A temperature control system and method for charging and discharging process of lithium battery module

Abstract

The application relates to the technical field of battery thermal management, and particularly discloses a lithium battery module charging and discharging process temperature control system and method, which comprises a thermal field dynamic perception module, a heat flow path prediction module, an adaptive regulation and control decision module and an executor array coordination module. By constructing a global temperature field digital twin model, predicting future heat flow paths and hot spots, and driving the executor array to perform spatial directional coordinated regulation and control, closed-loop, dynamic and accurate control of the internal temperature of the lithium battery module is realized, and the thermal safety and temperature uniformity are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of battery thermal management, and particularly relates to a lithium battery module charging and discharging process temperature control system and method. BACKGROUND

[0002] As a high-efficiency energy storage device, lithium batteries are widely used in electric vehicles, energy storage power stations, portable electronic devices and other fields. Their performance and safety directly affect the reliable operation of the entire system. A large amount of heat is generated during the charging and discharging process of the lithium battery module. If the heat cannot be dissipated in time and evenly, the internal temperature distribution of the module will be uneven, and local overheating may cause thermal runaway, which seriously affects the battery life and brings serious safety hazards. Therefore, accurate monitoring and effective control of the working temperature of the lithium battery module is one of the core technologies to ensure its safe and efficient operation.

[0003] Temperature control during the charging and discharging process of the lithium battery module is a key technology direction to improve the safety and reliability of the battery system. This technology aims to monitor the internal temperature field distribution of the module in real time, and dynamically adjust the thermal management strategy based on the monitoring data, so as to maintain the battery temperature within a safe and efficient working range, prevent local overheating and optimize battery performance.

[0004] The prior art usually uses a limited number of temperature sensors arranged at fixed positions inside the module for temperature monitoring. This static sensor layout scheme has significant defects: since the sensor positions are fixed, its monitoring range is limited and it is difficult to cover the dynamic heat migration path caused by uneven current distribution, internal resistance difference and changes in heat dissipation conditions during the charging and discharging process, especially in the key areas such as cell connection sheets and busbars where concentrated heat is easily generated, forming a monitoring blind area.

[0005] This results in the system being unable to capture the formation and evolution of local hot spots in real time and accurately, causing a serious delay in overheating warning and failing to provide timely and accurate control basis for the thermal management system, greatly increasing the risk of battery thermal runaway. SUMMARY

[0006] The purpose of the present application is to provide a lithium battery module charging and discharging process temperature control system and method to solve the problem of monitoring blind area caused by fixed position temperature sensor layout in the prior art, and the resulting technical contradiction of not capturing dynamic heat migration path and local hot spot in time and accurately, which leads to delay and failure of thermal management control.

[0007] To achieve the above purpose, the present application provides a lithium battery module charging and discharging process temperature control system. The system includes a thermal field dynamic perception module, a heat flow path prediction module, an adaptive control decision module and an actuator array coordination module.

[0008] The thermal field dynamic perception module is used to construct and update the global temperature field digital twin model of the lithium battery module in real time. The module includes a high-density distributed temperature sensing network and a data fusion and reconstruction unit. The high-density distributed temperature sensing network is composed of multiple miniature temperature sensor nodes deployed in key heat-sensitive areas inside the lithium battery module, including but not limited to the tab connection of each cell, the surface of the bus bar inside the module, the center point of the gap between cells, and the inner surface of the module shell.

[0009] The data fusion and reconstruction unit receives raw temperature data from all temperature sensor nodes in real time, and based on the preset three-dimensional geometric model of the lithium battery module and the material thermal physical property parameter database, uses the Kriging spatial interpolation algorithm to perform spatial interpolation calculation on the sparse sensor measurement point data, to generate a three-dimensional temperature field distribution map covering the entire module internal space with a preset spatial resolution, i.e. the global temperature field digital twin model. The model is updated at a preset refresh frequency.

