A temperature regulation method and system based on embedded lithium battery

Abstract

The present application relates to the technical field of lithium battery temperature regulation, particularly a temperature regulation method and system based on embedded lithium batteries. By constructing a temperature regulation perception model and an adaptive power management system, the present application realizes accurate real-time monitoring and dynamic regulation of lithium battery temperature. By introducing a temperature propagation model, a dynamic battery model and a compensation factor, the present application improves temperature prediction accuracy and system response capability. By using a comprehensive thermal state evaluation function and an innovative temperature regulation method, the present application realizes comprehensive thermal state evaluation and flexible temperature control. Multi-level temperature risk management ensures system safety. This multi-level, multi-angle temperature regulation strategy significantly improves battery life, safety and performance, providing a reliable and efficient energy solution for electric vehicles and other equipment.

Description

Technical Field

[0001] This invention relates to the field of lithium battery temperature control technology, and in particular to a temperature regulation method and system based on an embedded lithium battery. Background Technology

[0002] With the rapid development of the electric vehicle industry, lithium-ion batteries, as its core power source, are receiving increasing attention. Lithium-ion batteries generate a large amount of heat during high-power charging and discharging, making battery temperature control and management a key factor affecting their performance and safety. In recent years, embedded temperature sensing and intelligent thermal management technologies have been gradually applied to battery systems. By monitoring battery temperature distribution in real time and dynamically adjusting charging and discharging power, the battery can be maintained within an ideal temperature range. Simultaneously, significant progress has been made in battery temperature prediction methods based on electrochemical-thermal coupling models, providing a theoretical basis for precise temperature control. Furthermore, the application of novel thermal management materials such as phase change materials (PCMs) has further improved the heat dissipation efficiency and temperature uniformity of battery packs. The performance of electric vehicles largely depends on the performance of their battery packs. The large amount of heat generated by lithium-ion batteries during high-power discharge can, in some cases, shorten battery life or pose safety hazards. This presents a challenge to manufacturing efficient and reliable battery packs for electric vehicles.

[0003] However, existing lithium battery temperature regulation technologies still have many shortcomings. First, most temperature sensing systems use discrete sensor arrangements, making it difficult to accurately describe the internal temperature field of the battery, thus limiting the formulation of temperature control strategies. Second, traditional power management algorithms are often based on simplified thermal models, failing to fully consider the complex heat conduction mechanisms and electrochemical reactions within the battery that affect temperature distribution, reducing the accuracy and real-time performance of temperature control. Furthermore, existing thermal management systems are mostly passive, making it difficult to adaptively adjust based on the battery's dynamic operating state and environmental changes, thus failing to achieve an optimal balance between battery performance and safety. Finally, the lack of in-depth research on the evolution of temperature characteristics throughout the battery's life cycle makes it difficult to optimize temperature management strategies for the battery aging process during long-term operation. To obtain optimal performance from the battery pack, the thermal management system needs to: regulate the battery to operate within the required temperature range; and reduce the unevenness of temperature distribution. To quantify the impact of temperature on battery performance, and thus its impact on electric vehicles, a temperature-dependent battery performance model is required.

[0004] Therefore, there is an urgent need to develop an embedded lithium battery temperature regulation system that integrates intelligent temperature sensing, adaptive power management, and closed-loop thermal control to achieve precise temperature control of the battery, extend its service life, and improve its safety performance. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is how to regulate the battery to operate within the required temperature range; and how to reduce the unevenness of temperature distribution in lithium.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, embodiments of the present invention provide a temperature regulation method based on an embedded lithium battery, which includes: collecting battery data, constructing a temperature regulation sensing model, and monitoring battery temperature changes in real time through the temperature regulation sensing model.

[0009] An adaptive power management system is built based on changes in battery temperature. The output of the temperature regulation sensing model is used as the input of the adaptive power management system to dynamically adjust the battery charging and discharging power and optimize battery performance.

[0010] An integrated thermal management system is established by combining a temperature regulation sensing model and adaptive power management. The battery temperature is judged by the threshold output by the temperature regulation sensing model and adaptive power management. The integrated thermal management system prevents battery overheating and precisely regulates the battery temperature.

[0011] As a preferred embodiment of the temperature regulation method based on embedded lithium battery described in this invention, the battery data collected includes the temperature of various points inside the battery, the ambient temperature, the battery operating status, the battery health status, and other relevant parameters.

[0012] Battery internal temperature: core cell temperature, battery surface temperature, and temperature distribution at different locations;

[0013] Ambient temperature: the ambient temperature around the battery and the temperature of the heat dissipation system;

[0014] Battery operating status: charging current, discharging current, battery voltage;

[0015] Time-related data: temperature change rate, historical temperature data;

[0016] Battery health status: battery cycle count, battery capacity degradation;

[0017] Other relevant parameters: ambient humidity, vibration data, or shock data.

[0018] As a preferred embodiment of the temperature regulation method based on an embedded lithium battery described in this invention, the method involves: collecting battery data, constructing a temperature regulation sensing model, and monitoring battery temperature changes in real time through the temperature regulation sensing model; the temperature regulation sensing model includes a temperature propagation model, a dynamic battery model, and a temperature regulation strategy. Based on the temperature at different locations inside the battery, and considering the influence of the environment and the heat dissipation system, a temperature propagation model is constructed, which uses the following expression:

[0019]

[0020] Where Θ(x,t) is the temperature prediction at position x at time t; T env It refers to the ambient temperature; T i It is the battery temperature measured by the i-th sensor; x i It is the position of the i-th sensor; k i T is the attenuation coefficient at position i, indicating how heat is distributed from that point to the rest of the battery; cool It is the temperature of the cooling system; f cool It is the contribution of the heat dissipation system temperature to the total temperature, and it is a function of time to take into account the change of heat dissipation efficiency over time.

[0021] Based on the temperature at different locations inside the battery, and the influence of the battery's operating state on the battery temperature, a dynamic battery model can be constructed, which uses the following expression:

[0022]

[0023] Where Q(t) is the amount of heat generated at time t; η is the thermal efficiency coefficient; and These are the charging and discharging currents, respectively; R(T) is the battery internal resistance at temperature T, which depends on the internal temperature and can be estimated using a weighted average.

