New energy automobile battery heat dissipation method, system, equipment and medium

Abstract

The invention provides a new energy automobile battery heat dissipation method, system and device and a medium. The method comprises the following steps: identifying a hot spot region with abnormal temperature and a plurality of normal temperature partitions in a target battery pack; based on the thermal diffusion characteristics of the temperature in the hot spot area, combining with the current operation state of the new energy vehicle to analyze the thermal regulation and control load of the hot spot area, and then obtaining the cooling mode of the target battery pack; the thermal runaway critical deviation of the temperature in the hot spot area is determined by combining the temperature rise gradient of the hot spot area with the heat dissipation change characteristics of all the normal temperature subareas in the cooling mode; determining the heat dissipation compensation gain of the hot spot area according to the heat dissipation contribution degree and the thermal runaway critical deviation of each thermal gradient distribution characteristic to the heat dissipation process of the battery pack; and adjusting the heat dissipation temperature of the hot spot area according to the heat dissipation compensation gain. According to the technical scheme provided by the invention, the heat diffusion rule of the temperature abnormal region in the battery pack and the heat influence relationship of the surrounding normal temperature subregion can be identified.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery temperature control, and more particularly to a new energy vehicle battery heat dissipation method, system, device and medium. BACKGROUND

[0002] In various electronic devices and new energy application scenarios, battery performance is crucial. During the charging and discharging process of the battery, internal chemical reactions generate heat, causing temperature changes. If the temperature is too high, the battery life will be significantly shortened, and even thermal runaway problems will be caused. If the temperature is too low, the battery capacity will be reduced, and the charging and discharging efficiency will also be greatly discounted. With the continuous improvement of battery energy density, the heat generation problem is becoming increasingly prominent. Therefore, accurate and efficient battery temperature control is the key to ensuring battery performance, prolonging service life, and improving safety.

[0003] In existing battery temperature control, temperature sensors are usually used to monitor battery temperature in real time. When the temperature exceeds the safe range, the system will start countermeasures. For example, when the temperature is too high, the air cooling system uses fans to make air flow to remove heat, and the liquid cooling system transmits heat to the radiator through cooling liquid circulation to dissipate heat. When the temperature is too low, the resistance heating element is powered to generate heat to raise the battery temperature, thereby ensuring that the battery is in the best working condition. However, in the process of new energy vehicle battery heat dissipation, due to the large number of battery monomers in the battery pack and the complex spatial layout, the heat diffusion law of the temperature abnormal area in the battery pack and the thermal influence relationship of the surrounding normal temperature partition are difficult to identify, which leads to inaccurate temperature regulation of the temperature abnormal area, thereby reducing the accuracy of battery heat dissipation. Therefore, how to identify the heat diffusion law of the temperature abnormal area in the battery pack and the thermal influence relationship of the surrounding normal temperature partition, and then realize accurate temperature regulation of the temperature abnormal area, has become a difficult problem in the industry. SUMMARY

[0004] The present application provides a new energy vehicle battery heat dissipation method, system, device and medium, which can identify the heat diffusion law of the temperature abnormal area in the battery pack and the thermal influence relationship of the surrounding normal temperature partition.

[0005] In a first aspect, the present application provides a new energy vehicle battery heat dissipation method, comprising the following steps: identifying a temperature abnormal hot spot area and a plurality of normal temperature partitions in a target battery pack of a new energy vehicle; based on the heat diffusion characteristics of the temperature in the hot spot area and the current operating state of the new energy vehicle, analyzing the heat regulation load of the current hot spot area, and then selecting a cooling mode corresponding to the target battery pack according to the heat regulation load; monitoring a temperature rise gradient of the hotspot area in real time, predicting a thermal state of the current hotspot area by combining the temperature rise gradient with heat dissipation change characteristics of each normal temperature partition in the cooling mode, and then obtaining a thermal runaway critical deviation of the temperature in the hotspot area; extracting thermal gradient distribution characteristics from a thermal distribution map of each normal temperature partition, and then performing heat dissipation compensation analysis on heat dissipation temperature of the hotspot area according to heat dissipation contribution degrees of a heat dissipation process of the target battery pack and the thermal runaway critical deviation, to obtain a heat dissipation compensation gain of the hotspot area; adjusting the heat dissipation temperature of the hotspot area according to the heat dissipation compensation gain.

[0006] In some embodiments, identifying the hotspot area with temperature abnormities and the plurality of normal temperature partitions in the target battery pack of the new energy vehicle specifically includes: obtaining temperature data of the target battery pack of the new energy vehicle; constructing a temperature distribution matrix of the target battery pack of the new energy vehicle according to the temperature data; calculating deviation values of temperature values of each monitoring point in the temperature distribution matrix from a temperature threshold value, and then obtaining a temperature deviation value matrix; constructing an area with a temperature deviation value greater than an abnormality judgment threshold value in the temperature deviation value matrix as the hotspot area with temperature abnormities in the target battery pack; dividing an area less than or equal to the abnormality judgment threshold value in the temperature deviation value matrix into a normal temperature partition, and then obtaining the plurality of normal temperature partitions in the target battery pack.

[0007] In some embodiments, analyzing the thermal regulation load of the current hotspot area based on thermal diffusion characteristics of the temperature in the hotspot area and a current operating state of the new energy vehicle specifically includes: extracting time-series temperature data of the hotspot area in a current time period, and determining thermal diffusion characteristics of the temperature in the hotspot area according to the time-series temperature data; generating a thermal diffusion field model according to the thermal diffusion characteristics and a spatial distribution density of battery monomers in the hotspot area; obtaining current operating state parameters of the new energy vehicle, and inputting the operating state parameters into the thermal diffusion field model to extract a temperature variation trend; calculating the thermal regulation load of the current hotspot area based on the temperature variation trend.