[0010] The thermal flow path prediction module is used to predict the dynamic migration path of heat and potential hot spot locations within a future time window based on current and historical temperature field states. The module includes a heat source calculation unit and a computational fluid dynamics simulation unit. The heat source calculation unit receives the module total current, total voltage, and cell voltage data provided by the battery management system in real time, and calculates the instantaneous heat generation rate of each calculation grid cell in the module at the current location based on the Joule law and the electrochemical heat generation model.

[0011] The computational fluid dynamics simulation unit takes the current three-dimensional temperature field distribution map output by the thermal field dynamic perception module as the initial boundary condition, and takes the instantaneous heat generation rate distribution output by the heat source calculation unit as the volume heat source, calls the preset fluid control equation and heat transfer equation for numerical solution, simulates the coupling process of air or cooling medium flow and heat conduction, convection inside the module within a preset time length, predicts the evolution trend of the temperature field, and identifies the area where the temperature gradient exceeds the preset safety threshold, marking it as a potential hot spot area.

[0012] The adaptive regulation and control decision module is used to generate targeted and spatially differentiated thermal management regulation and control instructions based on the output of the thermal flow path prediction module. The module includes a strategy mapping unit and an instruction generation unit. The strategy mapping unit has a multi-dimensional strategy query table built-in, which takes the spatial coordinates of the predicted potential hot spot area, the temperature exceeding amplitude, and the temperature rise rate as input indexes, and maps the corresponding optimal composite control strategy.

[0013] The optimal composite control strategy is a combination of specific parameters, including the number of actuators to be started, the target power level of each actuator, and the start-stop timing relationship between different actuators. The instruction generation unit generates a digital control instruction sequence containing specific actuator address code, power control parameters, and timestamps according to the optimal composite control strategy output by the strategy mapping unit.

[0014] The actuator array coordination module is used to receive and execute the control instructions issued by the adaptive control decision module to implement precise spatial directional thermal management of the module. This module includes an actuator array composed of multiple independently controllable micro actuators and an array drive controller. The micro actuators in the actuator array are divided into two types according to their physical effects:

[0015] The first type is active heat dissipation actuators, including micro fans and semiconductor cooling plates;

[0016] The second type is heat flow guiding actuators, including micro guide vanes and variable thermal resistance material sheets.

[0017] These micro actuators are embedded in the inner wall of the heat dissipation duct of the lithium battery module or installed on the surface of the key cells in a matrix form.

[0018] The array drive controller parses the received digital control instruction sequence and accurately controls the operating state and power output of each addressed micro actuator through independent pulse width modulation channels or digital analog conversion channels, so that it works according to the required time and intensity of the instruction, thereby forming a dynamic adjustable forced convection field or local cooling field matching the predicted heat flow path inside the module, achieving directional suppression and heat dissipation of the predicted hot spot.

[0019] Further, the Kriging spatial interpolation algorithm used by the data fusion and reconstruction unit in the thermal field dynamic perception module has a variogram model that is adaptively selected according to the material composition of different regions inside the lithium battery module. For the cell body region, a Gaussian variogram model is used to reflect its continuous and smooth temperature change characteristics; for the metal connecting piece and busbar region, an exponential variogram model is used to better fit the possible temperature change characteristics. The data fusion and reconstruction unit will dynamically adjust the main parameters of the variogram, including the range and base value, to ensure that the interpolation model matches the current actual thermal field physical characteristics, before each interpolation calculation.

[0020] Further, the computational fluid dynamics simulation unit in the heat flow path prediction module operates in a simplified real-time simulation framework. This framework performs model reduction on the complete fluid control equations and heat transfer equations by pre-obtaining the eigenmodes of the flow field and temperature field of the module under different typical working conditions through high-fidelity offline simulation; in online prediction, the current temperature field and heat source distribution are projected onto the low-dimensional subspace formed by these preset eigenmodes for solving, greatly reducing the amount of calculation, so that the temperature field prediction within a future 5-30 second time window can be completed within 100 milliseconds, meeting the requirements of real-time control.