[0024] By combining a temperature propagation model and a dynamic battery model, the battery's operating state is adjusted to achieve real-time adjustment of the charging and discharging current. It self-regulates based on instantaneous temperature feedback and uses integral control to compensate for previous temperature errors, thereby controlling the battery temperature to maintain it within a safe range, extending battery life, and ensuring safe operation. The expression is as follows:

[0025]

[0026] Among them, I adj (t) is the adjusted charging or discharging current; K p K i These are the proportional and integral gains, respectively, T target It is used in PID controllers; It is the target temperature; It is the average temperature of the battery.

[0027] As a preferred embodiment of the temperature regulation method based on embedded lithium battery described in this invention, wherein: the adaptive power management is constructed according to the battery temperature change, and the output of the temperature regulation sensing model is used as the input of the adaptive power management to dynamically adjust the battery charging and discharging power and optimize battery performance;

[0028] The adaptive power management includes obtaining temperature data from a temperature control model, constructing a power adjustment model based on the obtained temperature data, further optimizing the power adjustment model by introducing temperature impact compensation, and obtaining the final charge and discharge power by combining the basic power adjustment model and temperature impact compensation.

[0029] Temperature data is obtained from the temperature control model. First, the average temperature of the battery, as defined in the previous temperature control model, is used as the basic input of this model. Real-time temperature information of the battery is obtained as the basis for dynamic power adjustment. The charging and discharging power is dynamically adjusted according to the battery temperature, and the power adjustment model is constructed using the following expression:

[0030]

[0031] Where P(t) is the charging / discharging power at time t; P0 is the base power, which is the maximum charging / discharging power of the battery under standard operating conditions; T target It is the target temperature; The average battery temperature is obtained from the temperature control model; τ is the temperature response constant, used to adjust the sensitivity of the power response.

[0032] Base power adjustment: An exponential decay function is used to adjust the base power to ensure that the relationship between power adjustment and battery temperature is non-linear, in order to accommodate the high sensitivity of battery temperature to performance;

[0033] By introducing temperature impact compensation, the power adjustment model is further optimized. This temperature impact compensation includes introducing a compensation factor to address performance fluctuations that may occur when temperatures change rapidly. The compensation factor smooths the transition of power adjustments and reduces the impact of rapid temperature changes. It is expressed as follows:

[0034] The rate of change of battery temperature is obtained from the temperature control model;

[0035] Combining base power adjustment and temperature effect compensation, the final charge and discharge power is obtained. The overall adjusted power output takes into account base power adjustment and temperature change compensation to ensure that the battery maintains optimal performance under all operating conditions. It is expressed by the following formula:

[0036] Pfinal (t)=P(t)·(1+C(t))

[0037] Among them, P final (t) is the final adjusted power.

[0038] As a preferred embodiment of the temperature regulation method based on embedded lithium battery described in this invention, the method involves: establishing an integrated thermal management system by combining a temperature regulation sensing model and adaptive power management; judging the battery temperature by the threshold output by the temperature regulation sensing model and adaptive power management; preventing battery overheating based on the integrated thermal management system; and precisely regulating the battery temperature.

[0039] The integrated thermal management system includes defining a comprehensive thermal state evaluation function, a temperature regulation strategy, and a final charge / discharge strategy adjustment.

[0040] The comprehensive thermal state evaluation function assesses the battery's thermal state by combining temperature deviation and power output, ensuring dynamic adjustment based on the battery's actual performance to accurately evaluate the battery's real-time thermal environment. A thermal state evaluation function is defined, which evaluates the battery's thermal state based on the output of an intelligent temperature sensing model and adaptive power management, and it uses the following expression:

[0041]

[0042] Where Z(t) is the comprehensive thermal state evaluation at time t; T actual (t) is the actual temperature obtained through a temperature control sensing model; T target This is the target operating temperature of the battery; P final (t) is the power adjustment value obtained from the adaptive power management system; is the adjustment coefficient, and λ, μ, and ν are used to balance the effects of temperature deviation and power output, respectively.

[0043] As a preferred embodiment of the temperature regulation method based on embedded lithium batteries described in this invention, the temperature regulation strategy utilizes a combination of hyperbolic tangent and sine functions to achieve dynamic temperature adjustment. The hyperbolic tangent function provides a smooth and effective regulation mechanism, while the sine function introduces periodic adjustment to adapt to fluctuations in ambient temperature. Based on the output of the thermal state evaluation function, the battery temperature regulation strategy is defined using the following expression:

[0044] R adjust (t)=δ·tanh(Z(t))+∈·sin(ω·t)

[0045] Among them, R adjust (t) represents the time-dependent temperature regulation strategy; δ, ∈, and ω are regulation coefficients used to adjust the response sensitivity and introduce periodic regulation to cope with external temperature fluctuations.

[0046] The final charge / discharge strategy adjustment comprehensively considers the effects of power management and temperature regulation to ensure that the battery maintains optimal performance and safe operation under various operating conditions. Based on the output of the thermal state evaluation function, the battery temperature regulation strategy is defined using the following expression:

[0047] P updated (t)=P final (t)·(1+R adjust (t))

[0048] Among them, P updated (t) is the final adjusted power after temperature regulation.

[0049] As a preferred embodiment of the temperature regulation method based on embedded lithium battery described in this invention, when the battery temperature is less than a first threshold: the battery temperature risk level is determined to be low risk; the system will monitor the real-time temperature of the battery and confirm that the temperature is lower than the safety threshold through an intelligent temperature sensing model, at which point it is determined to be a low-risk state; at the same time, a routine performance report is sent to the maintenance department, and a low alarm state is displayed through the user interface for routine risk awareness and basic maintenance training.

[0050] When the first threshold is between < battery temperature and the second threshold: the battery temperature risk level is determined to be medium risk; the intelligent temperature sensing model detects that the battery temperature is between the first and second thresholds, and the adaptive power management system adjusts the charging and discharging power according to the current temperature; at the same time, an early warning message is sent to the technical support department, and a medium-intensity alarm is generated and displayed through the system interface for risk communication and basic emergency drills. In addition, the early warning response plan is activated and corresponding technical intervention is prepared.

