Lithium ion battery thermal management method and related device

Abstract

The invention discloses a lithium ion battery thermal management method, a lithium ion battery thermal management device, thermal management equipment and a computer readable storage medium, and the method comprises the steps: obtaining battery data, and obtaining environment data from a sensor; performing thermal characteristic simulation on the battery data based on a battery thermal model to obtain simulation data; reasoning the battery data and the environment data based on a deep learning prediction model to obtain a temperature prediction result; performing temperature control strategy matching based on the simulation data and the temperature prediction result to obtain a thermal management control instruction; and executing a corresponding thermal management operation based on the operation instruction. According to the method, the battery heat management effect is improved, and the dangerous situation caused by battery heat accumulation is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery management, and more specifically, relates to a lithium-ion battery thermal management method, a lithium-ion battery thermal management device, a thermal management equipment, and a computer-readable storage medium. Background Art

[0002] Lithium-ion batteries are widely used in modern electric vehicles, smartphones, laptops and other portable devices due to their high energy density, long life and relative lightness. However, these batteries encounter a series of problems related to thermal management in actual use. If the heat generated by the battery during charging and discharging cannot be effectively managed, it will have a significant impact on the performance, safety and life of the battery. Therefore, the design and optimization of the battery thermal management system becomes crucial.

[0003] In the related technologies, battery thermal management methods usually include two methods: passive heat dissipation and active heat dissipation. Passive heat dissipation systems use the thermal conductivity and radiation heat dissipation of materials, which are often simple but have limited effects. Active heat dissipation systems actively remove the heat generated by the battery through devices such as fans, liquid cooling or heat pipes. These systems can solve the problems caused by heat concentration to a certain extent, but their design and control often rely on fixed strategies and rules. For these traditional methods, the biggest disadvantage is that their static or simple control strategies are difficult to cope with complex actual usage situations, such as load changes, ambient temperature fluctuations, etc. However, most of the above technologies rely on simple models or rules, and it is difficult to fully consider the complex situations in actual operations.

[0004] Therefore, how to improve the effect of battery thermal management and avoid dangerous situations caused by battery heat accumulation is a key issue that technical personnel in this field are concerned about. Summary of the invention

[0005] The purpose of the present application is to provide a lithium-ion battery thermal management method, a lithium-ion battery thermal management device, a thermal management equipment and a computer-readable storage medium, which improves the effect of battery thermal management and avoids dangerous situations caused by battery heat accumulation.

[0006] In view of the above defects or improvement needs of the prior art, the present invention provides a lithium ion battery thermal management method, comprising:

[0007] Obtaining battery data and environmental data from sensors; wherein the battery data includes temperature data, structural temperature data, and charge and discharge rate data; the environmental data includes peripheral temperature data, geographic data, and flood season data;

[0008] Performing thermal characteristic simulation on the battery data based on a battery thermal model to obtain simulation data; wherein the simulation data includes heat conduction data, heat source data, and heat dissipation path data;

[0009] Inferring the battery data and the environmental data based on a deep learning prediction model to obtain a temperature prediction result;

[0010] Perform temperature control strategy matching based on the simulation data and the temperature prediction result to obtain a thermal management control instruction;

[0011] A corresponding thermal management operation is performed based on the operation instruction.

[0012] Optionally, the process of constructing the battery thermal model includes:

[0013] Obtain battery thermal characteristics data, heat source data, and heat dissipation structure data;

[0014] Based on finite element analysis or computational fluid dynamics, heat conduction simulation is performed on the battery thermal characteristic data, the heat source data, and the heat dissipation structure data to obtain the battery thermal model.

[0015] Optionally, the process of training the deep learning prediction model includes:

[0016] Get historical battery data and historical environmental data;

[0017] The deep learning model is trained based on the historical battery data and the historical environmental data to obtain the deep learning prediction model.

[0018] Optionally, also include:

[0019] Determine whether the temperature is in an extreme temperature condition based on the temperature data, the structure temperature data, and the peripheral temperature data;

[0020] If so, the charge and discharge power is adjusted based on a preset adjustment ratio.