[0021] Further, the strategy mapping unit in the adaptive regulation and control decision module generates a multi-dimensional strategy query table through offline training of a deep reinforcement learning algorithm. The training process is carried out in a virtual environment containing a high-fidelity thermal fluid simulation model and a battery electro-thermal coupling model. The reinforcement learning agent takes the predicted hotspot area state as the observation value, the action combination of the actuator array as the action space, and the maintenance of module temperature uniformity, the minimization of regulation and control energy consumption, and the avoidance of actuator frequent start-stop as the comprehensive reward function. Through millions of scene trials and strategy iterations, the query table that can map the observation state to the optimal action strategy is finally obtained. After the system is deployed, the query table can be regularly updated through incremental learning based on actual operation data.

[0022] Further, the micro guide vane in the actuator array coordination module has a vane angle driven by a micro stepping motor, which can be continuously adjusted within a range of 0 degrees to 90 degrees. The variable thermal resistance material sheet is made of a doped polymer composite material, and its thermal conductivity can be linearly changed within a range of 0.5 watts per meter kelvin to 5 watts per meter kelvin under the action of an external applied control voltage of 0 volts to 5 volts. The array drive controller dynamically changes the thermal conduction path resistance of the local area where the variable thermal resistance material sheet is located by adjusting the voltage applied across the variable thermal resistance material sheet, thereby actively guiding the heat flow direction.

[0023] The application also provides a lithium battery module charging and discharging process temperature control method applied to the above-mentioned system, which is automatically executed by the system and includes the following steps:

[0024] Step 1, system initialization, loading lithium battery module three-dimensional model, material thermal physical property parameters, preset variation function model set, reduced simulation eigenmode database, and multi-dimensional strategy query table.

[0025] Step 2, the heat field dynamic perception module continuously collects temperature data of each monitoring point in the module through a high-density distributed temperature sensing network, and uses the data fusion and reconstruction unit to construct and update the global temperature field digital twin model in real time based on the Kriging spatial interpolation algorithm and the adaptive variation function model.

[0026] Step 3, the heat flow path prediction module synchronously acquires current temperature field data and electrical parameters of the battery management system, calculates the instantaneous heat generation rate distribution through a heat source calculation unit, and performs rapid numerical simulation on the temperature field evolution within a preset future time window based on a reduced-order model through a computational fluid dynamics simulation unit, predicts the heat flow path, and identifies potential hot spot areas.

[0027] Step 4, the strategy mapping unit of the adaptive regulation and control decision module queries a multi-dimensional strategy query table according to the predicted potential hot spot area information, obtains the corresponding optimal composite regulation and control strategy, and converts it into specific digital regulation and control instruction sequences through an instruction generation unit.

[0028] Step 5, the array drive controller of the actuator array coordination module receives the regulation and control instruction sequences, parses and drives the corresponding micro fans, semiconductor refrigeration plates, guide vanes, and variable thermal resistance material plates in the actuator array, and cooperates with the specified power, angle, or thermal resistance value to generate a target regulation and control field inside the module.

[0029] Step 6, the system cyclically executes steps 2 to 5 at a frequency of no less than 10 Hz, realizing closed-loop, dynamic, and spatially oriented temperature control during the charging and discharging process of the entire lithium battery module.

[0030] Compared with the prior art, the beneficial effects of the present application are:

[0031] 1、The present application fundamentally eliminates the inherent monitoring blind area of fixed sensor layout by deploying a high-density distributed temperature sensing network and integrating advanced spatial interpolation algorithms, and builds a global temperature field digital twin model. The system can present the three-dimensional temperature distribution inside the module in real time with high spatial resolution, accurately capture the subtle temperature changes of key areas such as cell connection plates and busbars, and provide an unprecedented panoramic and high-precision data basis for thermal management, achieving comprehensive perception of thermal state.

[0032] 2、The present application innovatively introduces a heat flow path real-time prediction mechanism based on computational fluid dynamics. The system not only monitors the current temperature, but also simulates the migration and accumulation process of heat in the future short time, identifies potential hot spots in advance. This paradigm shift from "passive monitoring" to "active prediction" enables the thermal management system to intervene in regulation and control before local overheating actually occurs, eliminating the risk of overheating in its infancy, and significantly improving the timeliness of early warning and the proactivity of control.

