Efficient solid-state battery energy storage heat management system applied to new energy

Abstract

The invention relates to the technical field of new energy, in particular to an efficient solid-state battery energy storage heat management system applied to new energy. The battery management unit is used for monitoring the voltage, the current and the temperature of each battery unit and generating a charge state and a health state; the power conversion unit is used for executing a charging and discharging instruction and a charging and discharging rate instruction transmitted by the battery management unit; the heat management unit is used for dynamically adjusting cooling and heating measures; and the safety protection unit is used for taking corresponding alarm and protection measures according to data monitored by the battery management unit. The system not only enhances the physical authenticity of the model, but also improves the capability of adapting to complex working conditions, and can more efficiently transfer redundant energy to a battery unit in a low charge state, thereby reducing energy loss and heat loss, and improving the stability and economic benefit of the system.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a high-efficiency solid-state battery energy storage thermal management system applied to new energy. Background Art

[0002] As global attention to sustainable development and reducing carbon emissions continues to increase, new energy technologies (such as solar energy, wind energy, etc.) have been rapidly developed. These renewable energy sources are intermittent and volatile, and require efficient energy storage systems to smooth output and provide a stable supply of electricity. Solid-state batteries have become an ideal choice for new energy storage systems due to their high energy density, long life, fast charging and discharging capabilities, and safety. However, despite the significant advantages of solid-state batteries in energy storage, their performance and life are still significantly affected by temperature. Too high or too low temperatures will cause battery efficiency to decrease and may even cause safety issues. Therefore, an efficient thermal management system is essential to ensure the stability and reliability of solid-state batteries under various operating conditions. Efficient thermal management can not only optimize the chemical reaction rate of the battery and improve the energy conversion efficiency, but also extend the battery life and reduce maintenance costs.

[0003] Traditional temperature regulation models often only consider basic heat conduction, convection and radiation processes, resulting in inaccurate model predictions. In addition, temperature regulation models usually use fixed parameters and cannot dynamically adapt to changes in the surface state of the battery (such as oxidation, contamination, etc.), resulting in poor temperature control effects under complex working conditions. Therefore, an efficient solid-state battery energy storage thermal management system for new energy is designed. Summary of the invention

[0004] The purpose of the present invention is to provide an efficient solid-state battery energy storage thermal management system applied to new energy, so as to solve the problem that the temperature regulation model proposed in the above background technology usually adopts fixed parameters and cannot dynamically adapt to changes in the surface state of the battery (such as oxidation, pollution, etc.), resulting in poor temperature control effect under complex working conditions.

[0005] To achieve the above object, the present invention aims to provide a high-efficiency solid-state battery energy storage thermal management system applied to new energy, comprising: A battery management unit, which is used to monitor the voltage, current and temperature of each battery cell, generate a charge state and a health state, and send charge and discharge instructions and charge and discharge rate instructions to the power conversion unit, while recording and analyzing the charge state and health state of each monitored battery cell, and evenly distribute the power distribution in the solid-state battery pack based on energy recovery; A power conversion unit, the power conversion unit is used to execute the charge and discharge instructions and the charge and discharge rate instructions transmitted from the battery management unit; A thermal management unit, the thermal management unit is used to monitor the temperature data of the battery management unit and the working state of the power conversion unit, dynamically adjust the cooling and heating measures, and introduce the heat source generated by the power conversion unit and the influence factors of the battery surface state change in the process of dynamically adjusting the cooling and heating measures for optimization; Among them, the thermal management unit includes a temperature monitoring module, a temperature adjustment module and a path design module; The safety protection unit is used to take corresponding alarm and protection measures according to the data monitored by the battery management unit. At the same time, when the thermal management unit cannot reduce the temperature of the battery pack to a safe range, the safety protection unit intervenes and implements backup protection measures.

[0006] As a further improvement of the technical solution, in the battery management unit, the power distribution in the solid-state battery pack is evenly distributed based on energy recovery, and the specific steps are as follows: The charge state and energy state of each battery cell are monitored in real time. When it is detected that the charge state of a battery cell is higher than that of other battery cells, the energy recovery circuit is started to transfer the excess energy to the battery cell with low charge state. The recovered energy is intelligently allocated according to the current state and demand of the battery cell with low charge state. In the process of intelligently allocating recovered energy, factors affecting energy efficiency are introduced for optimization.

