New energy vehicle battery heat dissipation methods, systems, equipment and media

Abstract

This application provides a method, system, device, and medium for heat dissipation of new energy vehicle batteries. It identifies hotspot areas with abnormal temperatures and multiple normal temperature zones within a target battery pack. Based on the thermal diffusion characteristics of the hotspot areas and the current operating status of the new energy vehicle, it analyzes the thermal regulation load of the hotspot areas, thereby obtaining the cooling mode of the target battery pack. It determines the critical deviation for thermal runaway in the hotspot areas by combining the temperature rise gradient of the hotspot areas with the heat dissipation change characteristics of each normal temperature zone under the cooling mode. It determines the heat dissipation compensation gain of the hotspot areas based on the heat dissipation contribution of each thermal gradient distribution characteristics to the battery pack's heat dissipation process and the critical deviation for thermal runaway. Finally, it adjusts the heat dissipation temperature of the hotspot areas based on the heat dissipation compensation gain. The technical solution provided by this application can identify the thermal diffusion pattern of abnormal temperature areas within the battery pack and the thermal influence relationship with the surrounding normal temperature zones.

Description

Technical Field

[0001] This application relates to the field of battery temperature control technology, and more specifically, to a method, system, device, and medium for heat dissipation of new energy vehicle batteries. Background Technology

[0002] Battery performance is crucial in various electronic devices and new energy applications. During charging and discharging, internal chemical reactions generate heat, leading to temperature changes. If the temperature is too high, the battery life will be significantly shortened, or even thermal runaway. If the temperature is too low, the battery capacity will decrease, and the charging and discharging efficiency will be greatly reduced. As the energy density of batteries continues to increase, the heat generation problem becomes more and more significant. Therefore, precise and efficient battery temperature control has become the key to ensuring battery performance, extending service life, and improving safety.

[0003] In existing battery temperature control systems, temperature sensors are typically used to monitor battery temperature in real time. When the temperature exceeds the safe range, the system activates countermeasures. For example, when the temperature is too high, the air-cooling system uses a fan to circulate air and remove heat, while the liquid-cooling system transfers heat to the radiator through coolant circulation. When the temperature is too low, resistance heating elements are energized to generate heat and raise the battery temperature, thus ensuring the battery is in optimal operating condition. However, in the heat dissipation process of new energy vehicle batteries, due to the large number of individual battery cells and their complex spatial layout within the battery pack, it is difficult to identify the thermal diffusion patterns of abnormal temperature areas within the battery pack and their thermal impact on the surrounding normal temperature zones. This leads to inaccurate temperature control in abnormal temperature areas, thereby reducing the precision of battery heat dissipation. Therefore, identifying the thermal diffusion patterns of abnormal temperature areas within the battery pack and their thermal impact on the surrounding normal temperature zones, and thus achieving precise temperature control in abnormal temperature areas, has become a challenge for the industry. Summary of the Invention

[0004] This application provides a method, system, device, and medium for heat dissipation of new energy vehicle batteries, which can identify the thermal diffusion pattern of abnormal temperature areas within the battery pack and the thermal influence relationship between the surrounding normal temperature zones.

[0005] In a first aspect, this application provides a method for heat dissipation of a new energy vehicle battery, comprising the following steps:

[0006] Identify hotspots of abnormal temperatures and multiple normal temperature zones within the target battery pack of a new energy vehicle;

[0007] Based on the thermal diffusion characteristics of the temperature in the hot spot area and the current operating status of the new energy vehicle, the thermal regulation load of the current hot spot area is analyzed, and then the cooling mode corresponding to the target battery pack is selected according to the thermal regulation load.

[0008] When the target battery pack is in the cooling mode, the temperature rise gradient of the hot spot area is monitored in real time. The thermal state of the current hot spot area is predicted by combining the temperature rise gradient with the heat dissipation change characteristics of each normal temperature zone in the cooling mode, and then the thermal runaway critical deviation of the temperature in the hot spot area is obtained.

[0009] Thermal gradient distribution features are extracted from the thermal distribution maps of each normal temperature zone. Then, based on the thermal gradient distribution features, the heat dissipation contribution of the target battery pack heat dissipation process and the thermal runaway critical deviation are used to perform heat dissipation compensation analysis on the heat dissipation temperature of the hot spot area, and the heat dissipation compensation gain of the hot spot area is obtained.

[0010] The heat dissipation temperature of the hot spot area is adjusted according to the heat dissipation compensation gain.

[0011] In some embodiments, identifying hotspots with abnormal temperatures and multiple normal temperature zones within a target battery pack in a new energy vehicle specifically includes:

[0012] Acquire temperature data of the target battery pack in a new energy vehicle;

[0013] Construct a temperature distribution matrix for the target battery pack in the new energy vehicle based on the temperature data;

[0014] The deviation of the temperature value at each monitoring point in the temperature distribution matrix from the temperature threshold is calculated, thereby obtaining the temperature deviation matrix;

[0015] The regions in the temperature deviation matrix where the temperature deviation value is greater than the anomaly detection threshold are constructed as hotspot regions of temperature anomalies within the target battery pack;

[0016] The regions in the temperature deviation matrix that are less than or equal to the anomaly determination threshold are divided into normal temperature zones, thereby obtaining multiple normal temperature zones within the target battery pack.

[0017] In some embodiments, analyzing the thermal control load of the current hotspot area based on the thermal diffusion characteristics of the temperature in the hotspot area and the current operating status of the new energy vehicle specifically includes:

[0018] Extract the time-series temperature data of the hotspot area within the current time period, and determine the thermal diffusion characteristics of the temperature in the hotspot area based on the time-series temperature data;

[0019] A thermal diffusion field model is generated based on the aforementioned thermal diffusion characteristics and the spatial distribution density of battery cells within the hotspot area.

[0020] Obtain the current operating status parameters of the new energy vehicle, and input the operating status parameters into the thermal diffusion field model to extract the temperature change trend;

[0021] The thermal control load of the current hot spot area is calculated based on the temperature change trend.

