Mobile energy storage battery heat dissipation method and device based on phase change heat dissipation and medium

Abstract

The invention discloses a mobile energy storage battery heat dissipation method and device based on phase change heat dissipation and a medium, and the method comprises the steps: carrying out the multi-source data fusion processing of the temperature parameters of a mobile energy storage battery, so as to obtain the temperature state of the mobile energy storage battery; performing environment adaptive threshold analysis on the temperature state of the mobile energy storage battery to obtain a battery heat dissipation strategy; based on a battery heat dissipation strategy, through heat dissipation execution action matching, determining heat dissipation process data of the mobile energy storage battery; determining first heat dissipation strategy optimization data through battery health state analysis according to the heat dissipation process data of the mobile energy storage battery; performing heat dissipation system performance analysis on the heat dissipation process data of the mobile energy storage battery, and determining second heat dissipation strategy optimization data; and performing weighted average on the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data to obtain a mobile energy storage battery heat dissipation optimization strategy. According to the mobile energy storage battery, the technical problems of low heat dissipation efficiency and poor heat dissipation strategy strain of the mobile energy storage battery in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the technical field of heat dissipation of energy storage batteries, and in particular to a heat dissipation method, device and medium for a mobile energy storage battery based on phase change heat dissipation. Background Art

[0002] With the rapid development of new energy technologies, large-capacity mobile energy storage batteries are finding widespread application in numerous fields, such as electric vehicles and mobile power stations. However, batteries generate significant heat during charging and discharging. If heat cannot be dissipated effectively and promptly, the battery overheats, leading to a host of problems. Excessive temperatures accelerate chemical reactions within the battery, reducing charging and discharging efficiency and severely impacting performance and service life. Furthermore, excessive temperatures can trigger thermal runaway, posing a significant safety hazard.

[0003] Currently, common battery cooling technologies include air cooling and liquid cooling. Air cooling uses fans and other devices to force air flow to remove heat, but the cooling efficiency is relatively low, especially when the battery generates a lot of heat. Air cooling is also easily affected by ambient temperature. Liquid cooling uses a circulating coolant to absorb heat, which improves the cooling efficiency. However, it also comes with system complexity, high costs, and the risk of coolant leakage. Therefore, there is an urgent need for an efficient, reliable, and cost-effective cooling technology for large-capacity mobile energy storage batteries. Summary of the Invention

[0004] The embodiments of the present application provide a mobile energy storage battery heat dissipation method, device and medium based on phase change heat dissipation, which solve the technical problems of low heat dissipation efficiency and poor adaptability of heat dissipation strategies of mobile energy storage batteries in the prior art.

[0005] In a first aspect, an embodiment of the present application provides a mobile energy storage battery heat dissipation method based on phase change heat dissipation, characterized in that the method includes: obtaining mobile energy storage battery temperature parameters, and performing multi-source data fusion processing on the mobile energy storage battery temperature parameters to obtain the mobile energy storage battery temperature state; performing environmental adaptive threshold analysis on the mobile energy storage battery temperature state to obtain a battery heat dissipation strategy; based on the battery heat dissipation strategy, determining mobile energy storage battery heat dissipation process data through heat dissipation execution action matching; determining first heat dissipation strategy optimization data based on the mobile energy storage battery heat dissipation process data through battery health status analysis; performing heat dissipation system performance analysis on the mobile energy storage battery heat dissipation process data to determine second heat dissipation strategy optimization data; and weighted averaging the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data to obtain the mobile energy storage battery heat dissipation optimization strategy.

[0006] In one implementation of the present application, multi-source data fusion processing is performed on the temperature parameters of the mobile energy storage battery to obtain the temperature state of the mobile energy storage battery, specifically including: performing Kalman filtering denoising on the temperature parameters of the mobile energy storage battery to obtain denoised temperature parameters; performing sliding window mean processing on the denoised temperature parameters to obtain temperature parameters to be differentiated that eliminate the influence of instantaneous fluctuations; mapping the temperature parameters to be differentiated to the battery body area of ​​the mobile energy storage battery to obtain the temperature state of the mobile energy storage battery.

