions.

[0071] The parameter adjustment module is used to determine the flow rates of the electrolyte and cooling medium, as well as their initial temperatures, based on electrode surface and cell temperature data, electrolyte inlet and outlet temperatures, and cooling medium inlet and outlet temperatures, for the corresponding heat dissipation scenario. Figure 2 As shown, the specific process is as follows:

[0072] Using battery temperature improvement as the clustering index, operating samples in the electrolyte adjustment group, cooling medium adjustment group, and synergistic adjustment group were clustered separately to determine the cluster centers with the largest mean battery temperature improvement. This resulted in the three optimal cluster centers for each of the three groups. Temperature improvement directly reflects the effect of parameters on improving temperature unevenness; using it as the basis for clustering is an effect-oriented parameter selection method that aligns with the problem-solving objectives.

[0073] This embodiment also uses the K-means clustering algorithm to aggregate scattered historical samples according to their improvement degree, focusing on the optimal cluster rather than all samples, reducing the computational workload of subsequent mapping relationship establishment and working condition matching, and avoiding judgment delays caused by data redundancy.

[0074] For the optimal cluster center in each adjustment group, the operating sample corresponding to the optimal cluster center is taken as the benchmark sample. The electrode surface and cell temperature data, electrolyte inlet and outlet temperatures and cooling medium inlet and outlet temperatures of each benchmark sample are used as reference standards. The adjustment parameters corresponding to the operating sample are used as pointing values. A one-to-one mapping relationship between the reference standards and pointing values ​​is established to form a mapping table.

[0075] The optimized mapping table reduces redundant data, eliminating the need for repeated calculations during subsequent matching; it allows for direct querying. Furthermore, the grouped mean avoids parameter fluctuations in individual samples, resulting in more stable output parameters. Moreover, the mapping table is a reusable parameter library, allowing subsequent parameter optimization to be based on this benchmark parameter, avoiding adjustments from scratch and shortening the fine-tuning cycle.

[0076] It should be noted that in the electrolyte adjustment group, the adjustment parameters are the adjusted flow rate and initial temperature of the electrolyte, while the flow rate and initial temperature of the cooling medium remain unchanged; in the cooling medium adjustment group, the adjustment parameters are the adjusted flow rate and initial temperature of the cooling medium, while the flow rate and initial temperature of the electrolyte remain unchanged; in the coordinated adjustment group, the adjustment parameters are the adjusted flow rates and initial temperatures of both the electrolyte and the cooling medium.

[0077] After determining the heat dissipation scenario, the Euclidean distance between the electrode surface and cell temperature data of the battery to be adjusted, the electrolyte inlet and outlet temperatures, and the cooling medium inlet and outlet temperatures and various reference standards is calculated. The reference standard corresponding to the minimum Euclidean distance is determined, and the pointing value is determined based on the mapping relationship. The corresponding flow rates of electrolyte and cooling medium, as well as the initial temperatures of electrolyte and cooling medium, are obtained.

[0078] By using Euclidean distance as a comprehensive comparison reference standard, the similarity between the current working conditions and the historical best scenarios can be fully considered, avoiding parameter mismatch caused by matching one indicator but large differences in other indicators.

[0079] The parameter optimization module is used to monitor the internal temperature of the battery after determining the flow rate and initial temperature. If the temperature is uneven, it determines the type of temperature unevenness and adjusts the flow rate and initial temperature accordingly. Figure 3 As shown, the specific process is as follows:

[0080] The system continuously monitors the charging process and dynamically adjusts the acquisition cycle. By removing outliers and interpolating missing values, the effective data after preprocessing in the current cycle is divided into electrode surface temperature data, cell thickness direction temperature data, and cell body surface temperature data. The mean square error of electrode surface temperature, mean square error of cell thickness direction temperature, and mean square error of cell body surface temperature in the current monitoring cycle are calculated respectively.

[0081] If the root mean square deviation of the electrode surface temperature, the root mean square deviation of the cell thickness temperature, and the root mean square deviation of the cell surface temperature are all less than the corresponding temperature equalization threshold, then temperature equalization is determined. Otherwise, temperature imbalance is determined, and a normal parameter adaptation signal is output to the BMS to maintain the current electrolyte / cooling medium flow rate and initial temperature, continuing monitoring according to the set cycle. The root mean square deviation transforms temperature equalization into a comparable quantitative indicator, resolving the error problem of subjective judgment of temperature difference magnitude.

[0082] It should be noted that the temperature equalization threshold includes the electrode surface equalization threshold, the thickness direction equalization threshold, and the body surface equalization threshold. The calibration of the three temperature equalization thresholds is carried out through battery bench experiments. Three types of root mean square error data are collected under different charge and discharge conditions when the temperature is uniform. The maximum value is then used as the temperature equalization threshold.