[0022] In some embodiments, selecting the cooling mode corresponding to the target battery pack based on the thermal regulation load specifically includes:

[0023] Obtain the adaptation parameter library for preset cooling modes;

[0024] The thermal regulation load is matched with the adaptation parameter library, and the cooling mode corresponding to the thermal regulation load is extracted as the cooling mode corresponding to the target battery pack.

[0025] In some embodiments, the thermal state of the current hot spot region is predicted by combining the temperature rise gradient with the heat dissipation change characteristics of each normal temperature zone under the cooling mode, thereby obtaining the critical deviation of thermal runaway temperature in the hot spot region. Specifically, this includes:

[0026] Determine the heat dissipation characteristics of each normal temperature zone under the cooling mode;

[0027] Based on the temperature rise gradient and the heat dissipation change characteristics of each normal temperature zone, establish the heat conduction deviation relationship between the hot spot area and each normal temperature zone.

[0028] Based on the aforementioned heat conduction deviation relationship, predict the change in thermal state of the hot spot region under the current cooling conditions;

[0029] The critical deviation of thermal runaway in the hot spot region is determined based on the thermal state change value and the target thermal equilibrium state of the target battery pack.

[0030] In some embodiments, extracting thermal gradient distribution features from the thermal distribution maps of each normal temperature zone specifically includes:

[0031] Obtain the heat distribution map of each normal temperature zone;

[0032] Select a normal temperature zone as the selected normal temperature zone, and perform spatial temperature difference operation on the heat distribution map corresponding to the selected normal temperature zone to obtain the temperature gradient vector field of the selected normal temperature zone.

[0033] The thermal gradient distribution characteristics of the selected normal temperature zone are determined based on the temperature gradient vector field.

[0034] Continue to determine the thermal gradient distribution characteristics of the remaining normal temperature zones.

[0035] In some embodiments, a temperature sensor array acquires temperature data of a target battery pack in a new energy vehicle.

[0036] Secondly, this application provides a new energy vehicle battery cooling system, comprising:

[0037] The identification module is used to identify hot spots with abnormal temperatures and multiple normal temperature zones within the target battery pack of a new energy vehicle.

[0038] The processing module is used to analyze the thermal control load of the current hot spot area based on the thermal diffusion characteristics of the temperature in the hot spot area and the current operating status of the new energy vehicle, and then select the cooling mode corresponding to the target battery pack according to the thermal control load.

[0039] The processing module is also used to monitor the temperature rise gradient of the hot spot area in real time when the target battery pack is in the cooling mode, and predict the thermal state of the current hot spot area by combining the temperature rise gradient with the heat dissipation change characteristics of each normal temperature zone in the cooling mode, thereby obtaining the thermal runaway critical deviation of the temperature in the hot spot area.

[0040] The processing module is also used to extract thermal gradient distribution features from the thermal distribution maps of each normal temperature zone, and then perform heat dissipation compensation analysis on the heat dissipation temperature of the hot spot area based on the heat dissipation contribution of each thermal gradient distribution feature to the heat dissipation process of the target battery pack and the thermal runaway critical deviation, so as to obtain the heat dissipation compensation gain of the hot spot area.

[0041] The execution module is used to adjust the heat dissipation temperature of the hot spot area according to the heat dissipation compensation gain.

[0042] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described new energy vehicle battery heat dissipation method.

[0043] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for cooling a new energy vehicle battery.

[0044] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0045] The new energy vehicle battery heat dissipation method, system, equipment, and medium provided in this application first identify hotspot areas with abnormal temperatures and multiple normal temperature zones within the target battery pack of the new energy vehicle; secondly, based on the thermal diffusion characteristics of the temperature in the hotspot areas and the current operating state of the new energy vehicle, the thermal regulation load of the current hotspot areas is analyzed, and then the cooling mode corresponding to the target battery pack is selected according to the thermal regulation load; further, when the target battery pack is in the cooling mode, the temperature rise gradient of the hotspot areas is monitored in real time, and the thermal state of the current hotspot areas is predicted by combining the temperature rise gradient with the heat dissipation change characteristics of each normal temperature zone in the cooling mode, thereby obtaining the thermal runaway critical deviation of the temperature in the hotspot areas; then, the thermal gradient distribution characteristics are extracted from the thermal distribution maps of each normal temperature zone, and then the heat dissipation temperature of the hotspot areas is analyzed for heat dissipation compensation based on the heat dissipation contribution of each thermal gradient distribution characteristic to the heat dissipation process of the target battery pack and the thermal runaway critical deviation, thereby obtaining the heat dissipation compensation gain of the hotspot areas; finally, the heat dissipation temperature of the hotspot areas is adjusted according to the heat dissipation compensation gain.

[0046] Therefore, this application can identify the thermal diffusion pattern of abnormal temperature areas within a battery pack and the thermal impact relationship with surrounding normal temperature zones, thereby achieving precise temperature control of abnormal temperature areas. Firstly, identifying hotspots with abnormal temperatures and multiple normal temperature zones within the target battery pack of a new energy vehicle can effectively identify areas with excessively high temperatures that affect overall thermal management safety, providing target locations for implementing focused cooling or localized control. This avoids the identification bias caused by the large number of battery cells and complex spatial layout within the target battery pack. Secondly, based on the thermal diffusion characteristics of the hotspot area and the current operating status of the new energy vehicle, the thermal control load of the current hotspot area is analyzed, upgrading the thermal control behavior from responsive to predictive. This allows the cooling system to intervene proactively based on predicted loads instead of relying on temperature triggers, effectively reducing the risk of thermal runaway. Furthermore, the cooling mode corresponding to the target battery pack is selected according to the thermal control load to provide effective cooling for the hotspot area. Finally, through temperature rise gradient... The thermal state of the current hot spot area is predicted by combining the heat dissipation change characteristics of each normal temperature zone under cooling mode, thereby obtaining the thermal runaway critical deviation of the temperature in the hot spot area. This measures the degree to which the current thermal state deviates from the expected safe operating range of the system, thus providing an evaluation basis for heat dissipation control strategies. Then, thermal gradient distribution characteristics are extracted from the thermal distribution maps of each normal temperature zone. Based on the heat dissipation contribution of each thermal gradient distribution characteristic to the heat dissipation process of the target battery pack and the thermal runaway critical deviation, heat dissipation compensation analysis is performed on the heat dissipation temperature of the hot spot area to obtain the heat dissipation compensation gain of the hot spot area. This identifies the thermal diffusion law of the abnormal temperature area in the target battery pack and the thermal influence relationship of the surrounding normal temperature zones, thereby avoiding inaccurate temperature control of the abnormal temperature area. Finally, the heat dissipation temperature of the hot spot area is adjusted according to the heat dissipation compensation gain. In summary, the technical solution provided by this application can identify the thermal diffusion law of the abnormal temperature area in the battery pack and the thermal influence relationship of the surrounding normal temperature zones, thereby achieving precise temperature control of the abnormal temperature area. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of an application scenario architecture for a new energy vehicle battery heat dissipation method according to some embodiments of this application;