[0007] In one implementation of the present application, an environmental adaptive threshold analysis is performed on the temperature state of a mobile energy storage battery to obtain a battery heat dissipation strategy, specifically including: obtaining ambient temperature data of the mobile energy storage battery, and performing time series prediction on the ambient temperature data to obtain ambient temperature change prediction data; performing battery heat generation trend analysis on the mobile energy storage battery by battery type to determine characteristic parameters of the mobile energy storage battery; based on the ambient temperature change prediction data and the characteristic parameters of the mobile energy storage battery, obtaining a battery heat dissipation strategy through battery temperature characteristic state analysis; wherein the battery heat dissipation strategy includes: active heat dissipation regulation and phase change heat dissipation regulation.

[0008] In one implementation of the present application, based on the battery heat dissipation strategy, the heat dissipation execution action matching is used to determine the heat dissipation process data of the mobile energy storage battery. Specifically, when the battery heat dissipation strategy is active heat dissipation adjustment, the matching heat dissipation execution action is to control the air flow by adjusting the fan speed to adjust the heat dissipation power, and determine the heat dissipation process data of the mobile energy storage battery corresponding to the adjusted heat dissipation power; when the battery heat dissipation strategy is phase change heat dissipation adjustment, the matching heat dissipation execution action is to absorb the heat dissipated by the battery through the phase change material, convert the sensible heat of the mobile energy storage battery into the latent heat of the phase change material, and determine the heat dissipation process data of the mobile energy storage battery corresponding to the heat dissipated by the battery absorbed by the phase change material.

[0009] In one implementation of the present application, first heat dissipation strategy optimization data is determined based on the heat dissipation process data of the mobile energy storage battery through battery health status analysis, specifically including: obtaining battery status parameters, and based on the battery status parameters, determining the battery cycle health through cycle health analysis; according to the heat dissipation process data of the mobile energy storage battery and the battery cycle health, obtaining a heat dissipation strategy optimization node through health impact mapping of battery heat dissipation; and performing execution parameter optimization on the heat dissipation strategy optimization node to determine the first heat dissipation strategy optimization data.

[0010] In one implementation of the present application, a heat dissipation system performance analysis is performed on the heat dissipation process data of the mobile energy storage battery to determine the second heat dissipation strategy optimization data, specifically including: obtaining the heat dissipation system state parameters, and based on the heat dissipation system state parameters, determining the heat dissipation system cycle performance through periodic performance analysis; according to the heat dissipation process data of the mobile energy storage battery and the heat dissipation system cycle performance, obtaining the heat dissipation system execution optimization node through performance impact mapping of battery heat dissipation; and optimizing the power consumption parameters of the heat dissipation system execution optimization node to determine the second heat dissipation strategy optimization data.

[0011] In one implementation of the present application, the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data are weighted averaged to obtain the mobile energy storage battery heat dissipation optimization strategy, specifically including: adaptively configuring the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data to obtain a weighted weight parameter; based on the weighted weight parameter, weighted averaging the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data to obtain the mobile energy storage battery heat dissipation optimization strategy.

[0012] In one implementation of the present application, after weighted averaging the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data to obtain the mobile energy storage battery heat dissipation optimization strategy, the method further includes: visualizing the mobile energy storage battery heat dissipation optimization strategy to obtain a visualized energy storage battery heat dissipation optimization strategy.

[0013] In a second aspect, an embodiment of the present application further provides a mobile energy storage battery heat dissipation device based on phase change heat dissipation, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: obtain mobile energy storage battery temperature parameters, and perform multi-source data fusion processing on the mobile energy storage battery temperature parameters to obtain the mobile energy storage battery temperature state; perform environmental adaptive threshold analysis on the mobile energy storage battery temperature state to obtain a battery heat dissipation strategy; based on the battery heat dissipation strategy, determine the mobile energy storage battery heat dissipation process data through heat dissipation execution action matching; determine the first heat dissipation strategy optimization data based on the mobile energy storage battery heat dissipation process data through battery health status analysis; perform heat dissipation system performance analysis on the mobile energy storage battery heat dissipation process data to determine the second heat dissipation strategy optimization data; and perform weighted average of the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data to obtain the mobile energy storage battery heat dissipation optimization strategy.