[0051] When the battery temperature exceeds the second threshold: the battery temperature risk level is determined to be high risk; when the temperature exceeds the second threshold, ITMS immediately conducts an advanced assessment, the adaptive power management system performs emergency power adjustment to reduce the temperature to a safe range as soon as possible, and at the same time sends emergency warning information to all relevant departments and users. The system interface generates and displays a high-intensity virtual alarm scenario for evacuation and rescue strategy formulation. Meanwhile, the system automatically starts the emergency cooling procedure and other necessary emergency response measures.

[0052] Secondly, embodiments of the present invention provide a temperature regulation system based on an embedded lithium battery, which includes...

[0053] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the temperature regulation method based on an embedded lithium battery as described in the first aspect of the present invention.

[0054] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the temperature regulation method based on an embedded lithium battery as described in the first aspect of the present invention.

[0055] The beneficial effects of this invention are as follows: by collecting battery data and constructing a temperature regulation sensing model, real-time monitoring of battery temperature changes is achieved. This method not only improves the accuracy of temperature monitoring but also captures subtle changes in the internal temperature distribution of the battery, providing an accurate data foundation for subsequent temperature regulation, thereby significantly improving the response speed and control precision of the entire system.

[0056] By using the output of the temperature regulation sensing model as the input to adaptive power management, dynamic adjustment of charging and discharging power is achieved. This closed-loop control mechanism can quickly adjust the battery's operating state according to real-time temperature changes, effectively preventing local overheating and significantly improving battery life and safety.

[0057] By combining a temperature regulation sensing model with adaptive power management to establish an integrated thermal management system, precise control of battery temperature is achieved. This integrated approach not only prevents battery overheating but also maintains the battery's optimal operating temperature under various complex conditions, thereby maximizing battery performance output and mitigating battery degradation caused by thermal stress.

[0058] By introducing a temperature propagation model and a dynamic battery model, the system can more accurately predict the temperature distribution and trends inside the battery. This method considers the influence of the battery's internal heat conduction mechanism and electrochemical reactions on temperature, greatly improving the accuracy of temperature prediction and providing the possibility for developing more refined temperature control strategies.

[0059] By introducing a compensation factor to optimize the power adjustment model, the system can better cope with rapid temperature changes. This method improves the system's dynamic response capability and reduces performance losses and safety risks that may result from drastic temperature fluctuations.

[0060] By defining a comprehensive thermal state evaluation function, the system can comprehensively assess the battery's thermal state. This method not only considers temperature deviation but also incorporates the impact of power output, enabling the system to more fully grasp the battery's operating state and thus formulate a more reasonable temperature control strategy.

[0061] By introducing a combination of hyperbolic tangent and sine functions for temperature regulation, dynamic and precise temperature adjustment is achieved. This innovative regulation method ensures smooth adjustment while adapting to periodic fluctuations in ambient temperature, greatly improving the flexibility and adaptability of temperature control.

[0062] By setting different temperature thresholds and corresponding risk levels, the system can achieve multi-level temperature risk management. This hierarchical management approach not only improves system security but also enables the implementation of appropriate measures based on different risk levels, achieving rational resource allocation and optimized system response.

[0063] In summary, our solution, based on an embedded lithium battery temperature regulation method and system, achieves precise temperature control through multi-level, multi-angle temperature monitoring and control strategies. This not only improves battery lifespan and safety but also optimizes battery performance output, providing a more reliable and efficient energy solution for electric vehicles and other devices using lithium batteries. Attached Figure Description

[0064] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a flowchart of a temperature regulation method based on an embedded lithium battery;

[0066] Figure 2 This is a diagram of a computer device based on a temperature regulation method using an embedded lithium battery. Detailed Implementation

[0067] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0068] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0069] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0070] Example 1

[0071] Reference Figures 1-2This is the first embodiment of the present invention, which provides a temperature regulation method based on an embedded lithium battery, including:

[0072] S100: Collects battery data, builds a temperature regulation sensing model, and monitors battery temperature changes in real time through the temperature regulation sensing model;

[0073] S101: The battery data collected includes the temperature of various points inside the battery, the ambient temperature, the battery operating status, the battery health status, and other relevant parameters.

[0074] Battery internal temperature: core cell temperature, battery surface temperature, and temperature distribution at different locations;

[0075] Ambient temperature: the ambient temperature around the battery and the temperature of the heat dissipation system;

[0076] Battery operating status: charging current, discharging current, battery voltage;

[0077] Time-related data: temperature change rate, historical temperature data;

[0078] Battery health status: battery cycle count, battery capacity degradation;

[0079] Other relevant parameters: ambient humidity, vibration data, or shock data.

[0080] S102: The battery data is collected to construct a temperature regulation sensing model, which is used to monitor battery temperature changes in real time. The temperature regulation sensing model includes a temperature propagation model, a dynamic battery model, and a temperature regulation strategy. Based on the temperature at different locations inside the battery, and considering the influence of the environment and the heat dissipation system, a temperature propagation model is constructed, which uses the following expression:

[0081]

[0082] Where Θ(x,t) is the temperature prediction at position x at time t; T env It refers to the ambient temperature; T i It is the battery temperature measured by the i-th sensor; x i It is the position of the i-th sensor; k i T is the attenuation coefficient at position i, indicating how heat is distributed from that point to the rest of the battery; cool It is the temperature of the cooling system; f cool It is the contribution of the heat dissipation system temperature to the total temperature, and it is a function of time to take into account the change of heat dissipation efficiency over time.

[0083] Based on the temperature at different locations inside the battery, and the influence of the battery's operating state on the battery temperature, a dynamic battery model can be constructed, which uses the following expression:

[0084]

[0085] Where Q(t) is the amount of heat generated at time t; η is the thermal efficiency coefficient; and These are the charging and discharging currents, respectively; R(T) is the battery internal resistance at temperature T, which depends on the internal temperature and can be estimated using a weighted average.

[0086] By combining a temperature propagation model and a dynamic battery model, the battery's operating state is adjusted to achieve real-time adjustment of the charging and discharging current. It self-regulates based on instantaneous temperature feedback and uses integral control to compensate for previous temperature errors, thereby controlling the battery temperature to maintain it within a safe range, extending battery life, and ensuring safe operation. The expression is as follows:

[0087]

[0088] Among them, I adj (t) is the adjusted charging or discharging current; K p K i These are the proportional and integral gains, respectively, T target It is used in PID controllers; It is the target temperature; It is the average temperature of the battery.