[0048] Figure 2 This is an exemplary flowchart of a new energy vehicle battery heat dissipation method according to some embodiments of this application;

[0049] Figure 3 This is an exemplary flowchart illustrating the determination of the temperature rise gradient according to some embodiments of this application;

[0050] Figure 4This is a schematic diagram of the structure of a new energy vehicle battery cooling system according to some embodiments of this application;

[0051] Figure 5 This is a schematic diagram of the structure of a computer device for implementing a heat dissipation method for a new energy vehicle battery, according to some embodiments of this application. Detailed Implementation

[0052] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] refer to Figure 1 This figure is a schematic diagram of an application scenario architecture for a new energy vehicle battery heat dissipation method according to some embodiments of this application. The application scenario architecture includes a data acquisition terminal, a communication network, a server, and a data storage system. The data acquisition terminal and the server are directly or indirectly connected through the communication network. The data acquisition terminal collects temperature data of the battery pack in the new energy vehicle and uploads it to the server. The server identifies hotspot areas with abnormal temperatures and multiple normal temperature zones within the battery pack based on the temperature data. Based on the thermal diffusion characteristics of the temperature in the hotspot areas and the current operating status of the new energy vehicle, the server analyzes the thermal regulation load of the current hotspot area and then selects the corresponding cooling system for the target battery pack based on the thermal regulation load. In the cooling mode, when the target battery pack is in the cooling mode, the temperature rise gradient of the hot spot area is monitored in real time. The thermal state of the current hot spot area is predicted by combining the temperature rise gradient with the heat dissipation change characteristics of each normal temperature zone in the cooling mode, thereby obtaining the thermal runaway critical deviation of the temperature in the hot spot area. The thermal gradient distribution characteristics are extracted from the thermal distribution map of each normal temperature zone. Then, the heat dissipation temperature of the hot spot area is analyzed by heat dissipation compensation based on the heat dissipation contribution of each thermal gradient distribution characteristics to the heat dissipation process of the target battery pack and the thermal runaway critical deviation, thereby obtaining the heat dissipation compensation gain of the hot spot area. The heat dissipation temperature of the hot spot area is adjusted according to the heat dissipation compensation gain.

[0054] refer to Figure 2 The figure is an exemplary flowchart of a new energy vehicle battery heat dissipation method according to some embodiments of this application. The new energy vehicle battery heat dissipation method mainly includes the following steps:

[0055] In step 101, hot spots with abnormal temperatures and multiple normal temperature zones are identified within the target battery pack of the new energy vehicle.

[0056] In some embodiments, identifying hotspots with abnormal temperatures and multiple normal temperature zones within a target battery pack in a new energy vehicle can be achieved using the following steps:

[0057] Acquire temperature data of the target battery pack in a new energy vehicle;

[0058] Construct a temperature distribution matrix for the target battery pack in the new energy vehicle based on the temperature data;

[0059] The deviation of the temperature value at each monitoring point in the temperature distribution matrix from the temperature threshold is calculated, thereby obtaining the temperature deviation matrix;

[0060] The regions in the temperature deviation matrix where the temperature deviation value is greater than the anomaly detection threshold are constructed as hotspot regions of temperature anomalies within the target battery pack;

[0061] The regions in the temperature deviation matrix that are less than or equal to the anomaly determination threshold are divided into normal temperature zones, thereby obtaining multiple normal temperature zones within the target battery pack.

[0062] In specific implementation, firstly, temperature data of the target battery pack in the new energy vehicle is acquired through a temperature sensor array. The temperature sensor array is deployed on each individual battery cell inside the target battery pack. The temperature data represents the temperature values ​​at various monitoring points of the target battery pack during operation, collected in real time by the temperature sensors. Secondly, based on the physical arrangement of the monitoring points inside the target battery pack, the temperature values ​​in the temperature data are mapped according to their corresponding spatial locations to construct a temperature distribution matrix for the target battery pack in the new energy vehicle. Each position in this temperature distribution matrix corresponds to a monitoring point, and the element values ​​in the temperature distribution matrix are the temperature values ​​of the corresponding monitoring points. The temperature distribution matrix refers to a temperature matrix structure constructed based on the temperature values ​​at different monitoring points within the target battery pack, used for... The temperature distribution matrix reflects the spatial distribution of the target battery pack. Further, it calculates the deviation of the temperature value at each monitoring point from the temperature threshold, and arranges all deviation values ​​in their original positions to generate a temperature deviation matrix. The temperature threshold can be set according to actual needs or expert knowledge; no specific limitation is made here. Then, using an existing region growing algorithm, the regions in the temperature deviation matrix with temperature deviation values ​​greater than the anomaly detection threshold are constructed as hotspot regions of temperature anomalies within the target battery pack. Finally, using an existing region growing algorithm, the regions in the temperature deviation matrix with temperature deviation values ​​less than or equal to the anomaly detection threshold are divided into normal temperature regions, and these normal temperature regions are uniformly divided according to the scale of the hotspot regions, thus obtaining multiple normal temperature zones within the target battery pack. Further details are omitted here.

