Energy storage battery temperature control method and system based on thermal coupling model

Abstract

The application discloses a kind of energy storage battery temperature control method and system based on thermal coupling model, it is related to data analysis technical field, the method includes: the electrical data and temperature data of energy storage battery are filtered and handled;Determine ohmic heat and reversible heat based on the electrical data and temperature data to determine target heating power;Based on target heating power, construct heat diffusion model, set the boundary condition of convection heat transfer form, construct temperature control optimization model based on heat diffusion model and boundary condition;Temperature distribution analysis in future time domain is carried out based on temperature control optimization model;Objective function and constraint condition are constructed, power scheduling analysis is carried out based on temperature distribution information, objective function and constraint condition, and temperature control processing of energy storage battery is carried out based on power scheduling information.The application significantly reduces the auxiliary operation energy consumption of energy storage system, to realize the efficient, safe and intelligent operation of energy storage system.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a method and system for temperature control of energy storage batteries based on a thermal coupling model. Background Technology

[0002] With the rapid development of new energy power generation and distributed power grids, energy storage systems are playing an increasingly important role in the power system. Lithium-ion batteries, with their high energy density, long cycle life, and fast response speed, have become the mainstream technology for energy storage systems. However, batteries generate Joule heat and reaction heat during charging and discharging. If heat dissipation is not timely or the temperature distribution is uneven, it can lead to battery performance degradation, accelerated capacity loss, and even safety hazards such as thermal runaway. Therefore, battery thermal management technology has become a critical aspect of the design and operation of energy storage systems.

[0003] Existing battery thermal management methods mainly include liquid cooling, air cooling, and phase change material cooling, and their control strategies mostly adopt fixed threshold control methods. In scenarios with environmental changes or complex operating conditions, these methods often cannot respond in real time to the nonlinear thermal behavior inside the battery, which can easily lead to problems such as excessive local temperature rise or uneven temperature distribution, thereby affecting the overall lifespan and safety of the system.

[0004] In recent years, researchers have begun to introduce thermally coupled models to describe the internal thermal behavior of batteries. These models combine the electrochemical reaction process with the heat conduction process, establishing a combined electro-thermal equation to accurately describe the battery's temperature distribution and dynamic changes. However, existing control algorithms still have shortcomings. For example, model parameters rely on experimental fitting, making it difficult to adapt to complex operating environments and aging conditions; and most control algorithms target single-point temperatures, failing to achieve dynamic coordination of multi-dimensional temperature fields. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a method and system for temperature control of energy storage batteries based on a thermal coupling model. While strictly ensuring the safe operation boundary of the battery, it effectively avoids energy waste caused by overcooling and greatly improves the flexibility and adaptability of the temperature control algorithm when facing multi-source heterogeneous data and changing environments.

[0006] To address the aforementioned technical problems, this invention provides a method for temperature control of energy storage batteries based on a thermal coupling model, the method comprising: The sampling module collects electrical and temperature data of the energy storage battery, and filters the electrical and temperature data to obtain filtered electrical and temperature data. The ohmic heat and reversible heat are determined based on the filtered electrical and temperature data, and the target heating power is determined based on the ohmic heat and reversible heat. A heat diffusion model is constructed based on the target heat generation power, boundary conditions for convective heat transfer are set, and a temperature control optimization model is constructed based on the heat diffusion model and boundary conditions. Based on the temperature control optimization model, the temperature distribution in the future time domain is analyzed to obtain temperature distribution information in the future time domain. An objective function and constraints are constructed, and power scheduling analysis is performed based on the temperature distribution information, objective function, and constraints to obtain power scheduling information. Based on the power scheduling information, temperature control processing of the energy storage battery is performed.

[0007] Optionally, the step of filtering the electrical data and temperature data to obtain filtered electrical data and temperature data includes: The current data in the electrical data is filtered by least squares to obtain the current data after least squares filtering. Low-pass filtering is performed on the voltage data in the temperature and electrical data to obtain low-pass filtered temperature and voltage data. Based on the current data after least squares filtering and the low-pass filtered temperature and voltage data, the filtered electrical and temperature data are determined.

[0008] Optionally, determining ohmic heat and reversible heat based on filtered electrical and temperature data, and determining the target heating power based on the ohmic heat and reversible heat, includes: The effective current component is determined based on the filtered electrical and temperature data, and the open-circuit voltage is determined based on the effective current component and the filtered electrical and temperature data. The ohmic heat is determined based on the effective current component and the battery internal resistance function, the reversible heat is determined based on the effective current component and the open-circuit voltage, and the target heating power per unit time is determined based on the ohmic heat and the reversible heat.