[0083] When there is temperature imbalance, a parameter adjustment signal is output to the BMS to determine whether the imbalance type is electrode surface-dominated, thickness direction-dominated, or a combination of both. The process for determining the imbalance type is as follows:

[0084] Electrode-surface dominant imbalance occurs only when the mean square deviation of the current electrode surface temperature is greater than or equal to the corresponding temperature equalization threshold, and the mean square deviation of the temperature in the cell thickness direction and the mean square deviation of the temperature on the cell surface are both less than the corresponding temperature equalization threshold.

[0085] The thickness-direction dominant imbalance occurs only when the mean square difference of temperature in the current cell thickness direction is greater than or equal to the corresponding temperature equilibrium threshold, and the mean square difference of temperature on the electrode surface and the mean square difference of temperature on the cell body surface are both less than the corresponding temperature equilibrium threshold.

[0086] If the above conditions are not met, it is considered a composite imbalance.

[0087] After determining the type of temperature imbalance, the flow rate is adjusted upward according to the set flow rate range first, and the improvement of the temperature mean square error is calculated. If the imbalance is still not achieved, the flow rate is adjusted upward. If the improvement of the temperature mean square error decreases, the flow rate is adjusted back to the flow rate corresponding to the maximum improvement of the temperature mean square error, and the initial temperature is adjusted downward according to the set temperature range until the temperature is balanced.

[0088] For example, the flow rate adjustment range is 20%, and the temperature adjustment range is 5°C, taking electrode surface-dominant imbalance adjustment as an example:

[0089] First, adjust the electrolyte flow rate upwards by the set range, while keeping the cooling medium parameters unchanged; shorten the acquisition cycle to 5 seconds / time, acquire temperature data for one cycle, and calculate the new standard deviation of electrode surface temperature, standard deviation of cell thickness temperature, and standard deviation of cell surface temperature; then calculate the improvement in temperature standard deviation, specifically the average of the differences between the standard deviations before and after the adjustments; then perform a temperature equalization judgment. If the temperature is equal, stop the adjustment and resume the original monitoring cycle; if it is not equal, continue the adjustment.

[0090] If the improvement in the mean square error of temperature is less than that of the previous cycle, the electrolyte flow rate is immediately adjusted back to the flow rate corresponding to the maximum improvement in the mean square error of temperature, and the initial electrolyte temperature is adjusted downward by the set range. After adjusting the initial temperature, the monitoring and improvement range calculation are repeated until all mean square errors are less than the corresponding threshold.

[0091] It should be noted that if the electrolyte flow rate is still uneven even at the maximum flow rate, the maximum flow rate will be maintained and the initial temperature will be continuously reduced. If this is still ineffective, a severe temperature unevenness alarm will be triggered.

[0092] For complex imbalances, the electrolyte flow rate and cooling medium flow rate are adjusted upwards simultaneously, and the initial temperature is also adjusted at the same time.

[0093] A flow battery heat dissipation management method, such as Figure 4 As shown, it includes the following steps:

[0094] S1. Determine whether there is temperature unevenness inside the battery based on electrode surface and cell temperature data.

[0095] S2. If there is uneven temperature, the heat dissipation scenario is determined based on the cell temperature data, charging current density and cooling medium outlet temperature.

[0096] S3. Based on the electrode surface and cell temperature data, the electrolyte inlet and outlet temperatures, and the cooling medium inlet and outlet temperatures, determine the flow rates of the electrolyte and cooling medium corresponding to the heat dissipation scenario, as well as the initial temperatures of the electrolyte and cooling medium.

[0097] S4. After determining the flow rate and initial temperature, monitor the internal temperature balance of the battery. If the temperature is uneven, determine the type of temperature imbalance and adjust the flow rate and initial temperature based on the type of temperature imbalance.