[0063] It should be noted that, in this application, the hot spot area refers to a local hot spot section within the target battery pack where there is a risk of thermal runaway. By identifying the hot spot area, areas with excessively high temperatures that affect the overall thermal management safety can be effectively identified, providing target locations for implementing key cooling or local control. In this application, the normal temperature zone refers to a local area within the target battery pack where the temperature deviation is within a stable range. It is used to provide a thermal stability reference area as a benchmark model for heat dissipation trend assessment and anomaly comparison.

[0064] In step 102, the thermal control load of the current hot spot area is analyzed based on the thermal diffusion characteristics of the temperature in the hot spot area and the current operating status of the new energy vehicle. Then, the cooling mode corresponding to the target battery pack is selected according to the thermal control load.

[0065] It should be noted that, in this application, the thermal regulation load represents the actual heat dissipation capacity required by the hot spot area. The thermal regulation load is used to guide the adjustment amount of the current cooling strategy and is a control target value that quantifies the intensity of temperature control intervention. By determining the thermal regulation load, the thermal control behavior can be upgraded from a responsive to a predictive approach, so that the cooling system no longer relies on temperature triggering but intervenes in advance based on the predicted load, effectively reducing the risk of thermal runaway.

[0066] In some embodiments, the thermal control load of the current hotspot area can be determined by analyzing the thermal diffusion characteristics of the temperature in the hotspot area in conjunction with the current operating status of the new energy vehicle, using the following steps:

[0067] Extract the time-series temperature data of the hotspot area within the current time period, and determine the thermal diffusion characteristics of the temperature in the hotspot area based on the time-series temperature data;

[0068] A thermal diffusion field model is generated based on the aforementioned thermal diffusion characteristics and the spatial distribution density of battery cells within the hotspot area.

[0069] Obtain the current operating status parameters of the new energy vehicle, and input the operating status parameters into the thermal diffusion field model to extract the temperature change trend;

[0070] The thermal control load of the current hot spot area is calculated based on the temperature change trend.

[0071] In specific implementation, firstly, within the spatial range of the hotspot area, temperature data at multiple time points is collected for each battery cell using a temperature sensor, thereby constructing time-series temperature data of the hotspot area within the current time period. The hotspot area is divided into regular voxel grid cells, with each grid cell's center point corresponding to a temperature value. The temperature change value between adjacent time points for each grid cell within the hotspot area is calculated using a temperature gradient-based differential method, and the average temperature change value is used as the thermal diffusion characteristic of the temperature in the hotspot area. Secondly, the spatial distribution density of battery cells within the hotspot area is calculated according to existing density calculation methods. Based on the heat conduction control equation, the thermal diffusion characteristic is used as the initial heat source behavior input, and the normalized spatial distribution density is used as an adjustment factor for local specific heat capacity and thermal conductivity to construct a thermal diffusion field model. Then, the current operating status parameters of the new energy vehicle are retrieved through the vehicle control system. These operating status parameters include vehicle speed, cooling system status, battery power output value, and ambient temperature. The parameters in these operating status parameters are input into the relevant modules of the thermal diffusion field model according to their categories. For example, the battery power output value is mapped to the heat generation rate of a single battery cell, the cooling system state is set as the boundary heat dissipation condition, vehicle speed and ambient temperature are used to adjust the thermal convection boundary condition, and the thermal diffusion field model performs simulation calculations within a set time step, outputting a predicted temperature change data sequence for the hot spot area, and using the temperature change curve obtained by least-squares fitting of the predicted temperature change data sequence as the temperature change trend; finally, the thermal regulation load of the current hot spot area is calculated based on the temperature change trend, that is: extracting the maximum temperature rise rate (i.e., the rate corresponding to the point where the temperature rises the fastest) and the minimum temperature rise rate (i.e., the rate corresponding to the point where the temperature rises the slowest) in the temperature change trend, and converting the difference between the maximum temperature rise rate and the minimum temperature rise rate into the thermal regulation load of the current hot spot area through equivalent heat capacity (i.e., the product of the equivalent heat capacity corresponding to the hot spot area and the difference is used as the thermal regulation load of the current hot spot area), the equivalent heat capacity represents the conversion parameter for converting the difference between the maximum temperature rise rate and the minimum temperature rise rate into the thermal regulation load, the specifics of which can be given based on expert knowledge, and will not be elaborated here.

[0072] It should be noted that, in this embodiment, the thermal diffusion characteristics represent the heat conduction trend of the temperature in the hot spot area over time; the thermal diffusion field model in this embodiment represents a system of heat conduction equations that simulate the temperature change over time and space based on the temperature diffusion characteristics in the hot spot area and the arrangement density of battery cells in the space, used to reconstruct and predict the temperature change process of the hot spot area under specified boundary conditions; the temperature change trend in this embodiment represents the temperature change of the hot spot area within the predicted time, used as preliminary information to determine the magnitude of the heat load and the urgency of regulation.

[0073] In some embodiments, selecting the cooling mode corresponding to the target battery pack based on the thermal regulation load can be achieved by the following steps:

[0074] Obtain the adaptation parameter library for preset cooling modes;

[0075] The thermal regulation load is matched with the adaptation parameter library, and the cooling mode corresponding to the thermal regulation load is extracted as the cooling mode corresponding to the target battery pack.

[0076] In specific implementation, firstly, an adaptation parameter library for preset cooling modes is obtained. This library includes all cooling modes supported by the current vehicle model. The cooling modes include air cooling, water cooling, phase change material assisted cooling, or a combination of the above cooling methods. A corresponding adaptation parameter range (i.e., thermal regulation load range) is preset for each cooling mode. The thermal regulation load range can be set by training the thermal regulation load of the cooling mode based on machine learning, which will not be elaborated here. Then, the thermal regulation load is used as an input quantity and compared with the thermal load matching range of each cooling mode in the adaptation parameter library. If the thermal regulation load falls within the load response range of a certain cooling mode, then that mode is taken as the cooling mode corresponding to the target battery pack.

[0077] In this embodiment, the cooling mode adaptation parameter library is a parameter set containing all defined cooling modes and their adaptation parameter ranges. It is used to quickly select cooling schemes with cooling execution capabilities based on the input thermal control load, and is an important matching basis for the initial screening of thermal control strategies. In this application, the cooling mode represents a cooling control scheme suitable for temperature control in hot areas under the current vehicle operating state, and is the final control decision result after precise matching of hot and cold loads and cooling capacity.