[0009] Optionally, the expression for the open-circuit voltage is: , in, Open circuit voltage, This refers to the battery terminal voltage. For the effective current component, This is a temperature-dependent function of the battery's internal resistance.

[0010] Optionally, the expression for the target heating power is: , in, For the target heating power, For Ohm heat, It is a reversible heat. For the effective current component, This is a temperature-dependent function of the battery's internal resistance, where T represents the temperature data. This is the open-circuit voltage.

[0011] Optionally, the expression for the thermal diffusion model is: , in, For material density, Here, T represents specific heat capacity, t represents temperature, and k represents thermal conductivity. Target heating power; The expression for the boundary condition is: , Where k is thermal conductivity, T is temperature, n is a unit vector, and h is the heat transfer coefficient. The ambient temperature.

[0012] Optionally, the expression for the temperature control optimization model is:

[0013] in, Here is the temperature data at time k+1, and A is the heat conduction matrix. Here is the temperature data at time k, and B is the cooling control matrix. Let be the power of the cooling device at time k, and E be the input matrix for the heat generation term. Let be the target heat generation power at time k, and F be the ambient temperature influence matrix. Let k be the ambient temperature at time k.

[0014] Optionally, the construction of the objective function and constraints, and the performance of power scheduling analysis based on the temperature distribution information, the objective function, and the constraints to obtain power scheduling information, includes: An objective function is constructed based on a preset optimization objective level. The minimum safe temperature boundary, the maximum safe temperature boundary, the maximum cooling device power boundary, and the cooling power change rate boundary are determined. Constraint conditions are then constructed based on the minimum safe temperature boundary, the maximum safe temperature boundary, the maximum cooling device power boundary, and the cooling power change rate boundary. Based on the temperature distribution information, objective function, and constraints, a particle swarm optimization algorithm is used to perform power scheduling analysis and obtain power scheduling information.

[0015] Optionally, the expression for the objective function is: , Where J is the objective function, The future time domain length is given, and α, β, and γ are weighting factors. , and For weighted matrices, The temperature data at time k is... Let be the power of the cooling device at time k. For the target temperature, The power of the cooling device at time k-1; The expression for the constraint condition is: , in, As the minimum safe temperature boundary, The temperature data at time k is... Let be the power of the cooling device at time k. The power of the cooling device at time k-1 For the maximum safe temperature boundary, This is the maximum power boundary for the cooling system. This represents the boundary of the cooling power change rate.

[0016] In addition, the present invention also provides a thermally coupled model-based energy storage battery temperature control system, the system comprising: a sampling module, a communication module, a main control module, and a power module, wherein the communication module is connected to the sampling module and the main control module respectively, the main control module is connected to the power module, and the system is configured to execute the above-described thermally coupled model-based energy storage battery temperature control method.

[0017] In this embodiment of the invention, electrical and temperature data of the energy storage battery are collected and filtered. Ohmic heat and reversible heat are determined based on the filtered electrical and temperature data, and the target heating power is determined based on the ohmic heat and reversible heat. A heat diffusion model is constructed based on the target heating power, boundary conditions for convective heat transfer are set, and a temperature control optimization model is constructed based on the heat diffusion model and boundary conditions. This model can accurately capture and reflect the dynamic temperature change of the energy storage battery in response to complex charging and discharging conditions and load impacts, overcoming the shortcomings of traditional models in sensing internal hot spots. The temperature distribution in the future time domain is analyzed based on a temperature control optimization model to obtain temperature distribution information in the future time domain. Objective function and constraints are constructed, and power scheduling analysis is performed based on temperature distribution information, objective function and constraints to obtain power scheduling information. Based on the power scheduling information, temperature control processing of energy storage batteries is carried out, which improves the flexibility and adaptability of temperature control algorithm in the face of multi-source heterogeneous data and variable environment. While strictly ensuring the safe operation boundary of the battery, it effectively avoids energy waste caused by overcooling and significantly reduces the auxiliary operation energy consumption of the energy storage system, thereby realizing the efficient, safe and intelligent operation of the energy storage system. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the energy storage battery temperature control method based on a thermal coupling model in an embodiment of the present invention. Figure 2 This is a flowchart illustrating a thermally coupled battery temperature control method based on a thermal coupling model in another embodiment of the present invention. Figure 3 This is a schematic diagram of the structural composition of an energy storage battery temperature control system based on a thermal coupling model in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating the energy storage battery temperature control method based on a thermal coupling model in an embodiment of the present invention. The method includes: S11: Based on the sampling module, electrical data and temperature data of the energy storage battery are collected, and the electrical data and temperature data are filtered to obtain filtered electrical data and temperature data; In the specific implementation of this invention, the current data in the electrical data is subjected to least squares filtering to obtain the least squares filtered current data; the temperature data and the voltage data in the electrical data are subjected to low-pass filtering to obtain low-pass filtered temperature data and voltage data; and the filtered electrical data and temperature data are determined based on the least squares filtered current data and the low-pass filtered temperature and voltage data, which can filter out interference and ensure that the obtained data is more accurate.

