A heat dissipation control method and system for an energy storage device

Abstract

The present invention belongs to the technical field of temperature control, and provides a heat dissipation control method and system for an energy storage device. The method includes: identifying the charge and discharge state of the energy storage device, obtaining first environmental data of the environment where the energy storage device is located during the charge and discharge state process, and predicting a first heat quantity to be dissipated according to the charge and discharge state and the first environmental data; when the energy storage device is in a charging state and meets the charging end condition, obtaining second environmental data of the environment where the energy storage device is located during the charging state process, and predicting a second heat quantity to be dissipated according to the second environmental data and the expected usage information of the energy storage device; determining the working parameters of the cooling fan according to the first heat quantity to be dissipated and the second heat quantity to be dissipated, and controlling the cooling fan to perform heat dissipation operation on the energy storage device according to the working parameters. The present invention determines targeted working parameters for the cooling fan based on the charge and discharge state, environmental data, and charging stage of the energy storage device, realizing intelligent heat dissipation control of the energy storage device.

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 an energy storage device. Background Art

[0002] The heat dissipation control of an energy storage device (such as a battery pack) is a key technology to ensure its stable operation and extend its lifespan. In the prior art, a heat dissipation 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 heat dissipation fan starts to quickly dissipate the heat generated inside the energy storage device. However, the working parameters of the heat dissipation fan in the above method are fixed, that is, the heat dissipation fan dissipates heat from the energy storage device at a fixed rotation speed / power, etc., resulting in poor balance between heat dissipation efficiency and heat dissipation economy, which needs to be improved. Summary of the Invention

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

[0004] The present invention discloses a heat dissipation control method for an energy storage device, the method comprising the following steps: identifying the charging and discharging state of the energy storage device, the charging and discharging state including a charging state, a discharging state, and acquiring first environmental data of the environment where the energy storage device is located during the charging and discharging state, predicting a first heat quantity to be dissipated according to the charging and discharging state and the first environmental data; when the charging and discharging state of the energy storage device is the charging state and the charging end condition is satisfied, acquiring second environmental data of the environment where the energy storage device is located during the charging state, predicting a second heat quantity to be dissipated according to the second environmental data and the expected usage information of the energy storage device; determining the working parameters of the heat dissipation fan according to the first heat quantity to be dissipated and the second heat quantity 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 charging and discharging state of the energy storage device includes: photographing a video image of the energy storage device, determining a storage device of the energy storage device according to the video image, and identifying a charging and discharging attribute of the storage device, the charging and discharging attribute including a charging attribute, a discharging attribute; if the charging and discharging attribute of the storage device is the charging attribute, determining that the charging and discharging state of the energy storage device is the charging state; if the charging and discharging attribute of the storage device is the discharging attribute, determining that the charging and discharging state of the energy storage device is the discharging state.

[0006] Optionally, obtaining the first environmental data of the environment where the energy storage device is located during the charge and discharge states includes: taking a panoramic image of the environment where the energy storage device is located, and using a convolutional network to respectively extract the enclosure data, heat dissipation device data, and heating element data of the environment from the panoramic image; performing matrix processing and integration on the enclosure data, the heat dissipation device data, and the heating element data respectively to obtain the first environmental data of the environment where the energy storage device is located during the charge and discharge states.