[0078] In step 103, when the target battery pack is in the cooling mode, the temperature rise gradient of the hot spot area is monitored in real time. The thermal state of the current hot spot area is predicted by combining the temperature rise gradient with the heat dissipation change characteristics of each normal temperature zone in the cooling mode, thereby obtaining the critical deviation of thermal runaway temperature in the hot spot area.

[0079] In some embodiments, reference Figure 3 As shown in the figure, this is an exemplary flowchart of determining the temperature rise gradient according to some embodiments of this application. In this embodiment, when the target battery pack is in the cooling mode, real-time monitoring of the temperature rise gradient of the hot spot area can be achieved by the following steps:

[0080] First, in step 1031, while the target battery pack is in the cooling mode, real-time temperature data of the hot spot area is continuously collected at a set sampling frequency;

[0081] Then, in step 1032, a temperature change curve of the hot spot area is constructed based on the real-time temperature data;

[0082] Finally, in step 1033, the temperature rise gradient of the hot spot region is calculated using the temperature change curve.

[0083] In specific implementation, firstly, when the target battery pack is in the cooling mode, real-time temperature data of the hot spot area is continuously collected by a temperature sensor at a set sampling frequency (e.g., 1Hz). The sampling frequency is determined by both the battery thermal response rate and the cooling mode response time to ensure that minute temperature fluctuations can be captured. Secondly, the real-time temperature data is stored in time-stamp order to form a time series, and a temperature change curve of the hot spot area is constructed based on this time series. The temperature change curve has time as the horizontal axis and temperature value as the vertical axis. Then, the temperature rise gradient of the hot spot area is calculated through the temperature change curve. That is, for each temperature change point on the temperature change curve, the first-order difference method is used to extract the temperature increment and time increment at the temperature change point, and the quotient of the temperature increment and the time increment is taken as the temperature rise gradient at the temperature change point, thereby obtaining the temperature rise gradient of the hot spot area.

[0084] It should be noted that, in this embodiment, the real-time temperature data refers to the set of current temperature values ​​corresponding to each sensor node in the hot spot area collected by the temperature sensor array at predetermined time intervals during continuous operation of the cooling mode. This data forms the basis of continuous time-domain data reflecting the current thermal state changes in the hot spot area. In this embodiment, the temperature change curve represents a two-dimensional temperature-time function graph plotted based on the changes in continuous temperature values ​​at each sampling point in the hot spot area over time. This graph is an important visualization structure characterizing the thermal response trend of the hot spot area. In this application, the temperature rise gradient represents the rate of temperature change in the hot spot area per unit time. This is a key dynamic thermal indicator for measuring the current cooling efficiency and judging the effectiveness of the temperature control response. By determining the temperature rise gradient, it is possible to determine whether the current cooling method responds in a timely and effective manner, and whether there is a cooling lag or heat backflow phenomenon, thereby allowing for timely adjustment or alarm processing of the cooling control strategy.

[0085] In some embodiments, the thermal state of the current hot spot region is predicted by combining the temperature rise gradient with the heat dissipation change characteristics of each normal temperature zone under the cooling mode, thereby obtaining the critical deviation of thermal runaway temperature in the hot spot region. This can be achieved by the following steps:

[0086] Determine the heat dissipation characteristics of each normal temperature zone under the cooling mode;

[0087] Based on the temperature rise gradient and the heat dissipation change characteristics of each normal temperature zone, establish the heat conduction deviation relationship between the hot spot area and each normal temperature zone.

[0088] Based on the aforementioned heat conduction deviation relationship, predict the change in thermal state of the hot spot region under the current cooling conditions;

[0089] The critical deviation of thermal runaway in the hot spot region is determined based on the thermal state change value and the target thermal equilibrium state of the target battery pack.

[0090] In specific implementation, firstly, historical temperature change data of each normal temperature zone under the cooling mode is acquired, and a moving average filtering algorithm is used to remove data noise. For each normal temperature zone, the heat flux density of the historical temperature change data corresponding to the normal temperature zone (i.e., calculated by the Fourier heat conduction function (q=-k・∇T, where q is heat flux density, k is thermal conductivity, and ∇T is temperature rise gradient)) is used as the heat dissipation change characteristic, thereby obtaining the heat dissipation change characteristics of each normal temperature zone under the cooling mode. Secondly, based on the temperature rise gradient and the heat dissipation change characteristics of each normal temperature zone, a heat conduction deviation relationship between the hot spot area and each normal temperature zone is established, that is: the temperature rise gradient is used as an input parameter to the Fourier heat conduction function, and... The heat flux density of the current hot spot region is output from the Fourier heat conduction function. The absolute difference between the heat flux density and the heat dissipation change characteristics of each normal temperature zone is taken as the heat conduction deviation relationship between the hot spot region and the normal temperature zone, thus obtaining the heat conduction deviation relationship between the hot spot region and each normal temperature zone. Then, based on the heat conduction deviation relationship, the thermal state change value of the hot spot region under the current cooling conditions is predicted. That is, a heat conduction response relationship matrix is ​​constructed according to the distribution order of all heat conduction deviation relationships in the normal temperature zone. The heat conduction response relationship matrix is ​​used as a boundary condition and substituted into the thermodynamic model (i.e., the three-dimensional unsteady-state heat conduction equation). Combined with the current cooling conditions (i.e., air cooling wind speed and liquid cooling flow rate parameters), a numerical iterative algorithm (i.e., Gaussian-) is used. The equations are solved using the Seidel iterative method to obtain the thermal state change value of the hot spot region under the current cooling conditions. Finally, based on the target thermal equilibrium state of the target battery pack (i.e., the pre-set normal operating temperature range of the target battery pack, which can be set according to expert knowledge), the thermal state change value is compared with the target temperature range. The thermal runaway critical deviation of the temperature in the hot spot region is calculated by the absolute error in the error calculation method (i.e., the absolute difference between the median temperature of the target temperature range and the thermal state change value is taken as the thermal runaway critical deviation).