[0007] As a further improvement of the technical solution, the battery management unit intelligently distributes the recovered energy according to the current state and demand of the low-charge state battery cells or other parts of the solid-state battery energy storage system, as follows: S11. Calculate the charge state and energy state of each battery cell: ; in, For the The state of charge of each battery cell; For the The current charge of each battery cell; For the The maximum capacity of each battery cell; ; in, For the The energy state of each battery cell; For the The voltage of each battery cell; For the The current of each battery cell; is the sampling time interval; S12, calculating the difference between the state of charge of each battery cell and the average state of charge, and determining whether it exceeds a threshold value. If so, starting the energy recovery circuit: ; ; in, For the The difference between the state of charge of each battery cell and the average state of charge; is the average state of charge of all battery cells; is the total number of battery cells; ; if , then the energy recovery circuit is started, where is the charge state threshold; S13, calculating the energy to be transferred, calculating the allocation ratio according to the current state and demand of the low-charge state battery cell, and calculating the energy that each low-charge state battery cell should receive according to the allocation ratio; ; in, for the energy transferred; The energy of a battery cell in a high state of charge; The energy of a battery cell in a low state of charge; ; in, For the The allocation ratio of low-charge state battery cells; is the target charge state; for all low state-of-charge battery cells; For the The state of charge of the low state-of-charge battery cells; According to the distribution ratio, calculate the energy that each low-charge state battery cell should receive: ; in, For the The energy that each battery cell should receive; S14. Based on the calculation results, the energy recovery circuit is started to transfer the excess energy from the battery cells with high charge state to the battery cells with low charge state, and the charge state and energy state of each battery cell are continuously monitored.

[0008] As a further improvement of the technical solution, the influencing factors of energy efficiency are introduced into the battery management unit during the intelligent allocation of recovered energy for optimization, and the optimization is as follows: ; in, For the The internal resistance of each battery cell; For the The reference internal resistance of each battery cell; is the internal resistance temperature coefficient; For the The temperature of each battery cell; is the reference temperature; ; in, For the Energy efficiency of each battery cell; For the The current of each battery cell; ; in, After optimization The allocation ratio of low-charge state battery cells; According to the optimized allocation ratio, the energy that each low-charge state battery cell should receive is calculated: ; in, After optimization The energy that each battery cell should receive.

[0009] As a further improvement of the technical solution, the temperature monitoring module in the thermal management unit collects the temperature data of each battery cell in real time through the temperature sensor installed on the battery cell, and monitors the temperature change of each battery cell to detect overheating or low temperature in time; The temperature regulation module dynamically adjusts the cooling and heating measures according to the basis provided by the temperature monitoring module and the working state of the power conversion unit, so that the battery operates within a suitable temperature range. In the process of dynamically adjusting the cooling and heating measures, the heat source generated by the power conversion unit and the influence factors of the change of the battery surface state are introduced for optimization; The path design module is used to optimize the heat conduction path to ensure that the heat generated by the battery can be quickly conducted to the radiator or cooling medium.

[0010] As a further improvement of the technical solution, the temperature monitoring module obtains the temperature distribution and temperature gradient of each battery cell to identify temperature abnormal points.

[0011] As a further improvement of the technical solution, the temperature adjustment module has the following specific steps: S31. Based on the principles of heat transfer and fluid mechanics, a physical temperature model of the battery pack is established. Combined with actual operating data, an empirical model is established. The physical model and the empirical model are combined to form a hybrid model. S32, setting the optimal temperature range and the maximum allowable temperature deviation; S33, using a PID controller to dynamically adjust the output of the cooling or heating device according to the temperature deviation and rate of change, introducing fuzzy logic control, adjusting the control parameters according to the fuzzy rules of temperature and working status, and dynamically adjusting the control parameters according to the real-time status and historical data of the battery pack; S34. Implement control strategies.

[0012] As a further improvement of the technical solution, in S31, the details are as follows: The physical model of the battery pack includes the heat conduction equation, the convection heat transfer equation and the radiation heat transfer equation; Heat conduction equation: ; in, is the density of the material; is the specific heat capacity of the material; is the temperature distribution function, expressed in spatial coordinates and time The temperature at is the thermal conductivity of the material; is the heat source term, expressed in spatial coordinates and time Heat generation rate at Convective heat transfer equation: ; in, is the convective heat transfer coefficient; is the temperature of the battery surface; is the temperature of the cooling medium; is the temperature gradient in the normal direction of the battery surface; Radiation heat transfer equation: ; in, is the surface emissivity; is the ambient temperature; is the Stefan-Boltzmann constant, approximately ; The empirical model is established as follows: ; in, is the predicted temperature; For in time Input feature vectors, including current, voltage, ambient temperature, and state of charge; The physical model and the empirical model are combined to form a hybrid model as follows: ; in, Temperature prediction for the hybrid model; Temperature prediction for physical models; Temperature prediction for the empirical model; is the dynamic weight coefficient.

