Heat dissipation control method and system for energy storage equipment

Abstract

The invention belongs to the technical field of temperature control, and provides a heat dissipation control method and system for energy storage equipment. The method comprises the steps that the charging and discharging state of the energy storage equipment is recognized, first environment data of the environment where the energy storage equipment is located in the charging and discharging state process is obtained, and a first to-be-dissipated heat amount is predicted according to the charging and discharging state and the first environment data; when the energy storage device is in the charging state and meets the charging ending condition, second environment data of the environment where the energy storage device is located in the charging state process is obtained, and a second to-be-dissipated heat amount is predicted according to the second environment data and the expected use information of the energy storage device; and working parameters of the cooling fan are determined according to the first to-be-cooled capacity and the second to-be-cooled capacity, and the cooling fan is controlled to execute cooling operation on the energy storage equipment according to the working parameters. Based on the charging and discharging state, the environment data and the charging stage of the energy storage equipment, the targeted working parameters are determined for the heat dissipation fan, and intelligent heat dissipation control over the energy storage equipment is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and in particular to a heat dissipation control method and system for energy storage equipment. Background Art

[0002] Heat dissipation control of energy storage devices (such as battery packs) is a key technology to ensure their stable operation and extend their life. In the prior art, a cooling fan is often installed on the energy storage device to dissipate heat for the energy storage device. Specifically, during the charging and discharging process of the energy storage device, the cooling fan is started to facilitate the rapid dissipation of the heat generated inside the energy storage device. However, the working parameters of the cooling fan in the above method are fixed, that is, the cooling fan dissipates heat for the energy storage device at a fixed speed / power, etc., resulting in a poor balance between the heat dissipation efficiency and the heat dissipation economy, which needs to be improved. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a heat dissipation control method and system for energy storage equipment, an electronic device, a computer storage medium and a computer program product.

[0004] The present invention discloses a heat dissipation control method for an energy storage device, the method comprising the following steps: identifying the charge and discharge state of the energy storage device, the charge and discharge state comprising a charging state and a discharging state, and acquiring first environmental data of an environment in which the energy storage device is located during the charge and discharge state, and predicting a first amount of heat to be dissipated according to the charge and discharge state and the first environmental data; when the charge and discharge state of the energy storage device is a charging state and a charging end condition is met, acquiring second environmental data of an environment in which the energy storage device is located during the charging state, and predicting a second amount of heat to be dissipated according to the second environmental data and expected use information of the energy storage device; determining working parameters of a heat dissipation fan according to the first amount of heat to be dissipated and the second amount of heat to be dissipated, and controlling the heat dissipation fan to perform a heat dissipation operation on the energy storage device according to the working parameters.

[0005] Optionally, the identifying the charge and discharge state of the energy storage device includes: shooting a video image of the energy storage device, determining a storage device of the energy storage device according to the video image, and identifying the charge and discharge properties of the storage device, the charge and discharge properties including charging properties and discharging properties; if the charge and discharge properties of the storage device are charging properties, then determining that the charge and discharge state of the energy storage device is a charging state; if the charge and discharge properties of the storage device are discharging properties, then determining that the charge and discharge state of the energy storage device is a discharging state.

[0006] Optionally, the obtaining of first environmental data of the environment in which the energy storage device is located during the charging and discharging state includes: taking a panoramic image of the environment in which the energy storage device is located, and using a convolutional network to respectively extract enclosure data, heat dissipation device data, and heating body data of the environment from the panoramic image; and matrixing and integrating the enclosure data, the heat dissipation device data, and the heating body data, respectively, to obtain the first environmental data of the environment in which the energy storage device is located during the charging and discharging state.

[0007] Optionally, predicting the first amount of heat to be dissipated based on the charge and discharge state and the first environmental data includes: obtaining device parameter information of the energy storage device, inputting the device parameter information and the charge and discharge state into a Transformer-based heat depth prediction model, and the heat depth prediction model outputs a third amount of heat to be dissipated; inputting the first environmental data into a heat dissipation difficulty assessment model, and the heat dissipation difficulty assessment model outputs a first heat dissipation difficulty coefficient; wherein the first heat dissipation difficulty coefficient is a value greater than 1, and the heat dissipation difficulty assessment model is constructed based on a graph convolutional neural network; and multiplying the third amount of heat to be dissipated by the first heat dissipation difficulty coefficient to obtain the first amount of heat to be dissipated.