[0008] In some embodiments, selecting a cooling mode corresponding to the target battery pack according to the thermal regulation load specifically includes: obtaining an adaptive parameter library of a preset cooling mode; The thermal control load is matched with the adaptation parameter library, and a cooling mode corresponding to the thermal control load is extracted as a cooling mode corresponding to the target battery pack.

[0009] In some embodiments, the thermal state of the current hotspot 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 hotspot area is obtained, specifically including: Determine heat dissipation change characteristics of each normal temperature partition under the cooling mode; Establishing a heat conduction deviation relationship between the hotspot area and each normal temperature partition based on the temperature rise gradient and the heat dissipation change characteristics of each normal temperature partition; Predicting a thermal state change value of the hot spot area under current cooling conditions based on the heat conduction deviation relationship; A thermal runaway critical deviation of the temperature in the hot spot area is determined according to the thermal state change value and a target thermal equilibrium state of a target battery pack.

[0010] In some embodiments, extracting thermal gradient distribution features from the thermal distribution map of each normal temperature zone specifically includes: Obtain thermal distribution maps of each normal temperature zone; Selecting a normal temperature partition as a selected normal temperature partition, performing a spatial temperature difference operation on a thermal distribution map corresponding to the selected normal temperature partition, and obtaining a temperature gradient vector field of the selected normal temperature partition; determining a thermal gradient distribution characteristic of a selected normal temperature zone according to the temperature gradient vector field; Continue to determine the thermal gradient distribution characteristics of the remaining normal temperature zones.

[0011] In some embodiments, a temperature sensor array acquires temperature data of a target battery pack in a new energy vehicle.

[0012] In a second aspect, the present application provides a new energy vehicle battery heat dissipation system, comprising: Identification module, used to identify abnormal temperature hot spots and multiple normal temperature zones within the target battery pack of new energy vehicles; a processing module configured to analyze a 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 state of the new energy vehicle, and then select a cooling mode corresponding to the target battery pack according to the thermal control load; The processing module is further configured to monitor a temperature rise gradient of the hotspot area in real time when the target battery pack is in the cooling mode, predict a thermal state of the current hotspot area by combining the temperature rise gradient with heat dissipation change characteristics of each normal temperature partition in the cooling mode, and obtain a thermal runaway critical deviation of the temperature in the hotspot area. The processing module is further configured to extract thermal gradient distribution characteristics from a thermal distribution map of each normal temperature partition, perform heat dissipation compensation analysis on heat dissipation temperature of the hotspot area according to a heat dissipation contribution of a heat dissipation process of the target battery pack and the thermal runaway critical deviation, and obtain a heat dissipation compensation gain of the hotspot area. The execution module is configured to adjust the heat dissipation temperature of the hotspot area according to the heat dissipation compensation gain.

[0013] In a third aspect, a computer device is provided, which includes a memory and a processor. The memory stores a code. The processor is configured to acquire the code and execute the new energy automobile battery heat dissipation method.

[0014] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the new energy automobile battery heat dissipation method.

[0015] The technical scheme provided by the embodiments of the present application has the following beneficial effects: In the new energy automobile battery heat dissipation method, system, device and medium provided by the present application, first, a hotspot area with abnormal temperature and multiple normal temperature partitions in a target battery pack of a new energy automobile are identified. Second, a thermal regulation load of the current hotspot area is analyzed based on thermal diffusion characteristics of the temperature in the hotspot area and a current operating state of the new energy automobile, and then a cooling mode corresponding to the target battery pack is selected according to the thermal regulation load. Third, a temperature rise gradient of the hotspot area is monitored in real time when the target battery pack is in the cooling mode, a thermal state of the current hotspot area is predicted by combining the temperature rise gradient with heat dissipation change characteristics of each normal temperature partition in the cooling mode, and a thermal runaway critical deviation of the temperature in the hotspot area is obtained. Fourth, thermal gradient distribution characteristics are extracted from a thermal distribution map of each normal temperature partition, heat dissipation compensation analysis is performed on heat dissipation temperature of the hotspot area according to a heat dissipation contribution of a heat dissipation process of the target battery pack and the thermal runaway critical deviation, and a heat dissipation compensation gain of the hotspot area is obtained. Finally, the heat dissipation temperature of the hotspot area is adjusted according to the heat dissipation compensation gain.

[0016] It can be seen that the present application can identify the thermal diffusion law of the abnormal temperature area in the battery pack and the thermal influence relationship between the surrounding normal temperature partitions, thereby realizing precise control of the temperature of the abnormal temperature area; first, identifying the hot spot area and multiple normal temperature partitions of the target battery pack in the new energy vehicle with abnormal temperature, which can effectively identify the area where the temperature is too high and affects the overall thermal management safety, and provide a target position for implementing key cooling or local control, thereby avoiding the identification deviation of the thermal diffusion law of the abnormal temperature area caused by the large number of battery cells and complex spatial layout in the target battery pack; secondly, based on the thermal diffusion characteristics of the temperature in the hot spot area and the current operating status of the current new energy vehicle, the thermal control load of the current hot spot area is analyzed to realize the upgrade of the thermal control behavior from responsive to predictive, so that the cooling system no longer relies on temperature triggering but performs pre-intervention based on the predicted load, effectively reducing the risk of thermal runaway, and selecting the cooling mode corresponding to the target battery pack according to the thermal control load to provide an effective cooling method for the hot spot area; further, through the temperature rise gradient structure The thermal state of the current hot spot area is predicted based on the heat dissipation change characteristics of each normal temperature partition under the cooling mode, and then the thermal runaway critical deviation of the temperature in the hot spot area is obtained to measure the degree to which the current thermal state deviates from the expected safe operating range of the system, thereby providing an evaluation basis for the heat dissipation control strategy; then, the thermal gradient distribution characteristics are extracted from the thermal distribution map of each normal temperature partition, and then the heat dissipation contribution of the target battery pack heat dissipation process and the thermal runaway critical deviation of each thermal gradient distribution characteristic are analyzed on the heat dissipation temperature of the hot spot area to obtain the heat dissipation compensation gain of the hot spot area to identify the heat diffusion law of the temperature abnormal area in the target battery pack and the thermal influence relationship between the surrounding normal temperature partitions, thereby avoiding inaccurate temperature control of the temperature abnormal 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 the present application can identify the heat diffusion law of the temperature abnormal area in the battery pack and the thermal influence relationship between the surrounding normal temperature partitions, thereby realizing precise control of the temperature of the temperature abnormal area. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of an application scenario architecture of a new energy vehicle battery heat dissipation method according to some embodiments of the present application; Figure 2 is an exemplary flow chart of a method for dissipating heat from a new energy vehicle battery according to some embodiments of the present application; Figure 3 is an exemplary flow chart for determining a temperature rise gradient according to some embodiments of the present application; Figure 4 This is a schematic structural diagram of a new energy vehicle battery heat dissipation system according to some embodiments of the present application; Figure 5is a structural schematic diagram of a computer device for implementing a new energy vehicle battery heat dissipation method according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the technical solutions of the present application, the technical solutions of the present application will be described in detail below in combination with the drawings in the specification and specific embodiments.