[0013] As a further improvement of the technical solution, the heat source generated by the power conversion unit and the influencing factors of the battery surface state change are introduced into the thermal management unit during the dynamic adjustment of the cooling and heating measures for optimization. The optimized equation is specifically: Heat conduction equation: ; ; ; in, is the internal heat source term; is the current density; is the internal resistance of the battery cell; The heat source term generated by power conversion; is the efficiency loss coefficient of power conversion; For power conversion in time Power loss; Dirac Function, indicating the location of power conversion ; Convective heat transfer equation: ; ; in, is the dynamic convection heat transfer coefficient; is the base convection heat transfer coefficient; is the speed correlation coefficient; is the base flow velocity; is the flow velocity of the cooling medium; is the speed index; Radiation heat transfer equation: ; ; in, is the dynamic surface emissivity; is the reference surface emissivity; is the temperature correlation coefficient; is the temperature change; The empirical model is established as follows: ; in, is the predicted temperature after optimization; For the optimized time An input feature vector including current, voltage, ambient temperature, charge state, internal resistance, cooling medium flow velocity and power loss of the power conversion unit (2); The physical model and the empirical model are combined to form a hybrid model as follows: ; in, Temperature prediction for the optimized hybrid model; Temperature prediction for the optimized physical model; Temperature prediction for the optimized empirical model; is the dynamic weight coefficient.

[0014] As a further improvement of the technical solution, the S34 is specifically as follows: Initialize temperature targets and control parameters, start temperature monitoring and control programs; continuously collect temperature and operating status data, and update model inputs; predict future temperature trends based on current status and models; determine the operating status of the cooling or heating device based on the prediction results and control strategy; send control instructions to the cooling or heating device to adjust its operating status; adjust control parameters based on actual temperature feedback.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This is used in the high-efficiency solid-state battery energy storage thermal management system for new energy. In the process of dynamically adjusting cooling and heating measures, the heat source generated by power conversion and the influencing factors of battery surface state changes are introduced for optimization, which not only enhances the physical reality of the model, but also improves its ability to adapt to complex working conditions, ensuring that the battery can be kept within the optimal operating temperature range under various operating conditions, thereby extending battery life and improving system reliability and overall performance; 2. In the high-efficiency solid-state battery energy storage thermal management system applied to new energy, the factors affecting energy efficiency are introduced for optimization in the process of intelligent distribution of recovered energy, which can significantly improve the performance and reliability of the entire solid-state battery energy storage system; by dynamically adjusting the energy distribution ratio by considering the actual working conditions of each battery cell, the system can more efficiently transfer excess energy to battery cells in low charge states, thereby reducing energy loss and heat loss. This enables the solid-state battery energy storage system to maintain optimal performance under various working conditions, improving the stability and economic benefits of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A system block diagram of the present invention as a whole; Figure 2 is a system block diagram of the thermal management unit in the present invention; The meaning of each number in the figure is: 1. Battery management unit; 2. Power conversion unit; 3. Thermal management unit; 31. Temperature monitoring module; 32. Temperature regulation module; 33. Path design module; 4. Safety protection unit. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example See also Figure 1-2 As shown, a high-efficiency solid-state battery energy storage thermal management system applied to new energy is provided, including a battery management unit 1, a power conversion unit 2, a thermal management unit 3 and a safety protection unit 4.

[0019] The battery management unit 1 is used to monitor the voltage, current and temperature of each battery cell, generate the charge state and health state, ensure that the battery operates within a safe working range, and send charge and discharge instructions and charge and discharge rate instructions to the power conversion unit 2. At the same time, the charge state and health state of each monitored battery cell are recorded and analyzed, and the power distribution in the solid-state battery pack is evenly distributed based on energy recovery; the charge and discharge process is optimized to extend the service life of the battery, and support system maintenance and fault diagnosis through data recording and analysis functions; In the battery management unit 1, the power distribution in the solid-state battery pack is evenly distributed based on energy recovery. The specific steps are as follows: The charge state and energy state of each battery cell are monitored in real time. When it is detected that the charge state of a battery cell is higher than that of other battery cells, the energy recovery circuit is started to transfer the excess energy to the battery cell with low charge state. The recovered energy is intelligently allocated according to the current state and demand of the battery cell with low charge state to ensure that the energy utilization efficiency of the system is maximized. In the process of intelligently allocating recovered energy, factors affecting energy efficiency are introduced for optimization.