[0098] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0099] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0100] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0102] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A flow channel thermal management system for a flow battery, the system comprising: The method comprises the following steps: A temperature uniformity judgment module is used to determine whether there is temperature unevenness in the battery interior based on electrode surface and cell temperature data; A heat dissipation scene determination module is used to determine the heat dissipation scene based on cell temperature data, charging current density and cooling medium outlet temperature when there is temperature unevenness; The average of the difference between the maximum and minimum values of the temperature of each monitoring point in the thickness direction of the cell is obtained based on the historical cell temperature data, and the standard deviation of the body surface temperature is calculated based on the temperature of each monitoring point on the surface of the cell body; The maximum and minimum values of the temperature of each monitoring point on the electrode surface are obtained based on the average of the difference between the maximum and minimum values of the temperature of each monitoring point on the electrode surface; The historical battery operation sample data meeting the requirements are screened based on the temperature difference in the thickness direction of the cell, the standard deviation of the body surface temperature and the temperature range of the electrode surface; Each operation sample in the historical battery operation sample data is simulated, and the electrolyte single variable adjustment, the cooling medium single variable adjustment and the collaborative adjustment are respectively carried out, and the temperature improvement degree of the adjusted battery is evaluated; 2. The flow channel thermal management system of a flow battery of claim 1, wherein, The operation samples are classified into the electrolyte adjustment group, the cooling medium adjustment group or the collaborative adjustment group based on the battery temperature improvement degree; The maximum cell temperature and the cell temperature range are calculated based on the cell temperature data in the operation samples, and the maximum cell temperature, the cell temperature range, the charging current density and the cooling medium outlet temperature are used as clustering indexes to respectively cluster the operation samples in the electrolyte adjustment group, the cooling medium adjustment group or the collaborative adjustment group; The most operation sample clustering clusters corresponding to the electrolyte adjustment group, the cooling medium adjustment group or the collaborative adjustment group are determined, and statistical analysis is performed, and the average of the corresponding maximum temperature, the average of the temperature range, the average of the charging current density and the average of the cooling medium outlet temperature are used as the scene matching reference values; The cell temperature data, the charging current density and the cooling medium outlet temperature are subjected to Euclidean distance calculation with the scene matching reference values, and the corresponding heat dissipation scene is determined based on the smallest Euclidean distance; An adjustment parameter determination module is used to determine the flow rates of the electrolyte and the cooling medium and the initial temperatures of the electrolyte and the cooling medium corresponding to the heat dissipation scene based on the electrode surface and cell temperature data, the electrolyte inlet and outlet temperatures and the cooling medium inlet and outlet temperatures; A parameter optimization module is used to perform temperature balancing monitoring on the battery interior after the flow rates and the initial temperatures are determined, to determine the temperature imbalance type if the temperature is not balanced, and to adjust the flow rates and the initial temperatures based on the temperature imbalance type. The process of determining whether there is temperature unevenness in the battery interior based on the electrode surface and cell temperature data comprises the following steps: The electrode surface is subjected to temperature monitoring based on the embedded film sensor of the bipolar plate, the thickness direction of the cell is subjected to temperature monitoring based on the minimally invasive fiber grating sensor, and the surface of the electrode is subjected to temperature monitoring based on the distributed sensor on the surface of the cell body; The electrode surface temperature data, the cell thickness direction temperature data and the cell body surface temperature data are obtained at a set period; The electrode surface temperature data, the cell thickness direction temperature data and the cell body surface temperature data of each period are respectively subjected to statistical analysis, and the electrode surface temperature mean square deviation, the cell thickness direction temperature mean square deviation and the cell body surface temperature mean square deviation are calculated; The average gradient of the electrode surface temperature mean square deviation, the average gradient of the cell thickness direction temperature mean square deviation, and the average gradient of the cell surface temperature mean square deviation are calculated. If any of the electrode surface temperature mean square deviation, the cell thickness direction temperature mean square deviation, and the cell surface temperature mean square deviation, or any of the average gradient of the electrode surface temperature mean square deviation, the average gradient of the cell thickness direction temperature mean square deviation, and the average gradient of the cell surface temperature mean square deviation is greater than the corresponding set threshold value, then there is temperature unevenness inside the battery, and if none of them is greater than the corresponding set threshold value, then the temperature inside the battery is uniform.

3. The flow channel thermal management system of a flow battery of claim 1, wherein, The process of screening the required historical battery operation sample data based on the cell thickness direction temperature difference, the cell surface temperature standard deviation, and the electrode surface temperature range is as follows: The first quartile, the third quartile, and the interquartile range of the cell thickness direction temperature difference, the cell surface temperature standard deviation, and the electrode surface temperature range are calculated respectively. If the cell thickness direction temperature difference, the cell surface temperature standard deviation, and the electrode surface temperature range of a certain historical battery operation sample data are greater than or equal to the corresponding third quartile and less than the third quartile plus one and a half times the interquartile range, then it is the required historical battery operation sample data.

4. The flow channel thermal management system of a flow battery of claim 3, wherein, The process of obtaining the battery temperature improvement degree is as follows: The electrode surface temperature mean square deviation, the cell thickness direction temperature mean square deviation, and the cell surface temperature mean square deviation before and after adjustment are calculated respectively. The comprehensive mean square deviations before and after adjustment are obtained by weighted summation of the electrode surface temperature mean square deviation, the cell thickness direction temperature mean square deviation, and the cell surface temperature mean square deviation before and after adjustment respectively. The difference between the comprehensive mean square deviation after adjustment and the comprehensive mean square deviation before adjustment is taken as the battery temperature improvement degree.