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

[0008] Optionally, when the charge and discharge state of the energy storage device is in the charging state and meets the charging end condition, obtaining the second environmental data of the environment where the energy storage device is located during the charging state, and predicting the second heat dissipation amount to be dissipated according to the second environmental data and the expected usage information of the energy storage device includes: retrieving the expected end time of the energy storage device, calculating the remaining duration between the current time and the expected end time, and when the remaining duration reaches the duration threshold, it is determined that the charging state meets the charging end condition; obtaining the second environmental data of the environment where the energy storage device is located during the charging state, and the second environmental data includes the same type of characteristic data as the first environmental data, using the heat depth prediction model to perform prediction processing on the device parameter information, charging state, and the second environmental data of the energy storage device to obtain a fourth heat dissipation amount to be dissipated; classifying and judging the expected usage information of the energy storage device, if the expected usage information is of the usage type, then multiplying the fourth heat dissipation amount to be dissipated by a first coefficient to obtain the second heat dissipation amount to be dissipated; if the expected usage information is of the storage type, then multiplying the fourth heat dissipation amount to be dissipated by a second coefficient to obtain the second heat dissipation amount 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 by the following method: using the heat dissipation difficulty evaluation model to evaluate the second environmental data to obtain a second heat dissipation difficulty coefficient, and determining the duration threshold according to 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 includes a processing device and a storage device. 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 states of the energy storage device, where the charge and discharge states include 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 states, and predicting a first heat dissipation amount to be dissipated based on the charge and discharge states and the first environmental data; when the charge and discharge state of the energy storage device is the charging state and the charging end condition is satisfied, obtaining second environmental data of the environment in which the energy storage device is located during the charging state, and predicting a second heat dissipation amount to be dissipated based on the second environmental data and the expected usage information of the energy storage device; determining the working parameters of the cooling fan based on the first heat dissipation amount to be dissipated and the second heat dissipation amount to be dissipated, and controlling the cooling fan to perform the heat dissipation operation on the energy storage device according to the working parameters.

[0011] The present invention also discloses an electronic device, including: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, where the processor executes the computer program to implement the method as described in any one of the preceding paragraphs.

[0012] The present invention also discloses a computer storage medium. The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method as described in any one of the preceding paragraphs.

[0013] The present invention also discloses a computer program product. The computer program product contains computer code, and when the computer code is executed by a processor of an electronic device, it implements the method as described in any one of the preceding paragraphs.

[0014] The beneficial effect of the present invention is that: the present invention realizes predicting the expected heat dissipation amount to be dissipated by the energy storage device (i.e., the above-mentioned first heat dissipation amount to be dissipated and the second heat dissipation amount to be dissipated) based on the charge and discharge states of the energy storage device, the environmental data of the environment in which it is located, 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. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0016] Figure 1It is a schematic flow chart of a heat dissipation control method for an energy storage device 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 schematic structural diagram of a heat dissipation control system for an energy storage device disclosed in an embodiment of the present invention. Detailed implementation manners

[0019] The following specific embodiments illustrate the implementation manners of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0020] In addition, the technical features involved in different implementation manners 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 method of arranging heat dissipation fans on an energy storage device is often used to dissipate heat from the energy storage device. Specifically, during the charging and discharging process of the energy storage device, the heat dissipation fans are started to quickly dissipate the heat generated inside the energy storage device. However, the working parameters of the heat dissipation fans in the above method are fixed, that is, the heat dissipation fans dissipate heat from the energy storage device at a fixed rotation speed / power, etc., resulting in poor balance between heat dissipation efficiency and heat dissipation economy, which needs to be improved.

[0022] For the above technical problems, as Figure 1 shown, an embodiment of the present invention discloses a heat dissipation control method for an energy storage device. The method includes the following steps: identifying the charging and discharging state of the energy storage device, where the charging and discharging state includes a charging state, a discharging state, and obtaining first environmental data of the environment where the energy storage device is located during the charging and discharging state, and predicting a first heat quantity to be dissipated according to the charging and discharging state and the first environmental data; when the charging and discharging state of the energy storage device is the charging state and meets the charging end condition, obtaining second environmental data of the environment where the energy storage device is located during the charging state, and predicting a second heat quantity to be dissipated according to the second environmental data and the expected usage information of the energy storage device; determining the working parameters of the heat dissipation fan according to the first heat quantity to be dissipated and the second heat quantity to be dissipated, and controlling the heat dissipation fan to perform the heat dissipation operation on the energy storage device according to the working parameters.

[0023] The heat dissipation control of the energy storage device in the present invention is divided into two stages, namely the first stage during the middle and early stages of charge and discharge and the last stage of charging. Different heat dissipation working parameters are adopted in the above two stages. Specifically, first, the charge and discharge states of the energy storage device are identified, including the charging state, the discharging state, and the first environmental data of the energy storage device during the charge and discharge states are obtained, and the first heat dissipation amount to be dissipated is predicted according to the charge and discharge states and the first environmental data. Then, the working parameters of the cooling fan are determined according to the first heat dissipation amount to be dissipated, and the cooling fan is controlled to dissipate heat from the energy storage device according to the working parameters during the middle and early stages of charge and discharge of the energy storage device.