[0020] This balanced distribution strategy based on energy recovery not only ensures uniform power distribution in each unit of the battery pack, prolongs battery life, but also improves the overall energy utilization efficiency of the system. After introducing the factors affecting energy efficiency for optimization, the system can dynamically adjust the energy distribution ratio according to the actual working conditions of each battery unit (such as temperature, internal resistance, etc.), further reducing energy loss and improving the efficiency of energy recovery and redistribution. This not only enhances the performance stability of the battery pack, but also significantly improves the reliability and economic benefits of the entire energy storage system, providing a more efficient and sustainable solution for new energy applications.

[0021] Intelligently distribute the recovered energy based on the current state and demand of the low-state-of-charge battery cells or other parts of the solid-state battery energy storage system, as follows: S11. Calculate the charge state and energy state of each battery cell: ; in, For the The state of charge of each battery cell; For the The current charge of each battery cell; For the The maximum capacity of each battery cell; ; in, For the The energy state of each battery cell; For the The voltage of each battery cell; For the The current of each battery cell; is the sampling time interval; By calculating the charge state and energy state of each battery cell in real time, the overall condition of the battery pack can be accurately understood. Battery cells with high and low charge states can be quickly identified, providing a basis for subsequent energy recovery and distribution.

[0022] S12, calculating the difference between the state of charge of each battery cell and the average state of charge, and determining whether it exceeds a threshold value. If so, starting the energy recovery circuit: ; ; in, For the The difference between the state of charge of each battery cell and the average state of charge; is the average state of charge of all battery cells; is the total number of battery cells; ; if , then the energy recovery circuit is started, where is the charge state threshold; By calculating the difference between the charge state of each battery cell and the average charge state, the battery cells that need energy recovery and distribution can be discovered in a timely manner. The charge state of each cell in the battery pack can also be kept relatively uniform, extending battery life and improving the overall performance and reliability of the system.

[0023] S13, calculating the energy to be transferred, calculating the allocation ratio according to the current state and demand of the low-charge state battery cell, and calculating the energy that each low-charge state battery cell should receive according to the allocation ratio; ; in, for the energy transferred; The energy of a battery cell in a high state of charge; The energy of a battery cell in a low state of charge; ; in, For the The allocation ratio of low-charge state battery cells; is the target charge state; for all low state-of-charge battery cells; For the The state of charge of the low state-of-charge battery cells; According to the distribution ratio, calculate the energy that each low-charge state battery cell should receive: ; in, For the The energy that each battery cell should receive; By calculating the allocation ratio of each low-charge state battery cell, it ensures that energy is reasonably allocated to the battery cells that need it most, thereby improving energy utilization efficiency. By dynamically adjusting the allocation ratio, the charge state within the battery pack can be more effectively balanced, energy loss can be reduced, and the overall performance of the system can be improved.

[0024] S14. Based on the calculation results, start the energy recovery circuit to transfer excess energy from the high-charge state battery cell to the low-charge state battery cell or other parts of the solid-state battery energy storage system, and continuously monitor the charge state and energy state of each battery cell to ensure the accuracy and safety of energy transfer; In the process of intelligent distribution of recovered energy, factors affecting energy efficiency are introduced for optimization. The optimization results are as follows: ; in, For the The internal resistance of each battery cell; For the The reference internal resistance of each battery cell; is the internal resistance temperature coefficient; For the The temperature of each battery cell; is the reference temperature; ; in, For the Energy efficiency of each battery cell; For the The current of each battery cell; ; in, After optimization The allocation ratio of low-charge state battery cells; According to the optimized allocation ratio, the energy that each low-charge state battery cell should receive is calculated: ; in, After optimization The energy that each battery cell should receive.