[0021] Optionally, also include:

[0022] Use thermal imaging technology to identify the inside of the battery and obtain the local heat source location and local temperature data;

[0023] When the local heat source position is the battery core position and the local temperature data is greater than a temperature threshold, the cooling system is controlled to perform local heat dissipation processing based on the local heat source position and the local temperature data.

[0024] Optionally, also include:

[0025] Based on the battery aging algorithm, the acquired physical property parameters and historical operation data are calculated to obtain an aging degree index; wherein the aging degree index includes an internal resistance offset value and a capacity attenuation value;

[0026] The aging degree indicator is added to the battery data.

[0027] Optionally, also include:

[0028] Acquire battery internal humidity data and battery external humidity data through a humidity sensor;

[0029] The battery internal humidity data and the battery external humidity data are added to the environmental data.

[0030] The present application also provides a lithium-ion battery thermal management device, comprising:

[0031] A data acquisition module is used to obtain battery data and environmental data from sensors; wherein the battery data includes temperature data, structural temperature data, and charge and discharge rate data; the environmental data includes peripheral temperature data, geographic data, and flood season data;

[0032] A thermal model simulation module, used to simulate the thermal characteristics of the battery data based on the battery thermal model to obtain simulation data; wherein the simulation data includes heat conduction data, heat source data, and heat dissipation path data;

[0033] A prediction model reasoning module, used to reason the battery data and the environmental data based on a deep learning prediction model to obtain a temperature prediction result;

[0034] An operation instruction generation module, used for matching a temperature control strategy based on the simulation data and the temperature prediction result to obtain a thermal management control instruction;

[0035] The instruction execution module is used to execute the corresponding thermal management operation based on the operation instruction.

[0036] The present application also provides a thermal management device, comprising:

[0037] Memory for storing computer programs;

[0038] The processor is used to implement the steps of the lithium-ion battery thermal management method as described above when executing the computer program.

[0039] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the lithium-ion battery thermal management method as described above are implemented.

[0040] A lithium-ion battery thermal management method provided by the present application includes: obtaining battery data and obtaining environmental data from a sensor; wherein the battery data includes temperature data, structural temperature data, and charge and discharge rate data; the environmental data includes peripheral temperature data, geographic data, and flood season data; thermal characteristic simulation of the battery data is performed based on a battery thermal model to obtain simulation data; wherein the simulation data includes heat conduction data, heat source data, and heat dissipation path data; reasoning the battery data and the environmental data based on a deep learning prediction model to obtain a temperature prediction result; matching a temperature control strategy based on the simulation data and the temperature prediction result to obtain a thermal management control instruction; and executing a corresponding thermal management operation based on the operation instruction.

[0041] It has the following beneficial effects:

[0042] By comprehensively applying battery data and environmental data, and using deep learning prediction models and thermal characteristics simulation, intelligent and dynamic thermal management is achieved. It can accurately predict battery temperature in real time and generate optimized temperature control strategies based on the prediction results and thermal characteristics data. By executing precise thermal management control instructions, the system can effectively adjust the cooling mechanism and charging and discharging strategies, thereby significantly improving the performance stability and safety of the battery. It not only improves the operating efficiency of the battery and extends its service life, but also significantly reduces the safety hazards caused by abnormal temperature, thereby enhancing the reliability of the battery system and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0044] Figure 1 A flow chart of a lithium-ion battery thermal management method provided in an embodiment of the present application;

[0045] Figure 2 A schematic diagram of the structure of a lithium-ion battery thermal management device provided in an embodiment of the present application;

[0046] Figure 3 A schematic diagram of the structure of the thermal management device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The purpose of the present application is to provide a lithium-ion battery thermal management method, a lithium-ion battery thermal management device, a thermal management equipment and a computer-readable storage medium, which improves the effect of battery thermal management and avoids dangerous situations caused by battery heat accumulation.

[0048] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0049] The lithium-ion battery thermal management method provided by the present application is described below through an embodiment.