[0019] Reference Figure 1 The figure is a schematic diagram of an application scenario architecture of a new energy vehicle battery heat dissipation method according to some embodiments of the present application. The application scenario architecture includes a collection terminal, a communication network, a server end, and a data storage system. The collection terminal is directly or indirectly connected with the server end through the communication network. The collection terminal collects temperature data of a battery pack in a new energy vehicle and uploads the temperature data to the server terminal. The server end identifies a hotspot area of temperature anomaly and a plurality of normal temperature partitions in the battery pack according to the temperature data. Based on the heat diffusion characteristics of the temperature in the hotspot area and the current operating state of the new energy vehicle, the heat regulation load of the current hotspot area is analyzed, and then the cooling mode corresponding to the target battery pack is selected according to the heat regulation load. When the target battery pack is in the cooling mode, the temperature rise gradient of the hotspot area is monitored in real time. The heat state of the current hotspot area is predicted by combining the temperature rise gradient with the heat dissipation change characteristics of each normal temperature partition in the cooling mode, and then the heat runaway critical deviation of the temperature in the hotspot area is obtained. The heat gradient distribution characteristics are extracted from the heat distribution map of each normal temperature partition, and then the heat dissipation temperature of the hotspot area is analyzed for heat dissipation compensation according to the heat dissipation contribution of the target battery pack in the heat dissipation process and the heat runaway critical deviation, and the heat dissipation compensation gain of the hotspot area is obtained. The heat dissipation temperature of the hotspot area is adjusted according to the heat dissipation compensation gain.

[0020] Reference Figure 2 The figure is an exemplary flowchart of a new energy vehicle battery heat dissipation method according to some embodiments of the present application. The new energy vehicle battery heat dissipation method mainly includes the following steps: In step 101, a hotspot area of temperature anomaly and a plurality of normal temperature partitions in a target battery pack in a new energy vehicle are identified.

[0021] In some embodiments, the identification of the hotspot area of temperature anomaly and the plurality of normal temperature partitions in the target battery pack in the new energy vehicle can be implemented by the following steps, that is: Obtain temperature data of the target battery pack in the new energy vehicle; Construct a temperature distribution matrix of the target battery pack in the new energy vehicle according to the temperature data; a temperature deviation value matrix is obtained by calculating the deviation of the temperature value of each monitoring point in the temperature distribution matrix from a temperature threshold value, wherein the temperature threshold value can be set according to actual requirements or set according to expert knowledge; The region in the temperature deviation value matrix where the temperature deviation value is greater than the abnormality determination threshold is constructed as a hotspot region of temperature abnormality in the target battery pack. The region in the temperature deviation value matrix where the temperature deviation value is less than or equal to the abnormality determination threshold is divided into a normal temperature partition, and a plurality of normal temperature partitions in the target battery pack are obtained.

[0022] In a specific implementation, first, temperature data of a target battery pack in a new energy vehicle is obtained by a temperature sensor array, the temperature sensor array is arranged on each battery monomer inside the target battery pack in the new energy vehicle, and the temperature data represents temperature values of each monitoring point of the target battery pack in a running process, which are collected by the temperature sensor in real time. Second, the temperature values in the temperature data are mapped according to their corresponding spatial positions according to the physical arrangement order of each monitoring point inside the target battery pack, to construct a temperature distribution matrix of the target battery pack in the new energy vehicle, each position of the temperature distribution matrix corresponds to a monitoring point, and an element value in the temperature distribution matrix is a temperature value of the corresponding monitoring point. The temperature distribution matrix refers to a temperature matrix structure constructed according to temperature values at different monitoring points in the target battery pack, and is used to reflect a temperature spatial distribution state of the target battery pack. Further, deviation values of the temperature values of each monitoring point in the temperature distribution matrix from a temperature threshold value are calculated, and all the deviation values are arranged according to the original positions to generate a temperature deviation value matrix. The temperature threshold value can be set according to actual requirements or set according to expert knowledge, which is not limited herein. Then, a region in the temperature deviation value matrix where the temperature deviation value is greater than an abnormality determination threshold is constructed as a hotspot region of temperature abnormality in the target battery pack by using an existing region growing algorithm. Finally, a region in the temperature deviation value matrix where the temperature deviation value is less than or equal to the abnormality determination threshold is divided into a normal temperature region by using the existing region growing algorithm, and the normal temperature region is uniformly divided according to the size of the hotspot region, to obtain a plurality of normal temperature partitions in the target battery pack, which is not described herein again.