[0025] Introducing factors affecting energy efficiency for optimization in the process of intelligently distributing recovered energy can significantly improve the performance and reliability of the entire solid-state battery energy storage system; by dynamically adjusting the energy distribution ratio by considering the actual working conditions of each battery cell (such as temperature, internal resistance, etc.), the system can more efficiently transfer excess energy to battery cells in a low charge state, thereby reducing energy loss and heat loss. This optimization not only ensures a more uniform distribution of power among cells in the battery pack and extends the battery life, but also improves the overall energy utilization efficiency and reduces the operating cost of the system. Ultimately, this enables the solid-state battery energy storage system to maintain optimal performance under various operating conditions, improving the stability and economic benefits of the system.

[0026] The power conversion unit 2 is used to execute the charge and discharge instructions and charge and discharge rate instructions transmitted from the battery management unit 1, so as to keep the battery in the best state; When the power conversion unit 2 is charged and discharged at high power, the battery pack will generate a lot of heat. The thermal management unit 3 must dynamically adjust the cooling or heating measures according to the working state of the power conversion unit 2 to ensure that the battery pack will not be damaged due to excessive temperature. At the same time, the efficiency of the power conversion unit 2 will also be affected by the temperature. The optimization of the thermal management unit 3 can improve the energy efficiency of the entire system.

[0027] The thermal management unit 3 is used to monitor the temperature data of the battery management unit 1 and the working state of the power conversion unit 2, dynamically adjust the cooling and heating measures, ensure that the battery system maintains the optimal working temperature under different environmental conditions, and introduce the heat source generated by the power conversion unit 2 and the influence factors of the battery surface state change in the process of dynamically adjusting the cooling and heating measures for optimization; The thermal management unit 3 includes a temperature monitoring module 31, a temperature adjustment module 32 and a path design module 33; The temperature monitoring module 31 in the thermal management unit 3 collects the temperature data of each battery cell in real time through the temperature sensor installed on the battery cell, and monitors the temperature change of each battery cell, and promptly detects overheating or low temperature phenomena, providing a basis for the temperature adjustment module 32; The temperature monitoring module 31 obtains the temperature distribution and temperature gradient of each battery cell to identify temperature abnormal points.

[0028] The temperature regulating module 32 dynamically adjusts the cooling and heating measures according to the basis provided by the temperature monitoring module 31 and the working state of the power conversion unit 2, so that the battery operates within a suitable temperature range. In the process of dynamically adjusting the cooling and heating measures, the heat source generated by the power conversion unit 2 and the influence factors of the change of the battery surface state are introduced for optimization; Develop intelligent temperature control algorithms to dynamically adjust the operating status of the cooling or heating device according to the real-time temperature and working status of the battery pack to ensure that the battery pack is always within the optimal operating temperature range; The specific steps of the temperature adjustment module 32 are as follows: S31. Based on the principles of heat transfer and fluid mechanics, a physical temperature model of the battery pack is established to describe the heat conduction, convection and radiation processes inside and outside the battery pack. Combined with actual operating data, an empirical model is established to capture the temperature variation of the battery pack under different operating conditions. The physical model and the empirical model are combined to form a hybrid model to improve the accuracy and robustness of the model. In S31, the details are as follows: The physical model of the battery pack includes the heat conduction equation, the convection heat transfer equation and the radiation heat transfer equation; Heat conduction equation: ; in, is the density of the material; is the specific heat capacity of the material; is the temperature distribution function, expressed in spatial coordinates and time The temperature at is the thermal conductivity of the material; is the heat source term, expressed in spatial coordinates and time Heat generation rate at Convective heat transfer equation: ; in, is the convective heat transfer coefficient; is the temperature of the battery surface; is the temperature of the cooling medium; is the temperature gradient in the normal direction of the battery surface; Radiation heat transfer equation: ; in, is the surface emissivity; is the ambient temperature; is the Stefan-Boltzmann constant, approximately ; The empirical model is established as follows: ; in, is the predicted temperature; For in time Input feature vectors, including current, voltage, ambient temperature, and state of charge; The physical model and the empirical model are combined to form a hybrid model as follows: ; in, Temperature prediction for the hybrid model; Temperature prediction for physical models; Temperature prediction for the empirical model; It is a dynamic weight coefficient, which is adjusted according to real-time data and model performance; In the process of dynamically adjusting the cooling and heating measures, the heat source generated by the power conversion unit 2 and the influencing factors of the battery surface state change are introduced for optimization. The optimized equation is specifically: Heat conduction equation: ; ; ; in, is the internal heat source term, which takes into account the heat generated by the chemical reaction and electrochemical reaction inside the battery; is the current density; is the internal resistance of the battery cell; The heat source term generated by power conversion; is the efficiency loss coefficient of power conversion; For power conversion in time Power loss; is the Dirac delta function, indicating the location of power conversion ; Convective heat transfer equation: ; ; in, is the dynamic convection heat transfer coefficient, taking into account the cooling medium flow velocity and turbulence effect; is the base convection heat transfer coefficient; is the speed correlation coefficient; is the base flow velocity; is the flow velocity of the cooling medium; is the speed index; Radiation heat transfer equation: ; ; in, is the dynamic surface emissivity, taking into account changes in surface conditions (such as oxidation, contamination, etc.); is the reference surface emissivity; is the temperature correlation coefficient; is the temperature change; The empirical model is established as follows: ; in, is the predicted temperature after optimization; For the optimized time The input feature vector includes current, voltage, ambient temperature, charge state, internal resistance, cooling medium flow velocity and power loss of the power conversion unit 2; The physical model and the empirical model are combined to form a hybrid model as follows: ; in, Temperature prediction for the optimized hybrid model; Temperature prediction for the optimized physical model; Temperature prediction for the optimized empirical model; is the dynamic weight coefficient; In the process of dynamically adjusting cooling and heating measures, the heat sources generated by the power conversion unit 2 (such as inverters, DC-DC converters, etc.) and the influencing factors of the changes in the battery surface state are introduced for optimization, which can significantly improve the accuracy and robustness of the temperature regulation module. Specifically, by considering the working state of the power conversion unit 2 and the heat it generates, the model can more comprehensively capture all heat sources in the system, thereby more accurately predicting the temperature distribution of the battery pack. At the same time, the introduction of the influence of changes in the battery surface state (such as oxidation, contamination, etc.) on the radiation heat transfer coefficient enables the model to more realistically reflect the actual physical process and improve the accuracy of temperature prediction. This comprehensive consideration not only enhances the physical authenticity of the model, but also improves its ability to adapt to complex working conditions, ensuring that the battery can remain within the optimal operating temperature range under various operating conditions, thereby extending the battery life and improving the reliability and overall performance of the system.