[0024] Meanwhile, if the charge and discharge state of the energy storage device is the charging state and meets the charging end condition, that is, when the charging is about to be completed, the second environmental data of the environment where the energy storage device is located during the charging state is obtained, and the second heat dissipation amount to be dissipated is predicted according to the second environmental data and the expected usage information of the energy storage device. Then, the new working parameters of the cooling fan are determined according to the second heat dissipation amount to be dissipated, and the cooling fan is controlled to dissipate heat from the energy storage device according to the new working parameters during the middle and last stages of charge and discharge of the energy storage device.

[0025] Therefore, the present invention realizes the prediction of the expected heat dissipation amount of the energy storage device (i.e., the above-mentioned first heat dissipation amount and second heat dissipation amount) based on the charge and discharge states of the energy storage device, the environmental data of the environment where it is located, and the charging stage, and accordingly determines the targeted working parameters for the cooling fan, thereby realizing the 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 rotation speed, power, etc. These working parameters and the heat dissipation amount to be dissipated are pre-configured in a comparison table, and each set of associated data in the comparison table represents the association relationship between the working parameters of the cooling fan and the corresponding heat dissipation amount to be dissipated. Obviously, the higher the heat dissipation amount to be dissipated, the higher the corresponding rotation speed and power.

[0027] Optionally, the identification of the charge and discharge state of the energy storage device includes: photographing a video image of the energy storage device, determining the storage device of the energy storage device according to the video image, and identifying the charge and discharge attributes of the storage device, where the charge and discharge attributes include a charging attribute and a discharging attribute; if the charge and discharge attribute of the storage device is the charging attribute, it is determined that the charge and discharge state of the energy storage device is the charging state; if the charge and discharge attribute of the storage device is the discharging attribute, it is determined that the charge and discharge state of the energy storage device is the discharging state.

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

[0029] Optionally, obtaining the first environmental data of the environment where the energy storage device is located during the charging and discharging state includes: capturing a panoramic image of the environment where 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; performing matrix processing and integration on the enclosure data, the heat dissipation device data, and the heating body data respectively to obtain the first environmental data of the environment where the energy storage device is located during the charging and discharging state.

[0030] In this embodiment, the specific situation of the environment where the energy storage device is located will affect the difficulty of heat dissipation of the energy storage device, mainly including the enclosure data, heat dissipation device data, and heating body data of the environment. Among them, the enclosure data refers to the degree of enclosure of the environment, such as a fully enclosed environment or a semi-enclosed environment; the heat dissipation device data refers to the device dedicated to discharging the heat in the environment to the outside of the environment in the environment, such as a ventilation system, which is arranged on the peripheral 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 body data refers to the number of other heating bodies in the environment and the expected value of the heat dissipated into the environment, etc. Other heating bodies are, for example, other energy storage devices, electrical control devices, etc., and the expected value of the heat can be generally determined according to the number and type of other heating bodies.

[0031] During specific implementation, control the camera to capture a panoramic image of the environment where the energy storage device is located, use a convolutional network to extract the above-mentioned enclosure data, heat dissipation device data, and heating body data of the environment from the panoramic image, then perform matrix processing on these data, and finally integrate the processed feature matrices to obtain the first environmental data of the environment where the energy storage device is located during the charging and discharging state.

[0032] Optionally, the first heat dissipation amount to be dissipated predicted according to the charge-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-discharge state into a heat depth prediction model based on Transformer, and the heat depth prediction model outputs a third heat dissipation amount to be dissipated; inputting the first environmental data into a heat dissipation difficulty evaluation model, and the heat dissipation difficulty evaluation 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 evaluation model is constructed based on a graph convolutional neural network; multiplying the third heat dissipation amount to be dissipated by the first heat dissipation difficulty coefficient to obtain the first heat dissipation amount to be dissipated.