[0089] S102: The battery data is collected to construct a temperature regulation sensing model, which is used to monitor battery temperature changes in real time. The temperature regulation sensing model includes a temperature propagation model, a dynamic battery model, and a temperature regulation strategy. Based on the temperature at different locations inside the battery, and considering the influence of the environment and the heat dissipation system, a temperature propagation model is constructed, which uses the following expression:

[0090]

[0091] Where Θ(x,t) is the temperature prediction at position x at time t; T env It refers to the ambient temperature; T i It is the battery temperature measured by the i-th sensor; x i It is the position of the i-th sensor; k i T is the attenuation coefficient at position i, indicating how heat is distributed from that point to the rest of the battery; cool It is the temperature of the cooling system; f cool It is the contribution of the heat dissipation system temperature to the total temperature, and it is a function of time to take into account the change of heat dissipation efficiency over time.

[0092] Based on the temperature at different locations inside the battery, and the influence of the battery's operating state on the battery temperature, a dynamic battery model can be constructed, which uses the following expression:

[0093]

[0094] Where Q(t) is the amount of heat generated at time t; η is the thermal efficiency coefficient; and These are the charging and discharging currents, respectively; R(T) is the battery internal resistance at temperature T, which depends on the internal temperature and can be estimated using a weighted average.

[0095] By combining a temperature propagation model and a dynamic battery model, the battery's operating state is adjusted to achieve real-time adjustment of the charging and discharging current. It self-regulates based on instantaneous temperature feedback and uses integral control to compensate for previous temperature errors, thereby controlling the battery temperature to maintain it within a safe range, extending battery life, and ensuring safe operation. The expression is as follows:

[0096]

[0097] Among them, I adj (t) is the adjusted charging or discharging current; K p K i These are the proportional and integral gains, respectively, T target It is used in PID controllers; It is the target temperature; It is the average temperature of the battery.

[0098] S200: Adaptive power management is built based on changes in battery temperature. The output of the temperature regulation sensing model is used as the input of adaptive power management to dynamically adjust the battery charging and discharging power and optimize battery performance.

[0099] S201: The adaptive power management based on battery temperature changes is constructed, and the output of the temperature regulation sensing model is used as the input of the adaptive power management to dynamically adjust the battery charging and discharging power and optimize battery performance.

[0100] The adaptive power management includes obtaining temperature data from a temperature control model, constructing a power adjustment model based on the obtained temperature data, further optimizing the power adjustment model by introducing temperature impact compensation, and obtaining the final charge and discharge power by combining the basic power adjustment model and temperature impact compensation.

[0101] Temperature data is obtained from the temperature control model. First, the average temperature of the battery, as defined in the previous temperature control model, is used as the basic input of this model. Real-time temperature information of the battery is obtained as the basis for dynamic power adjustment. The charging and discharging power is dynamically adjusted according to the battery temperature, and the power adjustment model is constructed using the following expression:

[0102]

[0103] Where P(t) is the charging / discharging power at time t; P0 is the base power, which is the maximum charging / discharging power of the battery under standard operating conditions; T target It is the target temperature; The average battery temperature is obtained from the temperature control model; τ is the temperature response constant, used to adjust the sensitivity of the power response.

[0104] Base power adjustment: An exponential decay function is used to adjust the base power to ensure that the relationship between power adjustment and battery temperature is non-linear, in order to accommodate the high sensitivity of battery temperature to performance;

[0105] By introducing temperature impact compensation, the power adjustment model is further optimized. This temperature impact compensation includes introducing a compensation factor to address performance fluctuations that may occur when temperatures change rapidly. The compensation factor smooths the transition of power adjustments and reduces the impact of rapid temperature changes. It is expressed as follows:

[0106]

[0107] The rate of change of battery temperature is obtained from the temperature control model;

[0108] Combining base power adjustment and temperature effect compensation, the final charge and discharge power is obtained. The overall adjusted power output takes into account base power adjustment and temperature change compensation to ensure that the battery maintains optimal performance under all operating conditions. It is expressed by the following formula:

[0109] P final (t)=P(t)·(1+C(t))

[0110] Among them, P final (t) is the final adjusted power.

[0111] S300: An integrated thermal management system is established by combining a temperature regulation sensing model and adaptive power management. The battery temperature is judged by the threshold output by the temperature regulation sensing model and adaptive power management. The integrated thermal management system prevents battery overheating and precisely regulates the battery temperature.

[0112] S301: The integrated thermal management system is established by combining the temperature regulation sensing model and adaptive power management. The battery temperature is judged by the threshold output by the temperature regulation sensing model and adaptive power management. The integrated thermal management system prevents battery overheating and precisely regulates the battery temperature.

[0113] The integrated thermal management system includes defining a comprehensive thermal state evaluation function, a temperature regulation strategy, and a final charge / discharge strategy adjustment.

[0114] The comprehensive thermal state evaluation function assesses the battery's thermal state by combining temperature deviation and power output, ensuring dynamic adjustment based on the battery's actual performance to accurately evaluate the battery's real-time thermal environment. A thermal state evaluation function is defined, which evaluates the battery's thermal state based on the output of an intelligent temperature sensing model and adaptive power management, and it uses the following expression:

[0115]

[0116] Where Z(t) is the comprehensive thermal state evaluation at time t; T actual (t) is the actual temperature obtained through a temperature control sensing model; T target This is the target operating temperature of the battery; P final (t) is the power adjustment value obtained from the adaptive power management system; is the adjustment coefficient, and λ, μ, and ν are used to balance the effects of temperature deviation and power output, respectively.

[0117] S302: The temperature regulation strategy utilizes a combination of hyperbolic tangent and sine functions to achieve dynamic temperature adjustment. The hyperbolic tangent function provides a smooth and effective regulation mechanism, while the sine function introduces periodic adjustment to adapt to fluctuations in ambient temperature. Based on the output of the thermal state evaluation function, the battery temperature regulation strategy is defined using the following expression:

[0118] R adjust (t)=δ·tanh(Z(t))+∈·sin(ω·t)

[0119] Among them, R adjust (t) represents the time-dependent temperature regulation strategy; δ, ∈, and ω are regulation coefficients used to adjust the response sensitivity and introduce periodic regulation to cope with external temperature fluctuations.