[0029] S32, set the optimal temperature range and the maximum allowable temperature deviation; the optimal temperature range is to The maximum allowable temperature deviation is ; S33, using a PID controller to dynamically adjust the output of the cooling or heating device according to the temperature deviation and rate of change, introducing fuzzy logic control, adjusting the control parameters according to the fuzzy rules of temperature and working status, improving the flexibility and robustness of the control, and dynamically adjusting the control parameters according to the real-time status and historical data of the battery pack to optimize the control effect; S34, real-time control strategy. Specifically: initialize temperature target and control parameters, start temperature monitoring and control program; continuously collect temperature and working status data, update model input; predict future temperature change trend based on current status and model; determine the operating status of cooling or heating device based on prediction results and control strategy; send control instructions to cooling or heating device to adjust its operating status; adjust control parameters based on actual temperature feedback to optimize control effect.

[0030] The path design module 33 is used to optimize the heat conduction path to ensure that the heat generated by the battery can be quickly transferred to the radiator or cooling medium. Through high thermal conductivity materials and reasonable design, the overall heat diffusion capacity is improved, local hot spots are reduced, and the heat dissipation efficiency of the system is improved; The safety protection unit 4 is used to take corresponding alarm and protection measures according to the data monitored by the battery management unit 1. At the same time, when the thermal management unit 3 cannot reduce the temperature of the battery pack to a safe range, the safety protection unit 4 intervenes and implements backup protection measures; The safety protection unit 4 is an important component to ensure the safe operation of the entire energy storage system. It uses a variety of protection measures, such as fuses, circuit breakers and emergency cut-off devices, to prevent abnormal situations such as short circuits, overcharging, and over-discharging. In addition, the unit also has fault detection and alarm functions, which can issue an alarm in time when the system is abnormal, ensuring that operators can take quick measures to avoid safety accidents.