[0033] 3、The application adopts an adaptive decision and actuator array collaborative regulation mechanism based on deep reinforcement learning optimization. The system can automatically match and execute a highly directional and accurately matched composite regulation strategy in space according to the predicted hot spot characteristics. By cooperatively controlling various types of micro actuators, a complex, dynamic and controllable forced convection and heat conduction regulation field can be formed inside the module, realizing accurate "targeted" cooling and heat flow diversion of the predicted hot spot. Compared with the traditional global uniform heat dissipation, this regulation method is more efficient, consumes less energy, and can effectively avoid the problem of local overcooling or insufficient heat dissipation.

[0034] 4、The application forms a high-speed closed loop of perception, prediction, decision and execution, realizes intelligent temperature control of the whole chain from macro module to micro hot spot and from current state to future trend. The system significantly improves the thermal safety and temperature uniformity of the lithium battery module under complex charging and discharging conditions, effectively prolongs the cycle life of the battery, and provides reliable technical support for the safe application of high-power and high-energy-density battery systems. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 is the overall technical scheme architecture diagram of the lithium battery module charging and discharging process temperature control system proposed by the application;

[0036] Figure 2 is the core principle framework diagram of the global temperature field digital twin model construction and heat flow path prediction in the application;

[0037] Figure 3 is the logic flow framework diagram of the adaptive regulation decision and actuator array cooperation in the application;

[0038] Figure 4 is the multi-level interaction relationship and data flow diagram of the thermal field dynamic perception module and the heat flow path prediction module in the application. DETAILED DESCRIPTION

[0039] Please refer to Figures 1 to 4 The application provides a specific embodiment of a lithium battery module charging and discharging process temperature control system and method. Please refer to the accompanying Figure 1 The system is a closed-loop control system integrating high-precision perception, real-time prediction, intelligent decision and accurate execution. The system takes the lithium battery module as the physical entity, realizes dynamic monitoring of the three-dimensional space temperature distribution inside the module, prediction of future thermal trends and active thermal management of spatial differentiation through a series of closely coupled hardware modules and software algorithms. During the operation of the whole system, each module interacts according to the preset logical sequence and data interface, forming a high-speed and adaptive control loop.

[0040] The physical carrier of the system is a standardized lithium battery module, which contains multiple lithium ion battery cells combined in series or parallel, and the battery cells are electrically connected through busbars made of metal. The entire module is packaged in a shell with an internal air duct structure. The core of the present application is to deeply integrate a sensor network, a computing unit, and an actuator based on the physical module, making it an intelligent thermal management entity.

[0041] First, the specific implementation details of the thermal field dynamic perception module are described. Please refer to the attached Figure 2 With the attached Figure 4 The core task of the thermal field dynamic perception module is to build and maintain a global temperature field digital twin model that is updated synchronously with the physical module. The module is composed of a high-density distributed temperature sensor network at the hardware layer and a data fusion and reconstruction unit at the software layer.

[0042] The specific deployment of the high-density distributed temperature sensor network follows a set of strict engineering guidelines. The selection of micro temperature sensor nodes is a digital output type patch thermocouple or a digital temperature sensor with an integrated circuit bus interface, with a volume of less than 2mm x 2mm x 1mm to ensure minimal interference with the module internal space and air flow. The deployment location is based on the "key thermal sensitive area" map determined by the pre-conducted finite element thermal simulation. Specifically, at the welding points of each battery cell's aluminum or copper tabs and busbars, one sensor node is deployed to monitor the Joule heat that may be generated due to contact resistance. On the surface of the copper or aluminum busbars connecting the battery cells inside the module, one sensor node is deployed every 50mm to monitor the temperature distribution on the busbars. In the center of the gap between adjacent battery cells, a micro probe type sensor is inserted vertically to monitor the battery cell side heat dissipation and gap air temperature.