[0014] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for heat dissipation of a mobile energy storage battery based on phase change heat dissipation, which stores computer-executable instructions, characterized in that the computer-executable instructions are configured to: obtain temperature parameters of a mobile energy storage battery, and perform multi-source data fusion processing on the temperature parameters of the mobile energy storage battery to obtain a temperature state of the mobile energy storage battery; perform an environmental adaptive threshold analysis on the temperature state of the mobile energy storage battery to obtain a battery heat dissipation strategy; based on the battery heat dissipation strategy, determine heat dissipation process data of the mobile energy storage battery through heat dissipation execution action matching; determine first heat dissipation strategy optimization data based on the heat dissipation process data of the mobile energy storage battery through battery health status analysis; perform heat dissipation system performance analysis on the heat dissipation process data of the mobile energy storage battery to determine second heat dissipation strategy optimization data; and perform weighted average of the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data to obtain a heat dissipation optimization strategy for the mobile energy storage battery.

[0015] The embodiments of the present application provide a mobile energy storage battery heat dissipation method, device, and medium based on phase change heat dissipation. By analyzing the heat dissipation strategy of the mobile energy storage battery in a temperature-varying environment and optimizing the heat dissipation system through active heat dissipation and phase change heat dissipation, the present application solves the technical problems of low heat dissipation efficiency and poor adaptability of heat dissipation strategies of mobile energy storage batteries in the prior art. This method realizes adaptive heat dissipation strategy analysis of mobile energy storage batteries in different environments, thereby improving the efficiency and reliability of heat dissipation of mobile energy storage batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A flow chart of a mobile energy storage battery heat dissipation method based on phase change heat dissipation provided in an embodiment of the present application; Figure 2 A schematic diagram of the internal structure of a mobile energy storage battery heat dissipation device based on phase change heat dissipation provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0018] The embodiments of the present application provide a mobile energy storage battery heat dissipation method, device, and medium based on phase change heat dissipation. By analyzing the heat dissipation strategy of the mobile energy storage battery in a temperature-varying environment and optimizing the heat dissipation system through active heat dissipation and phase change heat dissipation, the present application solves the technical problems of low heat dissipation efficiency and poor adaptability of heat dissipation strategies of mobile energy storage batteries in the prior art. This method realizes adaptive heat dissipation strategy analysis of mobile energy storage batteries in different environments, thereby improving the efficiency and reliability of heat dissipation of mobile energy storage batteries.

[0019] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0020] Figure 1 This is a flow chart of a mobile energy storage battery heat dissipation method based on phase change heat dissipation provided in an embodiment of the present application. Figure 1 As shown, the embodiment of the present application provides a mobile energy storage battery heat dissipation method based on phase change heat dissipation, which specifically includes the following steps: Step 101: Acquire temperature parameters of a mobile energy storage battery, and perform multi-source data fusion processing on the temperature parameters of the mobile energy storage battery to obtain a temperature state of the mobile energy storage battery.

[0021] For example, the temperature parameters of the mobile energy storage battery are obtained through a digital temperature sensor, and multi-source data fusion processing is performed on the mobile energy storage battery temperature parameters to obtain the temperature state of the mobile energy storage battery. This realizes the preprocessing of noise and fluctuation data of the mobile energy storage battery temperature data, improves the partition mapping of the local temperature of the battery, and improves the real-time detection accuracy of the key temperature state of the battery.

[0022] Specifically, multi-source data fusion processing is performed on the mobile energy storage battery temperature parameters to obtain the mobile energy storage battery temperature state, including: performing Kalman filtering denoising on the mobile energy storage battery temperature parameters to obtain denoised temperature parameters; performing sliding window mean processing on the denoised temperature parameters to obtain temperature parameters to be differentiated that eliminate the influence of instantaneous fluctuations; and mapping the temperature parameters to be differentiated to the battery body area of ​​the mobile energy storage battery to obtain the mobile energy storage battery temperature state.

[0023] In one embodiment, digital temperature sensors are deployed on the surface of battery cells, contact points of phase change materials, and inlet and outlet of liquid cooling plates to obtain temperature parameters of mobile energy storage batteries.

[0024] Kalman filtering is performed on the temperature parameters of mobile energy storage batteries to eliminate sensor noise, and the sliding window averaging method is used to process instantaneous fluctuations.