[0031] The above shows and describes 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 by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A high-efficiency solid-state battery energy storage thermal management system applied to new energy, characterized in that: include: A battery management unit (1), the battery management unit (1) being used to monitor the voltage, current and temperature of each battery cell, generate a charge state and a health state, and send charge and discharge instructions and charge and discharge rate instructions to the power conversion unit (2), while recording and analyzing the charge state and health state of each monitored battery cell, and evenly allocating the power distribution within the solid-state battery pack based on energy recovery; A power conversion unit (2), the power conversion unit (2) being used to execute the charge and discharge instructions and the charge and discharge rate instructions transmitted from the battery management unit (1); A thermal management unit (3), the thermal management unit (3) being used to monitor the temperature data of the battery management unit (1) and the working state of the power conversion unit (2), dynamically adjust cooling and heating measures, and introduce the heat source generated by the power conversion unit (2) and the influencing factors of the battery surface state change during the dynamic adjustment of the cooling and heating measures for optimization; The thermal management unit (3) includes a temperature monitoring module (31), a temperature regulating module (32) and a path design module (33); A safety protection unit (4) is used to take corresponding alarm and protection measures according to the data monitored by the battery management unit (1); at the same time, when the thermal management unit (3) is unable to reduce the temperature of the battery pack to a safe range, the safety protection unit (4) intervenes to implement backup protection measures.

2. The high-efficiency solid-state battery energy storage thermal management system for new energy according to claim 1 is characterized in that: In the battery management unit (1), the power distribution in the solid-state battery pack is evenly distributed based on energy recovery, and the specific steps are as follows: The charge state and energy state of each battery cell are monitored in real time. When it is detected that the charge state of a battery cell is higher than that of other battery cells, the energy recovery circuit is started to transfer the excess energy to the battery cell with low charge state. The recovered energy is intelligently allocated according to the current state and demand of the battery cell with low charge state. In the process of intelligently allocating recovered energy, factors affecting energy efficiency are introduced for optimization.

3. The high-efficiency solid-state battery energy storage thermal management system for new energy according to claim 2 is characterized in that: The battery management unit (1) intelligently distributes the recovered energy according to the current state and demand of the low-charge state battery cells or other parts of the solid-state battery energy storage system, as follows: S11. Calculate the charge state and energy state of each battery cell: ； in, For the The state of charge of each battery cell; For the The current charge of each battery cell; For the The maximum capacity of each battery cell; ； in, For the The energy state of each battery cell; For the The voltage of each battery cell; For the The current of each battery cell; is the sampling time interval; S12, calculating the difference between the state of charge of each battery cell and the average state of charge, and determining whether it exceeds a threshold value. If so, starting the energy recovery circuit: ； ； in, For the The difference between the state of charge of each battery cell and the average state of charge; is the average state of charge of all battery cells; is the total number of battery cells; ; if , then the energy recovery circuit is started, where is the charge state threshold; S13, calculating the energy to be transferred, calculating the allocation ratio according to the current state and demand of the low-charge state battery cell, and calculating the energy that each low-charge state battery cell should receive according to the allocation ratio; ； in, for the energy transferred; The energy of a battery cell in a high state of charge; The energy of a battery cell in a low state of charge; ； in, For the The allocation ratio of low-charge state battery cells; is the target charge state; for all low state-of-charge battery cells; For the The state of charge of the low state-of-charge battery cells; According to the distribution ratio, calculate the energy that each low-charge state battery cell should receive: ； in, For the The energy that each battery cell should receive; S14. Based on the calculation results, the energy recovery circuit is started to transfer the excess energy from the battery cells with high charge state to the battery cells with low charge state, and the charge state and energy state of each battery cell are continuously monitored.

4. The high-efficiency solid-state battery energy storage thermal management system for new energy according to claim 3 is characterized in that: The battery management unit (1) introduces factors influencing energy efficiency during the intelligent allocation of recovered energy for optimization, and the optimization is as follows: ； in, For the The internal resistance of each battery cell; For the The reference internal resistance of each battery cell; is the internal resistance temperature coefficient; For the The temperature of each battery cell; is the reference temperature; ； in, For the Energy efficiency of each battery cell; For the The current of each battery cell; ； in, After optimization The allocation ratio of low-charge state battery cells; According to the optimized allocation ratio, the energy that each low-charge state battery cell should receive is calculated: ； in, After optimization The energy that each battery cell should receive.

5. The high-efficiency solid-state battery energy storage thermal management system for new energy according to claim 4 is characterized in that: The temperature monitoring module (31) in the thermal management unit (3) collects temperature data of each battery cell in real time through a temperature sensor installed on the battery cell, and monitors the temperature change of each battery cell to promptly detect overheating or low temperature phenomena; The temperature regulating module (32) dynamically adjusts the cooling and heating measures according to the basis provided by the temperature monitoring module (31) and the working state of the power conversion unit (2), so that the battery operates within a suitable temperature range, and introduces the heat source generated by the power conversion unit (2) and the influencing factors of the change of the battery surface state during the dynamic adjustment of the cooling and heating measures for optimization; The path design module (33) is used to optimize the heat conduction path to ensure that the heat generated by the battery can be quickly conducted to the radiator or cooling medium.

