A lithium ion battery pack charging and discharging working temperature monitoring system

Abstract

The application discloses a lithium ion battery pack charging and discharging working temperature monitoring system and relates to the field of battery pack charging and discharging working temperature monitoring. The lithium ion battery pack charging and discharging working temperature monitoring system comprises the following steps: collecting two-dimensional temperature distribution images of the battery pack by a miniature thermal imager according to the geometric shape difference of the battery pack, extracting the highest working temperature and the maximum temperature difference between the battery cells in real time, and generating a life stress index representing the life attenuation rate of the battery; generating a hierarchical phase change intervention strategy based on the change trajectory and the cumulative value of the life stress index, outputting a differentiated trigger signal according to the hierarchical phase change intervention strategy, and finally implementing maximum power heat absorption on the highest temperature area and gradient power phase change on the area causing the temperature difference, so that the thermal state of the lithium ion battery pack is perceived, early warning of the life attenuation is realized, and the cooling resources are distributed on demand, thereby effectively improving the thermal safety and service life of the battery pack.

Description

Technical Field

[0001] This invention belongs to the field of battery pack charging and discharging operating temperature monitoring technology, and relates to a lithium-ion battery pack charging and discharging operating temperature monitoring system. Background Technology

[0002] In fields such as new energy vehicles and energy storage power stations, lithium-ion battery packs have become core energy storage components due to their advantages such as high energy density and long cycle life. However, lithium-ion batteries generate a lot of heat during charging and discharging, causing the battery temperature to rise. This not only affects their lifespan but also leads to temperature unevenness between the internal and external cells of the battery pack, thereby reducing overall performance and even causing thermal runaway in extreme cases.

[0003] Currently, existing technologies have proposed methods for monitoring the charging and discharging operating temperature of lithium-ion battery packs. For example, the invention patent with publication number CN118825505B proposes a lithium battery operating temperature management and control system. This system collects lithium battery temperature and performance data, as well as external environmental temperature and humidity data. Based on this, it calculates the current temperature status of the lithium battery, analyzes the ability of the lithium battery to maintain its voltage level under load conditions based on the current temperature status, generates lithium battery state characteristic information, and through real-time dynamic analysis of data, can accurately calculate the temperature status of the lithium battery in the current environment and predict future temperature fluctuations, making temperature control more precise and forward-looking.

[0004] However, although the above-mentioned existing technical solutions have achieved certain results in the management and control of lithium battery operating temperature, they still have the following shortcomings: First, in the actual operation of battery packs, the risk of thermal runaway often stems from the overheating of local cells or the huge temperature difference between cells. Existing technical solutions assess the overall temperature status by calculating the weighted average of lithium battery and external ambient temperature data. This method will mask the temperature peaks of specific cells inside the battery pack and the non-uniformity of spatial temperature distribution. At the same time, the calculation using standardized difference values ​​mainly reflects the deviation of battery temperature from a statistical average level, and cannot actively identify and locate local hot spots and temperature gradients caused by uneven internal resistance, loose connections or differences in cooling conditions. As a result, thermal management measures may lack pertinence and may overlook key cooling areas.

[0005] Secondly, in actual dynamic operation scenarios of lithium-ion battery packs, there are significant differences in the thermal state of different regions. It is necessary to adjust the allocation of cooling resources according to the real-time thermal state. However, the existing technical solutions only calculate the cooling demand index based on the overall temperature threshold deviation, and the environmental factor adjustment only makes uniform adjustments to the temperature threshold and control parameters. They fail to implement differentiated cooling strategies for different thermal state regions and cannot adapt the cooling power in real time, resulting in unreasonable allocation of cooling resources. This may lead to insufficient cooling of local high-temperature areas and over-cooling of non-high-temperature areas, resulting in energy waste. Summary of the Invention

[0006] In view of this, in order to solve the problems mentioned in the background art, the present invention provides a lithium-ion battery pack charging and discharging operating temperature monitoring system.

[0007] The objective of this invention can be achieved through the following technical solution: a lithium-ion battery pack charging and discharging operating temperature monitoring system, comprising: a global sensing module, used to acquire two-dimensional temperature distribution images of the battery pack through a miniature thermal imager.

[0008] The fusion diagnostic module is used to extract the highest operating temperature, the maximum temperature difference between cells, and the temperature change rate in real time based on the two-dimensional temperature distribution image, and generate a life stress index that characterizes the battery life decay rate through fusion calculation.

[0009] The strategy generation module is used to perform time-series analysis on the life stress index. When the change trajectory of the life stress index is detected to show an accelerating upward trend and a graded phase change intervention strategy is generated based on the cumulative integral value of the life stress index.