[0023] It should be noted that the hotspot region in the present application represents a local hotspot section in the target battery pack where a thermal runaway risk exists, and the hotspot region is determined to effectively identify a region where the temperature is too high and which affects the overall thermal management safety, to provide a target position for implementing key cooling or local regulation. The normal temperature partition in the present application represents a local region in the target battery pack where the temperature deviation value is within a stable range, and is used to provide a heat stable reference region as a benchmark model for heat dissipation trend evaluation and abnormality comparison.

[0024] In step 102, the thermal regulation load of the current hotspot area is analyzed based on the heat diffusion characteristics of the temperature in the hotspot area in combination with the current operating state of the new energy vehicle, and then the cooling mode corresponding to the target battery pack is selected according to the thermal regulation load.

[0025] It should be noted that the thermal regulation load in the present application represents the actual heat dissipation capacity value required by the hotspot area, and the thermal regulation load is used to guide the adjustment amount of the current cooling strategy, which is a control target value for quantifying the intervention intensity of temperature control. By determining the thermal regulation load, the behavior of the hotspot area can be upgraded from responsive to predictive, so that the cooling system no longer relies on temperature triggering but performs pre-intervention based on predicted load, effectively reducing the risk of thermal runaway.

[0026] In some embodiments, the thermal regulation load of the current hotspot area can be analyzed based on the heat diffusion characteristics of the temperature in the hotspot area in combination with the current operating state of the new energy vehicle by the following steps, that is: extracting the time series temperature data of the hotspot area in the current time period, and determining the heat diffusion characteristics of the temperature in the hotspot area according to the time series temperature data; generating a heat diffusion field model according to the heat diffusion characteristics in combination with the spatial distribution density of the battery monomers in the hotspot area; obtaining the current operating state parameters of the new energy vehicle, and inputting the operating state parameters into the heat diffusion field model to extract the temperature variation trend; calculating the thermal regulation load of the current hotspot area based on the temperature variation trend.

[0027] In a specific implementation, first, temperature data at multiple time points is collected by a temperature sensor for each battery monomer in a space range in which the hotspot area is located, so as to construct time-series temperature data of the hotspot area in a current time period, the hotspot area is divided into regular voxel grid units, each grid unit center point corresponds to a temperature value, a temperature change value between adjacent time points at each grid unit in the hotspot area is calculated by using a difference method based on a temperature gradient, and an average temperature change value is taken as a heat diffusion feature of the temperature in the hotspot area; second, a spatial distribution density of the battery monomer in the hotspot area is calculated according to an existing density calculation method, a heat diffusion field model is constructed by taking the heat diffusion feature as an initial heat source behavior input and taking a normalized spatial distribution density as an adjustment factor of local specific heat capacity and thermal conductivity according to a heat conduction control equation; then, current running state parameters of the new energy vehicle are called by a vehicle control system, the running state parameters include a vehicle speed, a cooling system state, a battery power output value and an ambient temperature of the vehicle, parameters in the running state parameters are input into related modules in the heat diffusion field model according to categories, for example, the battery power output value is mapped to a unit battery monomer heat generation rate, the cooling system state is set as a boundary heat dissipation condition, the vehicle speed and the ambient temperature are used to adjust a heat convection boundary condition, and simulation calculation is performed in a set time step by the heat diffusion field model, a predicted temperature change data sequence of the hotspot area is output, and a temperature change curve obtained by least square fitting of the predicted temperature change data sequence is taken as a temperature change trend; finally, a heat regulation load of the current hotspot area is calculated based on the temperature change trend, that is, a maximum temperature rise rate (a rate corresponding to a point at which the temperature rises fastest in the temperature change trend) and a minimum temperature rise rate (a rate corresponding to a point at which the temperature rises slowest in the temperature change trend) in the temperature change trend are extracted, and a difference between the maximum temperature rise rate and the minimum temperature rise rate is converted into the heat regulation load of the current hotspot area by using an equivalent heat capacity (a product of the equivalent heat capacity corresponding to the hotspot area and the difference is taken as the heat regulation load of the current hotspot area), the equivalent heat capacity represents a conversion parameter for converting the difference between the maximum temperature rise rate and the minimum temperature rise rate into the heat regulation load, and can be given according to expert knowledge, which will not be described herein.

[0028] It should be noted that the heat diffusion feature in the embodiment represents a heat conduction trend of the temperature in the hotspot area over time; the heat diffusion field model in the embodiment represents a heat conduction equation system for simulating temperature changes over time and space, which is established based on the temperature diffusion feature in the hotspot area and the arrangement density of the battery monomer in the space, and is used to restore and predict a temperature change process of the hotspot area under a specified boundary condition; and the temperature change trend in the embodiment represents a temperature change of the hotspot area in a prediction time, which is used as prior information for judging a heat load size and a regulation urgency degree.

[0029] In some embodiments, the selection of the cooling mode corresponding to the target battery pack according to the thermal regulation load can be achieved by the following steps, that is: obtaining an adaptive parameter library of preset cooling modes; matching the thermal regulation load with the adaptive parameter library, and extracting the cooling mode corresponding to the thermal regulation load as the cooling mode corresponding to the target battery pack.

[0030] In a specific implementation, first, an adaptive parameter library of preset cooling modes is obtained, the adaptive parameter library including all cooling modes supported by a current vehicle model, the cooling modes including air cooling, water cooling, phase change material assisted cooling, or a combined mode of the above cooling modes, and each cooling mode being preset with a corresponding adaptive parameter range (i.e., a thermal regulation load interval), which can be set according to machine learning of the thermal regulation load under the cooling mode, and thus is not described herein again. Then, the thermal regulation load is taken as an input, and is compared with the thermal load matching interval of each cooling mode in the adaptive parameter library. If the thermal regulation load falls within the load response interval of a cooling mode, the cooling mode is taken as the cooling mode corresponding to the target battery pack.