[0091] It should be noted that, in this embodiment, the heat dissipation change characteristic represents the dynamic response characteristics of the normal temperature zone during the heat release process under the action of the cooling mode, and is used to characterize the heat dissipation capacity and thermal stability trend that the normal temperature zone can provide in the controlled cooling environment; in this embodiment, the heat conduction deviation relationship represents the heat transfer deviation between the hot spot area and the surrounding normal temperature zone during the cooling process, and is used to reflect the thermal coupling strength between different temperature zones; in this embodiment, the thermal state change value represents the predicted thermal behavior index of the hot spot area temperature change over time, and is used to reflect whether the temperature of the hot spot area tends to stabilize under the current cooling mode, which is an important basis for evaluating the cooling effect and subsequent control strategies; in this application, the thermal runaway critical deviation represents the temperature deviation between the current predicted thermal state and the target thermal equilibrium state of the hot spot area, and is used to measure the degree to which the current thermal state deviates from the expected safe operating range of the system, which is an indicator for assessing whether thermal runaway exists in the hot spot area, and is also a direct quantitative basis for heat dissipation compensation or adjustment in the execution of control strategies.

[0092] In step 104, thermal gradient distribution features are extracted from the thermal distribution maps of each normal temperature zone. Then, based on the thermal gradient distribution features, the heat dissipation contribution of the target battery pack heat dissipation process and the thermal runaway critical deviation are used to perform heat dissipation compensation analysis on the heat dissipation temperature of the hot spot area, and the heat dissipation compensation gain of the hot spot area is obtained.

[0093] In some embodiments, extracting thermal gradient distribution features from the thermal distribution maps of each normal temperature zone can be achieved using the following steps:

[0094] Obtain the heat distribution map of each normal temperature zone;

[0095] Select a normal temperature zone as the selected normal temperature zone, and perform spatial temperature difference operation on the heat distribution map corresponding to the selected normal temperature zone to obtain the temperature gradient vector field of the selected normal temperature zone.

[0096] The thermal gradient distribution characteristics of the selected normal temperature zone are determined based on the temperature gradient vector field.

[0097] Continue to determine the thermal gradient distribution characteristics of the remaining normal temperature zones.

[0098] In specific implementation, firstly, real-time temperature data of each normal temperature zone is collected by temperature sensors during the stable operation period of the cooling mode. Based on the geometric arrangement of the temperature sensors, the collected temperature data is mapped to a two-dimensional thermal image structure corresponding to the battery pack structure according to the corresponding coordinate points, forming a thermal distribution map corresponding to each normal temperature zone. Secondly, a normal temperature zone is selected as the selected normal temperature zone, and the central difference method is used to perform spatial temperature difference operation on the thermal distribution map corresponding to the selected normal temperature zone to obtain the temperature gradient vector field of the selected normal temperature zone. Specifically, the temperature values ​​of adjacent points in the thermal distribution map can be subtracted in the horizontal (X-axis) and vertical (Y-axis) directions respectively to form a two-dimensional temperature gradient vector corresponding to each thermal distribution point. Then, the magnitude of the temperature gradient vector is used as an element of the temperature gradient vector field to obtain the temperature gradient vector field of the selected normal temperature zone. Then, the average thermal gradient in the temperature gradient vector field is used as the thermal gradient distribution feature of the selected normal temperature zone. Finally, the thermal gradient distribution features of the remaining normal temperature zones are determined by the method of "determining the thermal gradient distribution feature of the selected normal temperature zone based on the temperature gradient vector field".

[0099] It should be noted that, in this embodiment, the heat distribution map represents the temperature distribution state map in the normal temperature zone, which is used to reflect the heat diffusion pattern and temperature uniformity of each normal temperature zone within a specified time period. In this embodiment, the temperature gradient vector field refers to a spatial field composed of multiple two-dimensional vectors representing the rate of temperature change within the normal temperature zone, which is used to characterize the trend and intensity of temperature change at different locations. In this application, the heat gradient distribution feature represents the characteristic parameters describing the intensity of temperature change in the spatial distribution, which is used to reflect the heat dissipation uniformity, heat diffusion direction stability, and local heat concentration degree of the region.

[0100] In some embodiments, the heat dissipation compensation analysis of the hot spot region is performed based on the heat dissipation contribution of each thermal gradient distribution characteristic to the heat dissipation process of the target battery pack and the critical deviation of thermal runaway, and the heat dissipation compensation gain of the hot spot region can be obtained by the following steps:

[0101] Determine the contribution of each thermal gradient distribution characteristic to the heat dissipation process of the target battery pack;

[0102] Determine the thermal impact characteristics of the normal temperature zone during the heat dissipation process of the target battery pack based on all heat dissipation contributions.

[0103] The heat dissipation compensation gain of the hot spot region is generated based on the thermal impact characteristics and the thermal runaway critical deviation.

[0104] In specific implementation, firstly, the contribution of each thermal gradient distribution feature to the heat dissipation process of the target battery pack is determined. That is, the thermal gradient distribution feature of the entire area of ​​the target battery pack during the heat dissipation process is obtained as the global thermal gradient distribution feature, and the quotient of the thermal gradient distribution feature and the global thermal gradient distribution feature is taken as the contribution of the thermal gradient distribution feature to the heat dissipation process of the target battery pack. Then, the thermal impact feature of the normal temperature zone of the target battery pack heat dissipation process is determined according to all the heat dissipation contributions. That is, all the heat dissipation contributions are normalized to between 0 and 1 through minimum-maximum normalization, and the average value of all normalized heat dissipation contributions is taken as the thermal impact feature of the normal temperature zone of the target battery pack heat dissipation process. The thermal impact feature is a dimensionless parameter. Finally, the heat dissipation compensation gain of the hot spot area is generated according to the thermal impact feature and the thermal runaway critical deviation. That is, the thermal impact feature is used as the weight value of the thermal runaway critical deviation to obtain the weighted thermal runaway critical deviation, and the normalized weighted thermal runaway critical deviation is taken as the heat dissipation compensation gain of the hot spot area.