[0022] S12: Determine the ohmic heat and reversible heat based on the filtered electrical data and temperature data, and determine the target heating power based on the ohmic heat and reversible heat; In the specific implementation of this invention, the effective current component is determined based on the filtered electrical and temperature data, and the open-circuit voltage is determined based on the effective current component and the filtered electrical and temperature data; the ohmic heat is determined based on the effective current component combined with the battery internal resistance function, the reversible heat is determined based on the effective current component and the open-circuit voltage, and the target heating power per unit time is determined based on the ohmic heat and the reversible heat, thereby improving the accuracy of heating power calculation and providing reliable data support for subsequent temperature control optimization analysis.

[0023] S13: Construct a heat diffusion model based on the target heat generation power, set boundary conditions for convective heat transfer, and construct a temperature control optimization model based on the heat diffusion model and boundary conditions; In the specific implementation of this invention, a thermal diffusion model is constructed based on the target heat generation power, boundary conditions for convective heat transfer are set, and a temperature control optimization model is constructed based on the thermal diffusion model and boundary conditions to accurately capture and reflect the dynamic temperature change law of the energy storage battery in the process of coping with complex charging and discharging conditions and load impact.

[0024] S14: Based on the temperature control optimization model, perform temperature distribution analysis in the future time domain to obtain temperature distribution information in the future time domain; In the specific implementation of this invention, temperature distribution analysis in the future time domain is performed based on the temperature control optimization model to obtain temperature distribution information in the future time domain. By rolling the prediction of the future thermal state of the battery in the predicted time domain, an accurate data foundation can be provided for subsequent power scheduling analysis.

[0025] S15: Construct the objective function and constraints, and perform power scheduling analysis based on the temperature distribution information, objective function and constraints to obtain power scheduling information, and perform temperature control processing of the energy storage battery based on the power scheduling information.

[0026] In the specific implementation of this invention, an objective function is constructed based on a preset optimization objective level. The preset optimization objective level includes temperature field uniformity, thermal management energy consumption, and control action smoothness. The minimum safe temperature boundary, maximum safe temperature boundary, maximum cooling device power boundary, and cooling power change rate boundary are determined, and constraint conditions are constructed based on the minimum safe temperature boundary, maximum safe temperature boundary, maximum cooling device power boundary, and cooling power change rate boundary. Based on the temperature distribution information, objective function, and constraint conditions, a particle swarm optimization algorithm is used to perform power scheduling analysis to obtain power scheduling information, thereby achieving comprehensive optimal control of battery temperature deviation and overall system energy consumption. This proactive control strategy effectively avoids energy waste caused by overcooling while strictly ensuring the safe operation boundary of the battery, significantly reduces the auxiliary operation energy consumption of the energy storage system, and greatly improves the flexibility and adaptability of the temperature control algorithm when facing multi-source heterogeneous data and variable environments.

[0027] In this embodiment of the invention, electrical and temperature data of the energy storage battery are collected and filtered. Ohmic heat and reversible heat are determined based on the filtered electrical and temperature data, and the target heating power is determined based on the ohmic heat and reversible heat. A heat diffusion model is constructed based on the target heating power, boundary conditions for convective heat transfer are set, and a temperature control optimization model is constructed based on the heat diffusion model and boundary conditions. This model can accurately capture and reflect the dynamic temperature change of the energy storage battery in response to complex charging and discharging conditions and load impacts, overcoming the shortcomings of traditional models in sensing internal hot spots. The temperature distribution in the future time domain is analyzed based on a temperature control optimization model to obtain temperature distribution information in the future time domain. Objective function and constraints are constructed, and power scheduling analysis is performed based on temperature distribution information, objective function and constraints to obtain power scheduling information. Based on the power scheduling information, temperature control processing of energy storage batteries is carried out, which improves the flexibility and adaptability of temperature control algorithm in the face of multi-source heterogeneous data and variable environment. While strictly ensuring the safe operation boundary of the battery, it effectively avoids energy waste caused by overcooling and significantly reduces the auxiliary operation energy consumption of the energy storage system, thereby realizing the efficient, safe and intelligent operation of the energy storage system.