[0008] Optionally, when the charge and discharge state of the energy storage device is a charging state and the charging termination condition is met, second environmental data of the environment in which the energy storage device is located during the charging state is obtained, and a second amount of heat to be dissipated is predicted based on the second environmental data and the expected use information of the energy storage device, including: retrieving the expected end time of the energy storage device, calculating the remaining time between the current time and the expected end time, and when the remaining time reaches the time threshold, it is determined that the charging state meets the charging termination condition; obtaining the second environmental data of the environment in which the energy storage device is located during the charging state, the second environmental data and the first environmental data contain the same type of feature data, and using a thermal depth prediction model to predict and process the device parameter information, charging state, and the second environmental data of the energy storage device to obtain a fourth amount of heat to be dissipated; classifying and judging the expected use information of the energy storage device, if the expected use information is a use type, multiplying the fourth amount of heat to be dissipated by a first coefficient to obtain the second amount of heat to be dissipated; if the expected use information is a storage type, multiplying the fourth amount of heat to be dissipated by a second coefficient to obtain the second amount of heat to be dissipated; wherein, the first coefficient is less than the second coefficient, and the first coefficient is greater than or equal to 1, and the second coefficient is greater than 1.

[0009] Optionally, the duration threshold is determined in the following manner: use a heat dissipation difficulty assessment model to evaluate the second environmental data to obtain a second heat dissipation difficulty coefficient, and determine the duration threshold based on the second heat dissipation difficulty coefficient; wherein the duration threshold is positively correlated with the second heat dissipation difficulty coefficient.

[0010] The present invention also discloses a heat dissipation control system for an energy storage device, the system comprising a processing device and a storage device, the computer code stored in the storage device being called and executed by the processing device to implement the following steps: identifying the charge and discharge state of the energy storage device, the charge and discharge state comprising a charging state and a discharging state, and obtaining first environmental data of the environment in which the energy storage device is located during the charge and discharge state, and predicting a first amount of heat to be dissipated based on the charge and discharge state and the first environmental data; when the charge and discharge state of the energy storage device is a charging state and a charging termination condition is met, obtaining second environmental data of the environment in which the energy storage device is located during the charging state, and predicting a second amount of heat to be dissipated based on the second environmental data and expected use information of the energy storage device; determining working parameters of a cooling fan based on the first amount of heat to be dissipated and the second amount of heat to be dissipated, and controlling the cooling fan to perform heat dissipation operations on the energy storage device according to the working parameters.

[0011] The present invention also discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement any of the above methods.

[0012] The present invention also discloses a computer storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement any of the above methods.

[0013] The present invention also discloses a computer program product, which includes computer codes. When the computer codes are executed by a processor of an electronic device, any of the above methods is implemented.

[0014] The beneficial effect of the present invention is that the present invention predicts the estimated heat dissipation of the energy storage device (i.e., the first heat dissipation and the second heat dissipation mentioned above) based on the charging and discharging state of the energy storage device, the environmental data of the environment, and the charging stage, and accordingly determines targeted working parameters for the cooling fan, thereby realizing intelligent heat dissipation control of the energy storage device. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1It is a flow chart of a heat dissipation control method for energy storage equipment disclosed in an embodiment of the present invention.

[0017] Figure 2 It is a schematic diagram of a composite model disclosed in an embodiment of the present invention.

[0018] Figure 3 It is a structural schematic diagram of a heat dissipation control system of an energy storage device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0020] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0021] In the prior art, a cooling fan is often arranged on the energy storage device to dissipate heat from the energy storage device. Specifically, during the charging and discharging process of the energy storage device, the cooling fan is started to quickly dissipate the heat generated inside the energy storage device. However, the working parameters of the cooling fan in the above method are fixed, that is, the cooling fan dissipates heat from the energy storage device at a fixed speed / power, etc., resulting in a poor balance between the cooling efficiency and the cooling economy, which needs to be improved.