[0105] It should be noted that, in this embodiment, the heat dissipation contribution represents the degree to which the thermal gradient distribution characteristics promote the heat dissipation process of the target battery pack; in this embodiment, the thermal influence characteristics represent the influence parameters of the normal temperature region on the heat dissipation of the target battery pack region; in this application, the heat dissipation compensation gain represents the parameter used to regulate the heat dissipation temperature of the hot spot region. This heat dissipation compensation gain can effectively regulate the heat dissipation degree of the hot spot region. By determining the heat dissipation compensation gain, the cooling system can be effectively guided to implement targeted cooling operations, thereby achieving steady-state regulation of the hot spot temperature and ensuring that the target battery pack operates efficiently within the thermal safety range.

[0106] It should also be noted that the heat dissipation compensation analysis in this application refers to the analysis process of adjusting the heat dissipation temperature of the hot spot area. Specifically, the heat dissipation compensation analysis is performed on the heat dissipation temperature of the hot spot area based on the heat dissipation contribution of each thermal gradient distribution characteristic to the heat dissipation process of the target battery pack and the thermal runaway critical deviation. This involves: determining the heat dissipation contribution of each thermal gradient distribution characteristic to the heat dissipation process of the target battery pack; determining the thermal impact characteristics of the normal temperature zone of the target battery pack's heat dissipation process based on all heat dissipation contributions; and generating the heat dissipation compensation gain of the hot spot area based on the thermal impact characteristics and the thermal runaway critical deviation. This heat dissipation compensation gain is then used as the result of the heat dissipation compensation analysis, thereby completing the heat dissipation compensation analysis of the heat dissipation temperature of the hot spot area.

[0107] In step 105, the heat dissipation temperature of the hot spot area is adjusted according to the heat dissipation compensation gain.

[0108] In some embodiments, adjusting the heat dissipation temperature of the hot spot area according to the heat dissipation compensation gain can be achieved by the following steps:

[0109] The heat dissipation compensation gain is input into the temperature compensation model of the hot spot area to generate the target heat dissipation temperature adjustment curve;

[0110] Real-time monitoring of the heat dissipation temperature in hotspot areas, and feedback on the deviation between the heat dissipation temperature and the target heat dissipation temperature adjustment curve;

[0111] The heat dissipation compensation gain is updated based on the deviation feedback result to complete the process of adjusting the heat dissipation temperature of the hot spot area.

[0112] In specific implementation, firstly, the heat dissipation compensation gain is loaded as an input parameter into the temperature compensation model of the hot spot area. The temperature compensation model generates a target heat dissipation temperature adjustment curve. The temperature compensation model is constructed based on thermodynamic principles and uses a linear function to describe the relationship between heat dissipation temperature and heat dissipation compensation gain. For example, a first-order inertial element model can be used as the temperature compensation model, which will not be elaborated here. Then, the heat dissipation temperature of the hot spot area is monitored in real time by a temperature sensor, and the difference between the heat dissipation temperature and the expected temperature value on the target adjustment curve is compared to obtain a deviation feedback value, which will not be elaborated here. Finally, the heat dissipation compensation gain is updated according to the deviation feedback result using proportional-integral or proportional-integral-derivative methods to complete the process of adjusting the heat dissipation temperature of the hot spot area, which will not be elaborated here.

[0113] In this embodiment, the target heat dissipation temperature adjustment curve refers to the desired control trajectory used to guide the change of heat dissipation temperature in the hot spot area over time. This curve describes how the temperature of the hot spot area should gradually decrease or remain stable over time under a specified cooling mode, so as to achieve stable control of the thermal state. In this embodiment, the deviation feedback refers to the process of feeding back the temperature difference between the actual heat dissipation temperature and the preset target heat dissipation temperature adjustment curve during the heat dissipation control process of the hot spot area. It is used to measure the degree of deviation between the current system operating state and the ideal control state.

[0114] In another aspect, in some embodiments, this application provides a new energy vehicle battery cooling system, with reference to... Figure 4 The figure is a schematic diagram of the structure of a new energy vehicle battery cooling system according to some embodiments of this application. The new energy vehicle battery cooling system includes: an identification module 201, a processing module 202, and an execution module 203, which are described below:

[0115] The identification module 201 in this application is mainly used to identify hot spots with abnormal temperatures and multiple normal temperature zones inside the target battery pack of a new energy vehicle.

[0116] The processing module 202 in this application is mainly used to analyze the thermal control load of the current hot spot area based on the thermal diffusion characteristics of the temperature in the hot spot area and the current operating status of the new energy vehicle, and then select the cooling mode corresponding to the target battery pack according to the thermal control load.

[0117] The processing module 202 is also used to monitor the temperature rise gradient of the hot spot area in real time when the target battery pack is in the cooling mode, and predict the thermal state of the current hot spot area by combining the temperature rise gradient with the heat dissipation change characteristics of each normal temperature zone in the cooling mode, thereby obtaining the thermal runaway critical deviation of the temperature in the hot spot area.

[0118] In addition, the processing module 202 is also used to extract thermal gradient distribution features from the thermal distribution map of each normal temperature zone, and then perform heat dissipation compensation analysis on the heat dissipation temperature of the hot spot area based on the heat dissipation contribution of each thermal gradient distribution feature to the heat dissipation process of the target battery pack and the thermal runaway critical deviation, so as to obtain the heat dissipation compensation gain of the hot spot area.

[0119] The execution module 203 in this application is mainly used to adjust the heat dissipation temperature of the hot spot area according to the heat dissipation compensation gain.

[0120] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described new energy vehicle battery heat dissipation method.

[0121] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device for implementing a heat dissipation method for new energy vehicle batteries according to some embodiments of this application. The heat dissipation method for new energy vehicle batteries in the above embodiments can... Figure 5 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0122] The processor 301 can be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more devices used to control the execution of the new energy vehicle battery heat dissipation method in this application.

[0123] The communication bus 302 can be used to transmit information between the aforementioned components.

[0124] The memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0125] The memory 303 stores program code for executing the solution of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiments, the determination of the heat dissipation method for new energy vehicle batteries can be achieved by the processor 301 and one or more software modules in the program code in the memory 303.