[0025] A three-dimensional thermal model of the battery pack is established, and the temperature data is mapped to specific areas to obtain the temperature status of the mobile energy storage battery.

[0026] Step 102: Perform an environmental adaptive threshold analysis on the temperature state of the mobile energy storage battery to obtain a battery heat dissipation strategy.

[0027] For example, since the temperature change of the mobile energy storage battery in the power supply state is also affected by the environment in which the battery is located, in order to analyze the comprehensive impact of the battery self-heating and the ambient temperature band, the present application performs an environmental adaptive threshold analysis on the temperature state of the mobile energy storage battery, realizes the dynamic optimization of the heat dissipation strategy through a triple adaptive mechanism, determines the timing of active heat dissipation adjustment and phase change heat dissipation adjustment, and improves the practicality and stability of the battery heat dissipation strategy.

[0028] Specifically, an environmental adaptive threshold analysis is performed on the temperature state of the mobile energy storage battery to obtain a battery heat dissipation strategy, which specifically includes: obtaining ambient temperature data of the mobile energy storage battery and performing time series prediction on the ambient temperature data to obtain ambient temperature change prediction data; performing battery heat generation trend analysis on the mobile energy storage battery by battery type to determine the characteristic parameters of the mobile energy storage battery; based on the ambient temperature change prediction data and the characteristic parameters of the mobile energy storage battery, a battery heat dissipation strategy is obtained through battery temperature characteristic state analysis; wherein the battery heat dissipation strategy includes: active heat dissipation regulation and phase change heat dissipation regulation.

[0029] In one embodiment, a distributed temperature sensor network is deployed, and a time series prediction algorithm is used to analyze ambient temperature fluctuation patterns. Using a 5-10 minute prediction horizon, the algorithm continuously learns ambient temperature fluctuation patterns and predicts the impact of the external environment on battery temperature. The prediction results are then integrated with the output of the battery thermal model to form a composite temperature trend curve, generating predicted ambient temperature change data.

[0030] Then, a parameterized model of battery thermal characteristics is established, and a dedicated thermal parameter database is constructed for different battery types to record key battery characteristics. The BMS automatically identifies the battery type and calls the corresponding parameters. The heat generation rate is calculated based on real-time current and voltage data, and the temperature change trend is predicted using thermal dynamic equations to determine the characteristic parameters of the mobile energy storage battery.

[0031] Finally, a dynamic risk quantification mechanism is introduced to evaluate the thermal runaway risk by calculating the characteristic index of the area where the temperature accumulates beyond the standard, that is, the integral value of the amplitude and duration of the predicted temperature curve exceeding the safety threshold.

[0032] When the risk value is lower than the safety threshold, passive cooling is maintained; when the risk reaches a medium level, active cooling adjustment is started and the fan speed is automatically adjusted; under high-risk conditions, phase change cooling is activated.

[0033] Furthermore, when the ambient temperature has little effect on battery heat dissipation, the heat dissipation strategy can be automatically selected based on the battery temperature, which can further reduce the heat dissipation delay.

[0034] When the battery temperature is within the normal operating range (e.g., 25°C - 35°C), heat dissipation relies solely on the phase change cooling components, which absorb a small amount of heat using the latent heat of the phase change material to maintain a stable battery temperature. When the battery temperature exceeds this range and approaches the phase change temperature, the control module activates the air cooling component of the active cooling actuator to assist the phase change cooling and reduce the battery's heating rate. If the battery temperature rises further, reaching or exceeding the phase change temperature, the liquid cooling component of the active cooling actuator activates, working in conjunction with the air cooling and phase change cooling components to rapidly dissipate battery heat.

[0035] Step 103: Based on the battery heat dissipation strategy, heat dissipation execution action matching is performed to determine the heat dissipation process data of the mobile energy storage battery.

[0036] Specifically, based on the battery heat dissipation strategy, the heat dissipation execution action matching is used to determine the heat dissipation process data of the mobile energy storage battery. Specifically, when the battery heat dissipation strategy is active heat dissipation adjustment, the matching heat dissipation execution action is to control the air flow by adjusting the fan speed to adjust the heat dissipation power, and determine the heat dissipation process data of the mobile energy storage battery corresponding to the adjusted heat dissipation power; when the battery heat dissipation strategy is phase change heat dissipation adjustment, the matching heat dissipation execution action is to absorb the heat dissipated by the battery through the phase change material, convert the sensible heat of the mobile energy storage battery into the latent heat of the phase change material, and determine the heat dissipation process data of the mobile energy storage battery corresponding to the heat dissipated by the battery absorbed by the phase change material.

