Lithium battery intelligent BMS temperature control collaborative optimization method based on dynamic thermal management model

Abstract

The invention discloses a lithium battery intelligent BMS temperature control collaborative optimization method based on a dynamic thermal management model, and relates to the technical field of battery management. Initially setting a BMS temperature control strategy; performing dynamic thermal disturbance identification; optimizing temperature control collaborative parameters; and temperature control effect feedback correction. According to the invention, multi-physical field coupling modeling is carried out on the battery cell, the module and the PACK structure, so that accurate prediction of the heat production rule of the battery in different charging and discharging states is realized; according to the method, real-time thermal disturbance identification and classification are combined, cooperative operation parameters of multiple temperature control components are dynamically optimized, closed-loop optimization is formed through feedback correction and temperature control effect evaluation indexes, and comprehensive improvement of temperature uniformity, energy efficiency and cycle life is achieved, so that safe, efficient and intelligent operation of a lithium battery is guaranteed under complex working conditions and a decline state, and the safety and reliability of the lithium battery are improved. By introducing an adaptive algorithm and a machine learning technology, a temperature control strategy can be dynamically adjusted.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and specifically to an intelligent BMS temperature control collaborative optimization method for lithium batteries based on a dynamic thermal management model. Background Technology

[0002] With the rapid development of the new energy industry, lithium batteries, as core energy storage components, are widely used in electric vehicles, energy storage power stations, and other fields. The thermal characteristics of lithium batteries have a decisive impact on their safety, cycle life, and energy efficiency. The battery management system (BMS), as the "brain" of the lithium battery, directly affects the overall performance of the lithium battery through its temperature control strategy. Currently, traditional BMS temperature control methods mostly adopt static threshold control, triggering heat dissipation or heat preservation actions based solely on data from a single temperature sensor. This approach has the following shortcomings: it does not fully consider the differences in thermal conduction among cells, modules, and PACK structures, making it difficult to accurately reflect the dynamic thermal distribution of the lithium battery, resulting in a low degree of matching between the temperature control strategy and the actual thermal state; it lags in responding to dynamic thermal disturbances such as sudden changes in charge / discharge rates and abrupt changes in ambient temperature, easily leading to local overheating or uneven temperature distribution, affecting battery safety; the operating parameters of multiple temperature control components lack dynamic collaborative optimization, resulting in excessive energy loss or unstable temperature control effects; and the thermal characteristics of lithium batteries degrade with aging during cycling, and traditional methods do not adaptively adjust the thermal management model, leading to a significant decrease in temperature control accuracy after long-term use.

[0003] Therefore, there is an urgent need for an intelligent BMS temperature control collaborative optimization method based on a dynamic thermal management model to solve the problems of static control, weak anti-disturbance capability, and insufficient collaboration, and to achieve accurate temperature control and performance optimization throughout the entire life cycle of lithium batteries. Summary of the Invention

[0004] To address the aforementioned technical problems, a collaborative optimization method for intelligent BMS temperature control of lithium batteries based on a dynamic thermal management model is provided. This technical solution solves the problems mentioned above.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A collaborative optimization method for intelligent BMS temperature control in lithium batteries based on a dynamic thermal management model includes: S1. Lithium battery thermal characteristic modeling: thermal characteristic parameters of lithium battery cells, modules and PACK structures are collected and modeled. Combined with the heat generation law of lithium battery under different charge and discharge states, a dynamic thermal management basic model is constructed to ensure that the model can accurately reflect the actual thermal distribution state of lithium battery. S2, BMS temperature control strategy initial design: Based on the dynamic thermal management basic model, according to the safe operating temperature range of lithium battery, charge and discharge rate requirements and ambient temperature changes, the basic temperature control strategy of BMS is initially set, including heat dissipation start threshold, heat preservation trigger conditions and basic operating parameters of temperature control execution components. S3. Dynamic thermal disturbance identification: During the charging and discharging cycle of lithium battery, the cell temperature, temperature gradient and environmental thermal disturbance factors are monitored in real time. Thermal disturbances that exceed the prediction range of the basic model are identified and classified to determine the intensity, scope of influence and duration of the thermal disturbance. S4. Temperature control collaborative parameter optimization: Based on the dynamic thermal disturbance identification results and combined with the current working state of the lithium battery, the heat dissipation power, heat preservation strength and collaborative operation parameters of multiple temperature control components in the BMS temperature control strategy are dynamically optimized. S5. Temperature control effect feedback correction: During the execution of the optimized temperature control strategy, the temperature change curve, energy loss data and cycle life impact parameters of the lithium battery are continuously collected and compared with the preset temperature control target. If there is a deviation, the dynamic thermal management model and temperature control coordination parameters are corrected by feedback to form a closed-loop optimization.