5. A flow channel thermal management system for a flow battery according to claim 4, wherein, The process of classifying the operation samples into the electrolyte adjustment group, the cooling medium adjustment group, or the collaborative adjustment group based on the battery temperature improvement degree is as follows: For electrolyte single variable adjustment and cooling medium single variable adjustment, if the corresponding battery temperature improvement degree is greater than K times of the battery temperature improvement degree corresponding to other adjustment modes, then the corresponding battery operation state is classified into the electrolyte adjustment group or the cooling medium adjustment group, otherwise it is classified into the collaborative adjustment group.

6. The flow channel thermal management system of a flow battery of claim 1, wherein, The process of determining the flow rates of the electrolyte and the cooling medium corresponding to the heat dissipation scenario, and the initial temperatures of the electrolyte and the cooling medium based on the electrode surface and cell temperature data, the electrolyte inlet and outlet temperatures, and the cooling medium inlet and outlet temperatures is as follows: The operation samples in the electrolyte adjustment group, the cooling medium adjustment group, or the collaborative adjustment group are clustered respectively with the battery temperature improvement degree as the clustering index, and the cluster center with the maximum average battery temperature improvement degree is determined. For each cluster center, the electrode surface and cell temperature data, the electrolyte inlet and outlet temperatures, and the cooling medium inlet and outlet temperatures of the operation samples therein are taken as the reference standard, and the adjustment parameters corresponding to the operation samples are taken as the pointing values, and a mapping relationship between the reference standard and the pointing values is established. After determining the heat dissipation scenario, the electrode surface and cell temperature data of the battery to be adjusted, the electrolyte inlet and outlet temperatures and the cooling medium inlet and outlet temperatures are calculated with the Euclidean distance to each reference standard to determine the reference standard corresponding to the minimum Euclidean distance, determine the pointing value based on the mapping relationship, and obtain the corresponding electrolyte and cooling medium flow and the initial temperature of the electrolyte and cooling medium.

7. The flow channel thermal management system of a flow battery of claim 1, wherein, After determining the flow and initial temperature, the temperature of the battery interior is monitored for temperature balance, and if the temperature is not balanced, the temperature imbalance type is determined, and the flow and initial temperature are adjusted based on the temperature imbalance type. The electrode surface temperature variance, the cell thickness direction temperature variance and the cell surface temperature variance in the current monitoring period are calculated respectively. If the electrode surface temperature variance, the cell thickness direction temperature variance and the cell surface temperature variance are all less than the corresponding temperature balance threshold, it is determined that the temperature is balanced, otherwise the temperature is not balanced. When the temperature is not balanced, it is determined whether the imbalance type is electrode surface dominant imbalance or thickness direction dominant imbalance or composite imbalance. After determining the temperature imbalance type, the flow is adjusted upwards in priority according to the set flow amplitude, the temperature variance improvement amplitude is calculated, if it is still not balanced, the flow is continuously adjusted upwards, if the temperature variance improvement amplitude decreases, the flow is adjusted back to the flow corresponding to the maximum temperature variance improvement amplitude, and the initial temperature is adjusted downwards according to the set temperature amplitude, until the temperature is balanced.

8. The flow channel thermal management system of a flow battery of claim 7, wherein, The process of determining whether the imbalance type is electrode surface dominant imbalance or thickness direction dominant imbalance or composite imbalance when the temperature is not balanced is as follows: Only when the current electrode surface temperature variance is greater than or equal to the corresponding temperature balance threshold, and the cell thickness direction temperature variance and the cell surface temperature variance are both less than the corresponding temperature balance threshold, it is electrode surface dominant imbalance. Only when the current cell thickness direction temperature variance is greater than or equal to the corresponding temperature balance threshold, and the electrode surface temperature variance and the cell surface temperature variance are both less than the corresponding temperature balance threshold, it is thickness direction dominant imbalance. If the above conditions are not met, it is composite imbalance.

9. A method of thermal management of flow channels of a flow battery, by a system for thermal management of flow channels of a flow battery according to any one of claims 1 to 8, by performing the following steps, The steps include: Determine whether there is temperature imbalance in the battery interior based on the electrode surface and cell temperature data; If there is temperature imbalance, determine the heat dissipation scenario based on the cell temperature data, the charging current density and the cooling medium outlet temperature; Determine the flow of the electrolyte and the cooling medium corresponding to the heat dissipation scenario and the initial temperature of the electrolyte and the cooling medium based on the electrode surface and cell temperature data, the electrolyte inlet and outlet temperatures and the cooling medium inlet and outlet temperatures; After determining the flow and initial temperature, monitor the temperature of the battery interior for temperature balance, and if the temperature is not balanced, determine the temperature imbalance type and adjust the flow and initial temperature based on the temperature imbalance type.

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