[0010] The phase change triggering module is used to generate and output differentiated triggering signals that match the thermal state of each battery cell according to the graded phase change intervention strategy.

[0011] The partition execution module is used to implement maximum power phase change heat absorption in the region with the highest operating temperature based on differentiated trigger signals, and to implement gradient power phase change in the region involving the maximum temperature difference between cells.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention combines the differences in the geometric shape of the battery pack and collects two-dimensional temperature distribution images of the battery pack through a micro thermal imager. Based on this, the highest operating temperature and the maximum temperature difference between cells are extracted in real time, and a life stress index characterizing the battery life decay rate is generated. This solves the problem of relying on overall temperature weighted average and standardized difference calculation. It can actively locate local hot spots and areas with excessive temperature difference caused by uneven internal resistance, loose connection or different cooling conditions. This provides a reliable data basis for subsequent targeted thermal management and overcomes the defects of the prior art that rely on overall temperature weighted average.

[0013] (2) The present invention generates a graded phase change intervention strategy based on the change trajectory and cumulative value of the lifetime stress index, and outputs differentiated trigger signals accordingly. Finally, the maximum power heat absorption is implemented in the highest temperature region, and the gradient power phase change is implemented in the region that constitutes the temperature difference. This solves the problem of unreasonable allocation of cooling resources caused by uniform adjustment based only on the overall deviation, ensures priority cooling of key high temperature regions, effectively suppresses the risk of thermal runaway, and avoids over-cooling of non-critical regions, thereby improving the thermal consistency and life of the battery pack. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram showing the connections of the various modules in the system of the present invention.

[0016] Figure 2 This is a flowchart for determining the accelerated upward trend of the life stress index change trajectory in this invention.

[0017] Figure 3 The flowchart shows the generation of the life stress index intervention strategy and the setting of intervention priorities in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 As shown, this invention provides a lithium-ion battery pack charging and discharging operating temperature monitoring system, comprising: a global sensing module, a fusion diagnostic module, a strategy generation module, a phase change triggering module, and a partition execution module. All modules are connected in the order described above.

[0020] The global sensing module is used to acquire two-dimensional temperature distribution images of the battery pack using a miniature thermal imager.

[0021] Because different lithium-ion battery packs have significant differences in geometry and size, their surfaces may exhibit different structural features such as flat, curved, or angular surfaces. If a uniform deployment method is used for miniature thermal imagers, problems such as imaging angle deviation or incomplete coverage of the temperature measurement area may cause the acquired temperature images to fail to accurately reflect the actual temperature state of each area of ​​the battery pack, resulting in distortion of key parameters such as maximum operating temperature and temperature difference between cells.

[0022] To address this issue and ensure that the two-dimensional temperature distribution image can comprehensively capture temperature information at various locations within the battery pack, a miniature thermal imager deployment and image acquisition rule is used to address different surface structural features of the battery pack. Specifically, based on the geometric shape and size of the battery pack, it is determined whether the surface of the battery pack is curved, angular, or planar.

[0023] The process of determining whether the surface of a battery pack has curved or angular structures is achieved through the following steps: First, a three-dimensional geometric model of the battery pack to be determined is obtained. If no digital model of the battery pack is available, the battery pack entity is scanned using a three-dimensional scanning device to obtain three-dimensional point cloud data of its surface.

[0024] Subsequently, surface characteristic analysis is performed on the 3D geometric model or 3D point cloud data to calculate the curvature of each region on the outer surface of the battery pack. The curvature of each region is compared with a predefined curvature threshold: if the curvature of a region is consistently greater than the curvature threshold, the region is determined to be a curved structure; if a region is the boundary between two adjacent planes and its curvature changes abruptly, the region is determined to be an angular structure; if the curvature of all regions is consistently less than or equal to the curvature threshold, the surface of the battery pack is determined to be a planar structure.

[0025] It should be added that the above-mentioned predefined curvature threshold is determined with reference to the curvature characteristics of common lithium-ion battery pack structures, such as the surface curvature of a cylindrical cell module of approximately 0.02 to 0.05. The curvature of the transition area at the corners of the square battery cell module is approximately 0.01 to 0.03. The lower limit of the curvature range of the two types of structures mentioned above is taken as the predefined curvature threshold.

[0026] If the surface of the battery pack is a planar structure, then a placement point is set at the center of each planar area of ​​the battery pack.

[0027] The thermal conductivity of a planar structure is relatively uniform, and the central position can be used as a characteristic point of the temperature field in the planar region. It can achieve representative acquisition of the overall temperature state of the planar region with the fewest possible points, avoiding the increased cost and data processing load caused by redundant points.

[0028] If the surface of the battery pack is curved or has sharp edges, then according to the curvature or number of edges, additional placement points are added at the curved or sharp edges along the tangent plane and the normal direction.