[0120] The final charge / discharge strategy adjustment comprehensively considers the effects of power management and temperature regulation to ensure that the battery maintains optimal performance and safe operation under various operating conditions. Based on the output of the thermal state evaluation function, the battery temperature regulation strategy is defined using the following expression:

[0121] P updated (t)=Pfinal (t)·(1+R adjust (t))

[0122] Among them, P updated (t) is the final adjusted power after temperature regulation.

[0123] S303: When the battery temperature is less than the first threshold: the battery temperature risk level is determined to be low risk; the system will monitor the real-time temperature of the battery and confirm that the temperature is lower than the safety threshold through the intelligent temperature sensing model, at which point it is determined to be a low risk state; at the same time, a routine performance report is sent to the maintenance department, and the low alarm state is displayed through the user interface for routine risk awareness and basic maintenance training.

[0124] When the first threshold is between < battery temperature and the second threshold: the battery temperature risk level is determined to be medium risk; the intelligent temperature sensing model detects that the battery temperature is between the first and second thresholds, and the adaptive power management system adjusts the charging and discharging power according to the current temperature; at the same time, an early warning message is sent to the technical support department, and a medium-intensity alarm is generated and displayed through the system interface for risk communication and basic emergency drills. In addition, the early warning response plan is activated and corresponding technical intervention is prepared.

[0125] When the battery temperature exceeds the second threshold: the battery temperature risk level is determined to be high risk; when the temperature exceeds the second threshold, ITMS immediately conducts an advanced assessment, the adaptive power management system performs emergency power adjustment to reduce the temperature to a safe range as soon as possible, and at the same time sends emergency warning information to all relevant departments and users. The system interface generates and displays a high-intensity virtual alarm scenario for evacuation and rescue strategy formulation. Meanwhile, the system automatically starts the emergency cooling procedure and other necessary emergency response measures.

[0126] Furthermore, this embodiment also provides a temperature regulation system based on an embedded lithium battery, including,

[0127] The data acquisition module collects battery data, builds a temperature regulation sensing model, and monitors battery temperature changes in real time through the temperature regulation sensing model.

[0128] The module builds an adaptive power management system based on battery temperature changes, using the output of the temperature regulation sensing model as the input to the adaptive power management system to dynamically adjust the battery charging and discharging power and optimize battery performance.

[0129] The control module establishes an integrated thermal management system by combining a temperature control sensing model and adaptive power management. It judges the battery temperature based on the threshold output by the temperature control sensing model and adaptive power management, prevents battery overheating based on the integrated thermal management system, and precisely controls the battery temperature.

[0130] This embodiment also provides a computer device applicable to the temperature regulation method based on an embedded lithium battery, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the temperature regulation method based on an embedded lithium battery as proposed in the above embodiment.

[0131] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0132] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the temperature regulation method based on an embedded lithium battery as proposed in the above embodiments.

[0133] In summary, by collecting battery data and constructing a temperature regulation sensing model, real-time monitoring of battery temperature changes was achieved. This method not only improves the accuracy of temperature monitoring but also captures subtle changes in the internal temperature distribution of the battery, providing an accurate data foundation for subsequent temperature regulation and thus significantly improving the response speed and control precision of the entire system.

[0134] By using the output of the temperature regulation sensing model as the input to adaptive power management, dynamic adjustment of charging and discharging power is achieved. This closed-loop control mechanism can quickly adjust the battery's operating state according to real-time temperature changes, effectively preventing local overheating and significantly improving battery life and safety.

[0135] By combining a temperature regulation sensing model with adaptive power management to establish an integrated thermal management system, precise control of battery temperature is achieved. This integrated approach not only prevents battery overheating but also maintains the battery's optimal operating temperature under various complex conditions, thereby maximizing battery performance output and mitigating battery degradation caused by thermal stress.

[0136] By introducing a temperature propagation model and a dynamic battery model, the system can more accurately predict the temperature distribution and trends inside the battery. This method considers the influence of the battery's internal heat conduction mechanism and electrochemical reactions on temperature, greatly improving the accuracy of temperature prediction and providing the possibility for developing more refined temperature control strategies.

[0137] By introducing a compensation factor to optimize the power adjustment model, the system can better cope with rapid temperature changes. This method improves the system's dynamic response capability and reduces performance losses and safety risks that may result from drastic temperature fluctuations.

[0138] By defining a comprehensive thermal state evaluation function, the system can comprehensively assess the battery's thermal state. This method not only considers temperature deviation but also incorporates the impact of power output, enabling the system to more fully grasp the battery's operating state and thus formulate a more reasonable temperature control strategy.

[0139] By introducing a combination of hyperbolic tangent and sine functions for temperature regulation, dynamic and precise temperature adjustment is achieved. This innovative regulation method ensures smooth adjustment while adapting to periodic fluctuations in ambient temperature, greatly improving the flexibility and adaptability of temperature control.

[0140] By setting different temperature thresholds and corresponding risk levels, the system can achieve multi-level temperature risk management. This hierarchical management approach not only improves system security but also enables the implementation of appropriate measures based on different risk levels, achieving rational resource allocation and optimized system response.

[0141] In summary, our solution, based on an embedded lithium battery temperature regulation method and system, achieves precise temperature control through multi-level, multi-angle temperature monitoring and control strategies. This not only improves battery lifespan and safety but also optimizes battery performance output, providing a more reliable and efficient energy solution for electric vehicles and other devices using lithium batteries.

[0142] Example 2

[0143] Reference Figure 1 - Figure 2 This is the second embodiment of the present invention, which provides a temperature regulation method based on an embedded lithium battery. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0144] To verify the effectiveness of the temperature regulation method based on embedded lithium batteries proposed in this invention, a high-capacity lithium-ion battery pack used in electric vehicles was selected for testing. This battery pack consists of 100 individual cells connected in series and parallel, with a total capacity of 100 kWh and a rated voltage of 400 V. First, 20 high-precision temperature sensors were installed at key locations within the battery pack to monitor the internal temperature distribution in real time. Simultaneously, ambient temperature sensors and a cooling system temperature sensor were installed externally on the battery pack. To comprehensively evaluate battery performance, monitoring equipment for parameters such as current, voltage, and cycle count was also provided.