[0050] Please refer to Figure 1 , Figure 1 A flow chart of a lithium-ion battery thermal management method provided in an embodiment of the present application.

[0051] In this embodiment, the method may include:

[0052] S101, obtaining battery data, and obtaining environmental data from sensors; wherein the battery data includes temperature data, structural temperature data, and charge and discharge rate data; and the environmental data includes peripheral temperature data, geographic data, and flood season data;

[0053] This step aims to obtain battery data and environmental data from sensors. Battery data includes temperature data, structural temperature data, and charge and discharge rate data; environmental data includes ambient temperature data, geographic data, and flood season data.

[0054] These data provide the basis for subsequent thermal characteristic simulation and temperature prediction. Battery data includes temperature data, structural temperature data and charge and discharge rate data, which directly reflect the thermal state and heat load of the battery during use. Environmental data includes peripheral temperature data, geographical data and flood season data, which describe the actual working environment of the battery. These factors have an important impact on the thermal management of the battery.

[0055] It can be seen that this step provides a comprehensive data foundation, which helps to accurately evaluate the actual working environment and thermal load of the battery. By collecting multi-dimensional data, we can ensure that we have a full understanding of the battery and environmental conditions, and provide reliable data support for subsequent thermal characteristics simulation and prediction.

[0056] S102, simulating the thermal characteristics of the battery data based on the battery thermal model to obtain simulation data; wherein the simulation data includes heat conduction data, heat source data, and heat dissipation path data;

[0057] Based on S101, this step aims to simulate the thermal characteristics of the battery data based on the battery thermal model to obtain simulation data; wherein the simulation data includes heat conduction data, heat source data, and heat dissipation path data.

[0058] The battery thermal model simulates the thermal characteristics of the battery by establishing a mathematical model, including heat conduction, heat source distribution, and heat dissipation path. By inputting battery data, the model can predict the thermal behavior of the battery under different working conditions. The simulation data helps identify the heat source, heat conduction path, and heat dissipation, providing a basis for subsequent temperature control strategies.

[0059] Among them, heat conduction data: use the heat conduction model to calculate the transfer path and rate of heat inside and outside the battery. The model can be based on finite element analysis (FEA) or computational fluid dynamics (CFD) methods. Heat source data: generate heat source data based on the battery's charge and discharge rate and the chemical reaction inside the battery to simulate the heat generation of the battery under different conditions. Heat dissipation path data: analyze the heat flow path inside the battery through the model to identify the way heat is transferred to the cooling system.

[0060] As can be seen, this step provides a deep understanding of the thermal behavior inside the battery, helping to identify and solve potential thermal problems. The simulation data enables the thermal management system to foresee and optimize thermal management strategies during the design phase, thereby improving the performance and safety of the battery.

[0061] S103, inferring the battery data and the environmental data based on the deep learning prediction model to obtain a temperature prediction result;

[0062] Based on S102, this step aims to infer the battery data and environmental data based on the deep learning prediction model to obtain the temperature prediction result. Among them, the deep learning prediction model is trained by a large amount of battery and environmental data to learn the complex pattern of battery thermal behavior. The model is able to predict the future battery temperature, taking into account the current battery state and environmental factors. Deep learning technology can handle high-dimensional data and nonlinear relationships, thereby providing more accurate temperature prediction results.

[0063] Specifically, this step may also include: Data preprocessing: cleaning, standardizing and feature extraction of collected battery data and environmental data. Model training: using historical data to train deep learning models, such as convolutional neural networks or recurrent neural networks, to learn thermal patterns and environmental impacts in the data. Predictive reasoning: inputting real-time data into the trained model to generate future temperature prediction results.

[0064] It can be seen that this step provides high-precision temperature prediction, allowing the thermal management system to foresee the future state of the battery. The deep learning model can automatically process complex data relationships and improve the accuracy and reliability of the prediction.