[0033] In this embodiment, to predict the first heat dissipation amount 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 evaluation model based on a graph convolutional neural network. First, input the device parameter information of the energy storage device (including but not limited to battery temperature, battery internal resistance, battery material type, charge-discharge rate, etc., and these data can be obtained by communicating with the energy storage device management system of the charging station or new energy vehicle), and the charge-discharge state into the heat depth prediction model to obtain the third heat dissipation amount to be dissipated predicted by this model. The third heat dissipation amount to be dissipated is the expected heat generation amount in the charging state / discharging state predicted by the heat depth prediction model based on the own characteristics of this model of energy storage device.

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

[0035] The above-mentioned first heat dissipation amount to be dissipated is not the "true" expected heat generation amount of the energy storage device predicted, but an artificial parameter that has been slightly adjusted using the first heat dissipation difficulty coefficient. However, the first heat dissipation amount to be dissipated obtained after the adjustment is more in line with the actual situation of the environment where the energy storage device is located. The working parameters of the cooling fan determined based on the first heat dissipation amount to be dissipated can achieve the technical effect of better heat dissipation for 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 manner. The present invention does not limit their specific architectures, 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-discharge state of the energy storage device is the charging state and satisfies the charging end condition, second environmental data of the environment where the energy storage device is located during the charging process is obtained, and a second heat to be dissipated is predicted based on the second environmental data and the expected usage information of the energy storage device, including: retrieving the expected end time of the energy storage device, calculating the remaining duration between the current time and the expected end time, and determining that the charging state satisfies the charging end condition when the remaining duration reaches a duration threshold; obtaining the second environmental data of the environment where the energy storage device is located during the charging process, the second environmental data and the first environmental data including the same type of characteristic data, using the heat depth prediction model to perform prediction processing on the device parameter information, charging state, and the second environmental data of the energy storage device to obtain a fourth heat to be dissipated; classifying and judging the expected usage information of the energy storage device, if the expected usage information is the usage type, multiplying the fourth heat to be dissipated by a first coefficient to obtain the second heat to be dissipated; if the expected usage information is the storage type, multiplying the fourth heat to be dissipated by a second coefficient to obtain the second 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 the charging state and is about to end charging, the present invention is configured to predict the second heat to be dissipated based on the latest environmental data of the environment where the energy storage device is located, that is, the second environmental data, and the expected usage information of the energy storage device. Specifically as follows: first, obtain the second environmental data of the environment where the energy storage device is located during the charging process, the second environmental data and the first environmental data including the same type of characteristic data, which are respectively used to characterize the environmental conditions in the first half and the last stage of the charge-discharge process of the energy storage device. Similar to the foregoing, use the heat depth prediction model to perform prediction processing on the device parameter information, charging state, and second environmental data of the energy storage device to obtain a fourth heat to be dissipated.

[0039] Compared with the foregoing method for determining the first heat dissipation requirement, the present invention further considers the expected usage information of the energy storage device, classifying it into a usage type and a storage type. The usage type means that the energy storage battery will be quickly installed and used by other new energy vehicles after charging, while the storage type means that the energy storage battery will not be quickly installed and used by other new energy vehicles after charging, but will be stored in the swapping station for a period of time. In the usage type, the heat management system of the new energy vehicle itself will control the heat dissipation of the energy storage device, and there is no need to strengthen the heat dissipation based on the fourth heat dissipation requirement; while in the storage type, it is necessary to 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 swapping station.

[0040] For this reason, the present invention sets that when the expected usage information is of the usage type, a smaller first coefficient is used to appropriately increase the predicted fourth heat dissipation requirement; and when the expected usage information is of the storage type, a larger second coefficient is multiplied by the fourth heat dissipation requirement to respectively obtain the corresponding second heat dissipation requirement. Among them, the first coefficient can also be 1, that is, when the energy storage device is immediately installed and used by a new energy vehicle after charging, there is no need to strengthen its heat dissipation.

[0041] Among them, the expected end time can also be obtained by communicating with the energy storage device management system of the charging station or the new energy vehicle, and details are not described herein again.