[0022] In response to the above technical issues, such as Figure 1 As shown, an embodiment of the present invention discloses a heat dissipation control method for an energy storage device, the method comprising the following steps: identifying the charge and discharge state of the energy storage device, the charge and discharge state comprising a charging state and a discharging state, and acquiring first environmental data of an environment in which the energy storage device is located during the charge and discharge state, and predicting a first amount of heat to be dissipated according to the charge and discharge state and the first environmental data; when the charge and discharge state of the energy storage device is a charging state and a charging end condition is satisfied, acquiring second environmental data of an environment in which the energy storage device is located during the charging state, and predicting a second amount of heat to be dissipated according to the second environmental data and expected use information of the energy storage device; determining working parameters of a cooling fan according to the first amount of heat to be dissipated and the second amount of heat to be dissipated, and controlling the cooling fan to perform a heat dissipation operation on the energy storage device according to the working parameters.

[0023] The present invention divides the heat dissipation control of the energy storage device into two stages, namely the front stage of charging and discharging and the end stage of charging, and adopts different heat dissipation working parameters in the above two stages. Specifically, the charging and discharging state of the energy storage device is first identified, including the charging state, the discharging state, and the first environmental data of the energy storage device during the charging and discharging process is obtained, and the first heat dissipation amount to be dissipated is predicted based on the charging and discharging state and the first environmental data. Then, the working parameters of the cooling fan are determined based on the first heat dissipation amount, and the cooling fan is controlled to perform heat dissipation control on the energy storage device according to the working parameters in the front stage of charging and discharging of the energy storage device.

[0024] At the same time, if the charge and discharge state of the energy storage device is a charging state and the charging end condition is met, that is, charging is about to be completed, the second environmental data of the environment in which the energy storage device is located during the charging state is obtained, and the second amount of heat to be dissipated is predicted based on the second environmental data and the expected use information of the energy storage device. Then, the new working parameters of the cooling fan are determined based on the second amount of heat to be dissipated, and the cooling fan is controlled to perform heat dissipation control on the energy storage device according to the new working parameters at the end of the charging and discharging of the energy storage device.

[0025] Therefore, the present invention predicts the estimated heat dissipation of the energy storage device (i.e., the first heat dissipation and the second heat dissipation mentioned above) based on the charging and discharging state of the energy storage device, the environmental data of the environment, and the charging stage, and accordingly determines targeted working parameters for the cooling fan, thereby realizing intelligent heat dissipation control of the energy storage device.

[0026] It should be noted that the working parameters of the cooling fan include any one of the speed and power, etc. These working parameters and the amount of heat to be dissipated are pre-configured in a comparison table, and each set of associated data in the comparison table represents the relationship between the working parameters of the cooling fan and the corresponding amount of heat to be dissipated. Obviously, the higher the amount of heat to be dissipated, the higher the corresponding speed and power.

[0027] Optionally, the identifying the charge and discharge state of the energy storage device includes: shooting a video image of the energy storage device, determining a storage device of the energy storage device according to the video image, and identifying the charge and discharge properties of the storage device, the charge and discharge properties including charging properties and discharging properties; if the charge and discharge properties of the storage device are charging properties, then determining that the charge and discharge state of the energy storage device is a charging state; if the charge and discharge properties of the storage device are discharging properties, then determining that the charge and discharge state of the energy storage device is a discharging state.

[0028] In this embodiment, taking a battery swap station as an example, when a new energy vehicle enters the battery swap station, the automatic battery swap device removes the power battery pack, i.e., the energy storage device, from the new energy vehicle and places it in an internal storage device. The storage device is configured with charging attributes and discharging attributes, respectively. Correspondingly, the storage device with charging attributes is dedicated to replenishing the power battery pack with electric energy, while the discharging attribute is used to discharge the power battery pack. The discharge operation is, for example, to perform a discharge rate test on the power battery pack to evaluate the battery life / health status. The charge and discharge attributes of the storage device can be text, symbols, or graphics posted on the storage device. By taking a video image of the energy storage device, the charge and discharge attributes of the storage device where the energy storage device is placed can be quickly determined based on the video image, thereby determining the charge and discharge status of the energy storage device.