[0028] Example 2 Please see Figure 2 , Figure 2 This is a flowchart illustrating a thermally coupled battery temperature control method based on a thermal coupling model according to another embodiment of the present invention. The method includes: S201: Based on the sampling module, electrical data and temperature data of the energy storage battery are collected, and the electrical data and temperature data are filtered to obtain filtered electrical data and temperature data; In a specific implementation of this invention, the step of filtering the electrical data and temperature data to obtain filtered electrical data and temperature data includes: performing least squares filtering on the current data in the electrical data to obtain least squares filtered current data; performing low-pass filtering on the temperature data and voltage data in the electrical data to obtain low-pass filtered temperature data and voltage data; and determining the filtered electrical data and temperature data based on the least squares filtered current data and the low-pass filtered temperature and voltage data.

[0029] Specifically, the sampling module collects electrical and temperature data from the energy storage battery. This module includes temperature, current, and voltage sensors. A 16-bit resolution analog-to-digital converter is used in the sampling module to ensure the capture of minute current fluctuations. The sensors selected are Hall effect sensors and Rogowski coils, used not only for metering but also for capturing high-frequency current harmonics. These high-frequency harmonics cause additional skin effect losses in the battery; by acquiring data over a wide bandwidth, these losses are incorporated into the ohmic heat calculation, correcting the thermal prediction errors of traditional models. Strict synchronization between the three-phase current and temperature sampling is crucial. Misalignment of the time axes will decouple the calculation of the electrochemical reaction heat from the actual temperature response, leading to controller failure. The electrical data includes both current and voltage data. The current data in the electrical data is filtered using the least squares method to obtain the filtered current data. Least squares filtering is a signal processing method based on the least squares criterion, which aims to estimate the optimal value of the required signal or parameter from observation data containing noise or errors. Its core idea is to obtain the optimal filtering result by minimizing the sum of squares of the errors between the observed value and the estimated value, thereby eliminating random noise and enabling the accurate extraction of the effective current component for calculating ohmic heat, preventing the estimation deviation of heat generation power caused by noise.

[0030] Low-pass filtering is applied to the voltage data in the temperature and electrical data to obtain low-pass filtered temperature and voltage data. Low-pass filtering is used to remove high-frequency electromagnetic interference that does not affect the battery's temperature while retaining low-frequency components that reflect the battery's thermal inertia. The parameters of the low-pass filter are set, and the temperature and voltage data are filtered using the low-pass filter. The filtered electrical and temperature data are determined based on the current data after least squares filtering and the temperature and voltage data after low-pass filtering. In other words, the filtered electrical and temperature data are composed of the current data after least squares filtering and the temperature and voltage data after low-pass filtering.

[0031] S202: Determine the effective current component based on the filtered electrical data and temperature data, and determine the open-circuit voltage based on the effective current component and the filtered electrical data and temperature data; In a specific implementation of this invention, the expression for the open-circuit voltage is: , in, Open circuit voltage, This refers to the battery terminal voltage. For the effective current component, This is a temperature-dependent function of the battery's internal resistance.

[0032] Specifically, the effective current component is determined based on the filtered electrical and temperature data. Current data can be extracted from the filtered electrical data, and the current data is compensated based on the temperature data to obtain the effective current component. The open-circuit voltage is determined based on the effective current component and the filtered electrical and temperature data. The battery terminal voltage is extracted from the filtered electrical data. The temperature-related battery internal resistance function is identified using this electrical and temperature data. The battery terminal voltage and the effective current component are input into the expression for the open-circuit voltage to obtain the open-circuit voltage of the energy storage battery. The expression for the open-circuit voltage is: , in, Open circuit voltage, This refers to the battery terminal voltage. For the effective current component, This is a temperature-dependent function of the battery's internal resistance.

[0033] S203: Determine the ohmic heat based on the effective current component and the battery internal resistance function, determine the reversible heat based on the effective current component and the open circuit voltage, and determine the target heating power per unit time based on the ohmic heat and the reversible heat. In a specific implementation of this invention, the expression for the target heating power is: , in, For the target heating power, For Ohm heat, It is a reversible heat. For the effective current component, This is a temperature-dependent function of the battery's internal resistance, where T represents the temperature data. This is the open-circuit voltage.

[0034] Specifically, batteries generate two main types of heat during charging and discharging: ohmic heat and reversible heat. Ohmic heat is determined based on the effective current component and the battery's internal resistance function. Ohmic heat is caused by the battery's internal resistance. Reversible heat is determined based on the effective current component and the open-circuit voltage. Reversible heat is caused by the entropy change effect of the electrochemical reaction. By inputting ohmic heat and reversible heat into the expression for heating power, the target heating power of the energy storage battery per unit time is obtained. The sign of reversible heat is determined by the sign of the entropy change. For common lithium iron phosphate batteries, reversible heat accounts for approximately 5% to 10% of the total heat.