[0042] Optionally, 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.

[0043] In this embodiment, although the heat dissipation rate of the energy storage device can be increased by strengthening the heat dissipation wind speed / power of the heat dissipation fan to achieve the foregoing technical purpose. However, the long-term operation of the heat dissipation fan under strong load will cause its performance parameters to degrade prematurely, which is not conducive to long-term use. To solve this technical problem, the present invention controls the heat dissipation fan to perform enhanced heat dissipation on the energy storage battery in advance. Specifically: the second environmental data is evaluated using a heat dissipation difficulty evaluation model to obtain a second heat dissipation difficulty coefficient. The evaluation method of the second environmental data is the same as that of the foregoing first environmental data, and details are not described herein again. 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 indicates that the real-time heat dissipation difficulty of the environment where the energy storage device is located is greater. At this time, the set duration threshold is larger, that is, the heat dissipation fan is controlled earlier to strengthen the heat dissipation of the energy storage battery in advance. In this way, the heat dissipation load of the heat dissipation fan can be appropriately reduced through a longer strengthening heat dissipation duration, and its service life can be extended. When the second heat dissipation difficulty coefficient is smaller, it indicates that the real-time heat dissipation difficulty of the environment where the energy storage device is located is smaller. At this time, the set duration threshold is smaller, that is, the control of the heat dissipation fan to strengthen the heat dissipation of the energy storage battery in advance is delayed.

[0045] As Figure 3 shown, an embodiment of the present invention also discloses a heat dissipation control system for an energy storage device. The system includes a processing device and a storage device. 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, where the charge and discharge state includes a charging state, a discharging state, and obtaining first environmental data of the environment where the energy storage device is located during the charge and discharge state process, and predicting a first heat quantity 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 the charging state and the charging end condition is satisfied, obtaining second environmental data of the environment where the energy storage device is located during the charging state process, and predicting a second heat quantity to be dissipated based on the second environmental data and the expected usage information of the energy storage device; determining the working parameters of the heat dissipation fan based on the first heat quantity to be dissipated and the second heat quantity 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.

[0046] An embodiment of the present invention also discloses an electronic device, including: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, where the processor executes the computer program to implement the method as described in the foregoing embodiment.

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

[0048] An embodiment of the present invention also discloses a computer program product, where the computer program product includes computer code, and when the computer code is executed by a processor of an electronic device, the method as described in the foregoing embodiment is implemented.

[0049] The above computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0050] It should be understood that various forms of the processes shown above may be used, steps may be reordered, added, or deleted. For example, the steps recited in the present invention may be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0051] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within 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 charge state and a 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 a charge state and a charging end condition is satisfied, 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; 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, 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 the 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 the 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; The duration threshold is determined by: using a heat dissipation difficulty evaluation model to evaluate the second environment data to obtain a second heat dissipation difficulty coefficient, and determining the duration threshold according to the second heat dissipation difficulty coefficient; wherein the duration threshold is positively correlated with the second heat dissipation difficulty coefficient; In the usage type, the thermal management system of the new energy vehicle itself will control the heat dissipation of the energy storage equipment. At this time, there is no need to enhance the heat dissipation based on the fourth heat dissipation; in the storage type, it is necessary to fully dissipate the heat of the energy storage equipment 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.

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 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 includes 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 heat dissipation operations on the energy storage device according to the working parameters; 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, 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 the 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 the 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; The duration threshold is determined by: using a heat dissipation difficulty evaluation model to evaluate the second environment data to obtain a second heat dissipation difficulty coefficient, and determining the duration threshold according to the second heat dissipation difficulty coefficient; wherein the duration threshold is positively correlated with the second heat dissipation difficulty coefficient; In the usage type, the thermal management system of the new energy vehicle itself will control the heat dissipation of the energy storage equipment. At this time, there is no need to enhance the heat dissipation based on the fourth heat dissipation; in the storage type, it is necessary to fully dissipate the heat of the energy storage equipment 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.

6. 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 4.

7. 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 4.

8. 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 4 is implemented.

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