[0031] The adaptive parameter library of the cooling mode in this embodiment is a parameter set including all defined cooling modes and their adaptive parameter ranges, which is used to quickly filter out a cooling scheme with cooling execution capability according to the input thermal regulation load, and is an important matching basis for the preliminary screening of the thermal control strategy. The cooling mode in this application represents a cooling control scheme suitable for temperature control of a hot spot area under a current vehicle operating state, and is a final control decision result after accurate matching of the thermal load and the cooling capacity.

[0032] In step 103, the temperature rise gradient of the hot spot area is monitored in real time when the target battery pack is in the cooling mode, the thermal state of the current hot spot area is predicted by combining the heat dissipation change characteristics of each normal temperature partition under the cooling mode with the temperature rise gradient, and then the thermal runaway critical deviation of the temperature in the hot spot area is obtained.

[0033] In some embodiments, referring to Figure 3 FIG. 2 is an example flowchart for determining a temperature rise gradient, according to some embodiments of the present application. In this embodiment, the real-time monitoring of the temperature rise gradient of the hot spot area when the target battery pack is in the cooling mode can be achieved by the following steps: First, in step 1031, real-time temperature data of the hot spot area is continuously collected at a set sampling frequency when the target battery pack is in the cooling mode. Then, in step 1032, a temperature change curve of the hot spot area is constructed according to the real-time temperature data. Finally, in step 1033, the temperature rise gradient of the hotspot area is calculated through the temperature change curve.

[0034] In a specific implementation, first, when the target battery pack is in the cooling mode, real-time temperature data of the hotspot area is continuously collected by the temperature sensor at a set sampling frequency (e.g., 1 Hz), which is determined by the battery thermal response rate and the cooling mode response time to ensure that the temperature fluctuations can be captured; second, the real-time temperature data is stored in chronological order to form a time series, and a temperature change curve of the hotspot area is constructed based on the time series, where time is taken as the horizontal axis and temperature value is taken as the vertical axis; third, the temperature rise gradient of the hotspot 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, and thus the temperature rise gradient of the hotspot area is obtained.

[0035] It should be noted that, in this embodiment, the real-time temperature data refers to a set of current temperature values of each sensing node in the hotspot area collected by the temperature sensor array at a predetermined time interval during the continuous operation of the cooling mode, which is a continuous time domain data basis reflecting the current thermal state change of the hotspot area; the temperature change curve represents a temperature-time two-dimensional function graph drawn according to the change of the continuous temperature values of each sampling point in the hotspot area with time, which is an important visual structure representing the thermal response trend of the hotspot area; the temperature rise gradient in this application represents the change rate of the temperature in the hotspot area per unit time, which is a key dynamic thermal index for measuring the current cooling efficiency and judging whether the temperature control response is effective. By determining the temperature rise gradient, it can be judged whether the current cooling means responds in time and effectively, whether there is a cooling lag or thermal backflow phenomenon, so as to adjust or alarm the cooling control strategy in time.

[0036] In some embodiments, the thermal state of the current hotspot area is predicted by combining the temperature rise gradient with the heat dissipation change characteristics of each normal temperature partition in the cooling mode, and thus the thermal runaway critical deviation of the temperature in the hotspot area can be obtained by the following steps, that is: determining the heat dissipation change characteristics of each normal temperature partition in the cooling mode; establishing a thermal conduction deviation relationship between the hotspot area and each normal temperature partition according to the temperature rise gradient and the heat dissipation change characteristics of each normal temperature partition; predicting the thermal state change value of the hotspot area under the current cooling condition based on the thermal conduction deviation relationship; determining the thermal runaway critical deviation of the temperature in the hotspot area according to the thermal state change value and the target thermal equilibrium state of the target battery pack.

[0037] In a specific implementation, first, historical temperature change data of each normal temperature partition in the cooling mode is obtained, and a sliding average filtering algorithm is used to remove data noise. For each normal temperature partition, the heat flux density of the historical temperature change data corresponding to the normal temperature partition (i.e., calculated by the Fourier heat conduction function (q = -k ∇T, where q is the heat flux density, k is the thermal conductivity, and ∇T is the temperature rise gradient)) is calculated as the heat dissipation change feature, and then the heat dissipation change feature of each normal temperature partition in the cooling mode is obtained. Second, a heat conduction deviation relationship between the hotspot region and each normal temperature partition is established according to the temperature rise gradient and the heat dissipation change feature of each normal temperature partition, that is, the temperature rise gradient is input into the Fourier heat conduction function as an input parameter, and the heat flux density of the current hotspot region is output from the Fourier heat conduction function. The absolute difference between the heat flux density and the heat dissipation change feature of each normal temperature partition is taken as the heat conduction deviation relationship between the hotspot region and the normal temperature partition, and then the heat conduction deviation relationship between the hotspot region and each normal temperature partition is obtained. Then, the heat state change value of the hotspot region under the current cooling condition is predicted based on the heat conduction deviation relationship, that is, all heat conduction deviation relationships are constructed into a heat conduction response relationship matrix according to the distribution order of the normal temperature partition. The heat conduction response relationship matrix is taken as a boundary condition, substituted into a thermodynamic model (i.e., a three-dimensional unsteady heat conduction equation), combined with the current cooling condition (i.e., the air cooling wind speed and the liquid cooling flow parameter), and the equation is solved by a numerical iteration algorithm (i.e., the Gauss-Seidel iteration method) to obtain the heat state change value of the hotspot region under the current cooling condition. Finally, according to the target thermal equilibrium state of the target battery pack (i.e., a pre-set temperature range for normal operation of the target battery pack, which can be set according to expert knowledge), the heat state change value is compared with the target temperature interval, and the thermal runaway critical deviation of the temperature in the hotspot 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 interval and the heat state change value is taken as the thermal runaway critical deviation).