[0037] For example, when the battery control unit temperature reaches the phase change temperature of the phase change material, the phase change heat dissipation component begins to operate. The phase change material absorbs the heat dissipated by the battery, undergoing a physical change, converting the battery's sensible heat into latent heat, which is stored in the phase change material. Because the material temperature remains essentially unchanged during the phase change process, the battery temperature can be maintained within a relatively stable range for a certain period of time, preventing a sudden temperature increase.

[0038] Active cooling is achieved through the air cooling system, which adjusts fan speed to control air flow and thus cooling efficiency. When the battery temperature rises rapidly, the fan speed increases to increase air flow and improve cooling efficiency. When the temperature drops, the fan speed decreases to save energy and reduce noise. The liquid cooling system controls the cooling effect by adjusting the circulation pump flow rate and the coolant temperature. For example, when the battery temperature is too high, the circulation pump flow rate is increased to allow the coolant to absorb heat faster, while also reducing the temperature of the coolant entering the cold plate, enhancing heat dissipation.

[0039] The temperature, current, voltage and working status of each heat dissipation component during the entire heat dissipation process are recorded in real time. These data are stored in the data storage module for subsequent data analysis.

[0040] Furthermore, the phase change material filled in the phase change heat dissipation deployment component can be paraffin wax among organic phase change materials, such as industrial paraffin wax, which has many characteristics that meet the heat dissipation requirements of large-capacity mobile energy storage batteries.

[0041] Paraffin wax maintains stable physical and chemical properties during multiple solid-liquid phase transitions. It can continuously and stably exert its phase change heat dissipation effect under long-term battery charge and discharge cycles and frequent temperature changes. Its performance will not deteriorate due to long-term use, ensuring the long-term reliability of the heat dissipation system and high thermal stability.

[0042] When the battery temperature reaches the paraffin wax phase transition temperature (typically between 30°C and 60°C; select a paraffin wax with an appropriate melting point based on the actual battery operating temperature requirements), it absorbs a significant amount of latent heat as it transitions from solid to liquid. For example, common paraffin wax can have a phase transition latent heat of 200-300 J / g. This highly efficient absorbs excess heat generated by the battery, effectively slowing the temperature rise and maintaining the battery within an optimal operating temperature range. It offers a high phase transition latent heat, minimal supercooling, and is non-corrosive.

[0043] Step 104: Determine first heat dissipation strategy optimization data based on the mobile energy storage battery heat dissipation process data and battery health status analysis.

[0044] Illustratively, the present application determines the first heat dissipation strategy optimization data through battery health status analysis, thereby realizing dynamic analysis of the battery health status, and then inferring the impact of the heat dissipation strategy on battery health in a certain period, so as to optimize the heat dissipation strategy.

[0045] Specifically, according to the heat dissipation process data of the mobile energy storage battery, the first heat dissipation strategy optimization data is determined through battery health status analysis, including: obtaining battery status parameters, and based on the battery status parameters, determining the battery cycle health through cycle health analysis; according to the heat dissipation process data of the mobile energy storage battery and the battery cycle health, obtaining the heat dissipation strategy optimization node through health impact mapping of battery heat dissipation; and performing execution parameter optimization on the heat dissipation strategy optimization node to determine the first heat dissipation strategy optimization data.

[0046] Step 105: Perform a heat dissipation system performance analysis on the mobile energy storage battery heat dissipation process data to determine the second heat dissipation strategy optimization data.

[0047] Illustratively, the present application implements dynamic analysis of the cooling system performance by analyzing the cooling process data of the mobile energy storage battery, thereby inferring the impact of the cooling strategy on the cooling system performance in a certain period, so as to optimize the cooling strategy.