[0029] Curved surfaces can lead to heat accumulation due to curvature variations. For example, areas with greater curvature are prone to forming localized high-temperature points. Angular structures can exhibit differences in heat transfer gradients due to abrupt structural changes. For example, large temperature differences can easily occur on both sides of an angular structure. Adding points along the tangential plane can cover the temperature field of different curvature segments of the surface and different extension directions of the angular structure. Adding points along the normal direction can capture the local vertical temperature gradient of the surface or angular structure, avoiding the omission of key temperature information such as curvature-related high-temperature points and angular gradient temperature differences due to single or few point placements.

[0030] The miniature thermal imager is fixed at the deployment point, and a mechanical bracket is used to make the optical axis of each miniature thermal imager perpendicular to the tangent plane of the corresponding area on the surface of the battery pack, triggering the miniature thermal imager to acquire two-dimensional temperature distribution images of the battery pack.

[0031] The fusion diagnostic module is used to extract the highest operating temperature, the maximum temperature difference between cells, and the temperature change rate in real time based on the two-dimensional temperature distribution image, and to generate a life stress index that characterizes the battery life decay rate through fusion calculation.

[0032] Considering that the maximum operating temperature directly reflects whether the battery pack faces the risk of local overheating, overheating can accelerate electrolyte decomposition and electrode material aging, shortening battery life. The maximum temperature difference between cells reflects the thermal consistency level of each cell. Excessive temperature difference can easily lead to uneven charging and discharging, causing local overcharging or over-discharging, increasing safety hazards. The temperature change rate characterizes the dynamic evolution trend of the battery pack's thermal state. Sudden rises and falls in temperature may indicate abnormal faults such as internal short circuits or poor contact.

[0033] Based on this, the steps to extract the highest operating temperature, the maximum temperature difference between cells, and the rate of temperature change are as follows: the two-dimensional temperature distribution image of the battery pack is parsed into a digital temperature field matrix containing position coordinates and temperature values.

[0034] Iterate through all temperature values ​​in the digital temperature field matrix and output the maximum value as the real-time highest operating temperature of the battery pack.

[0035] Based on the cell locations marked in the two-dimensional temperature distribution image of the battery pack, the temperature value of each cell location is extracted from the digital temperature field matrix to determine the highest temperature of each cell.

[0036] The specific steps for calibrating the cell positions in the two-dimensional temperature distribution image of the battery pack are as follows: First, obtain the cell specifications, assembly layout scheme, and three-dimensional assembly drawings of the battery pack to be calibrated. Construct a two-dimensional physical coordinate system with the top left corner of the top surface of the battery pack as the origin, the horizontal direction as the X-axis, and the vertical direction as the Y-axis. Calculate the physical boundary coordinates of each cell and form a cell number-physical boundary coordinate correspondence table.

[0037] The two-dimensional temperature distribution image is then imported into the image processing system. A pixel coordinate system is established with the pixel at the top left corner of the image as the origin. At least three uniformly distributed fixed reference marks are selected in the non-cell area of ​​the battery pack. Their physical coordinates are measured and the corresponding pixel positions are located through image recognition algorithms. A linear mapping relationship between physical coordinates and pixel coordinates is constructed. Based on this mapping relationship, the physical boundary coordinates of the cells are converted into image pixel coordinates. The converted coordinates are fitted with a rectangular contour to determine the pixel contour range of each cell in the image. A cell number is assigned to each contour range.

[0038] Finally, select at least two cells from the edge and center of the battery pack and measure their surface temperature using an infrared thermometer. Compare the temperature with the average temperature within the corresponding cell pixel contour range extracted from the calibration mapping table. If the deviation exceeds ±0.5℃, readjust the reference mark positioning, coordinate mapping parameters, etc., and repeat the above steps until the deviation meets the temperature measurement accuracy requirements of the thermal imager, thus completing the final calibration of the cell position.

[0039] The highest temperatures of all battery cells are grouped into a set. The maximum and minimum values ​​are found by comparison in this set. The difference between the maximum and minimum values ​​is calculated, and the output is the maximum temperature difference between the battery cells.

[0040] Obtain two-dimensional temperature distribution images of the battery pack at the current moment and the previous moment, and calculate the change in temperature value at the same location point per unit time.

[0041] Iterate through all locations and find the temperature change with the largest absolute value, which is taken as the temperature change rate of the battery pack.

[0042] The steps for generating the life stress index, which characterizes the rate of battery life degradation, are as follows: divide the maximum operating temperature, the maximum temperature difference between cells, and the rate of temperature change by their respective preset reference values ​​to obtain three corresponding dimensionless values.