[0038] It should be noted that the heat dissipation change feature in the embodiment represents the dynamic response characteristic of the normal temperature partition reflecting the process of releasing heat to the outside under the action of the cooling mode, and is used to represent the heat dissipation capacity and heat stability trend that the normal temperature partition can provide in the controlled cooling environment; the heat conduction deviation relationship in the embodiment represents the heat energy transfer deviation between the hotspot region and the surrounding normal temperature partition in the cooling process, and is used to reflect the heat coupling strength between different temperature zones; the heat state change value in the embodiment represents the predicted heat behavior index of the change of the temperature of the hotspot region with time, and is used to reflect whether the temperature of the hotspot region tends to be stable under the current cooling mode, which is an important basis for evaluating the cooling effect and subsequent control strategy; the heat runaway critical deviation in the application represents the temperature deviation amplitude between the current predicted heat state of the hotspot region and the target heat balance state, and is used to measure the degree of deviation of the current heat state from the system expected safe operation interval, which is an index for evaluating whether the hotspot region exists heat runaway, and is also a direct quantitative basis for heat dissipation compensation or adjustment in the control strategy execution.

[0039] In step 104, the heat gradient distribution feature is extracted from the heat distribution map of each normal temperature partition, and then the heat dissipation contribution degree of the heat dissipation process of the target battery pack and the heat dissipation temperature of the hotspot region are analyzed according to the heat dissipation compensation of the heat runaway critical deviation of each heat gradient distribution feature, to obtain the heat dissipation compensation gain of the hotspot region.

[0040] In some embodiments, the heat gradient distribution feature extracted from the heat distribution map of each normal temperature partition can be realized by the following steps, that is: Obtaining the heat distribution map of each normal temperature partition; Selecting a normal temperature partition as a selected normal temperature partition, performing spatial temperature difference operation on the heat distribution map corresponding to the selected normal temperature partition to obtain the temperature gradient vector field of the selected normal temperature partition; Determining the heat gradient distribution feature of the selected normal temperature partition according to the temperature gradient vector field; Continue to determine the heat gradient distribution feature of the remaining normal temperature partition.

[0041] In a specific implementation, first, the temperature sensor collects real-time temperature data of each normal temperature partition during the stable operation period of the cooling mode, and according to the geometric arrangement position of the temperature sensor, the collected temperature data is mapped to a two-dimensional thermal image structure diagram corresponding to the battery pack structure according to the corresponding coordinate points, forming a thermal distribution diagram corresponding to each normal temperature partition. Secondly, a normal temperature partition is selected as a selected normal temperature partition, and a central difference method is used to perform spatial temperature difference operation on the thermal distribution diagram corresponding to the selected normal temperature partition to obtain a temperature gradient vector field of the selected normal temperature partition. Specifically, the difference between the temperature values of each thermal distribution point in the thermal distribution diagram in the horizontal (X-axis) and vertical (Y-axis) directions is calculated to form a two-dimensional temperature gradient vector corresponding to each thermal distribution point. Then, the amplitude of the temperature gradient vector is taken as an element of the temperature gradient vector field, and the temperature gradient vector field of the selected normal temperature partition is obtained. Then, the average thermal gradient in the temperature gradient vector field is taken as the thermal gradient distribution feature of the selected normal temperature partition. Finally, the thermal gradient distribution features of the remaining normal temperature partitions are determined by the determination method of "determining the thermal gradient distribution feature of the selected normal temperature partition according to the temperature gradient vector field".

[0042] It should be noted that the thermal distribution diagram in this embodiment represents the distribution state diagram of the temperature in the normal temperature partition, which is used to reflect the heat diffusion pattern and temperature uniformity of each normal temperature partition in a specified time period. The temperature gradient vector field in this embodiment refers to a spatial field composed of multiple two-dimensional vectors representing the rate of temperature change in the normal temperature partition, which is used to depict the change trend and change intensity of the temperature at different positions. The thermal gradient distribution feature in this application refers to a characteristic parameter describing the change intensity of the temperature 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.

[0043] In some embodiments, the heat dissipation compensation gain of the hotspot region can be obtained by performing heat dissipation compensation analysis on the heat dissipation temperature of the hotspot region according to the heat dissipation contribution degree of each thermal gradient distribution feature to the heat dissipation process of the target battery pack and the heat dissipation contribution degree of the heat runaway critical deviation. The following steps can be used to achieve this: Determine the heat dissipation contribution degree of each thermal gradient distribution feature to the heat dissipation process of the target battery pack. Determine the heat influence feature of the normal temperature partition of the target battery pack according to all the heat dissipation contribution degrees. Generate the heat dissipation compensation gain of the hotspot region according to the heat influence feature and the heat runaway critical deviation.

[0044] In a specific implementation, first, the heat dissipation contribution degree of each heat gradient distribution feature to the heat dissipation process of the target battery pack is determined, that is, the heat gradient distribution feature of the entire region of the target battery pack in the heat dissipation process is obtained as a global heat gradient distribution feature, the quotient of the heat gradient distribution feature and the global heat gradient distribution feature is taken as the heat dissipation contribution degree of the heat gradient distribution feature to the heat dissipation process of the target battery pack, and then the heat dissipation contribution degrees of the heat gradient distribution features to the heat dissipation process of the target battery pack are obtained; then, the heat influence feature of the normal temperature partition of the heat dissipation process of the target battery pack is determined according to all the heat dissipation contribution degrees, that is, all the heat dissipation contribution degrees are normalized to 0 to 1 through minimum-maximum normalization, and the average of all the normalized heat dissipation contribution degrees is taken as the heat influence feature of the normal temperature partition of the heat dissipation process of the target battery pack, the heat influence feature is a dimensionless parameter; finally, the heat dissipation compensation gain of the hot spot region is generated according to the heat influence feature and the thermal runaway critical deviation, that is, the heat influence feature is taken as the weight value of the thermal runaway critical deviation to obtain a 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 region.