[0145] The testing process consisted of three phases: standard operating condition testing, extreme operating condition testing, and long-term cycle testing. In the standard operating condition testing, the battery pack underwent charge-discharge cycles at 0.5C, 1C, and 2C rates at an ambient temperature of 25°C, with 50 cycles at each rate. The extreme operating condition testing included a -20°C low-temperature start-up test and a 45°C high-temperature fast-charging test. The long-term cycle testing was conducted under simulated real-world usage conditions, including combinations of different temperatures and charge-discharge rates, lasting for 6 months and accumulating 1000 complete charge-discharge cycles.

[0146] During testing, the temperature control sensing model of this invention processes data from various sensors in real time. The temperature propagation model uses equation (1) to predict the internal temperature distribution of the battery pack, taking into account data from 20 temperature sensors, ambient temperature, and the influence of the heat dissipation system. The dynamic battery model calculates the heat generation based on equation (2), taking into account the current charge / discharge state and the battery's internal resistance. The temperature control strategy adjusts the charge / discharge current in real time using equation (3) to maintain the optimal operating temperature.

[0147] The adaptive power management system dynamically adjusts the charging and discharging power using equation (4) based on the output of the temperature regulation sensing model. To cope with rapid temperature changes, a temperature impact compensation factor is introduced, as shown in equation (5). The final charging and discharging power is determined by equation (6), ensuring that the battery maintains optimal performance under various operating conditions.

[0148] The integrated thermal management system combines the outputs of the temperature regulation sensing model and adaptive power management. The instantaneous thermal environment of the battery is comprehensively evaluated using the thermal state evaluation function defined in equation (7). The temperature regulation strategy employs an innovative combination of hyperbolic tangent and sine functions, as shown in equation (8), achieving smooth and flexible temperature regulation. The final charge / discharge strategy is determined by equation (9), comprehensively considering the effects of power management and temperature regulation.

[0149] In addition, this system has a three-level temperature risk management mechanism. When the temperature is below 35℃, it is judged as low risk; when the temperature is between 35℃ and 45℃, it is judged as medium risk and the early warning mechanism is activated; when the temperature exceeds 45℃, it is judged as high risk and emergency cooling measures are immediately taken.

[0150] The test results are shown in the table below:

[0151]

[0152] By analyzing the above data, the following conclusions can be drawn:

[0153] Significantly improved temperature uniformity: Under standard operating conditions, the method of this invention reduces the maximum temperature difference inside the battery pack from 8.5℃ to 3.2℃, an improvement of 62.4%. This significant improvement is attributed to the accurate prediction of the temperature propagation model and the dynamic adjustment of the adaptive power management system. Uniform temperature distribution is crucial for extending battery life and improving safety, as it effectively reduces localized overheating and overcooling, and lowers internal battery stress.

[0154] Significantly improved extreme temperature adaptability: In a -20°C environment, the method of this invention reduces battery startup time from 180 seconds to 75 seconds, an improvement of 58.3%. This is mainly attributed to the rapid response of the temperature regulation sensing model to low-temperature environments and the intelligent preheating strategy of the adaptive power management system. Fast charging performance in a 45°C environment is also significantly improved, with the time to charge to 80% reduced from 45 minutes to 28 minutes, an improvement of 37.8%. This excellent performance in extreme temperatures greatly expands the applicability of electric vehicles in various climatic conditions.

[0155] Significantly extended cycle life: After 1000 complete charge-discharge cycles, the battery pack using the method of this invention achieved a capacity retention rate of 91%, which is 11 percentage points higher than that of the traditional method. This result fully demonstrates the effective maintenance of battery health during long-term use. Through precise temperature control and intelligent charge-discharge strategies, the aging rate of the battery is effectively slowed down, providing users with a longer-lasting user experience.

[0156] Improved charging efficiency: Average charging efficiency increased from 89% to 94%, a 5.6% increase. This improvement is primarily due to the optimized control of the charging process by the adaptive power management system and the effective management of heat generation during charging by the temperature control strategy. Higher charging efficiency not only means shorter charging time but also reduces energy loss during charging, improving overall energy utilization.

[0157] Safety performance is significantly improved: the incidence of temperature-related failures has decreased from 3.5 times / 1000 cycles to 0.8 times / 1000 cycles, a reduction of 77.1%. This significant improvement is mainly attributed to the multi-level temperature risk management mechanism and precise temperature control strategy of this invention. Through real-time monitoring and early warning, the system can take preventative measures before temperature anomalies occur, greatly reducing the probability of safety accidents.

[0158] Both energy density and power density are significantly improved: energy density increases from 220Wh / kg to 238Wh / kg, an increase of 8.2%; power density increases from 2000W / kg to 2350W / kg, an increase of 17.5%. This comprehensive performance improvement demonstrates that the invention not only effectively manages battery temperature but also unlocks the battery's potential performance by optimizing its operating state. Higher energy and power densities mean that electric vehicles can achieve longer driving range and stronger acceleration performance at the same weight.

[0159] In summary, the temperature regulation method based on embedded lithium batteries proposed in this invention demonstrates significant advantages in several key indicators. It not only improves battery lifespan and safety but also achieves comprehensive enhancements in extreme environment adaptability, charging efficiency, and overall performance. This comprehensive improvement provides strong technical support for the development of the electric vehicle industry and is expected to promote the widespread adoption of electric vehicles in a wider range of application scenarios.

[0160] Example 3

[0161] Reference Figure 1 - Figure 2 This is the third embodiment of the present invention, which provides a temperature regulation method based on an embedded lithium battery. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0162] The magnitude of the total heat generation rate from the battery pack under load determines the size and design of the cooling system. Within a battery cell, heat is generated by (i) entropy changes from electrochemical reactions and (ii) the Joule effect (or ohmic heating) caused by current transfer across the internal resistance. The heat generation rate in a battery can be calculated as follows:

[0163] q = I(E) oc -V)-ITdE oc / dT)

[0164] Where q is the heat generation rate, I is the current (I>0 for discharging, I<0 for charging), Eoc is the equilibrium battery voltage or open-circuit potential, V is the battery voltage, T is the temperature, and dEoc / dT is the temperature coefficient. The first term, I(Eoc-V), is the heat generated by the ohmic resistance and other internal resistances present in the battery. The second term, IT[dEoc / dT), is the heat generated or consumed by the reversible entropy change due to the electrochemical reaction of the battery.