[0006] Preferably, S1 specifically includes: Specific heat capacity, thermal conductivity and heat generation rate parameters of different types of lithium battery cells were collected, and heat generation data of the cells at different charge and discharge rates (0.5C to 5C) in the range of 0% to 100% SOC were obtained through experimental testing. Based on the structural layout of the module and PACK, a heat conduction path model is constructed to simulate the heat transfer process between cells and between cells and the outer casing. At the same time, boundary conditions of the environmental convection heat transfer coefficient and radiation heat transfer coefficient are introduced to form a dynamic thermal management basic model that includes multi-physics coupling. The basic model is validated by comparing the temperature distribution predicted by the model with the actual tested lithium battery temperature data. If the error exceeds 5%, the thermal parameters and heat transfer path are corrected until the model error meets the accuracy requirements.

[0007] Preferably, a temperature response coefficient is set in the dynamic thermal management model. This coefficient is the ratio of the cell temperature change rate to the thermal input power and is dynamically updated according to the cyclic aging degree of the lithium battery. When the number of cycles of the lithium battery exceeds 50% of the designed cycle life, if the temperature response coefficient decreases by more than 20%, it is determined that the thermal characteristics of the cell have significantly degraded, and the thermal parameter recalibration process is automatically triggered, and the thermal management model is adaptively adjusted.

[0008] The preferred initial design process for the BMS temperature control strategy is as follows: Set a safe temperature range for lithium batteries, including the minimum insulation temperature and the maximum heat dissipation start temperature. Divide this range into temperature sub-ranges, with each sub-range corresponding to a different temperature control intensity level. Based on the relationship between ambient temperature and safe temperature range, the basic startup logic of the temperature control actuator is set as follows: when the ambient temperature is lower than the minimum insulation temperature, the heating component is started first, and the heating power increases linearly with the difference between the ambient temperature and the minimum insulation temperature. When the ambient temperature is higher than the maximum heat dissipation startup temperature, the heat dissipation component is started, and the heat dissipation power increases stepwise with the difference between the ambient temperature and the maximum heat dissipation startup temperature. When the ambient temperature is within the safe temperature range, maintain basic insulation and low-power heat dissipation. The charge / discharge rate is used as a correction factor to dynamically adjust the temperature control threshold. During high-rate charge / discharge, the maximum heat dissipation start-up temperature is reduced by 5-8℃, and the minimum heat preservation temperature is increased by 3-5℃ to ensure thermal safety under extreme operating conditions.

[0009] Preferably, S3 specifically includes: By using NTC temperature sensors distributed at different locations in the lithium battery module, the cell temperature data is collected in real time, and the temperature gradient of adjacent cells is obtained. When the temperature gradient of adjacent cells is greater than 5°C, it is determined that there is a local thermal disturbance. Environmental thermal disturbance factors are monitored, including sudden changes in ambient temperature (more than 10°C within 10 minutes), direct sunlight, and sudden changes in ventilation conditions. These are quantified as a thermal disturbance intensity index H, with H ranging from 0 to 1. When H ≥ 0.6, it is considered a strong thermal disturbance. Thermal disturbances are classified and labeled: local thermal disturbances are labeled as type P, and strong environmental thermal disturbances are labeled as type E. The duration t of the thermal disturbance is also recorded. When t > 30 minutes, it is considered a persistent thermal disturbance.