[0065] S104, matching the temperature control strategy based on the simulation data and the temperature prediction result to obtain a thermal management control instruction;

[0066] Based on S103, this step aims to match the temperature control strategy based on the simulation data and the temperature prediction results to obtain thermal management control instructions. Combined with the thermal characteristics simulation data and the temperature prediction results, the thermal state of the battery is managed by formulating a suitable temperature control strategy. The temperature control strategy includes the adjustment of the cooling system, the optimization of the charging and discharging strategy, etc. By comparing the simulation data and the prediction results, the system can generate targeted control instructions and adjust the thermal management operation.

[0067] The simulation data and prediction results are input into the strategy generation module, which uses a rule engine or optimization algorithm to develop a temperature control strategy that adapts to the current battery status and environmental conditions. Then, based on the results of the strategy matching, specific thermal management control instructions are generated, such as adjusting the speed of the cooling fan, changing the flow of the liquid cooling system, or limiting the charging power.

[0068] It can be seen that this step realizes targeted and dynamic thermal management, and can adjust the thermal management strategy of the battery system in real time. Through scientific strategy matching, the efficiency and safety of the battery can be maximized.

[0069] S105, executing corresponding thermal management operations based on the operation instructions.

[0070] Based on S104, this step aims to perform corresponding thermal management operations based on the operation instructions. According to the generated thermal management control instructions, corresponding operations are implemented to adjust the thermal state of the battery. These operations may include adjusting the cooling system, changing the battery charging and discharging strategy, etc., so as to achieve effective temperature control.

[0071] It can be seen that this step may include: Execution of operation: converting control instructions into actual operations through actuators (such as fan controllers, liquid pump controllers, and control interfaces of battery management systems). Real-time adjustment: executing and adjusting thermal management operations in real time according to changes in battery and environmental conditions to ensure that the battery operates within a safe temperature range.

[0072] In summary, this method realizes intelligent and dynamic thermal management by comprehensively applying battery data and environmental data, using deep learning prediction models and thermal characteristics simulation. It can accurately predict the battery temperature in real time and generate optimized temperature control strategies based on the prediction results and thermal characteristics data. By executing precise thermal management control instructions, the system can effectively adjust the cooling mechanism and charging and discharging strategies, thereby significantly improving the performance stability and safety of the battery. It not only improves the operating efficiency of the battery and extends its service life, but also significantly reduces the safety hazards caused by abnormal temperature, thereby enhancing the reliability of the battery system and user experience.

[0073] Optional, battery thermal model building process, including:

[0074] Step 1, obtaining battery thermal characteristic data, heat source data, and heat dissipation structure data;

[0075] Step 2: Based on finite element analysis or computational fluid dynamics, a heat conduction simulation is performed on the battery thermal characteristic data, heat source data, and heat dissipation structure data to obtain a battery thermal model.

[0076] The battery thermal model simulates the heat conduction process inside the battery by building a physical model, usually based on finite element analysis or computational fluid dynamics methods to simulate the transfer and dissipation of heat in the battery and its impact on the battery structure. Thermal characteristic data, heat source data, and heat dissipation structure data provide the necessary input for the model to ensure the accuracy and reliability of the simulation results.

[0077] Among them, the thermal characteristic data of the battery (such as thermal conductivity), heat source data (such as heat generation during charging and discharging), and heat dissipation structure data (such as cooling channel design) are obtained through experimental measurement and data acquisition systems. Then, finite element analysis or computational fluid dynamics software is used to process these data and establish a thermal conduction model of the battery. These simulation tools can handle complex heat flow and transfer problems and generate a thermal model of the battery.

[0078] It can be seen that this option provides a detailed simulation of the internal thermal behavior of the battery, helping to understand and optimize the thermal management design of the battery. An accurate thermal model can predict the heat distribution under different operating conditions, reduce temperature problems in actual applications, and improve the overall stability and safety of the system.

[0079] Optional, deep learning prediction model training process, including:

[0080] Step 1, obtaining historical battery data and historical environmental data;

[0081] Step 2: Train the deep learning model based on historical battery data and historical environmental data to obtain a deep learning prediction model.