[0048] Specifically, a heat dissipation system performance analysis is performed on the heat dissipation process data of the mobile energy storage battery to determine the second heat dissipation strategy optimization data, including: obtaining the heat dissipation system state parameters, and determining the heat dissipation system cycle performance through periodic performance analysis based on the heat dissipation system state parameters; obtaining the heat dissipation system execution optimization node through performance impact mapping of battery heat dissipation according to the heat dissipation process data of the mobile energy storage battery and the heat dissipation system cycle performance; and optimizing the power consumption parameters of the heat dissipation system execution optimization node to determine the second heat dissipation strategy optimization data.

[0049] Step 106: Take a weighted average of the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data to obtain a heat dissipation optimization strategy for the mobile energy storage battery.

[0050] Specifically, the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data are weighted averaged to obtain the mobile energy storage battery heat dissipation optimization strategy, including: adaptively configuring the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data to obtain a weighted weight parameter; based on the weighted weight parameter, weighted averaging the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data to obtain the mobile energy storage battery heat dissipation optimization strategy.

[0051] Furthermore, after weighted averaging the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data to obtain the mobile energy storage battery heat dissipation optimization strategy, the method further includes: visualizing the mobile energy storage battery heat dissipation optimization strategy to obtain a visualized energy storage battery heat dissipation optimization strategy.

[0052] In one embodiment, a large-capacity mobile energy storage battery serves as the core power source of an electric vehicle. It is frequently charged and discharged during vehicle driving, especially when driving at high speeds, accelerating suddenly, or frequently starting and stopping. The battery will generate a lot of heat. A heat dissipation method based on phase change heat dissipation technology is adopted. The phase change material can absorb latent heat when the battery temperature rises, delaying the temperature rise, and the active heat dissipation components work together when necessary to quickly reduce the temperature. Taking a certain brand of electric vehicle as an example, after being equipped with this heat dissipation system, the battery temperature fluctuation range is controlled within 5°C under continuous high-intensity driving conditions, effectively avoiding battery performance degradation caused by overheating, improving the stability of vehicle cruising range by 15%, and extending the battery life by about 20%, significantly improving the overall performance of the electric vehicle and user experience.

[0053] As an essential component of urban public transportation, electric buses operate for extended periods of time daily, making frequent stops for charging and discharging. This makes battery heat generation a more prominent issue. A heat dissipation method that combines phase change cooling technology with active cooling precisely adapts to the complex and changing operating conditions of electric buses. In hot summer weather, even when the vehicle is operating at high loads for extended periods, this cooling system absorbs significant amounts of heat through the phase change material and promptly dissipates excess heat through air and liquid cooling, ensuring the battery remains within its optimal operating temperature range. This safeguards the normal operation and punctuality of buses, while reducing the risk of failures due to battery overheating and lowering vehicle maintenance costs.

[0054] Electric trucks are commonly used for cargo transportation, often carrying heavy loads and traveling long distances, placing extremely high demands on battery power and stability. Large-capacity batteries generate significant heat under conditions such as heavy-loaded hill climbing and prolonged high-speed driving. This heat dissipation method effectively addresses this challenge. The phase change material rapidly absorbs heat during the initial rise in battery temperature. Active heat dissipation actuators intelligently adjust the heat dissipation intensity based on temperature fluctuations, preventing safety issues such as thermal runaway from overheating. This ensures the safety and timeliness of cargo transportation and promotes the widespread adoption of electric trucks in the logistics industry.

[0055] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a mobile energy storage battery heat dissipation device based on phase change heat dissipation, the structure of which is as follows: Figure 2 shown.

[0056] Figure 2 This is a schematic diagram of the internal structure of a mobile energy storage battery heat dissipation device based on phase change heat dissipation provided in an embodiment of the present application. Figure 2 As shown, the equipment includes: at least one processor 201; and, a memory 202 communicatively coupled to the at least one processor; The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor 201 to enable the at least one processor 201 to: Acquire the temperature parameters of the mobile energy storage battery, and perform multi-source data fusion processing on the mobile energy storage battery temperature parameters to obtain the temperature state of the mobile energy storage battery; perform environmental adaptive threshold analysis on the temperature state of the mobile energy storage battery to obtain the battery heat dissipation strategy; based on the battery heat dissipation strategy, determine the heat dissipation process data of the mobile energy storage battery through heat dissipation execution action matching; determine the first heat dissipation strategy optimization data based on the heat dissipation process data of the mobile energy storage battery through battery health status analysis; perform heat dissipation system performance analysis on the heat dissipation process data of the mobile energy storage battery to determine the second heat dissipation strategy optimization data; and obtain the mobile energy storage battery heat dissipation optimization strategy by weighted averaging the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data.