[0010] Preferably, for P-type thermal disturbances, a local temperature control enhancement strategy is activated, which reduces the temperature of the local high-temperature cell to within ±2℃ of the adjacent cell temperature within 5 minutes by adjusting the speed of the cooling fan and the flow rate of the liquid cooling channel in the corresponding area; for E-type thermal disturbances, a global temperature control coordination strategy is activated, which synchronously adjusts the operating parameters of all temperature control components to ensure that the overall temperature fluctuation of the lithium battery is controlled within ±3℃.

[0011] Preferably, the temperature control coordination parameter optimization process is as follows: Establish a collaborative operation matrix for multiple temperature control components, where the matrix elements are the power ratio coefficients of different components, and determine the initial ratio coefficients based on the type and intensity of thermal disturbance. With the optimization objectives of minimizing lithium battery temperature uniformity and energy loss, the particle swarm optimization algorithm is used to iteratively solve the cooperative operation matrix to obtain the optimal power ratio parameters. When the lithium battery is in a low charge state (SOC<20%), an energy priority coefficient is introduced to correct the optimization target, prioritizing the energy efficiency of the temperature control system and allowing the maximum temperature difference to be relaxed to 6℃.

[0012] Preferably, a temperature control response delay threshold is set during the optimization process. When the actual response time of the temperature control component exceeds the temperature control response delay threshold (the temperature control response delay threshold is set to 1 to 3 seconds depending on the component type), the power output ratio of the component is automatically increased to shorten the response delay and ensure the real-time performance of the temperature control strategy.

[0013] Preferably, the temperature control effect feedback correction process is as follows: The lithium battery temperature curve is collected during the execution of the optimized temperature control strategy to obtain the average temperature deviation and temperature fluctuation variance. If the average temperature deviation is >2℃ or the temperature fluctuation variance is >4℃², the temperature control effect is deemed unsatisfactory. The heat transfer coefficient in the dynamic thermal management model is adjusted according to the deviation direction. At the same time, the cycle capacity decay rate of the lithium battery is collected. When the cycle capacity decay rate exceeds 0.5% in a single optimization cycle, the temperature control strategy is deemed to have an adverse effect on battery life. The operating intensity of the corresponding temperature control component is reduced, and the collaborative parameters are re-optimized.

[0014] Preferably, a temperature control effectiveness evaluation index is established, wherein the formula for calculating the temperature control effectiveness evaluation index is: In the formula, as well as These are the weighting coefficients. This is the difference between the actual average temperature and the target temperature. The highest temperature, For temperature fluctuation variance, The variance of the target temperature fluctuation. The cycle capacity decay rate of lithium batteries. The maximum allowable capacity decay rate; when When the current temperature control strategy is deemed optimal, the parameters are kept unchanged. Make slight corrections when necessary. When this happens, a full optimization process is triggered, and steps S2-S4 are re-executed.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes to construct a dynamic thermal management model, combining dynamic thermal disturbance identification, collaborative parameter optimization of multiple temperature control components, and a closed-loop feedback correction mechanism throughout the entire life cycle, to achieve precise, intelligent, and adaptive collaborative optimization of lithium battery BMS temperature control under all operating conditions. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of the present invention. Detailed Implementation