[0043] It should be noted that the maximum operating temperature reference value should be based on the cell specification sheet first, and the upper limit of the rated operating temperature indicated in the specification sheet should be used directly. If the specification sheet does not specify, refer to the temperature safety operating limit of this type of battery pack in the corresponding industry standard and take the smaller value between the two.

[0044] The maximum temperature difference between cells is directly adopted from the recommended limit for thermal consistency of cells as specified in the industry standard, such as less than or equal to 5°C. If the battery pack specification sheet has temperature difference requirements, the specification sheet shall prevail.

[0045] The benchmark value for temperature change rate is first selected from the normal maximum temperature change rate under the rated charge and discharge rate in the cell specification, and then the temperature change rate warning threshold in the industry thermal fault warning standard is referenced, and the smaller value between the two is taken.

[0046] The three dimensionless values ​​mentioned above are used as three components and combined in sequence to generate a three-dimensional stress state vector.

[0047] Specifically, the dimensionless value of the highest operating temperature is used as the first component of the three-dimensional stress state vector, the dimensionless value of the maximum temperature difference between cells is used as the second component, and the dimensionless value of the temperature change rate is used as the third component. The three-dimensional stress state vector is formed by combining the first, second, and third components in the order of the first, second, and third components, and is denoted as [α, β, γ], where α is the dimensionless value of the highest operating temperature, β is the dimensionless value of the maximum temperature difference between cells, and γ is the dimensionless value of the temperature change rate.

[0048] Call a predefined fusion transformation matrix with non-zero off-diagonal elements, perform matrix multiplication between the stress state vector and the fusion transformation matrix, and obtain the three-dimensional fusion result vector.

[0049] The predefined fusion transformation matrix with non-zero off-diagonal elements is of order 3×3, matching the dimension of the three-dimensional stress state vector [α, β, γ]. It is obtained through fitting with a large amount of experimental data: First, multivariate orthogonal experiments are designed for the three parameters, covering different parameter level combinations, such as α ranging from 0.4 to 1.4, β from 0.2 to 1.2, and γ from 0.3 to 1.3. Battery life degradation rate data are collected for each set of experiments, forming a sample set of the three parameter combinations and life degradation rate. Then, a positive correlation is constructed between the magnitude of the matrix-stress vector multiplication result and the life degradation rate. The least squares method is used to fit the sample set, and the initial values ​​of the diagonal and off-diagonal elements of the matrix are obtained through inversion. Finally, the fitting is tested through parameter combinations in non-orthogonal experiments, and the element values ​​are fine-tuned until the correlation between the theoretically calculated life stress index and the actual life degradation rate meets the standard, such as a Pearson correlation coefficient greater than or equal to 0.92. Finally, the optimized matrix is ​​predefined and fixed in the system.

[0050] A preferred example of a fusion transformation matrix is: .

[0051] The magnitude of the 3D fusion result vector is calculated and used as the life stress index.

[0052] The life stress index is a dimensionless comprehensive index used to quantify the life decay rate of lithium-ion battery packs under the combined effects of the three thermal stresses of maximum operating temperature, maximum temperature difference between cells, and temperature change rate during charging and discharging.

[0053] The strategy generation module is used to perform time-series analysis on the lifetime stress index. When the change trajectory of the lifetime stress index is detected to show an accelerating upward trend, a graded phase change intervention strategy is generated based on the cumulative integral value of the lifetime stress index.

[0054] The life stress index directly reflects the life evolution trend of the battery pack under thermal stress. If the index only rises slowly, it indicates that the battery is in the normal aging stage. If it rises at an accelerated rate, it indicates that the thermal stress coupling effect is intensified and the battery has entered a rapid life decay channel. Timely intervention is required to avoid a sudden drop in life or safety risks.

[0055] See Figure 2 As shown, the steps to determine the accelerating upward trend of the life stress index change trajectory are as follows: continuously receive the life stress index in chronological order to form a life stress index time series.

[0056] Based on the life stress index time series, the difference between the average values ​​of the life stress index within the two most recent consecutive equal time windows is calculated as the recent first-order rate of change.

[0057] The time window can be set according to the battery pack's operating cycle, such as 5 minutes or 10 minutes, but it is not limited to this and can be adjusted according to the battery pack's operating status.

[0058] The average life stress index within one equal-length window preceding the two most recent consecutive equal-length windows is calculated, and the difference between this and the average of the first window of the two equal-length windows is used as the historical first-order rate of change.

[0059] If the recent first-order rate of change is greater than zero and the recent first-order rate of change is greater than the historical first-order rate of change, then the trajectory of the life stress index is determined to show an accelerating upward trend.