[0045] It should be noted that the heat dissipation contribution degree in the embodiment represents an index of the promotion degree of the heat gradient distribution feature to the heat dissipation process of the target battery pack; the heat influence feature in the embodiment represents an influence parameter of the normal temperature region on the heat dissipation of the target battery pack region; the heat dissipation compensation gain in the application represents a parameter for regulating the heat dissipation temperature of the hot spot region, which can effectively adjust the heat dissipation degree of the hot spot region, and through the determination of the heat dissipation compensation gain, the cooling system can be effectively guided to implement targeted cooling operation, so as to realize the steady-state regulation of the hot spot temperature and ensure the efficient operation of the target battery pack within the thermal safety range.

[0046] It should also be noted that the heat dissipation compensation analysis in the application represents an analysis process of the heat dissipation temperature compensation adjustment of the hot spot region, wherein the heat dissipation temperature of the hot spot region is analyzed for heat dissipation compensation according to the heat dissipation contribution degree of each heat gradient distribution feature to the heat dissipation process of the target battery pack and the thermal runaway critical deviation, that is, the heat dissipation contribution degree of each heat gradient distribution feature to the heat dissipation process of the target battery pack is determined; the heat influence feature of the normal temperature partition of the heat dissipation process of the target battery pack is determined according to all the heat dissipation contribution degrees; and the heat dissipation compensation gain of the hot spot region is generated according to the heat influence feature and the thermal runaway critical deviation, that is, the heat dissipation compensation gain is taken as the result of the heat dissipation compensation analysis, and then the heat dissipation temperature of the hot spot region is analyzed for heat dissipation compensation.

[0047] In step 105, the heat dissipation temperature of the hot spot region is adjusted according to the heat dissipation compensation gain.

[0048] In some embodiments, adjusting the heat dissipation temperature of the hotspot region according to the heat dissipation compensation gain can be achieved by the following steps: inputting the heat dissipation compensation gain into a temperature compensation model of the hotspot region to generate a target heat dissipation temperature adjustment curve; monitoring the heat dissipation temperature of the hotspot region in real time and feeding back the deviation of the heat dissipation temperature from the target heat dissipation temperature adjustment curve; updating the heat dissipation compensation gain according to the deviation feedback result to complete the adjustment process of the heat dissipation temperature of the hotspot region.

[0049] In specific implementation, first, the heat dissipation compensation gain is loaded as an input parameter into the temperature compensation model of the hotspot region, and a target heat dissipation temperature adjustment curve is generated by the temperature compensation model. The temperature compensation model is constructed based on the principle of thermodynamics, and a linear function is used to describe the relationship between the heat dissipation temperature and the heat dissipation compensation gain. For example, a first-order inertia link model can be used as the temperature compensation model, which will not be described here. Then, the heat dissipation temperature of the hotspot region is monitored in real time by a temperature sensor, and the deviation 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 described here. Finally, the heat dissipation compensation gain is updated according to the deviation feedback result by using proportional-integral or proportional-integral-derivative to complete the adjustment process of the heat dissipation temperature of the hotspot region, which will not be described here.

[0050] In this embodiment, the target heat dissipation temperature adjustment curve refers to the expected control trajectory for guiding the heat dissipation temperature of the hotspot region to change over time. This curve describes how the temperature of the hotspot region should gradually decrease or maintain over time under a specified cooling mode to achieve stable regulation 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 regulation process of the hotspot region, which is used to measure the deviation between the current system operating state and the ideal control state.

[0051] In addition, another aspect of the present application provides a new energy vehicle battery heat dissipation system, which is described in detail with reference to Figure 4 The figure is a structural schematic diagram of a new energy vehicle battery heat dissipation system according to some embodiments of the present application, which includes an identification module 201, a processing module 202 and an execution module 203, which are described as follows: The identification module 201 is mainly used to identify the temperature abnormal hotspot region and multiple normal temperature partitions in the target battery pack in the new energy vehicle. 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 state of the new energy vehicle, and then select a cooling mode corresponding to the target battery pack according to the thermal control load; The processing module 202 is further configured to monitor the temperature rise gradient of the hotspot area in real time when the target battery pack is in the cooling mode, predict the thermal state of the current hotspot area based on the temperature rise gradient combined with the heat dissipation change characteristics of each normal temperature partition in the cooling mode, and thereby obtain a thermal runaway critical deviation of the temperature in the hotspot area; In addition, the processing module 202 is further configured to extract thermal gradient distribution features from the thermal distribution maps of each normal temperature zone, and then perform a 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, to obtain a heat dissipation compensation gain for the hot spot area; 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.

[0052] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned new energy vehicle battery heat dissipation method.

[0053] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a method for dissipating heat from a new energy vehicle battery according to some embodiments of the present application. The method for dissipating heat from a new energy vehicle battery in the above embodiment can be achieved by Figure 5 The computer device shown in FIG3 is implemented as shown in FIG3 , which includes at least one processor 301 , a communication bus 302 , a memory 303 and at least one communication interface 304 .

[0054] The processor 301 can be a general-purpose central processing unit (CPU), or an application-specific integrated circuit (ASIC) or one or more for controlling the execution of the new energy vehicle battery heat dissipation method in this application.

[0055] The communication bus 302 may be used to transmit information between the aforementioned components.

[0056] The memory 303 can be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magneto-optical disk, a magnetic disk or other magnetic storage device, or any other medium capable of storing desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto. The memory 303 can exist independently, and is connected to the processor 301 through the communication bus 302. The memory 303 can also be integrated with the processor 301.