6. The high-efficiency solid-state battery energy storage thermal management system for new energy according to claim 5 is characterized in that: The temperature monitoring module (31) obtains the temperature distribution and temperature gradient of each battery cell, thereby identifying temperature abnormal points.

7. The high-efficiency solid-state battery energy storage thermal management system for new energy according to claim 6 is characterized in that: The specific steps of the temperature adjustment module (32) are as follows: S31. Based on the principles of heat transfer and fluid mechanics, a physical temperature model of the battery pack is established. Combined with actual operating data, an empirical model is established. The physical model and the empirical model are combined to form a hybrid model. S32, setting the optimal temperature range and the maximum allowable temperature deviation; S33, using a PID controller to dynamically adjust the output of the cooling or heating device according to the temperature deviation and rate of change, introducing fuzzy logic control, adjusting the control parameters according to the fuzzy rules of temperature and working status, and dynamically adjusting the control parameters according to the real-time status and historical data of the battery pack; S34. Implement control strategies.

8. The high-efficiency solid-state battery energy storage thermal management system for new energy according to claim 7 is characterized in that: In the S31, the details are as follows: The physical model of the battery pack includes the heat conduction equation, the convection heat transfer equation and the radiation heat transfer equation; Heat conduction equation: ； in, is the density of the material; is the specific heat capacity of the material; is the temperature distribution function, expressed in spatial coordinates and time The temperature at is the thermal conductivity of the material; is the heat source term, expressed in spatial coordinates and time Heat generation rate at Convective heat transfer equation: ； in, is the convective heat transfer coefficient; is the temperature of the battery surface; is the temperature of the cooling medium; is the temperature gradient in the normal direction of the battery surface; Radiation heat transfer equation: ； in, is the surface emissivity; is the ambient temperature; is the Stefan-Boltzmann constant; The empirical model is established as follows: ； in, is the predicted temperature; For in time Input feature vectors, including current, voltage, ambient temperature, and state of charge; The physical model and the empirical model are combined to form a hybrid model as follows: ； in, Temperature prediction for the hybrid model; Temperature prediction for physical models; Temperature prediction for the empirical model; is the dynamic weight coefficient.

9. The high-efficiency solid-state battery energy storage thermal management system for new energy according to claim 8, characterized in that: In the process of dynamically adjusting the cooling and heating measures, the heat source generated by the power conversion unit (2) and the influencing factors of the change in the battery surface state are introduced into the thermal management unit (3) for optimization. The optimized equation is specifically: Heat conduction equation: ； ； ； in, is the internal heat source term; is the current density; is the internal resistance of the battery cell; The heat source term generated by power conversion; is the efficiency loss coefficient of power conversion; For power conversion in time Power loss; Dirac Function, indicating the location of power conversion ; Convective heat transfer equation: ； ； in, is the dynamic convection heat transfer coefficient; is the base convection heat transfer coefficient; is the speed correlation coefficient; is the base flow velocity; is the flow velocity of the cooling medium; is the speed index; Radiation heat transfer equation: ； ； in, is the dynamic surface emissivity; is the reference surface emissivity; is the temperature correlation coefficient; is the temperature change; The empirical model is established as follows: ； in, is the predicted temperature after optimization; For the optimized time An input feature vector including current, voltage, ambient temperature, charge state, internal resistance, cooling medium flow velocity and power loss of the power conversion unit (2); The physical model and the empirical model are combined to form a hybrid model as follows: ； in, Temperature prediction for the optimized hybrid model; Temperature prediction for the optimized physical model; Temperature prediction for the optimized empirical model; is the dynamic weight coefficient.

10. The high-efficiency solid-state battery energy storage thermal management system for new energy according to claim 9, characterized in that: The S34 is specifically as follows: Initialize temperature targets and control parameters, start temperature monitoring and control programs; continuously collect temperature and operating status data, and update model inputs; predict future temperature trends based on current status and models; determine the operating status of the cooling or heating device based on the prediction results and control strategy; send control instructions to the cooling or heating device to adjust its operating status; adjust control parameters based on actual temperature feedback.

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