[0043] On the inner surface of the module shell, corresponding to the air duct inlet, outlet, and corners, surface-mounted sensors are deployed with a grid density of 100mm x 100mm to monitor the boundary temperature conditions. All sensor nodes are connected through a redundantly designed integrated circuit bus network or wireless sensor network, with the bus controller integrated into the data fusion and reconstruction unit to synchronously collect the original temperature data of all nodes at a sampling frequency of no less than 100Hz. Each data packet contains a unique sensor node identification code, temperature measurement value, time stamp, and data verification code.

[0044] The data fusion and reconstruction unit is an embedded industrial computer or a high-performance field programmable gate array. It internally stores an accurate three-dimensional computer-aided design geometric model of the lithium battery module, which is discretized into a grid of millions of volume elements. At the same time, the unit is built-in with a database of material thermal physical parameters, which defines the precise density, specific heat capacity, and thermal conductivity parameters of each material such as the cell shell, electrode material, electrolyte, busbar copper material, and shell plastic, which are functions of temperature. After receiving the raw temperature data stream from the sensor network, the data fusion and reconstruction unit first performs data preprocessing, including rejecting outliers due to communication interference and performing sliding average filtering on the historical data of each sensor node to suppress noise. The preprocessed data is a set of spatially discrete, three-dimensional coordinate temperature point sets.

[0045] In order to generate a three-dimensional temperature field covering the entire internal continuous space of the module from these sparse point sets, the data fusion and reconstruction unit uses the Kriging spatial interpolation algorithm for calculation. The core of this algorithm is to construct a variogram function model that can reflect the spatial correlation of variables. In this embodiment, the algorithm does not use a single global variogram function, but adaptsively selects according to the material properties of the region where the interpolation point is located. The specific process is as follows: when the temperature of a certain position in the module needs to be estimated, the algorithm first queries the material type to which the position belongs in the three-dimensional geometric model. If the position is located inside or on the surface of the cell body, a Gaussian variogram function model is used, which mathematically represents the characteristics of the temperature changing smoothly and continuously in this region.

[0046] If the position is located in the metal connecting piece or busbar region, an exponential variogram function model is used, which can better fit the characteristics of local temperature changes caused by current skin effect or poor contact. Before each interpolation calculation, the data fusion and reconstruction unit calculates the temperature statistical characteristics of different material regions in real time based on the latest batch of sensor data, including variance and spatial autocorrelation length, and dynamically adjusts the key parameters of the selected variogram function, mainly the range and nugget values. The range parameter determines the influence range of spatial correlation, and the nugget value represents the overall fluctuation amplitude of the region temperature. Through this dynamic parameter adjustment, the interpolation model can adaptively match the actual thermal physical characteristics of the module under the current charging and discharging conditions, significantly improving the interpolation accuracy.

[0047] The interpolation calculation process is accelerated on a graphics processor. The algorithm takes the three-dimensional coordinates and temperatures of all sensor nodes as known conditions, and solves the Kriging equation set for all node coordinates of the entire module three-dimensional grid to output the temperature estimate of each grid node. These temperature values are organized into a three-dimensional array with a preset spatial resolution, for example, a resolution of 1 millimeter, which constitutes the global temperature field digital twin model. This model is updated at a refresh rate of 50 Hz, ensuring that it is highly synchronized with the actual thermal state of the physical module. The generated temperature field data is sent in real time to the heat flow path prediction module through a high-speed communication interface.

[0048] Next, the specific implementation of the heat flow path prediction module will be described in detail. Please refer to the accompanying drawings Figure 2 and the accompanying drawings Figure 4 The function of this module is to predict the dynamic migration process of heat in the next few seconds to tens of seconds based on the accurate temperature field and real-time electrical parameters at the current time. The module is composed of a heat source calculation unit and a computational fluid dynamics simulation unit in series.

[0049] The heat source calculation unit continuously obtains real-time data streams from the battery management system of the entire vehicle or energy storage system. These data include the total input and output current of the lithium battery module, the total terminal voltage, and the terminal voltage of each cell obtained through the internal monitoring circuit of the battery management system. The heat source calculation unit calculates the instantaneous heat generation rate of each spatial position inside the module according to these electrical parameters. The heat generation model is a composite model of Joule heat and electrochemical reaction heat.