[0029] Optionally, the obtaining of first environmental data of the environment in which the energy storage device is located during the charging and discharging state includes: taking a panoramic image of the environment in which the energy storage device is located, and using a convolutional network to respectively extract enclosure data, heat dissipation device data, and heating body data of the environment from the panoramic image; and matrixing and integrating the enclosure data, the heat dissipation device data, and the heating body data, respectively, to obtain the first environmental data of the environment in which the energy storage device is located during the charging and discharging state.

[0030] In this embodiment, the specific conditions of the environment in which the energy storage device is located will affect the difficulty of heat dissipation of the energy storage device, mainly including the closedness data of the environment, the heat dissipation device data, and the heating element data. Among them, the closedness data refers to the degree of closure of the environment, such as a fully closed environment and a semi-closed environment; the heat dissipation device data refers to the equipment configured in the environment that is dedicated to discharging the heat in the environment to the outside of the environment, such as a ventilation system, which is arranged on the surrounding wall / ceiling of the environment, while the heat dissipation fan in this solution is arranged on the surface of the energy storage device; the heating element data refers to the number of other heating elements in the environment, the expected value of heat dissipated to the environment, etc. Other heating elements are, for example, other energy storage devices, electrical control equipment, etc., and the expected value of heat can be roughly determined based on the number and type of other heating elements.

[0031] During specific implementation, the camera is controlled to capture a panoramic image of the environment in which the energy storage device is located, and a convolutional network is used to extract the above-mentioned closedness data, heat dissipation device data, and heat generating body data of the environment from the panoramic image. These data are then matrix processed, and finally the processed feature matrices are integrated to obtain the first environmental data of the environment in which the energy storage device is located during the charging and discharging process.

[0032] Optionally, predicting the first amount of heat to be dissipated based on the charge and discharge state and the first environmental data includes: obtaining device parameter information of the energy storage device, inputting the device parameter information and the charge and discharge state into a Transformer-based heat depth prediction model, and the heat depth prediction model outputs a third amount of heat to be dissipated; inputting the first environmental data into a heat dissipation difficulty assessment model, and the heat dissipation difficulty assessment model outputs a first heat dissipation difficulty coefficient; wherein the first heat dissipation difficulty coefficient is a value greater than 1, and the heat dissipation difficulty assessment model is constructed based on a graph convolutional neural network; and multiplying the third amount of heat to be dissipated by the first heat dissipation difficulty coefficient to obtain the first amount of heat to be dissipated.

[0033] In this embodiment, in order to predict the first amount of heat to be dissipated of the energy storage device, the present invention constructs two models, namely, a heat depth prediction model based on Transformer and a heat dissipation difficulty assessment model based on a graph convolutional neural network. First, the device parameter information of the energy storage device (including but not limited to battery temperature, battery internal resistance, battery material type, charge and discharge rate, etc., which can be obtained by communicating with the energy storage device management system of the charging station or new energy vehicle) and the charge and discharge status are input into the heat depth prediction model to obtain the third amount of heat to be dissipated predicted by the model. The third amount of heat to be dissipated is the expected amount of heat generated in the charging state / discharging state predicted by the heat depth prediction model based on the inherent characteristics of the energy storage device of this model.

[0034] However, as analyzed above, the heat dissipation of the energy storage device is actually affected by the specific conditions of the environment in which it is located. Therefore, if the working parameters of the cooling fan are determined based on the third amount of heat to be dissipated, the working intensity of the cooling fan will be low, and the energy storage device will not be able to obtain a better heat dissipation effect. In this regard, the present invention also inputs the first environmental data obtained above into the heat dissipation difficulty evaluation model to obtain the first heat dissipation difficulty coefficient predicted by the model (for example, 1.2, 1.3); and then multiplying the first heat dissipation difficulty coefficient by the third amount of heat to be dissipated obtained above to obtain the final first amount of heat to be dissipated.

[0035] The above-mentioned first amount of heat to be dissipated is not the "real" expected heat generation of the energy storage device obtained by prediction, but an artificial parameter after a certain degree of fine-tuning using the first heat dissipation difficulty coefficient. However, the first amount of heat to be dissipated obtained after fine-tuning is more in line with the actual situation of the environment in which the energy storage device is located. The working parameters of the cooling fan determined based on the first amount of heat to be dissipated can achieve a technical effect of better heat dissipation of the energy storage device.