[0057] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for heat dissipation of a mobile energy storage battery based on phase change heat dissipation stores computer executable instructions, wherein the computer executable instructions are set to: Acquire the temperature parameters of the mobile energy storage battery, and perform multi-source data fusion processing on the mobile energy storage battery temperature parameters to obtain the temperature state of the mobile energy storage battery; perform environmental adaptive threshold analysis on the temperature state of the mobile energy storage battery to obtain the battery heat dissipation strategy; based on the battery heat dissipation strategy, determine the heat dissipation process data of the mobile energy storage battery through heat dissipation execution action matching; determine the first heat dissipation strategy optimization data based on the heat dissipation process data of the mobile energy storage battery through battery health status analysis; perform heat dissipation system performance analysis on the heat dissipation process data of the mobile energy storage battery to determine the second heat dissipation strategy optimization data; and obtain the mobile energy storage battery heat dissipation optimization strategy by weighted averaging the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data.

[0058] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the IoT device and media embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0059] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0060] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0062] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0064] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0065] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0066] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology to store information. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0067] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0068] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A mobile energy storage battery heat dissipation method based on phase change heat dissipation, characterized in that: The method comprises: Acquiring a mobile energy storage battery temperature parameter, and performing multi-source data fusion processing on the mobile energy storage battery temperature parameter to obtain a mobile energy storage battery temperature state; Performing an environmental adaptive threshold analysis on the temperature state of the mobile energy storage battery to obtain a battery heat dissipation strategy; Based on the battery heat dissipation strategy, heat dissipation execution action matching is performed to determine the heat dissipation process data of the mobile energy storage battery; Determining first heat dissipation strategy optimization data based on the mobile energy storage battery heat dissipation process data and through battery health status analysis; Performing a heat dissipation system performance analysis on the heat dissipation process data of the mobile energy storage battery to determine the second heat dissipation strategy optimization data; The first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data are weighted averaged to obtain a mobile energy storage battery heat dissipation optimization strategy.

2. A mobile energy storage battery heat dissipation method based on phase change heat dissipation according to claim 1, characterized in that: Performing multi-source data fusion processing on the mobile energy storage battery temperature parameter to obtain the mobile energy storage battery temperature state specifically includes: Performing Kalman filtering denoising on the temperature parameter of the mobile energy storage battery to obtain a denoised temperature parameter; Performing sliding window mean processing on the denoised temperature parameters to obtain temperature parameters to be differentiated that eliminate the influence of instantaneous fluctuations; The temperature parameter to be distinguished is mapped to the battery body area of ​​the mobile energy storage battery to obtain the temperature state of the mobile energy storage battery.

3. The mobile energy storage battery heat dissipation method based on phase change heat dissipation according to claim 1, characterized in that: Performing an environmental adaptive threshold analysis on the temperature state of the mobile energy storage battery to obtain a battery heat dissipation strategy, specifically including: Acquiring ambient temperature data of the mobile energy storage battery and performing time series prediction on the ambient temperature data to obtain ambient temperature change prediction data; Performing a battery heat generation trend analysis on the mobile energy storage battery by battery type to determine characteristic parameters of the mobile energy storage battery; Based on the ambient temperature change prediction data and the characteristic parameters of the mobile energy storage battery, the battery heat dissipation strategy is obtained through battery temperature characteristic state analysis; wherein, the battery heat dissipation strategy includes: active heat dissipation regulation and phase change heat dissipation regulation.

4. A mobile energy storage battery heat dissipation method based on phase change heat dissipation according to claim 3, characterized in that: Based on the battery heat dissipation strategy, heat dissipation execution action matching is performed to determine the heat dissipation process data of the mobile energy storage battery, specifically including: In the case where the battery heat dissipation strategy is active heat dissipation adjustment, matching the heat dissipation execution action is controlling the air flow by adjusting the fan speed to adjust the heat dissipation power, and determining the mobile energy storage battery heat dissipation process data corresponding to the adjusted heat dissipation power; When the battery heat dissipation strategy is phase change heat dissipation regulation, the heat dissipation execution action is matched to absorb the heat emitted by the battery through the phase change material, convert the sensible heat of the mobile energy storage battery into the latent heat of the phase change material, and determine the mobile energy storage battery heat dissipation process data corresponding to the phase change material absorbing the heat emitted by the battery.