[0057] The memory 303 is configured to store program codes for implementing the solutions of the present application, and the processor 301 is configured to control the execution of the program codes. The program codes can include one or more software modules. The determination of the new energy vehicle battery heat dissipation method in the above embodiments can be implemented by one or more software modules in the program codes of the processor 301 and the memory 303.

[0058] The communication interface 304 is configured to communicate with other devices or communication networks, such as an Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc., using any transceiver-like device.

[0059] In a specific implementation, as an example, the computer device can include a plurality of processors, each of which can be a single-CPU processor or a multi-CPU processor. The processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0060] The computer device described above can be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device can be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of the present application do not limit the type of the computer device.

[0061] In addition, the present application also provides a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program is executed by a processor to realize the new energy automobile battery heat dissipation method.

[0062] Although the preferred embodiments of the present application have been described, those skilled in the art who are familiar with the basic inventive concept can make additional changes and modifications to the embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0063] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A method for dissipating heat from a new energy vehicle battery, characterized in that: The steps include: Identify abnormal temperature hotspots and multiple normal temperature zones within the target battery pack of new energy vehicles; Analyzing 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 state of the new energy vehicle, and then selecting a cooling mode corresponding to the target battery pack according to the thermal control 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, and the thermal state of the current hot spot area is predicted based on the temperature rise gradient combined 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; Extracting thermal gradient distribution features from the thermal distribution maps of each normal temperature zone, and then performing a 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, to obtain a heat dissipation compensation gain for the hot spot area; The heat dissipation temperature of the hot spot area is adjusted according to the heat dissipation compensation gain.

2. The method according to claim 1, wherein Identifying abnormal temperature hotspots and multiple normal temperature zones within the target battery pack of a new energy vehicle specifically includes: Obtain temperature data of the target battery pack in new energy vehicles; Constructing a temperature distribution matrix of a target battery pack in a new energy vehicle based on the temperature data; Calculating the deviation between the temperature value of each monitoring point in the temperature distribution matrix and the temperature threshold, thereby obtaining a temperature deviation value matrix; constructing areas in the temperature deviation value matrix where the temperature deviation value is greater than the abnormality determination threshold as hot spots of abnormal temperature in the target battery pack; The areas in the temperature deviation value matrix that are less than or equal to the abnormality determination threshold are divided into normal temperature partitions, thereby obtaining a plurality of normal temperature partitions in the target battery pack.

3. The method according to claim 1, wherein Based on the thermal diffusion characteristics of the temperature in the hot spot area and the current operating state of the new energy vehicle, the thermal control load of the current hot spot area is analyzed, which specifically includes: Extracting time-series temperature data of the hotspot area in a current time period, and determining a thermal diffusion characteristic of the temperature in the hotspot area based on the time-series temperature data; Generate a thermal diffusion field model based on the thermal diffusion characteristics and the spatial distribution density of battery cells in the hot spot area; Obtaining current operating state parameters of the new energy vehicle, and inputting the operating state parameters into the thermal diffusion field model to extract temperature change trends; The heat control load of the current hot spot area is calculated based on the temperature change trend.

4. The method according to claim 1, wherein Selecting a cooling mode corresponding to the target battery pack according to the thermal control load specifically includes: Obtaining the adaptation parameter library of the preset cooling mode; The thermal control load is matched with the adaptation parameter library, and a cooling mode corresponding to the thermal control load is extracted as a cooling mode corresponding to the target battery pack.

5. The method according to claim 1, wherein The thermal state of the current hotspot 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 hotspot area is obtained, specifically including: Determine heat dissipation change characteristics of each normal temperature partition under the cooling mode; Establishing a heat conduction deviation relationship between the hotspot area and each normal temperature partition based on the temperature rise gradient and the heat dissipation change characteristics of each normal temperature partition; Predicting a thermal state change value of the hot spot area under current cooling conditions based on the heat conduction deviation relationship; A thermal runaway critical deviation of the temperature in the hot spot area is determined according to the thermal state change value and a target thermal equilibrium state of a target battery pack.

6. The method according to claim 1, wherein Extracting thermal gradient distribution features from the thermal distribution map of each normal temperature zone specifically includes: Obtain thermal distribution maps of each normal temperature zone; Selecting a normal temperature partition as a selected normal temperature partition, performing a spatial temperature difference operation on a thermal distribution map corresponding to the selected normal temperature partition, and obtaining a temperature gradient vector field of the selected normal temperature partition; determining a thermal gradient distribution characteristic of a selected normal temperature zone according to the temperature gradient vector field; Continue to determine the thermal gradient distribution characteristics of the remaining normal temperature zones.

7. The method according to claim 2, wherein The temperature data of the target battery pack in the new energy vehicle is obtained through the temperature sensor array.

8. A new energy vehicle battery cooling system, characterized in that: include: Identification module, used to identify abnormal temperature hot spots and multiple normal temperature zones within the target battery pack of new energy vehicles; a processing module configured to analyze a 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 state of the new energy vehicle, and then select a cooling mode corresponding to the target battery pack according to the thermal control load; The processing module is further configured to monitor the temperature rise gradient of the hotspot area in real time when the target battery pack is in the cooling mode, and predict the thermal state of the current hotspot area based on the temperature rise gradient combined with the heat dissipation change characteristics of each normal temperature partition in the cooling mode, thereby obtaining a thermal runaway critical deviation of the temperature in the hotspot area; The processing module is further configured to extract thermal gradient distribution features from the thermal distribution maps of each normal temperature zone, and then perform a 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, to obtain a heat dissipation compensation gain for the hot spot area; An 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 stores a code, and the processor is configured to obtain the code and execute the new energy vehicle battery heat dissipation method according to 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 a processor, the new energy vehicle battery heat dissipation method according to any one of claims 1 to 7 is implemented.

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