[0017] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0018] Reference Figure 1 As shown, a collaborative optimization method for intelligent BMS temperature control of lithium batteries based on a dynamic thermal management model includes: S1. Lithium battery thermal characteristic modeling: thermal characteristic parameters of lithium battery cells, modules and PACK structures are collected and modeled. Combined with the heat generation law of lithium battery under different charge and discharge states, a dynamic thermal management basic model is constructed to ensure that the model can accurately reflect the actual thermal distribution state of lithium battery. S2, BMS temperature control strategy initial design: Based on the dynamic thermal management basic model, according to the safe operating temperature range of lithium battery, charge and discharge rate requirements and ambient temperature changes, the basic temperature control strategy of BMS is initially set, including heat dissipation start threshold, heat preservation trigger conditions and basic operating parameters of temperature control execution components. S3. Dynamic thermal disturbance identification: During the charging and discharging cycle of lithium battery, the cell temperature, temperature gradient and environmental thermal disturbance factors are monitored in real time. Thermal disturbances that exceed the prediction range of the basic model are identified and classified to determine the intensity, scope of influence and duration of the thermal disturbance. S4. Temperature control collaborative parameter optimization: Based on the dynamic thermal disturbance identification results and combined with the current working state of the lithium battery, the heat dissipation power, heat preservation strength and collaborative operation parameters of multiple temperature control components in the BMS temperature control strategy are dynamically optimized. S5. Temperature control effect feedback correction: During the execution of the optimized temperature control strategy, the temperature change curve, energy loss data and cycle life impact parameters of the lithium battery are continuously collected and compared with the preset temperature control target. If there is a deviation, the dynamic thermal management model and temperature control coordination parameters are corrected by feedback to form a closed-loop optimization.

[0019] S1 specifically includes: The specific heat capacity, thermal conductivity, and heat generation rate of different types of lithium battery cells were collected. Through experimental testing, heat generation data of the cells at different charge and discharge rates (0.5C to 5C) within the range of 0% to 100% SOC were obtained. Based on the structural layout of the module and PACK, a heat conduction path model is constructed to simulate the heat transfer process between cells and between cells and the outer casing. At the same time, boundary conditions of the environmental convection heat transfer coefficient and radiation heat transfer coefficient are introduced to form a dynamic thermal management basic model that includes multi-physics coupling. The basic model is validated by comparing the temperature distribution predicted by the model with the actual tested lithium battery temperature data. If the error exceeds 5%, the thermal parameters and heat transfer path are corrected until the model error meets the accuracy requirements. By collecting and experimentally testing thermal parameters such as specific heat capacity, thermal conductivity, and heat generation rate of different types of lithium battery cells, more accurate battery thermal characteristic modeling can be achieved. Particularly in acquiring heat generation data under various charge / discharge rates, a dynamic thermal management model with multi-physics coupling can be created, taking into account external factors such as environmental convection and radiation heat transfer, thus making the thermal model more adaptable to different application scenarios. The validation process ensures that the model error is less than 5%, improving simulation accuracy.

[0020] A temperature response coefficient is set in the dynamic thermal management model. This coefficient is the ratio of the cell temperature change rate to the thermal input power and is dynamically updated according to the cyclic aging degree of the lithium battery. When the number of cycles of the lithium battery exceeds 50% of the designed cycle life, if the temperature response coefficient decreases by more than 20%, it is determined that the thermal characteristics of the cell have significantly degraded, and the thermal parameter recalibration process is automatically triggered, and the thermal management model is adaptively adjusted.