[0035] The expression for the target heating power is: , in, For the target heating power, For Ohm heat, It is a reversible heat. For the effective current component, This is a temperature-dependent function of the battery's internal resistance, where T represents the temperature data. This is the open-circuit voltage.

[0036] S204: Construct a heat diffusion model based on the target heat generation power, set boundary conditions for convective heat transfer, and construct a temperature control optimization model based on the heat diffusion model and boundary conditions; In the specific implementation of this invention, the expression of the thermal diffusion model is: , in, For material density, Here, T represents specific heat capacity, t represents temperature, and k represents thermal conductivity. Target heating power; The expression for the boundary condition is: , Where k is thermal conductivity, T is temperature, n is a unit vector, and h is the heat transfer coefficient. The ambient temperature.

[0037] The expression for the temperature control optimization model is:

[0038] in, Here is the temperature data at time k+1, and A is the heat conduction matrix. Here is the temperature data at time k, and B is the cooling control matrix. Let be the power of the cooling device at time k, and E be the input matrix for the heat generation term. Let be the target heat generation power at time k, and F be the ambient temperature influence matrix. Let k be the ambient temperature at time k.

[0039] Specifically, a thermal diffusion model is constructed based on the target heat generation power. The thermal diffusion process of the battery module can be represented by the energy conservation equation, and the expression of the thermal diffusion model is as follows: , in, For material density, Here, T represents specific heat capacity, t represents temperature, and k represents thermal conductivity. The target heating power.

[0040] The boundary conditions are set to take into account the convective heat transfer mode. The expression for the boundary conditions is: , Where k is thermal conductivity, T is temperature, n is a unit vector, and h is the heat transfer coefficient. The ambient temperature.

[0041] A temperature control optimization model is constructed based on the aforementioned heat diffusion model and boundary conditions. The heat diffusion model is discretized using the finite volume method or finite difference method to obtain a discrete state model. This discrete state model is then updated using boundary conditions in the form of convective heat transfer to obtain the system state update equations, which constitute the final temperature control optimization model. The expression for the temperature control optimization model is as follows: , in, Here is the temperature data at time k+1, and A is the heat conduction matrix. Here is the temperature data at time k, and B is the cooling control matrix. Let be the power of the cooling device at time k, and E be the input matrix for the heat generation term. Let be the target heat generation power at time k, and F be the ambient temperature influence matrix. Let k be the ambient temperature at time k. The heat conduction matrix describes the thermal coupling between modules, and the cooling control matrix corresponds to the role of the liquid cooling or air cooling system.

[0042] S205: Based on the temperature control optimization model, perform temperature distribution analysis in the future time domain to obtain temperature distribution information in the future time domain; In the specific implementation of this invention, the temperature distribution in the future time domain is analyzed based on the temperature control optimization model to obtain the temperature distribution information in the future time domain. That is, the temperature distribution in the future time domain is predicted according to the temperature control optimization model, and the energy storage battery is judged to have overheating in a certain period of time in the future based on the analyzed temperature distribution.

[0043] S206: Construct an objective function based on a preset optimization objective level, determine the minimum safe temperature boundary, the maximum safe temperature boundary, the maximum cooling device power boundary, and the cooling power change rate boundary, and construct constraint conditions based on the minimum safe temperature boundary, the maximum safe temperature boundary, the maximum cooling device power boundary, and the cooling power change rate boundary; In the specific implementation of this invention, the expression of the objective function is: , Where J is the objective function, The future time domain length is given, and α, β, and γ are weighting factors. , and For weighted matrices, The temperature data at time k is... Let be the power of the cooling device at time k. For the target temperature, The power of the cooling device at time k-1; The expression for the constraint condition is: , in, As the minimum safe temperature boundary, The temperature data at time k is... Let be the power of the cooling device at time k. The power of the cooling device at time k-1 For the maximum safe temperature boundary, This is the maximum power boundary for the cooling system. This represents the boundary of the cooling power change rate.

[0044] Specifically, an objective function is constructed based on a preset optimization objective level, which includes three levels: uniformity of the battery temperature field; minimization of control energy consumption; and smoothness of control quantity changes (avoiding frequent start-stop). The objective function is constructed by combining these three aspects, and its expression is: , Where J is the objective function, The future time domain length is given, and α, β, and γ are weighting factors. , and For weighted matrices, The temperature data at time k is... Let be the power of the cooling device at time k. For the target temperature, Let be the power of the cooling device at time k-1. The first term of the objective function is used to constrain temperature distribution deviation, the second term is used to suppress energy consumption, and the third term is used to smooth the control output. A multi-dimensional objective function covering three aspects—temperature field uniformity, thermal management energy consumption, and control action smoothness (avoiding frequent equipment start-ups and shutdowns)—is constructed. By introducing an adaptively adjustable weight factor mechanism, the algorithm can achieve dynamic balance control under different operating conditions based on different grid load states and external environmental conditions in the distribution area. This greatly improves the flexibility and adaptability of the temperature control algorithm when facing multi-source heterogeneous data and changing environments.