5. The mobile energy storage battery heat dissipation method based on phase change heat dissipation according to claim 1, characterized in that: Based on the mobile energy storage battery heat dissipation process data, the first heat dissipation strategy optimization data is determined through battery health status analysis, specifically including: Obtaining battery status parameters, and determining the battery cycle health through cycle health analysis based on the battery status parameters; According to the mobile energy storage battery heat dissipation process data and the battery cycle health, a heat dissipation strategy optimization node is obtained through battery heat dissipation health impact mapping; Optimize execution parameters of the heat dissipation strategy optimization node to determine the first heat dissipation strategy optimization data.

6. The mobile energy storage battery heat dissipation method based on phase change heat dissipation according to claim 1, characterized in that: Performing a heat dissipation system performance analysis on the heat dissipation process data of the mobile energy storage battery to determine the second heat dissipation strategy optimization data, specifically including: Acquiring state parameters of the heat dissipation system, and determining the periodic performance of the heat dissipation system through periodic performance analysis based on the state parameters of the heat dissipation system; According to the mobile energy storage battery heat dissipation process data and the heat dissipation system periodic performance, a heat dissipation system execution optimization node is obtained through battery heat dissipation performance impact mapping; The power consumption parameters of the optimization node of the heat dissipation system are optimized to determine the second heat dissipation strategy optimization data.

7. The mobile energy storage battery heat dissipation method based on phase change heat dissipation according to claim 1, characterized in that: The first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data are weighted averaged to obtain a mobile energy storage battery heat dissipation optimization strategy, specifically including: Adaptively weighting the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data to obtain a weighted weight parameter; Based on the weighted weight parameter, the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data are weighted averaged to obtain the mobile energy storage battery heat dissipation optimization strategy.

8. The mobile energy storage battery heat dissipation method based on phase change heat dissipation according to claim 1, characterized in that: After obtaining the mobile energy storage battery heat dissipation optimization strategy by weighted averaging the first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data, the method further includes: The mobile energy storage battery heat dissipation optimization strategy is visualized to obtain a visualized energy storage battery heat dissipation optimization strategy.

9. A mobile energy storage battery heat dissipation device based on phase change heat dissipation, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Acquiring a mobile energy storage battery temperature parameter, and performing multi-source data fusion processing on the mobile energy storage battery temperature parameter to obtain a mobile energy storage battery temperature state; Performing an environmental adaptive threshold analysis on the temperature state of the mobile energy storage battery to obtain a battery heat dissipation strategy; Based on the battery heat dissipation strategy, heat dissipation execution action matching is performed to determine the heat dissipation process data of the mobile energy storage battery; Determining first heat dissipation strategy optimization data based on the mobile energy storage battery heat dissipation process data and through battery health status analysis; Performing a heat dissipation system performance analysis on the heat dissipation process data of the mobile energy storage battery to determine the second heat dissipation strategy optimization data; The first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data are weighted averaged to obtain a mobile energy storage battery heat dissipation optimization strategy.

10. A non-volatile computer storage medium for mobile energy storage battery heat dissipation based on phase change heat dissipation, storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Acquiring a mobile energy storage battery temperature parameter, and performing multi-source data fusion processing on the mobile energy storage battery temperature parameter to obtain a mobile energy storage battery temperature state; Performing an environmental adaptive threshold analysis on the temperature state of the mobile energy storage battery to obtain a battery heat dissipation strategy; Based on the battery heat dissipation strategy, heat dissipation execution action matching is performed to determine the heat dissipation process data of the mobile energy storage battery; Determining first heat dissipation strategy optimization data based on the mobile energy storage battery heat dissipation process data and through battery health status analysis; Performing a heat dissipation system performance analysis on the heat dissipation process data of the mobile energy storage battery to determine the second heat dissipation strategy optimization data; The first heat dissipation strategy optimization data and the second heat dissipation strategy optimization data are weighted averaged to obtain a mobile energy storage battery heat dissipation optimization strategy.

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