[0021] The initial design process for the BMS temperature control strategy is as follows: Set a safe temperature range for lithium batteries, including the minimum insulation temperature and the maximum heat dissipation start temperature. Divide this range into temperature sub-ranges, with each sub-range corresponding to a different temperature control intensity level. Based on the relationship between ambient temperature and safe temperature range, the basic startup logic of the temperature control actuator is set as follows: when the ambient temperature is lower than the minimum insulation temperature, the heating component is started first, and the heating power increases linearly with the difference between the ambient temperature and the minimum insulation temperature. When the ambient temperature is higher than the maximum heat dissipation startup temperature, the heat dissipation component is started, and the heat dissipation power increases stepwise with the difference between the ambient temperature and the maximum heat dissipation startup temperature. When the ambient temperature is within the safe temperature range, maintain basic insulation and low-power heat dissipation. The charge / discharge rate is used as a correction factor to dynamically adjust the temperature control threshold. During high-rate charge / discharge, the maximum heat dissipation start-up temperature is reduced by 5-8℃, and the minimum heat preservation temperature is increased by 3-5℃ to ensure thermal safety under extreme conditions. The BMS temperature control strategy not only considers the safe temperature range of lithium batteries, but also dynamically adjusts the temperature control parameters. Combined with changes in ambient temperature, SOC and charge / discharge rate, it can adapt to the temperature control requirements under different operating conditions. By setting different temperature control intensity levels and start-up logic, the BMS can more flexibly respond to the temperature control requirements under different environments and operating conditions. Especially under extreme operating conditions, the temperature control strategy can be adjusted in time to ensure the thermal safety of the battery.

[0022] S3 specifically includes: By using NTC temperature sensors distributed at different locations in the lithium battery module, the cell temperature data is collected in real time, and the temperature gradient of adjacent cells is obtained. When the temperature gradient of adjacent cells is greater than 5°C, it is determined that there is a local thermal disturbance. Environmental thermal disturbance factors are monitored, including sudden changes in ambient temperature (more than 10°C within 10 minutes), direct sunlight, and sudden changes in ventilation conditions. These are quantified as a thermal disturbance intensity index H, with H ranging from 0 to 1. When H ≥ 0.6, it is considered a strong thermal disturbance. Thermal disturbances are classified and labeled: local thermal disturbances are labeled as type P, and strong environmental thermal disturbances are labeled as type E. The duration t of the thermal disturbance is also recorded. When t > 30 minutes, it is considered a persistent thermal disturbance. The NTC temperature sensor collects cell temperature data in real time, dynamically monitors local and environmental thermal disturbances, and uses the thermal disturbance intensity index as an important parameter to capture and respond to environmental or local thermal changes in a timely manner. The identification and classification of thermal disturbances helps to accurately determine the source of thermal problems, thereby adopting different temperature control strategies. The identification of persistent thermal disturbances can also prevent long-term temperature fluctuations and ensure long-term stable operation of the battery.

[0023] For P-type thermal disturbances, a local temperature control enhancement strategy is activated. By adjusting the cooling fan speed and liquid cooling channel flow rate in the corresponding area, the temperature of the local high-temperature cell is reduced to within ±2℃ of the adjacent cell temperature within 5 minutes. For E-type thermal disturbances, a global temperature control coordination strategy is activated. The operating parameters of all temperature control components are adjusted synchronously to ensure that the overall temperature fluctuation of the lithium battery is controlled within ±3℃.

[0024] The optimization process for temperature control coordination parameters is as follows: Establish a collaborative operation matrix for multiple temperature control components, where the matrix elements are the power ratio coefficients of different components, and determine the initial ratio coefficients based on the type and intensity of thermal disturbance. With the optimization objectives of minimizing lithium battery temperature uniformity and energy loss, the particle swarm optimization algorithm is used to iteratively solve the cooperative operation matrix to obtain the optimal power ratio parameters. When the lithium battery is in a low charge state (SOC<20%), an energy priority coefficient is introduced to correct the optimization target, prioritizing the energy efficiency of the temperature control system and allowing the maximum temperature difference to be relaxed to 6℃. The multi-temperature control components in the temperature control system adopt a collaborative operation matrix and dynamically optimize the power ratio through a particle swarm optimization algorithm to ensure the battery temperature uniformity and energy efficiency. For low-charge states, the energy efficiency of the temperature control system is prioritized, and the temperature difference control is appropriately relaxed. During the optimization process, a temperature control response delay threshold is added to ensure the real-time performance of the temperature control strategy and avoid delays that lead to substandard temperature control effects. By dynamically adjusting the optimization target, the temperature control system can operate efficiently under different working conditions.