[0045] The optimization problem must satisfy physical and safety constraints. The minimum safe temperature boundary, maximum safe temperature boundary, maximum cooling device power boundary, and cooling power change rate boundary are determined. Based on these boundaries, constraint conditions are constructed, and their expressions are as follows: , in, As the minimum safe temperature boundary, The temperature data at time k is... Let be the power of the cooling device at time k. The power of the cooling device at time k-1 For the maximum safe temperature boundary, This is the maximum power boundary for the cooling system. The boundary condition for the cooling power change rate is defined. By setting constraints, multiple physical and safety constraints, including the upper and lower limits of the battery core temperature, the extreme values ​​of the cooling equipment power, and the change rate of control quantities, are strictly embedded in the multi-objective optimization process. This mechanism is equivalent to building a safety barrier at the algorithm's underlying layer, ensuring that the system can always operate stably within a safe temperature range under any extreme disturbance, effectively preventing lithium plating caused by overcooling or performance degradation and thermal runaway safety accidents caused by overheating, thus protecting asset safety.

[0046] S207: Based on the temperature distribution information, objective function, and constraints, power scheduling analysis is performed using the particle swarm optimization algorithm to obtain power scheduling information, and temperature control processing of the energy storage battery is performed based on the power scheduling information.

[0047] In the specific implementation of this invention, power scheduling analysis is performed using a particle swarm optimization algorithm based on the temperature distribution information, objective function, and constraints to obtain power scheduling information. Power scheduling analysis is an optimization problem, specifically a constrained quadratic programming problem, which can be solved using quadratic programming or an improved particle swarm optimization algorithm. The algorithm predicts the future temperature distribution over time. When the temperature distribution reaches the overheating standard, the objective function is minimized using the particle swarm optimization algorithm under constraints. Power scheduling information is obtained under the condition of minimizing the objective function. The expression for the power scheduling information can be: , in. For power scheduling information, J is the objective function. The temperature data at time k is... Let k be the power of the cooling device at time k. The power scheduling information includes cooling power and charging / discharging power.

[0048] Based on the power scheduling information, the main control module controls the power module to perform temperature control on the energy storage battery. The main control module uses a power scheduling algorithm to preemptively reduce the charging and discharging current of the battery cluster or preemptively activate the air-cooling / liquid-cooling system, achieving a shift from passive triggering to active defense. The power module includes a DC bus energy storage module and a four-quadrant power conversion module. DC bus energy storage module: This module is the core object directly controlled by the algorithm. The heat generated by its charging and discharging behavior is the main source of system disturbance. Through the temperature control algorithm, this module automatically finds the operating point with the minimum temperature rise when performing grid peak shaving and valley filling tasks. For example, in the high-power mode of two-phase input / output, the algorithm automatically limits the maximum continuous current to prevent reaching the thermal runaway boundary. Four-quadrant power conversion module: The switching losses of the converter are also one of the system's heat sources. The main control algorithm incorporates the converter's efficiency model into the total energy consumption objective function. By adjusting the pulse width modulation frequency or phase, while reducing switching losses, and in conjunction with battery thermal management, it achieves the lowest total energy consumption at the system level (battery + converter). It achieves dynamic allocation of cooling power, cooling medium flow rate and fan speed, thereby ensuring temperature balance while achieving optimal energy consumption.

[0049] Simultaneously, the system can utilize real-time monitoring data from the detection device to identify and dynamically correct key parameters within the model online. This allows the system to automatically calibrate model deviations and maintain extremely high temperature prediction accuracy and control stability even under complex conditions such as long-term battery cycle aging and drastic changes in outdoor temperature and humidity in the transformer substation area, thereby extending the system's full lifecycle service capability.