[0126] Communication interface 304 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0127] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0128] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0129] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described new energy vehicle battery heat dissipation method.

[0130] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0131] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for heat dissipation of a new energy vehicle battery, characterized in that, Includes the following steps: Identify hotspots of abnormal temperatures and multiple normal temperature zones within the target battery pack of a new energy vehicle; Based on the thermal diffusion characteristics of the temperature in the hot spot area and the current operating status of the new energy vehicle, the thermal regulation load of the current hot spot area is analyzed, and then the cooling mode corresponding to the target battery pack is selected according to the thermal regulation load. When the target battery pack is in the cooling mode, the temperature rise gradient of the hot spot area is monitored in real time. The thermal state of the current hot spot area is predicted by combining the temperature rise gradient with the heat dissipation change characteristics of each normal temperature zone in the cooling mode, and then the thermal runaway critical deviation of the temperature in the hot spot area is obtained. Thermal gradient distribution features are extracted from the thermal distribution maps of each normal temperature zone. Then, based on the thermal gradient distribution features, the heat dissipation contribution of the target battery pack heat dissipation process and the thermal runaway critical deviation are used to perform heat dissipation compensation analysis on the heat dissipation temperature of the hot spot area, and the heat dissipation compensation gain of the hot spot area is obtained. The heat dissipation temperature of the hot spot area is adjusted according to the heat dissipation compensation gain.

2. The method as described in claim 1, characterized in that, Identifying hotspots of abnormal temperatures and multiple normal temperature zones within the target battery pack of a new energy vehicle specifically includes: Acquire temperature data of the target battery pack in a new energy vehicle; Construct a temperature distribution matrix for the target battery pack in the new energy vehicle based on the temperature data; The deviation of the temperature value at each monitoring point in the temperature distribution matrix from the temperature threshold is calculated, thereby obtaining the temperature deviation matrix; The regions in the temperature deviation matrix where the temperature deviation value is greater than the anomaly detection threshold are constructed as hotspot regions of temperature anomalies within the target battery pack; The regions in the temperature deviation matrix that are less than or equal to the anomaly determination threshold are divided into normal temperature zones, thereby obtaining multiple normal temperature zones within the target battery pack.

3. The method as described in claim 1, characterized in that, Based on the thermal diffusion characteristics of the temperature in the hotspot area and the current operating status of the new energy vehicles, the thermal regulation load of the current hotspot area is specifically analyzed as follows: Extract the time-series temperature data of the hotspot area within the current time period, and determine the thermal diffusion characteristics of the temperature in the hotspot area based on the time-series temperature data; A thermal diffusion field model is generated based on the aforementioned thermal diffusion characteristics and the spatial distribution density of battery cells within the hotspot area. Obtain the current operating status parameters of the new energy vehicle, and input the operating status parameters into the thermal diffusion field model to extract the temperature change trend; The thermal control load of the current hot spot area is calculated based on the temperature change trend.

4. The method as described in claim 1, characterized in that, Selecting the cooling mode corresponding to the target battery pack based on the thermal regulation load specifically includes: Obtain the adaptation parameter library for preset cooling modes; The thermal regulation load is matched with the adaptation parameter library, and the cooling mode corresponding to the thermal regulation load is extracted as the cooling mode corresponding to the target battery pack.

5. The method as described in claim 1, characterized in that, By combining the temperature rise gradient with the heat dissipation change characteristics of each normal temperature zone under the cooling mode, the thermal state of the current hot spot area is predicted, and the critical deviation of thermal runaway in the hot spot area is obtained, specifically including: Determine the heat dissipation characteristics of each normal temperature zone under the cooling mode; Based on the temperature rise gradient and the heat dissipation change characteristics of each normal temperature zone, establish the heat conduction deviation relationship between the hot spot area and each normal temperature zone. Based on the aforementioned heat conduction deviation relationship, predict the change in thermal state of the hot spot region under the current cooling conditions; The critical deviation of thermal runaway in the hot spot region is determined based on the thermal state change value and the target thermal equilibrium state of the target battery pack.

6. The method as described in claim 1, characterized in that, Extracting thermal gradient distribution features from the thermal distribution maps of each normal temperature zone specifically includes: Obtain the heat distribution map of each normal temperature zone; Select a normal temperature zone as the selected normal temperature zone, and perform spatial temperature difference operation on the heat distribution map corresponding to the selected normal temperature zone to obtain the temperature gradient vector field of the selected normal temperature zone. The thermal gradient distribution characteristics of the selected normal temperature zone are determined based on the temperature gradient vector field. Continue to determine the thermal gradient distribution characteristics of the remaining normal temperature zones.

7. The method as described in claim 2, characterized in that, Temperature data of the target battery pack in a new energy vehicle is obtained through a temperature sensor array.

8. A heat dissipation system for a new energy vehicle battery, characterized in that, include: The identification module is used to identify hot spots with abnormal temperatures and multiple normal temperature zones within the target battery pack of a new energy vehicle. The processing module is used to analyze the thermal control load of the current hot spot area based on the thermal diffusion characteristics of the temperature in the hot spot area and the current operating status of the new energy vehicle, and then select the cooling mode corresponding to the target battery pack according to the thermal control load. The processing module is also used to monitor the temperature rise gradient of the hot spot area in real time when the target battery pack is in the cooling mode, and predict the thermal state of the current hot spot area by combining the temperature rise gradient with the heat dissipation change characteristics of each normal temperature zone in the cooling mode, thereby obtaining the thermal runaway critical deviation of the temperature in the hot spot area. The processing module is also used to extract thermal gradient distribution features from the thermal distribution maps of each normal temperature zone, and then perform heat dissipation compensation analysis on the heat dissipation temperature of the hot spot area based on the heat dissipation contribution of each thermal gradient distribution feature to the heat dissipation process of the target battery pack and the thermal runaway critical deviation, so as to obtain the heat dissipation compensation gain of the hot spot area. The execution module is used to adjust the heat dissipation temperature of the hot spot area according to the heat dissipation compensation gain.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the new energy vehicle battery heat dissipation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the new energy vehicle battery heat dissipation method as described in any one of claims 1 to 7.

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