[0082] The deep learning prediction model can learn the complex relationship between the thermal behavior of the battery and the environmental impact by training historical battery data and environmental data. The trained model can accurately predict the future battery temperature and provide support for dynamic thermal management strategies.

[0083] Obtain historical data: Collect a large amount of historical battery data and historical environmental data from the battery system.

[0084] Model training: Use this data to train deep learning models (such as convolutional neural networks (CNN) or long short-term memory networks (LSTM)). Improve prediction accuracy by continuously optimizing model parameters.

[0085] It can be seen that the deep learning model is able to handle complex nonlinear relationships and provide high-precision temperature prediction. Real-time prediction of battery temperature allows the thermal management system to take measures in advance to optimize the operating efficiency and safety of the battery.

[0086] Optionally, also include:

[0087] Step 1, judging whether it is in an extreme temperature condition based on temperature data, structural temperature data, and peripheral temperature data;

[0088] Step 2: If yes, adjust the charge and discharge power based on a preset adjustment ratio.

[0089] It can be seen that when the battery data or environmental data indicates that the battery is in extreme temperature conditions, the charging and discharging power needs to be adjusted to avoid overheating or overcooling of the battery and protect the battery from damage.

[0090] That is, the temperature sensor is used to monitor the temperature data of the battery and the structural temperature data in real time, and combined with the peripheral temperature data to determine whether there is an extreme temperature condition. When an extreme temperature is detected, the battery's charge and discharge power is dynamically adjusted according to the preset adjustment ratio to reduce the heat load or increase the cooling effect.

[0091] It can be seen that timely adjustment of charging and discharging power can effectively prevent battery performance degradation or safety risks under extreme temperature conditions, improve battery operation safety and reliability, and extend battery life.

[0092] Optionally, also include:

[0093] Step 1: Identify the inside of the battery by thermal imaging technology to obtain the local heat source location and local temperature data;

[0094] Step 2: When the local heat source position is the core position of the battery and the local temperature data is greater than the temperature threshold, the cooling system is controlled to perform local heat dissipation based on the local heat source position and the local temperature data.

[0095] It can be seen that the local heat source location inside the battery can be identified through thermal imaging technology, and targeted cooling can be performed based on the local temperature data. This method can optimize the efficiency of the cooling system and deal with the problem of heat concentration.

[0096] Thermal imaging recognition: Use thermal imaging technology to scan the inside of the battery to obtain the location and temperature data of the local heat source. Control cooling system: When a local heat source at the core position is identified and the temperature exceeds the threshold, the cooling system is controlled to perform local heat dissipation to reduce the impact of the heat source.

[0097] It can be seen that precise local cooling is provided to prevent battery performance degradation or safety issues caused by heat concentration. The responsiveness and pertinence of the thermal management system are improved, and the heat dissipation effect of the battery is optimized.

[0098] Optionally, also include:

[0099] Step 1, calculating the acquired physical property parameters and historical operation data based on the battery aging algorithm to obtain an aging degree index; wherein the aging degree index includes an internal resistance offset value and a capacity attenuation value;

[0100] Step 2: Add the aging index to the battery data.

[0101] Among them, the aging degree of the battery will affect its performance and thermal management requirements. By calculating the internal resistance offset value and capacity attenuation value of the battery, the aging degree index can be obtained and incorporated into the thermal management model to optimize the management strategy.

[0102] Calculate aging indicators: Use the battery aging algorithm to calculate the internal resistance offset value and capacity attenuation value based on the physical property parameters and historical operation data of the battery.

[0103] Data integration: Add aging indicators to battery data for use by thermal management systems.

[0104] It can be seen that by considering the aging degree of the battery, the thermal management system can adjust its strategy to adapt to the actual state of the battery and improve management efficiency. It enhances the adaptability and life prediction capabilities of the battery system and helps improve the overall reliability of the system.

[0105] Optionally, also include:

[0106] Step 1, obtaining battery internal humidity data and battery external humidity data through a humidity sensor;

[0107] Step 2: Add the battery internal humidity data and the battery external humidity data to the environmental data.