[0036] It should be noted that the heat depth prediction model and the heat dissipation difficulty assessment model respectively adopt the existing Transformer architecture and graph convolutional neural network, and are trained and tested in the existing way. The present invention does not limit their specific architecture, training and testing methods. Of course, the heat depth prediction model and the heat dissipation difficulty assessment model can also be integrated into a composite model, such as Figure 2 shown.

[0037] Optionally, when the charge and discharge state of the energy storage device is a charging state and the charging termination condition is met, second environmental data of the environment in which the energy storage device is located during the charging state is obtained, and a second amount of heat to be dissipated is predicted based on the second environmental data and the expected use information of the energy storage device, including: retrieving the expected end time of the energy storage device, calculating the remaining time between the current time and the expected end time, and when the remaining time reaches the time threshold, it is determined that the charging state meets the charging termination condition; obtaining the second environmental data of the environment in which the energy storage device is located during the charging state, the second environmental data and the first environmental data contain the same type of feature data, and using a thermal depth prediction model to predict and process the device parameter information, charging state, and the second environmental data of the energy storage device to obtain a fourth amount of heat to be dissipated; classifying and judging the expected use information of the energy storage device, if the expected use information is a use type, multiplying the fourth amount of heat to be dissipated by a first coefficient to obtain the second amount of heat to be dissipated; if the expected use information is a storage type, multiplying the fourth amount of heat to be dissipated by a second coefficient to obtain the second amount of heat to be dissipated; wherein, the first coefficient is less than the second coefficient, and the first coefficient is greater than or equal to 1, and the second coefficient is greater than 1.

[0038] In this embodiment, when the energy storage device is in a charging state and is about to end charging, the present invention is configured to predict the second amount of heat to be dissipated based on the latest environmental data of the environment in which the energy storage device is located, namely the second environmental data, and the expected use information of the energy storage device. Specifically, first obtain the second environmental data of the environment in which the energy storage device is located during the charging state. The second environmental data and the first environmental data contain the same type of characteristic data, which are respectively used to characterize the environmental conditions of the energy storage device in the front stage of charging and discharging and the end stage of charging. Similar to the above, the device parameter information, charging state, and second environmental data of the energy storage device are predicted and processed using a thermal depth prediction model to obtain the fourth amount of heat to be dissipated.

[0039] Compared with the aforementioned method of determining the first amount of heat to be dissipated, the present invention further considers the expected use information of the energy storage device and classifies it into use type and storage type. The use type means that the energy storage battery will be installed and used by other new energy vehicles soon after charging is completed, while the storage type means that the energy storage battery will not be installed and used by other new energy vehicles soon after charging is completed, but will be stored in the battery swap station for a period of time. Under the use type, the thermal management system of the new energy vehicle itself will control the heat dissipation of the energy storage device, and there is no need to enhance the heat dissipation on the basis of the fourth amount of heat to be dissipated; under the storage type, it is necessary to fully dissipate the heat of the energy storage device as much as possible to reduce the power consumption and load of the ventilation and cooling system in the storage space of the battery swap station.

[0040] In this regard, the present invention is set to use a smaller first coefficient to appropriately increase the predicted fourth heat dissipation when the expected use information is a use type; and when the expected use information is a storage type, use a larger second coefficient to multiply the fourth heat dissipation to obtain the corresponding second heat dissipation. Among them, the first coefficient can also be 1, that is, when the energy storage device is immediately installed and used by the new energy vehicle after charging, there is no need to enhance the heat dissipation.

[0041] The expected end time can also be obtained by communicating with the energy storage equipment management system of the charging station or the new energy vehicle, and the details will not be repeated here.

[0042] Optionally, the duration threshold is determined in the following manner: use a heat dissipation difficulty assessment model to evaluate the second environmental data to obtain a second heat dissipation difficulty coefficient, and determine the duration threshold based on the second heat dissipation difficulty coefficient; wherein the duration threshold is positively correlated with the second heat dissipation difficulty coefficient.