[0060] The first-order rate of change is used to characterize the average trend of the life stress index within the corresponding time window. A recent first-order rate of change greater than zero indicates that the life stress index is on the rise in the current stage. A recent first-order rate of change greater than the historical first-order rate of change indicates that the average speed of the index increase has further accelerated compared to the previous period, reflecting the characteristic of accelerated upward trend and capturing the turning point of the life stress index from a gradual increase to a rapid increase.

[0061] The calculation steps for the cumulative integral value of the life stress index are as follows: After the life stress index shows an accelerated upward trend, the life stress index value at the end of the preset period is calculated using the recent first-order rate of change as the basic rate of change.

[0062] It should be added that the preset cycle is determined based on the single charge-discharge cycle time of the battery and the daily operation and maintenance monitoring cycle of the energy storage battery.

[0063] By connecting the current life stress index value with the life stress index value at the end of the preset period, a data sequence is formed that extends from the current moment to the end of the preset period. Extrapolating future stress change trends based on historical data and the current state allows the system to identify accelerated degradation trends in advance, before the actual occurrence of thermal runaway risk.

[0064] The data sequence is integrally calculated over the entire time period from the current moment to the end of the preset period, and the result is used as the cumulative integral value of the life stress index.

[0065] See Figure 3 As shown, the region with the highest operating temperature is the core source of local overheating risk, which can directly lead to safety issues such as electrolyte decomposition and thermal runaway. The severity of this risk is higher than that of the temperature difference region between battery cells. The high-temperature side region with the largest temperature difference between battery cells is key to exacerbating charging-discharging imbalance and localized aging, and therefore requires secondary priority treatment. To avoid resource waste and disordered intervention, the following graded phase change intervention strategy steps are formulated: If the lifetime stress index shows an accelerating upward trend, but the cumulative integral value of the lifetime stress index does not exceed the damage tolerance, a warning-level intervention strategy is generated; this aims to achieve a balance between early risk warning and avoiding excessive consumption of phase change materials and system energy waste.

[0066] The aforementioned damage tolerance is a preset cumulative integral threshold for the life stress index, set based on experimental data of battery pack material characteristics: First, by using specifications and basic tests provided by the battery pack's core material suppliers, the material's tolerance limit parameters, such as the critical temperature for high-temperature aging of the cathode and the electrolyte decomposition initiation temperature, are obtained. Then, multi-gradient thermal stress conditions covering the material's tolerance range, near the tolerance limit, and beyond the tolerance limit are designed, and accelerated aging tests are conducted on the same batch of battery packs, simultaneously monitoring the cumulative integral value of the life stress index, the battery capacity decay rate, and the internal resistance growth rate. When the battery capacity decay rate reaches 20% or the internal resistance growth rate exceeds 50%, the integral value at this point is extracted as the critical integral value for material damage. After averaging the critical integral values ​​of all conditions, a safety redundancy coefficient of 5% to 10% is added to obtain the damage tolerance. Finally, 3 to 5 sets of actual application conditions are selected for verification to confirm the damage tolerance. For example, for a typical lithium-ion battery pack, based on accelerated aging tests, the damage tolerance can be set to 1000, but the actual value needs to be adjusted according to the battery material characteristics.

[0067] If the life stress index shows an accelerating upward trend and the cumulative integral value of the life stress index exceeds the damage tolerance, an intervention level intervention strategy is generated to quickly curb further damage accumulation and avoid a sudden drop in battery life or the occurrence of safety hazards.

[0068] For the intervention level strategy, the region with the highest operating temperature is identified as the first priority region, and the high-temperature side region that constitutes the largest temperature difference between cells is identified as the second priority region.

[0069] The area with the highest operating temperature is identified as the first priority area because it is the core thermal risk source that directly causes safety hazards such as electrolyte decomposition and thermal runaway, and has the highest degree of harm.

[0070] The high-temperature side region, which constitutes the largest temperature difference between battery cells, is identified as the second priority region because it mainly exacerbates the imbalance between charging and discharging and local aging, and its harm is secondary to that of the former. Identifying it according to this priority can achieve priority control of core risks and effectively curb the accumulation of damage and the spread of safety risks.

[0071] Set the intervention priority of the first priority area to the highest level, and the intervention priority of the second priority area to the second highest level.

[0072] The phase change triggering module is used to generate and output differentiated triggering signals that match the thermal state of each battery cell according to the graded phase change intervention strategy.

[0073] Specifically, the intervention level, target area identifier, and corresponding intervention priority information contained in the graded phase change intervention strategy are analyzed.

[0074] If the strategy is a warning level, a unified low-level warning signal will be generated.