[0165] Different types of lithium-ion batteries have different internal resistance and entropy change values. Thermal management strategies require accurate determination of these data. Since internal resistance depends on the battery temperature and SOC, it should be measured under various battery operating conditions. The equivalent internal resistance of flat batteries currently used in electric vehicles is proprietary and not provided in the published literature. However, this parameter has been measured for commercially available small-sized lithium-ion batteries that are structurally very similar to flat batteries. In this work, experimentally measured internal resistance data of a cylindrical SONY-US18650

[10] were used as a function of SOC and temperature for analysis. These experimental data were used for curve fitting for numerical implementation, from which the battery internal resistance calculation formula can be obtained as shown in the following formula:

[0166] R i =2.258×10 -6 SOC -0.3952 T = 20℃

[0167] R i =1.857×10 -6 SOC -0.2787 T = 30℃

[0168] R i =1.659×10 -6 SOC -0.1692 T = 40℃

[0169] Based on thermodynamic theory, the entropy change ΔS was obtained by measuring the dependence of the battery open-circuit voltage on different SOC temperatures:

[0170]

[0171] Experimental results show that within the temperature range of 20–40℃, ΔS is almost independent of temperature, and the change of ΔS with SOC approximately conforms to the following equation:

[0172] ΔS=99.88SOC-76.67 0≤SOC≤0.77

[0173] ΔS = 30 0.77 < SOC ≤ 87

[0174] ΔS = -20 0.87 < SOC ≤ 1

[0175] Taking these factors into account, the equation can be modified, and the internal heat generation rate in the power generation region of a lithium-ion battery during discharge can be expressed as:

[0176]

[0177] Compared to traditional battery heat dissipation calculation formulas, the third embodiment uses a simplified formula that can be calculated using only experimentally measured data. This method simplifies heat calculation and reduces the number of parameters required for the formula. This approach facilitates precise embedded thermal management of lithium batteries.

[0178] By fitting numerical curves to experimental data, the formula for calculating battery internal resistance can be obtained.

[0179] Within the temperature range of 20–40℃, ΔS is almost independent of temperature, and the equation for the variation of ΔS with SOC can be approximately simplified to obtain the equation.

[0180] A simplified equation for calculating battery heat generation rate in experimental analysis is presented, which mainly includes experimental fitting of the battery internal resistance calculation formula and approximation of entropy change ΔS.

[0181] According to the battery internal resistance calculation formula of claim 1, the characteristic is that the relationship between battery internal resistance and battery SOC is obtained by fitting known experimental data.

[0182] The approximate processing of entropy change ΔS according to claim 2 is characterized in that: the relationship between ΔS and SOC in the temperature range of 20 to 40℃ is obtained through experimental results.

[0183] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A temperature regulation method based on an embedded lithium battery, characterized in that: include, Collect battery data, build a temperature regulation sensing model, and monitor battery temperature changes in real time through the temperature regulation sensing model. The collected battery data is used to construct a temperature regulation sensing model, which monitors battery temperature changes in real time. The temperature regulation sensing model includes a temperature propagation model, a dynamic battery model, and a temperature regulation strategy. Based on the temperature at different locations inside the battery, and considering the influence of the environment and the heat dissipation system, a temperature propagation model is constructed, which uses the following expression: ； in, It is a temperature prediction at location x at time t; It is the ambient temperature; It is the battery temperature measured by the i-th sensor; It is the position of the i-th sensor; It is the attenuation coefficient at position i, indicating how heat is distributed from the position of the i-th sensor to other parts of the battery; It is the temperature of the heat dissipation system; It is the contribution of the heat dissipation system temperature to the total temperature, and it is a function of time to take into account the change of heat dissipation efficiency over time. Based on the temperature at different locations inside the battery, and the influence of the battery's operating state on the battery temperature, a dynamic battery model can be constructed, which uses the following expression: ； Where Q(t) is the amount of heat generated at time t; It is the thermal efficiency coefficient; and These are the charging and discharging currents, respectively; R(T) is the battery internal resistance at temperature T, which depends on the internal temperature and can be estimated using a weighted average. By combining a temperature propagation model and a dynamic battery model, the battery's operating state is adjusted to achieve real-time adjustment of the charging and discharging current. It self-regulates based on instantaneous temperature feedback and uses integral control to compensate for previous temperature errors, thereby controlling the battery temperature to maintain it within a safe range, extending battery life, and ensuring safe operation. The expression is as follows: ； in, It is the adjusted charging or discharging current; , These are proportional and integral gains, respectively. This is the target temperature of the battery; The average battery temperature is obtained from a temperature control model. An adaptive power management system is built based on changes in battery temperature. The output of the temperature regulation sensing model is used as the input of the adaptive power management system to dynamically adjust the battery charging and discharging power and optimize battery performance. The adaptive power management based on battery temperature changes uses the output of the temperature regulation sensing model as the input to dynamically adjust the battery charging and discharging power and optimize battery performance. The adaptive power management includes obtaining temperature data from a temperature control model, constructing a power adjustment model based on the obtained temperature data, further optimizing the power adjustment model by introducing temperature impact compensation, and obtaining the final charge and discharge power by combining the basic power adjustment model and temperature impact compensation. Temperature data is obtained from the temperature control model. First, the average temperature of the battery, as defined in the previous temperature control model, is used as the basic input of this model. Real-time temperature information of the battery is obtained as the basis for dynamic power adjustment. The charging and discharging power is dynamically adjusted according to the battery temperature, and the power adjustment model is constructed using the following expression: ； Where P(t) is the charging and discharging power at time t. This is the base power, which is the maximum charge and discharge power of the battery under standard operating conditions. This is the target temperature of the battery; It is the average battery temperature obtained from the temperature control model; τ is the temperature response constant, used to adjust the sensitivity of the power response; Base power adjustment: An exponential decay function is used to adjust the base power to ensure that the relationship between power adjustment and battery temperature is non-linear, in order to accommodate the high sensitivity of battery temperature to performance; By introducing temperature impact compensation, the power adjustment model is further optimized. This temperature impact compensation includes introducing a compensation factor to address performance fluctuations that may occur when temperatures change rapidly. The compensation factor smooths the transition of power adjustments and reduces the impact of rapid temperature changes. It is expressed as follows: ； in, It is the compensation factor for time t; It is a compensation coefficient, used to adjust the degree of compensation; It is the rate of change of battery temperature, obtained from the temperature control model; Combining base power adjustment and temperature effect compensation, the final charge and discharge power is obtained. The overall adjusted power output takes into account base power adjustment and temperature change compensation to ensure that the battery maintains optimal performance under all operating conditions. It is expressed by the following formula: ； in, This is the final adjusted power; An integrated thermal management system is established by combining a temperature regulation sensing model and adaptive power management. The battery temperature is judged by the threshold output by the temperature regulation sensing model and adaptive power management. The integrated thermal management system prevents battery overheating and precisely regulates the battery temperature.