[0025] During the optimization process, a temperature control response delay threshold is set. When the actual response time of the temperature control component exceeds the temperature control response delay threshold (the temperature control response delay threshold is set to 1 to 3 seconds depending on the component type), the power output ratio of the component is automatically increased to shorten the response delay and ensure the real-time performance of the temperature control strategy.

[0026] The temperature control effect feedback and correction process is as follows: The lithium battery temperature curve is collected during the execution of the optimized temperature control strategy to obtain the average temperature deviation and temperature fluctuation variance. If the average temperature deviation is >2℃ or the temperature fluctuation variance is >4℃², the temperature control effect is deemed unsatisfactory. The heat transfer coefficient in the dynamic thermal management model is adjusted according to the deviation direction. At the same time, the cycle capacity decay rate of the lithium battery is collected. When the cycle capacity decay rate exceeds 0.5% in a single optimization cycle, the temperature control strategy is deemed to have an adverse effect on battery life. The operating intensity of the corresponding temperature control component is reduced, and the collaborative parameters are re-optimized.

[0027] Establish a temperature control effectiveness evaluation index, wherein the formula for calculating the temperature control effectiveness evaluation index is: In the formula, as well as These are the weighting coefficients. This is the difference between the actual average temperature and the target temperature. The highest temperature, For temperature fluctuation variance, The variance of the target temperature fluctuation. The cycle capacity decay rate of lithium batteries. The maximum allowable capacity decay rate; when When the current temperature control strategy is deemed optimal, the parameters are kept unchanged. Make slight corrections when necessary. When this happens, a full optimization process is triggered, and steps S2-S4 are re-executed; By collecting temperature data in real time and using a temperature control effectiveness evaluation index, the current temperature control strategy is evaluated and corrected in real time. If the temperature control effect is not ideal, feedback adjustments will be made to reduce the operating intensity of the temperature control components and protect battery life. The temperature control effectiveness evaluation index integrates multiple factors such as temperature deviation, temperature fluctuation, and cycle capacity decay to ensure that the temperature control system maintains optimal performance under different operating environments. If it is found that the temperature control strategy has a negative impact on battery life, the system will automatically adjust to ensure long-term stable operation.

[0028] In summary, the advantages of this invention are as follows: Improve temperature control accuracy: By constructing a dynamic thermal management model that includes the cell, module and PACK structure, and combining it with multi-physics coupling analysis, the thermal distribution state of the lithium battery can be accurately reflected. The model error can be controlled, providing a reliable foundation for temperature control strategies.

[0029] Enhanced anti-disturbance capability: Through a dynamic thermal disturbance identification mechanism, local thermal disturbances and strong environmental thermal disturbances are monitored in real time, and targeted local reinforcement or global collaborative strategies are launched to ensure that the temperature fluctuation range is controlled within ±3℃, effectively avoiding the risk of local overheating.

[0030] Optimize collaborative efficiency: Establish a power ratio collaborative matrix for multiple temperature control components, and combine it with particle swarm optimization algorithm to achieve dynamic collaboration of heat dissipation and heat preservation components with the goal of minimizing temperature uniformity and energy loss. Under low power state (SOC<20%), further improve energy utilization efficiency through energy priority correction. Adaptable to the entire life cycle: Introducing a dynamic temperature response coefficient update mechanism to automatically trigger thermal parameter calibration and model adjustment for thermal characteristic degradation caused by lithium battery aging (cycle life > 50%), ensuring the adaptability and stability of temperature control strategy during long-term use; By correcting the temperature control effect through feedback, and combining indicators such as average temperature deviation, temperature fluctuation variance, and cycle capacity decay rate, an evaluation index is constructed to form a closed loop of modeling, optimization, feedback, and correction. This ensures that the temperature control strategy continuously meets actual needs, extends the cycle life of lithium batteries, and reduces safety hazards.