[0075] This low-level warning signal triggers two types of response actions: first, local warning indicator components within the system, such as warning LEDs, illuminate upon receiving the low-level signal; second, remote monitoring terminals, such as the battery management system (BMS) host computer, display the warning information in a pop-up window upon receiving the signal. Simultaneously, this low-level warning signal serves only as a risk alert trigger signal and does not trigger high-intensity intervention actions such as phase change material release or high-power operation of directional cooling fans, thus achieving the intended warning level positioning.

[0076] If the strategy is intervention level, then according to the target area priority, the control parameters of the maximum power level are mapped to the first priority area, and the control parameters of the medium power level are mapped to the second priority area, thereby generating an electronic trigger signal that includes the phase change power level.

[0077] The final determination of the medium power level needs to be verified through heat load matching tests. For example, if the heat load of the second priority area is only 50% of that of the first priority area, the medium power level can be set to 50% of the maximum power to ensure that the power matches the heat load and avoid excessive intervention.

[0078] If the rated power of the equipment is limited, such as a certain model of liquid cooling pump having a maximum power of only 150W, then the medium power level needs to be adjusted within the rated power range of the equipment, such as not exceeding 100W, while also meeting the requirement of being lower than the power of the first priority zone. In summary, the medium power level is usually 40% to 60% of the maximum power of the first priority zone.

[0079] The partition execution module is used to implement maximum power phase change heat absorption in the region with the highest operating temperature based on differentiated trigger signals, and to implement gradient power phase change in the region involving the maximum temperature difference between cells.

[0080] The aforementioned phase change material can be organic or inorganic. Its phase change temperature should match the battery's operating temperature range. The control mechanism adjusts the phase change power through electronic trigger signals, such as through a PID controller.

[0081] Since the area with the highest operating temperature is the core source of local overheating in the battery pack, its high temperature environment can easily trigger irreversible safety hazards such as electrolyte decomposition, electrode material pulverization, and thermal runaway. It is necessary to maximize the heat absorption efficiency per unit time through maximum power phase change heat absorption to achieve rapid cooling and curb the spread of safety risks.

[0082] Based on this, the maximum power phase change heat absorption steps for the region with the highest operating temperature are as follows: Based on the differentiated trigger signal, identify the battery cell or region that is calibrated as the first priority region.

[0083] Apply a phase change trigger signal of maximum power level to the identified target area to activate the phase change material in that area to enter the maximum power endothermic state.

[0084] The target area is the region with the highest operating temperature. Its high-temperature environment can easily trigger irreversible reactions such as electrolyte decomposition and accelerated pulverization of electrode materials, and may even lead to thermal runaway. It is necessary to curb the temperature rise with the highest efficiency. The maximum power heat absorption state of the phase change material corresponds to the peak value of its heat absorption efficiency per unit time. By maximizing the latent heat release rate of the phase change, the excess heat in the target area can be quickly absorbed, so as to achieve rapid temperature drop and stable control, and prevent the risk of high temperature from spreading to the surrounding areas.

[0085] The system continuously acquires real-time temperature data for the area. When the temperature in the area drops to a preset safety threshold, it reduces or stops the phase change heat absorption power in that area.

[0086] Although the target area is cooled by absorbing heat through phase change at maximum power, the battery may generate secondary heat during operation due to load fluctuations and environmental changes. Continuous monitoring can prevent premature shutdown and heat risk rebound, and at the same time prevent excessive heat absorption that could cause the temperature to drop below the battery's optimal operating temperature.

[0087] The preset safety threshold is directly adopted from the battery safety temperature range recommended by national standards.

[0088] In addition, the risks in the area involving the maximum temperature difference between cells are mainly manifested as uneven charging and discharging and accelerated local aging caused by the temperature difference. This risk is gradual and its severity is lower than that of direct overheating. Gradient power phase change can meet the temperature control requirements of this area and reduce the temperature difference while avoiding excessive consumption of phase change materials and waste of system energy due to excessive intervention. Therefore, the steps for implementing gradient power phase change in the area involving the maximum temperature difference between cells are as follows: Based on the differentiated trigger signal, identify the high-temperature side unit group and the low-temperature side unit group that constitute the maximum temperature difference between cells.

[0089] More specifically, the temperature data of all cells in the battery pack are collected in real time. After basic noise reduction processing, the temperature of each cell is compared pairwise to calculate the temperature difference between all cells and select the maximum temperature difference. Based on the maximum temperature difference, cells whose temperature is at the upper limit of the maximum temperature difference and are spatially adjacent are classified as high-temperature candidate units, and cells whose temperature is at the lower limit of the maximum temperature difference and are spatially adjacent are classified as low-temperature candidate units.

[0090] High-level cooling power is allocated to the high-temperature side unit group, and low-level cooling power is allocated to the low-temperature side unit group.