[0050] In this embodiment of the invention, electrical and temperature data of the energy storage battery are collected and filtered. Ohmic heat and reversible heat are determined based on the filtered electrical and temperature data, and the target heating power is determined based on the ohmic heat and reversible heat. A heat diffusion model is constructed based on the target heating power, boundary conditions for convective heat transfer are set, and a temperature control optimization model is constructed based on the heat diffusion model and boundary conditions. This model can accurately capture and reflect the dynamic temperature change of the energy storage battery in response to complex charging and discharging conditions and load impacts, overcoming the shortcomings of traditional models in sensing internal hot spots. The temperature distribution in the future time domain is analyzed based on a temperature control optimization model to obtain temperature distribution information in the future time domain. Objective function and constraints are constructed, and power scheduling analysis is performed based on temperature distribution information, objective function and constraints to obtain power scheduling information. Based on the power scheduling information, temperature control processing of energy storage batteries is carried out, which improves the flexibility and adaptability of temperature control algorithm in the face of multi-source heterogeneous data and variable environment. While strictly ensuring the safe operation boundary of the battery, it effectively avoids energy waste caused by overcooling and significantly reduces the auxiliary operation energy consumption of the energy storage system, thereby realizing the efficient, safe and intelligent operation of the energy storage system.

[0051] Example 3 Please see Figure 3 , Figure 3 This is a schematic diagram of the structural composition of an energy storage battery temperature control system based on a thermal coupling model in an embodiment of the present invention, as shown below. Figure 3 As shown, the system includes a sampling module, a communication module, a main control module, and a power module. The communication module is connected to the sampling module and the main control module, and the main control module is connected to the power module. The system is configured to execute the above-described energy storage battery temperature control method based on a thermal coupling model.

[0052] In the specific implementation of this invention, the sampling module is used to collect electrical data including phase current, neutral current, and voltage, as well as temperature data of key points of the battery module. The analog-to-digital converter of the sampling module uses 16-bit resolution to ensure that it can capture minute current fluctuations. The Hall effect sensor and Rogowski coil selected by the sensor are used not only for measurement but also for capturing high-frequency current harmonics. It is essential to ensure strict synchronization between the three-phase current and temperature sampling. If the time axis is not aligned, it will lead to decoupling between the calculation of electrochemical reaction heat and the actual temperature response, resulting in controller failure. Therefore, the sampling module is designed with a hardware synchronization triggering mechanism. The communication module adopts the same 4G / LoRa / RS-485 combination as the detection device. In particular, the RS-485 interface is used to stably send the fan speed command and liquid cooling pump flow command calculated by the algorithm to the underlying actuator in a high-interference industrial environment, ensuring the accurate execution of temperature control actions. The main control module is not only a logic controller but also a solver for the optimization algorithm. It runs the quadratic programming solution program proposed in this invention in real time. After receiving status data from the detection device, the main control module calculates the optimal power scheduling command based on the objective function, under the constraints of temperature boundaries and power change rate. When the algorithm predicts local overheating in the future time domain, the main control module will reduce the charging and discharging current of the battery cluster in advance, or start the air-cooling / liquid-cooling system of the power module in advance, realizing the transformation from passive triggering to active defense. The power module includes a DC bus energy storage module and a four-quadrant power conversion module. DC bus energy storage module: This module is the core object directly controlled by the algorithm. The heat generated by its charging and discharging behavior is the main source of disturbance in the system. Through temperature control algorithm, this module will automatically find the operating point with the minimum temperature rise when performing grid peak shaving and valley filling tasks. For example, in the high-power mode of two-phase input and output, the algorithm will automatically limit the maximum continuous current to prevent reaching the thermal runaway boundary. Four-quadrant power conversion module: The switching losses of the converter are also one of the heat sources of the system. The main control algorithm incorporates the converter's efficiency model into the total energy consumption objective function. By adjusting the pulse width modulation frequency or phase, it reduces switching losses while simultaneously managing battery thermal management, achieving the lowest possible total energy consumption at the system level (battery + converter). It also dynamically allocates cooling power, cooling medium flow rate, and fan speed, thereby ensuring temperature balance while achieving optimal energy consumption. In this embodiment of the invention, the energy storage battery temperature control system integrates a sampling module, a communication module, a main control module, and a power module. While strictly ensuring the safe operation boundary of the battery, it effectively avoids energy waste caused by overcooling and significantly reduces the auxiliary operation energy consumption of the energy storage system, thereby achieving efficient, safe, and intelligent operation of the energy storage system.

[0053] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0054] Furthermore, the above provides a detailed description of the energy storage battery temperature control method and system based on a thermal coupling model provided by the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for temperature control of energy storage batteries based on a thermal coupling model, characterized in that, The method includes: The sampling module collects electrical and temperature data of the energy storage battery, and filters the electrical and temperature data to obtain filtered electrical and temperature data. The ohmic heat and reversible heat are determined based on the filtered electrical and temperature data, and the target heating power is determined based on the ohmic heat and reversible heat. A heat diffusion model is constructed based on the target heat generation power, boundary conditions for convective heat transfer are set, and a temperature control optimization model is constructed based on the heat diffusion model and boundary conditions. Based on the temperature control optimization model, the temperature distribution in the future time domain is analyzed to obtain temperature distribution information in the future time domain. An objective function and constraints are constructed, and power scheduling analysis is performed based on the temperature distribution information, objective function, and constraints to obtain power scheduling information. Based on the power scheduling information, temperature control processing of the energy storage battery is performed.