[0043] In this embodiment, although the heat dissipation rate of the energy storage device can be improved by increasing the heat dissipation wind speed / power of the heat dissipation fan to achieve the aforementioned technical purpose. However, the long-term high load of the heat dissipation fan will cause its performance parameters to degrade prematurely, which is not conducive to long-term use. In response to this technical problem, the present invention controls the heat dissipation fan to enhance the heat dissipation of the energy storage battery in advance. Specifically: the second environmental data is evaluated using the heat dissipation difficulty assessment model to obtain the second heat dissipation difficulty coefficient. The assessment method for the second environmental data is the same as the aforementioned first environmental data, which will not be repeated here. Then, the duration threshold is determined based on the evaluated second heat dissipation difficulty coefficient and the corresponding positive correlation. The expression form of the positive correlation is not limited, for example, it can be a calculation formula or a comparison table.

[0044] Among them, when the second heat dissipation difficulty coefficient is larger, it means that the real-time heat dissipation difficulty of the environment where the energy storage device is located is greater. At this time, the time threshold is set to be larger, that is, the earlier the cooling fan is controlled to enhance the heat dissipation of the energy storage battery in advance, so that the heat dissipation load of the cooling fan can be appropriately reduced through a longer enhanced heat dissipation time, thereby increasing its service life; and when the second heat dissipation difficulty coefficient is smaller, it means that the real-time heat dissipation difficulty of the environment where the energy storage device is located is smaller. At this time, the time threshold is set to be smaller, that is, the cooling fan is controlled to enhance the heat dissipation of the energy storage battery in advance more slowly.

[0045] like Figure 3 As shown, an embodiment of the present invention further discloses a heat dissipation control system for an energy storage device, the system comprising a processing device and a storage device, the computer code stored in the storage device being called and executed by the processing device to implement the following steps: identifying the charge and discharge state of the energy storage device, the charge and discharge state comprising a charging state and a discharging state, and obtaining first environmental data of the environment in which the energy storage device is located during the charge and discharge state, and predicting a first amount of heat to be dissipated according to the charge and discharge state and the first environmental data; when the charge and discharge state of the energy storage device is a charging state and a charging end condition is met, obtaining second environmental data of the environment in which the energy storage device is located during the charging state, and predicting a second amount of heat to be dissipated according to the second environmental data and expected use information of the energy storage device; determining working parameters of a cooling fan according to the first amount of heat to be dissipated and the second amount of heat to be dissipated, and controlling the cooling fan to perform a heat dissipation operation on the energy storage device according to the working parameters.

[0046] An embodiment of the present invention further discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method described in the above embodiment.

[0047] An embodiment of the present invention further discloses a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in the above embodiment.

[0048] An embodiment of the present invention further discloses a computer program product, which includes computer code. When the computer code is executed by a processor of an electronic device, the method described in the above embodiment is implemented.

[0049] The computer-readable storage medium described above may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0050] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0051] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A heat dissipation control method for an energy storage device, characterized in that: The method comprises the following steps: identifying the charge and discharge state of the energy storage device, the charge and discharge state comprising a charging state and a discharging state, and acquiring first environmental data of the environment in which the energy storage device is located during the charge and discharge state, and predicting a first amount of heat to be dissipated according to the charge and discharge state and the first environmental data; when the charge and discharge state of the energy storage device is a charging state and a charging end condition is satisfied, acquiring second environmental data of the environment in which the energy storage device is located during the charging state, and predicting a second amount of heat to be dissipated according to the second environmental data and expected use information of the energy storage device; determining working parameters of a cooling fan according to the first amount of heat to be dissipated and the second amount of heat to be dissipated, and controlling the cooling fan to perform a heat dissipation operation on the energy storage device according to the working parameters.

2. A heat dissipation control method for energy storage equipment according to claim 1, characterized in that: The charge and discharge state of the energy storage device is identified, including: shooting a video image of the energy storage device, determining a storage device of the energy storage device according to the video image, and identifying the charge and discharge properties of the storage device, wherein the charge and discharge properties include charging properties and discharging properties; if the charge and discharge properties of the storage device are charging properties, then determining that the charge and discharge state of the energy storage device is a charging state; if the charge and discharge properties of the storage device are discharging properties, then determining that the charge and discharge state of the energy storage device is a discharging state.