[0091] Allocating high-level cooling power to the high-temperature side cell group is to quickly reduce its temperature to curb the risk of electrode material aging, electrolyte decomposition and thermal runaway caused by overheating, and to reduce the temperature difference with the low-temperature side to improve the charging and discharging balance of the battery pack.

[0092] The reason for allocating low-level cooling power to the low-temperature side cell group is that there is no significant risk of overheating in this area. Low power is sufficient to meet the needs of reducing the temperature difference, while avoiding excessive cooling that would cause the temperature to drop below the battery's optimal operating temperature and lead to performance degradation.

[0093] Within the preset monitoring period, the maximum temperature difference between battery cells is continuously calculated. If the current temperature difference is greater than the temperature difference in the previous period, it is determined to be an increasing trend of temperature difference; otherwise, it is determined to be a decreasing or stable trend of temperature difference.

[0094] It should be noted that the preset monitoring cycle is obtained by using basic experiments to obtain the average thermal response time of the battery cell from temperature change to stabilization under charging and discharging conditions. One-third to one-half of this average thermal response time is taken as the base cycle to ensure timely capture of dynamic changes in temperature difference. Combined with the response time of the cooling system from startup to reaching the target cooling power, the base cycle is made not less than this response time to avoid misjudgment of trends due to ineffective cooling. Finally, the preset monitoring cycle is determined, for example, 30 seconds to 1 minute.

[0095] When a trend of increasing temperature difference is determined, the cooling power level of the high-temperature side cell group is increased, or the cooling power level of the low-temperature side cell group is maintained. The increase in temperature difference can be directly slowed down by accelerating the heat removal rate of the high-temperature side, and the cooling power level of the low-temperature side cell group can be maintained. This can avoid the performance degradation caused by the low-temperature side temperature falling below the battery's optimal operating temperature due to excessive cooling, and at the same time prevent the low-temperature side temperature from further decreasing and exacerbating the temperature difference.

[0096] When the temperature difference is determined to be decreasing or stabilizing, the current cooling power level of each cell group is maintained. The current power is sufficient to ensure the thermal balance of the battery pack, which can avoid the increase in system energy consumption and operation fluctuations caused by additional adjustment of cooling power, thereby continuously ensuring the thermal stability of the battery pack.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 lithium-ion battery pack charging and discharging operating temperature monitoring system, characterized in that: include: The global sensing module is used to acquire two-dimensional temperature distribution images of the battery pack using a miniature thermal imager; The fusion diagnostic module is used to extract the highest operating temperature, the maximum temperature difference between cells and the temperature change rate in real time based on the two-dimensional temperature distribution image, and generate a life stress index that characterizes the battery life decay rate through fusion calculation. The strategy generation module is used to perform time-series analysis on the life stress index. When the change trajectory of the life stress index is detected to show an accelerating upward trend, a graded phase change intervention strategy is generated based on the cumulative integral value of the life stress index. The phase change triggering module is used to generate and output differentiated triggering signals that match the thermal state of each battery cell according to the graded phase change intervention strategy. The partition execution module is used to implement maximum power phase change heat absorption in the region with the highest operating temperature based on differentiated trigger signals, and to implement gradient power phase change in the region involving the maximum temperature difference between cells. The full-domain perception module is as follows: Based on the geometric shape and size of the battery pack, it determines whether the surface of the battery pack is curved, angular, or planar; if the surface of the battery pack is planar, it sets a deployment point at the center of each planar area of ​​the battery pack. If the surface of the battery pack is curved or has sharp edges, then according to the curvature or number of edges, add points along the tangent plane and normal direction at the curved or sharp edges. The miniature thermal imager is fixed at the deployment point, and the optical axis of each miniature thermal imager is perpendicular to the tangent plane of the corresponding area on the surface of the battery pack by a mechanical bracket, triggering the miniature thermal imager to acquire two-dimensional temperature distribution images of the battery pack. The steps for extracting the maximum operating temperature, the maximum temperature difference between cells, and the rate of temperature change are as follows: The two-dimensional temperature distribution image of the battery pack is parsed into a digital temperature field matrix containing position coordinates and temperature values; all temperature values ​​in the digital temperature field matrix are traversed, and the maximum value is output as the real-time maximum operating temperature of the battery pack; based on the cell positions marked in the two-dimensional temperature distribution image of the battery pack, the temperature value of each cell position is extracted from the digital temperature field matrix to determine the maximum temperature of each cell; the maximum temperatures of all cells are grouped into a set, and the maximum and minimum values ​​are found by comparison within this set, the difference between the maximum and minimum values ​​is calculated, and the result is output as the maximum temperature difference between cells; the two-dimensional temperature distribution images of the battery pack at the current moment and the previous moment are obtained, and the change in temperature value at the same location point per unit time is calculated; all location points are traversed, and the temperature change with the largest absolute value is found as the rate of temperature change of the battery pack; The steps for generating the life stress index, which characterizes the rate of battery life degradation, are as follows: Divide the maximum operating temperature, the maximum temperature difference between cells, and the temperature change rate by their respective preset benchmark values ​​to obtain three corresponding dimensionless values; combine these three dimensionless values ​​as three components in sequence to generate a three-dimensional stress state vector; call a predefined fusion transformation matrix with non-zero off-diagonal elements, and perform matrix multiplication on the stress state vector and the fusion transformation matrix to obtain a three-dimensional fusion result vector; calculate the magnitude of the three-dimensional fusion result vector as the life stress index. The steps for generating a graded phase change intervention strategy are as follows: If the lifetime stress index shows an accelerating upward trend, but the cumulative integral value of the lifetime stress index does not exceed the damage tolerance, a warning-level intervention strategy is generated; if the lifetime stress index shows an accelerating upward trend and the cumulative integral value of the lifetime stress index exceeds the damage tolerance, an intervention-level intervention strategy is generated; for the intervention-level strategy, the region where the highest operating temperature is located is identified as the first priority region, and the high-temperature side region that constitutes the largest temperature difference between cells is identified as the second priority region; the intervention priority of the first priority region is set to the highest level, and the intervention priority of the second priority region is set to the second highest level.