[0050] For busbars, connecting sheets and other pure conductor regions, the heat generation rate is calculated according to Joule's law, with the formula: wherein, is the volumetric heat generation rate, is the current density, is the material resistivity. The current density distribution is obtained by proportionally distributing the pre-calibrated finite element electromagnetic simulation results combined with the real-time total current.

[0051] For the cell body region, the heat generation rate calculation uses the Bernham electrochemical heat generation model, which divides the heat generation into reversible heat and irreversible heat. Irreversible heat is composed of Ohmic internal resistance and polarization internal resistance heat, and reversible heat is related to the entropy heat coefficient of the cell and the charging and discharging current. The heat source calculation unit internally stores a table of internal resistance parameters and entropy heat coefficient parameters for each cell at different states of charge and temperatures, and through table lookup interpolation, combined with real-time current, voltage and cell local temperature from the digital twin model, the heat generation rate of each calculation grid element inside each cell is calculated. Finally, the heat source calculation unit outputs a three-dimensional distribution map of instantaneous volumetric heat generation rate that is perfectly aligned with the three-dimensional temperature field grid.

[0052] The computational fluid dynamics simulation unit is responsible for performing fast forward simulation prediction. It receives two key inputs:

[0053] One is the current three-dimensional temperature field distribution provided by the thermal field dynamic perception module, which serves as the initial temperature field for the simulation;

[0054] The other is the instantaneous heat generation rate distribution provided by the heat source calculation unit, which serves as the volumetric heat source term for the simulation.

[0055] Traditional computational fluid dynamics simulation calculations are computationally intensive and cannot meet real-time control requirements. Therefore, the present embodiment adopts a real-time simulation framework based on model reduction. The core of this framework is an "eigenmode" database. Before system deployment, a large number of simulations of typical working conditions for the target lithium battery module need to be performed through high-fidelity, offline computational fluid dynamics simulation. These working conditions cover different ambient temperatures, different charging and discharging rates, and different cooling air volumes. From these high-fidelity simulation results, a number of eigenmodes that can represent the main dynamic characteristics of the flow field and temperature field of the module are extracted through eigenvalue orthogonal decomposition. Typically, the first 20 to 50 modes can capture more than 95% of the energy. These eigenmodes and their corresponding eigenvalues are stored in the database of the computational fluid dynamics simulation unit.

[0056] During online prediction, the computational fluid dynamics simulation unit does not directly solve the complete Navier-Stokes equations and energy equations. Instead, it projects the current three-dimensional temperature field and three-dimensional heat generation rate distribution onto the low-dimensional subspace spanned by the eigenmodes to obtain a set of low-dimensional modal coefficients. In the low-dimensional space, the fluid control equations and heat transfer equations are simplified to a set of ordinary differential equations with respect to these modal coefficients. Solving this set of ordinary differential equations with significantly reduced dimension, we can quickly obtain the evolution trajectory of the modal coefficients in the future time period. Finally, the predicted modal coefficients at the future time are reconstructed in reverse to obtain the three-dimensional temperature field prediction result at the future time. The entire prediction process can be completed within 100 milliseconds for a time window of 5 to 30 seconds in the future, fully meeting the timing requirements of real-time control. In the prediction results output by the simulation unit, areas where the temperature prediction value exceeds a pre-set safety threshold, such as 45 degrees Celsius, or the temperature gradient exceeds 0.1 degrees Celsius per millimeter, are marked as "potential hot spot areas". The predicted temperature field evolution sequence clearly reveals the main migration path of the heat flow.

[0057] The specific implementation of the adaptive regulation and control decision module is then described. Please refer to the accompanying Figure 3 The role of this module is to convert the predicted, abstract thermal threat into specific, executable hardware control commands. The module includes a strategy mapping unit and an instruction generation unit.