[0031] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A collaborative optimization method for intelligent BMS temperature control of lithium batteries based on a dynamic thermal management model, characterized in that, The specific process of temperature control collaborative optimization is as follows: S1. Lithium battery thermal characteristic modeling: thermal characteristic parameters of lithium battery cells, modules and PACK structures are collected and modeled. Combined with the heat generation law of lithium battery under different charge and discharge states, a dynamic thermal management basic model is constructed to ensure that the model can accurately reflect the actual thermal distribution state of lithium battery. S2, BMS temperature control strategy initial design: Based on the dynamic thermal management basic model, according to the safe operating temperature range of lithium battery, charge and discharge rate requirements and ambient temperature changes, the basic temperature control strategy of BMS is initially set, including heat dissipation start threshold, heat preservation trigger conditions and basic operating parameters of temperature control execution components. S3. Dynamic thermal disturbance identification: During the charging and discharging cycle of lithium battery, the cell temperature, temperature gradient and environmental thermal disturbance factors are monitored in real time. Thermal disturbances that exceed the prediction range of the basic model are identified and classified to determine the intensity, scope of influence and duration of the thermal disturbance. S4. Temperature control collaborative parameter optimization: Based on the dynamic thermal disturbance identification results and combined with the current working state of the lithium battery, the heat dissipation power, heat preservation strength and collaborative operation parameters of multiple temperature control components in the BMS temperature control strategy are dynamically optimized. S5. Temperature control effect feedback correction: During the execution of the optimized temperature control strategy, the temperature change curve, energy loss data and cycle life impact parameters of the lithium battery are continuously collected and compared with the preset temperature control target. If there is a deviation, the dynamic thermal management model and temperature control coordination parameters are corrected by feedback to form a closed-loop optimization.

2. The intelligent BMS temperature control collaborative optimization method for lithium batteries based on a dynamic thermal management model according to claim 1, characterized in that, S1 specifically includes: Specific heat capacity, thermal conductivity and heat generation rate parameters of different types of lithium battery cells were collected, and heat generation data of the cells were obtained through experimental testing within a set range and at different charge and discharge rates. Based on the structural layout of the module and PACK, a heat conduction path model is constructed to simulate the heat transfer process between cells and between cells and the outer casing. At the same time, boundary conditions of the environmental convection heat transfer coefficient and radiation heat transfer coefficient are introduced to form a dynamic thermal management basic model that includes multi-physics coupling. The basic model is validated by comparing the temperature distribution predicted by the model with the actual tested lithium battery temperature data. If the error exceeds the set value, the thermal parameters and heat transfer path are corrected until the model error meets the accuracy requirements.

3. The intelligent BMS temperature control collaborative optimization method for lithium batteries based on a dynamic thermal management model according to claim 2, characterized in that: A temperature response coefficient is set in the dynamic thermal management model. This coefficient is the ratio of the cell temperature change rate to the thermal input power. It is dynamically updated according to the cyclic aging degree of the lithium battery. When the number of cycles of the lithium battery exceeds half of the designed cycle life, if the temperature response coefficient drops by more than the set threshold, it is determined that the thermal characteristics of the cell have significantly degraded. The thermal parameter recalibration process is automatically triggered, and the thermal management model is adaptively adjusted.

4. The intelligent BMS temperature control collaborative optimization method for lithium batteries based on a dynamic thermal management model according to claim 1, characterized in that, The initial design process for the BMS temperature control strategy is as follows: Set a safe temperature range for lithium batteries, including the minimum insulation temperature and the maximum heat dissipation start temperature. Divide this range into temperature sub-ranges, with each sub-range corresponding to a different temperature control intensity level. Based on the relationship between ambient temperature and safe temperature range, the basic startup logic of the temperature control actuator is set as follows: when the ambient temperature is lower than the minimum insulation temperature, the heating component is started first, and the heating power increases linearly with the difference between the ambient temperature and the minimum insulation temperature. When the ambient temperature is higher than the maximum heat dissipation startup temperature, the heat dissipation component is started, and the heat dissipation power increases stepwise with the difference between the ambient temperature and the maximum heat dissipation startup temperature. When the ambient temperature is within the safe temperature range, maintain basic insulation and low-power heat dissipation. The charge / discharge rate is used as a correction factor to dynamically adjust the temperature control threshold. During high-rate charge / discharge, the maximum heat dissipation start-up temperature is lowered, and the minimum heat preservation temperature is raised.