2. The energy storage battery temperature control method based on a thermal coupling model according to claim 1, characterized in that, The step of filtering the electrical data and temperature data to obtain filtered electrical data and temperature data includes: The current data in the electrical data is filtered by least squares to obtain the current data after least squares filtering. Low-pass filtering is performed on the voltage data in the temperature and electrical data to obtain low-pass filtered temperature and voltage data. Based on the current data after least squares filtering and the low-pass filtered temperature and voltage data, the filtered electrical and temperature data are determined.

3. The energy storage battery temperature control method based on a thermal coupling model according to claim 1, characterized in that, The process of determining ohmic heat and reversible heat based on filtered electrical and temperature data, and determining the target heating power based on the ohmic heat and reversible heat, includes: The effective current component is determined based on the filtered electrical and temperature data, and the open-circuit voltage is determined based on the effective current component and the filtered electrical and temperature data. The ohmic heat is determined based on the effective current component and the battery internal resistance function, the reversible heat is determined based on the effective current component and the open-circuit voltage, and the target heating power per unit time is determined based on the ohmic heat and the reversible heat.

4. The energy storage battery temperature control method based on a thermal coupling model according to claim 3, characterized in that, The expression for the open-circuit voltage is: ， in, Open circuit voltage, This refers to the battery terminal voltage. For the effective current component, This is a temperature-dependent function of the battery's internal resistance.

5. The energy storage battery temperature control method based on a thermal coupling model according to claim 3, characterized in that, The expression for the target heating power is: ， in, For the target heating power, For Ohm heat, It is a reversible heat. For the effective current component, This is a temperature-dependent function of the battery's internal resistance, where T represents the temperature data. This is the open-circuit voltage.

6. The energy storage battery temperature control method based on a thermal coupling model according to claim 1, characterized in that, The expression for the heat diffusion model is: ， in, For material density, Here, T represents specific heat capacity, t represents temperature, and k represents thermal conductivity. Target heating power; The expression for the boundary condition is: ， Where k is thermal conductivity, T is temperature, n is a unit vector, and h is the heat transfer coefficient. The ambient temperature.

7. The energy storage battery temperature control method based on a thermal coupling model according to claim 1, characterized in that, The expression for the temperature control optimization model is: ， in, Here is the temperature data at time k+1, and A is the heat conduction matrix. Here is the temperature data at time k, and B is the cooling control matrix. Let be the power of the cooling device at time k, and E be the input matrix for the heat generation term. Let be the target heat generation power at time k, and F be the ambient temperature influence matrix. Let k be the ambient temperature at time k.

8. The energy storage battery temperature control method based on a thermal coupling model according to claim 1, characterized in that, The process of constructing the objective function and constraints, and performing power scheduling analysis based on the temperature distribution information, objective function, and constraints to obtain power scheduling information includes: An objective function is constructed based on a preset optimization objective level. The minimum safe temperature boundary, the maximum safe temperature boundary, the maximum cooling device power boundary, and the cooling power change rate boundary are determined. Constraint conditions are then constructed based on the minimum safe temperature boundary, the maximum safe temperature boundary, the maximum cooling device power boundary, and the cooling power change rate boundary. Based on the temperature distribution information, objective function, and constraints, a particle swarm optimization algorithm is used to perform power scheduling analysis and obtain power scheduling information.

9. The energy storage battery temperature control method based on a thermal coupling model according to claim 8, characterized in that, The expression for the objective function is: ， Where J is the objective function, The future time domain length is given, and α, β, and γ are weighting factors. , and For weighted matrices, The temperature data at time k is... Let be the power of the cooling device at time k. For the target temperature, The power of the cooling device at time k-1; The expression for the constraint condition is: ， in, As the minimum safe temperature boundary, The temperature data at time k is... Let be the power of the cooling device at time k. The power of the cooling device at time k-1 For the maximum safe temperature boundary, This is the maximum power boundary for the cooling system. This represents the boundary of the cooling power change rate.

10. A temperature control system for an energy storage battery based on a thermal coupling model, characterized in that, The system includes a sampling module, a communication module, a main control module, and a power module. The communication module is connected to the sampling module and the main control module, and the main control module is connected to the power module. The system is configured to execute the energy storage battery temperature control method based on the thermal coupling model as described in any one of claims 1-9.

Images