2. The lithium-ion battery pack charging and discharging operating temperature monitoring system according to claim 1, characterized in that: The steps to determine if the life stress index shows an accelerating upward trend are as follows: The life stress index is continuously received in chronological order to form a life stress index time series. Based on the life stress index time series, the difference between the average values ​​of the life stress index within the two most recent consecutive equal time windows is calculated as the recent first-order rate of change. The average life stress index within one equal-length window preceding the two most recent consecutive equal-length windows is calculated, and the difference between this and the average of the first window of the two equal-length windows is used as the historical first-order rate of change. If the recent first-order rate of change is greater than zero and is greater than the historical first-order rate of change, then the trajectory of the life stress index is determined to show an accelerating upward trend.

3. The lithium-ion battery pack charging and discharging operating temperature monitoring system according to claim 1, characterized in that: The steps for calculating the cumulative integral value of the life stress index are as follows: After the life stress index shows an accelerated upward trend, the life stress index value at the end of the preset period is calculated using the recent first-order rate of change as the basic rate of change. Connect the current life stress index value with the life stress index value at the end of the preset period to form a data sequence that extends from the current moment to the end of the preset period in time. The data sequence is integrally calculated over the entire time period from the current moment to the end of the preset period, and the result is used as the cumulative integral value of the life stress index.

4. The lithium-ion battery pack charging and discharging operating temperature monitoring system according to claim 3, characterized in that: The steps for generating the differentiated trigger signal are as follows: The intervention level, target area identifier, and corresponding intervention priority information contained in the graded phase change intervention strategy are analyzed. If the strategy is a warning level, a unified low-level warning signal will be generated; If the strategy is intervention level, then according to the target area priority, the control parameters of the maximum power level are mapped to the first priority area, and the control parameters of the medium power level are mapped to the second priority area, thereby generating an electronic trigger signal that includes the phase change power level.

5. The lithium-ion battery pack charging and discharging operating temperature monitoring system according to claim 1, characterized in that: The steps for implementing maximum power phase change heat absorption in the region of highest operating temperature are as follows: Based on the differentiated trigger signal, identify the battery cell or region that is marked as the first priority region; Apply a phase change trigger signal of maximum power level to the identified target area to activate the phase change material in that area to enter the maximum power endothermic state; The system continuously acquires real-time temperature data for the area. When the temperature in the area drops to a preset safety threshold, it reduces or stops the phase change heat absorption power in that area.

6. The lithium-ion battery pack charging and discharging operating temperature monitoring system according to claim 1, characterized in that: The steps for implementing gradient power phase transition in the region involving the maximum temperature difference between battery cells are as follows: Based on the differentiated trigger signals, identify the high-temperature side unit group and the low-temperature side unit group that constitute the largest temperature difference between the cells; High-level cooling power is allocated to the high-temperature side unit group, and low-level cooling power is allocated to the low-temperature side unit group. Within the preset monitoring period, the maximum temperature difference between the cells is continuously calculated. If the current temperature difference is greater than the temperature difference in the previous period, it is determined to be an increasing trend of temperature difference; otherwise, it is determined to be a decreasing or stable trend of temperature difference. When it is determined that the temperature difference is widening, the cooling power level of the high-temperature side unit group is increased, or the cooling power level of the low-temperature side unit group is maintained. When the temperature difference is determined to be decreasing or stabilizing, the current cooling power level of each unit group is maintained.

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