5. The intelligent BMS temperature control collaborative optimization method for lithium batteries based on a dynamic thermal management model according to claim 1, characterized in that, S3 specifically includes: By using NTC temperature sensors distributed at different locations in the lithium battery module, the cell temperature data is collected in real time to obtain the temperature gradient between adjacent cells. Monitor environmental thermal disturbance factors, including sudden changes in ambient temperature, direct sunlight, and abrupt changes in ventilation conditions, and quantify them as thermal disturbance intensity indices; Thermal disturbances are classified and labeled. Local thermal disturbances are labeled as type P, and strong environmental thermal disturbances are labeled as type E. The duration of thermal disturbances is also recorded. When the duration of thermal disturbance is greater than 30 minutes, it is determined to be a persistent thermal disturbance.

6. The intelligent BMS temperature control collaborative optimization method for lithium batteries based on a dynamic thermal management model according to claim 5, characterized in that, For P-type thermal disturbances, a local temperature control enhancement strategy is initiated by adjusting the cooling fan speed and liquid cooling flow rate in the corresponding area; for E-type thermal disturbances, a global temperature control coordination strategy is initiated by synchronously adjusting the operating parameters of all temperature control components.

7. The intelligent BMS temperature control collaborative optimization method for lithium batteries based on a dynamic thermal management model according to claim 1, characterized in that, The optimization process for temperature control coordination parameters is as follows: Establish a collaborative operation matrix for multiple temperature control components, where the matrix elements are the power ratio coefficients of different components, and determine the initial ratio coefficients based on the type and intensity of thermal disturbance. With the optimization objectives of minimizing lithium battery temperature uniformity and energy loss, the particle swarm optimization algorithm is used to iteratively solve the cooperative operation matrix to obtain the optimal power ratio parameters. When the lithium battery is in a low charge state, an energy priority coefficient is introduced to correct the optimization target and prioritize the energy efficiency of the temperature control system.

8. The intelligent BMS temperature control collaborative optimization method for lithium batteries based on a dynamic thermal management model according to claim 7, characterized in that, During the optimization process, a temperature control response delay threshold is set. When the actual response time of the temperature control component exceeds the temperature control response delay threshold, the power output ratio of that component is automatically increased.

9. The intelligent BMS temperature control collaborative optimization method for lithium batteries based on a dynamic thermal management model according to claim 1, characterized in that, The temperature control effect feedback and correction process is as follows: The temperature curve of the lithium battery during the execution of the optimized temperature control strategy is collected to obtain the average temperature deviation and temperature fluctuation variance. The heat transfer coefficient in the dynamic thermal management model is adjusted according to the deviation direction. At the same time, the cycle capacity decay rate of the lithium battery is collected. When the cycle capacity decay rate exceeds 0.5% in a single optimization cycle, it is determined that the temperature control strategy has an adverse effect on the battery life. The operating intensity of the corresponding temperature control component is reduced, and the collaborative parameters are re-optimized.

10. The intelligent BMS temperature control collaborative optimization method for lithium batteries based on a dynamic thermal management model according to claim 9, characterized in that: Establish a temperature control effectiveness evaluation index, wherein the formula for calculating the temperature control effectiveness evaluation index is: ; In the formula, as well as These are the weighting coefficients. This is the difference between the actual average temperature and the target temperature. The highest temperature, For temperature fluctuation variance, The variance of the target temperature fluctuation. The cycle capacity decay rate of lithium batteries. This represents the maximum allowable capacity decay rate.

Images