2. The temperature regulation method based on an embedded lithium battery as described in claim 1, characterized in that: The battery data collected includes the temperature at various points inside the battery, the ambient temperature, the battery's operating status, the battery's health status, and other relevant parameters. Battery internal temperature: core cell temperature, battery surface temperature, and temperature distribution at different locations; Ambient temperature: the ambient temperature around the battery and the temperature of the heat dissipation system; Battery operating status: charging current, discharging current, battery voltage; Time-related data: temperature change rate, historical temperature data; Battery health status: battery cycle count, battery capacity degradation; Other relevant parameters: ambient humidity, vibration data, or shock data.

3. The temperature regulation method based on an embedded lithium battery as described in claim 2, characterized in that: The integrated thermal management system is established by combining a temperature regulation sensing model and adaptive power management. The battery temperature is judged by the threshold output by the temperature regulation sensing model and adaptive power management. The integrated thermal management system prevents battery overheating and precisely regulates the battery temperature. The integrated thermal management system includes defining a comprehensive thermal state evaluation function, a temperature regulation strategy, and a final charge / discharge strategy adjustment. The comprehensive thermal state evaluation function assesses the battery's thermal state by combining temperature deviation and power output, ensuring dynamic adjustment based on the battery's actual performance to accurately evaluate the battery's real-time thermal environment. A thermal state evaluation function is defined, which evaluates the battery's thermal state based on the output of an intelligent temperature sensing model and adaptive power management, and it uses the following expression: ； in, It is a comprehensive thermal state evaluation over time t; It is the actual temperature obtained through a temperature regulation sensing model; This is the target temperature of the battery; It is the power adjustment value obtained from the adaptive power management system; , , These are adjustment coefficients, used to balance the effects of temperature deviation and power output, respectively.

4. The temperature regulation method based on an embedded lithium battery as described in claim 3, characterized in that: The temperature regulation strategy utilizes a combination of hyperbolic tangent and sine functions to achieve dynamic temperature adjustment. The hyperbolic tangent function provides a smooth and effective regulation mechanism, while the sine function introduces periodic adjustment to adapt to fluctuations in ambient temperature. Based on the output of the thermal state evaluation function, the battery temperature regulation strategy is defined using the following expression: ； in, It is a temperature regulation strategy based on time t; , , It is the adjustment coefficient, used to adjust the sensitivity of the response and introduce periodic adjustments to cope with external temperature fluctuations; The final charge / discharge strategy adjustment comprehensively considers the effects of power management and temperature regulation to ensure that the battery maintains optimal performance and safe operation under various operating conditions. Based on the output of the thermal state evaluation function, the battery temperature regulation strategy is defined using the following expression: ； in, It is the final adjusted power after temperature regulation.

5. The temperature regulation method based on an embedded lithium battery as described in claim 4, characterized in that: When the battery temperature is less than the first threshold: the battery temperature risk level is judged as low risk; the system will monitor the real-time temperature of the battery and confirm that the temperature is lower than the safety threshold through the intelligent temperature sensing model, at which point it is judged as a low risk state; at the same time, a routine performance report is sent to the maintenance department and the low alarm status is displayed through the user interface for routine risk awareness and basic maintenance training. When the first threshold is less than the battery temperature and the second threshold is between: the battery temperature risk level is determined to be medium risk; The intelligent temperature sensing model detects that the battery temperature is between the first and second thresholds, and the adaptive power management system adjusts the charging and discharging power according to the current temperature. At the same time, early warning information is sent to the technical support department, and a medium-intensity alarm is generated and displayed through the system interface for risk communication and basic emergency drills. In addition, the early warning response plan is activated and corresponding technical intervention is prepared. When the battery temperature exceeds the second threshold: the battery temperature risk level is determined to be high risk; when the temperature exceeds the second threshold, ITMS immediately conducts an advanced assessment, the adaptive power management system performs emergency power adjustment to reduce the temperature to a safe range as soon as possible, and at the same time sends emergency warning information to all relevant departments and users. The system interface generates and displays a high-intensity virtual alarm scenario for evacuation and rescue strategy formulation. Meanwhile, the system automatically starts the emergency cooling procedure and other necessary emergency response measures.

6. A temperature regulation system based on an embedded lithium battery, based on the temperature regulation method based on an embedded lithium battery according to any one of claims 1 to 5, characterized in that: It also includes, The data acquisition module collects battery data, builds a temperature regulation sensing model, and monitors battery temperature changes in real time through the temperature regulation sensing model. The module builds an adaptive power management system based on battery temperature changes, using the output of the temperature regulation sensing model as the input to the adaptive power management system to dynamically adjust the battery charging and discharging power and optimize battery performance. The control module establishes an integrated thermal management system by combining a temperature control sensing model and adaptive power management. It judges the battery temperature based on the threshold output by the temperature control sensing model and adaptive power management, prevents battery overheating based on the integrated thermal management system, and precisely controls the battery temperature.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the temperature regulation method based on an embedded lithium battery as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the temperature regulation method based on an embedded lithium battery as described in any one of claims 1 to 5.

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