3. A heat dissipation control method for energy storage equipment according to claim 2, characterized in that: Obtaining first environmental data of the environment in which the energy storage device is located during the charging and discharging state, including: taking a panoramic image of the environment in which the energy storage device is located, and using a convolutional network to respectively extract the enclosure data, heat dissipation device data, and heating body data of the environment from the panoramic image; matrixing and integrating the enclosure data, the heat dissipation device data, and the heating body data, respectively, to obtain the first environmental data of the environment in which the energy storage device is located during the charging and discharging state.

4. The heat dissipation control method of an energy storage device according to claim 3, characterized in that: Predicting a first amount of heat to be dissipated according to the charge and discharge state and the first environmental data includes: acquiring device parameter information of an energy storage device, inputting the device parameter information and the charge and discharge state into a Transformer-based heat depth prediction model, and the heat depth prediction model outputs a third amount of heat to be dissipated; inputting the first environmental data into a heat dissipation difficulty assessment model, and the heat dissipation difficulty assessment model outputs a first heat dissipation difficulty coefficient; wherein the first heat dissipation difficulty coefficient is a value greater than 1, and the heat dissipation difficulty assessment model is constructed based on a graph convolutional neural network; and multiplying the third amount of heat to be dissipated by the first heat dissipation difficulty coefficient to obtain the first amount of heat to be dissipated.

5. A heat dissipation control method for energy storage equipment according to claim 4, characterized in that: When the charge and discharge state of the energy storage device is a charging state and the charging end condition is met, the second environmental data of the environment in which the energy storage device is located during the charging state is obtained, and the second amount of heat to be dissipated is predicted based on the second environmental data and the expected use information of the energy storage device, including: calling the expected end time of the energy storage device, calculating the remaining time between the current time and the expected end time, and when the remaining time reaches the time threshold, it is determined that the charging state meets the charging end condition; obtaining the second environmental data of the environment in which the energy storage device is located during the charging state, the second environmental data and the first environmental data contain the same type of feature data, and using the thermal depth prediction model to predict and process the device parameter information, the charging state, and the second environmental data of the energy storage device to obtain a fourth amount of heat to be dissipated; classifying and judging the expected use information of the energy storage device, if the expected use information is a use type, multiplying the fourth amount of heat to be dissipated by a first coefficient to obtain the second amount of heat to be dissipated; if the expected use information is a storage type, multiplying the fourth amount of heat to be dissipated by a second coefficient to obtain the second amount of heat to be dissipated; wherein, the first coefficient is less than the second coefficient, and the first coefficient is greater than or equal to 1, and the second coefficient is greater than 1.

6. A heat dissipation control method for energy storage equipment according to claim 5, characterized in that: The duration threshold is determined in the following manner: the second environmental data is evaluated using a heat dissipation difficulty evaluation model to obtain a second heat dissipation difficulty coefficient, and the duration threshold is determined based on the second heat dissipation difficulty coefficient; wherein the duration threshold is positively correlated with the second heat dissipation difficulty coefficient.

7. A heat dissipation control system for an energy storage device, the system comprising a processing device and a storage device, characterized in that: The computer code stored in the storage device is called and executed by the processing device to implement the following steps: identifying the charge and discharge state of the energy storage device, the charge and discharge state including the charge state and the discharge state, and obtaining first environmental data of the environment in which the energy storage device is located during the charge and discharge state, and predicting a first amount of heat to be dissipated according to the charge and discharge state and the first environmental data; when the charge and discharge state of the energy storage device is the charge state and the charging end condition is met, obtaining second environmental data of the environment in which the energy storage device is located during the charge state, and predicting a second amount of heat to be dissipated according to the second environmental data and expected use information of the energy storage device; determining working parameters of a cooling fan according to the first amount of heat to be dissipated and the second amount of heat to be dissipated, and controlling the cooling fan to perform heat dissipation operations on the energy storage device according to the working parameters.

8. An electronic device, comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 6.

9. A computer storage medium storing a computer program, characterized in that: The computer program is executed by a processor to implement the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that: The computer program product includes computer codes, and when the computer codes are executed by a processor of an electronic device, the method according to any one of claims 1 to 6 is implemented.

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