[0108] Among them, humidity also affects the thermal management and performance of the battery. By monitoring the humidity data inside and outside the battery, we can have a more comprehensive understanding of the impact of environmental conditions on the battery and take it into account in thermal management.

[0109] Humidity sensor: Install a humidity sensor to monitor the humidity data inside and outside the battery in real time.

[0110] Data Integration: Add this humidity data to environmental data so that it can be comprehensively considered in the thermal management process.

[0111] It can be seen that comprehensive monitoring of humidity effects is provided, enabling the thermal management system to optimize the battery management strategy under various environmental conditions, which helps to improve the safety and performance stability of the battery under different humidity conditions.

[0112] The following is an introduction to a lithium-ion battery thermal management device provided in an embodiment of the present application. The lithium-ion battery thermal management device and the lithium-ion battery thermal management method described below can be referenced to each other.

[0113] Please refer to Figure 2 , Figure 2 A schematic diagram of the structure of a lithium-ion battery thermal management device provided in an embodiment of the present application.

[0114] In this embodiment, the device may include:

[0115] The data acquisition module 100 is used to obtain battery data and environmental data from sensors; wherein the battery data includes temperature data, structural temperature data, and charge and discharge rate data; the environmental data includes peripheral temperature data, geographic data, and flood season data;

[0116] The thermal model simulation module 200 is used to simulate the thermal characteristics of the battery data based on the battery thermal model to obtain simulation data; wherein the simulation data includes heat conduction data, heat source data, and heat dissipation path data;

[0117] The prediction model reasoning module 300 is used to reason the battery data and the environmental data based on the deep learning prediction model to obtain the temperature prediction result;

[0118] An operation instruction generation module 400 is used to match the temperature control strategy based on the simulation data and the temperature prediction result to obtain a thermal management control instruction;

[0119] The instruction execution module 500 is used to execute corresponding thermal management operations based on the operation instructions.

[0120] This application also provides thermal management equipment, please refer to Figure 3 , Figure 3This is a schematic diagram of the structure of a thermal management device provided in an embodiment of the present application. The thermal management device may include:

[0121] Memory for storing computer programs;

[0122] The processor is used to implement the steps of any of the above-mentioned lithium-ion battery thermal management methods when executing a computer program.

[0123] like Figure 3 , which is a schematic diagram of the composition structure of a thermal management device, the thermal management device may include: a processor 10, a memory 11, a communication interface 12 and a communication bus 13. The processor 10, the memory 11, and the communication interface 12 all communicate with each other through the communication bus 13.

[0124] In the embodiment of the present application, the processor 10 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic devices.

[0125] The processor 10 may call a program stored in the memory 11. Specifically, the processor 10 may execute operations in an embodiment of the abnormal IP identification method.

[0126] The memory 11 is used to store one or more programs, which may include program codes, and the program codes include computer operation instructions. In the embodiment of the present application, the memory 11 at least stores programs for implementing the following functions:

[0127] Obtain battery data and environmental data from sensors; battery data includes temperature data, structural temperature data, and charge and discharge rate data; environmental data includes ambient temperature data, geographic data, and flood season data;

[0128] Performing thermal characteristic simulation on battery data based on a battery thermal model to obtain simulation data; wherein the simulation data includes heat conduction data, heat source data, and heat dissipation path data;

[0129] Infer the battery data and environmental data based on the deep learning prediction model to obtain the temperature prediction result;

[0130] Match the temperature control strategy based on the simulation data and the temperature prediction results to obtain the thermal management control instructions;

[0131] Execute corresponding thermal management operations based on the operation instructions.

[0132] In a possible implementation, the memory 11 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function, etc.; the data storage area may store data created during use.

[0133] In addition, the memory 11 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0134] The communication interface 12 may be an interface of a communication module, and is used to connect to other devices or systems.

[0135] Of course, it should be noted that Figure 3 The structure shown does not constitute a limitation on the thermal management device in the embodiment of the present application. In actual applications, the thermal management device may include Figure 3 More or fewer components than shown, or combinations of certain components.

[0136] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned lithium-ion battery thermal management methods can be implemented.

[0137] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0138] For an introduction to the computer-readable storage medium provided in this application, please refer to the above method embodiment, and this application will not go into details here.

[0139] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0140] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0141] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0142] The above is a detailed introduction to a lithium-ion battery thermal management method, a lithium-ion battery thermal management device, a thermal management equipment, and a computer-readable storage medium provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A lithium-ion battery thermal management method, characterized in that: include: Obtaining battery data and environmental data from sensors; wherein the battery data includes temperature data, structural temperature data, and charge and discharge rate data; the environmental data includes peripheral temperature data, geographic data, and flood season data; Performing thermal characteristic simulation on the battery data based on a battery thermal model to obtain simulation data; wherein the simulation data includes heat conduction data, heat source data, and heat dissipation path data; Inferring the battery data and the environmental data based on a deep learning prediction model to obtain a temperature prediction result; Perform temperature control strategy matching based on the simulation data and the temperature prediction result to obtain a thermal management control instruction; A corresponding thermal management operation is performed based on the operation instruction.

2. The lithium-ion battery thermal management method according to claim 1, characterized in that: The process of constructing the battery thermal model includes: Obtain battery thermal characteristics data, heat source data, and heat dissipation structure data; Based on finite element analysis or computational fluid dynamics, heat conduction simulation is performed on the battery thermal characteristic data, the heat source data, and the heat dissipation structure data to obtain the battery thermal model.

3. The lithium-ion battery thermal management method according to claim 1, characterized in that: The process of deep learning prediction model training includes: Get historical battery data and historical environmental data; The deep learning model is trained based on the historical battery data and the historical environmental data to obtain the deep learning prediction model.

4. The lithium-ion battery thermal management method according to claim 1, characterized in that: Also includes: Determine whether the temperature is in an extreme temperature condition based on the temperature data, the structure temperature data, and the peripheral temperature data; If so, the charge and discharge power is adjusted based on a preset adjustment ratio.

5. The lithium-ion battery thermal management method according to claim 1, characterized in that: Also includes: Use thermal imaging technology to identify the inside of the battery and obtain the local heat source location and local temperature data; When the local heat source position is the battery core position and the local temperature data is greater than a temperature threshold, the cooling system is controlled to perform local heat dissipation processing based on the local heat source position and the local temperature data.

6. The lithium-ion battery thermal management method according to claim 1, characterized in that: Also includes: Based on the battery aging algorithm, the acquired physical property parameters and historical operation data are calculated to obtain an aging degree index; wherein the aging degree index includes an internal resistance offset value and a capacity attenuation value; The aging degree indicator is added to the battery data.

7. The lithium-ion battery thermal management method according to claim 1, characterized in that: Also includes: Acquire battery internal humidity data and battery external humidity data through a humidity sensor; The battery internal humidity data and the battery external humidity data are added to the environmental data.

8. A lithium-ion battery thermal management device, characterized in that: include: A data acquisition module is used to obtain battery data and environmental data from sensors; wherein the battery data includes temperature data, structural temperature data, and charge and discharge rate data; the environmental data includes peripheral temperature data, geographic data, and flood season data; A thermal model simulation module, used to simulate the thermal characteristics of the battery data based on the battery thermal model to obtain simulation data; wherein the simulation data includes heat conduction data, heat source data, and heat dissipation path data; A prediction model reasoning module, used to reason the battery data and the environmental data based on a deep learning prediction model to obtain a temperature prediction result; An operation instruction generation module, used for matching a temperature control strategy based on the simulation data and the temperature prediction result to obtain a thermal management control instruction; The instruction execution module is used to execute the corresponding thermal management operation based on the operation instruction.

9. A thermal management device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the lithium-ion battery thermal management method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the lithium-ion battery thermal management method according to any one of claims 1 to 7 are implemented.

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