[0058] The core asset of the policy mapping unit is a multi-dimensional policy lookup table. This lookup table is not written by artificial experience rules, but is generated by deep reinforcement learning algorithm offline training in a virtual environment. The training environment is a high-fidelity digital twin simulation platform that integrates battery thermal coupling model, computational fluid dynamics model and physical effect model of actuator array. The observation space of the reinforcement learning agent is the feature vector of the predicted potential hot spot area, which includes the three-dimensional space bounding box coordinates of the hot spot area, the amplitude of the highest temperature in the area exceeding the safety threshold, the average temperature rise rate of the area, and the relative position of the hot spot area in the module. The action space of the agent is the combination of all possible actions of all micro actuators, such as 10 gears for each micro fan, 10 power levels for each semiconductor refrigeration sheet, 10 angles for each guide vane, and 10 voltage levels for each variable thermal resistance material sheet.

[0059] The reward function of the agent is a multi-objective weighted sum, mainly including 3 items: negative temperature uniformity index, i.e. encouraging temperature distribution uniformity; negative total energy consumption, i.e. encouraging energy saving; negative actuator action change penalty, i.e. encouraging stable control to avoid frequent switching. The agent learns how to choose a set of actuator actions to maximize the long-term cumulative reward under various observed thermal threat states through millions of scenario trial and error and policy iteration in the virtual environment through the proximal policy optimization algorithm. The finally converged strategy is discretized and stored as a multi-dimensional lookup table.

[0060] In the actual system, after receiving the potential hot spot area feature vector from the thermal flow path prediction module, the policy mapping unit directly looks up the table as an index and outputs the corresponding "optimal composite control strategy" within milliseconds. The strategy is a structure that explicitly lists the unique identification code list of the micro actuators that need to be activated, the target power level or target angle value or target voltage value corresponding to each actuator in the list, and the start-stop timing relationship between different actuators, such as requiring the guide vane to rotate to the specified angle first, and then starting the micro fan in the corresponding area after a delay of 200 milliseconds.

[0061] The instruction generation unit is responsible for translating the structured strategy output by the policy mapping unit into low-level control instructions that can be directly recognized by the actuator array coordination module. The instruction generation unit generates specific digital control instruction sequences according to the type and communication protocol of the actuators. For micro fans and semiconductor refrigeration sheets, the instructions include actuator address code, target pulse width modulation duty cycle value, and effective timestamp.

[0062] For micro-guide vanes, the instruction contains actuator address code, target angle value, rotation speed, and effective timestamp. For variable thermal resistance material sheet, the instruction contains actuator address code, target control voltage value, voltage ramping rate, and effective timestamp. All these instructions are encapsulated in a transactional instruction package, ensuring that either all are executed correctly or all are rolled back in case of error. The instruction package is sent to the actuator array coordination module through real-time Ethernet or controller area network bus.

[0063] Finally, the detailed description of the specific implementation of the actuator array coordination module is described. Please refer to the accompanying drawings Figure 1 and the accompanying Figure 3 The module is the final execution mechanism of the system to exert influence on the physical world, which is composed of an actuator array and an array drive controller.

[0064] The actuator array is a set of micro actuators customized and embedded according to the module structure. In the active heat dissipation actuator, the diameter of the micro fan is between 10 mm and 20 mm, which is driven by a brushless DC motor and embedded on the side wall or partition of the module heat dissipation air duct. Its blowing direction can be perpendicular to the main flow of the air duct or at an angle. The size of the semiconductor refrigeration sheet is 10 mm x 10 mm x 3 mm, which is tightly attached to the specific position of the battery cell shell identified in advance as the hot spot through the heat-conducting silicone grease. Its cold face faces the battery cell, and its hot face is connected to the micro heat dissipation fin and placed in the air duct.

[0065] In the heat flow guide actuator, the micro guide vane is made of aluminum alloy sheet with a thickness of 0.5 mm, a width of about 5 mm, and a length covering the height of the air duct cross section. The vane rotating shaft is driven by a micro stepping motor, which has a step angle of 1.8 degrees. Through a reduction gear set, continuous and precise positioning of the vane within the range of 0 degrees to 90 degrees can be achieved. At 0 degrees, the vane is parallel to the airflow direction, and at 90 degrees, it is completely perpendicular to change or block the airflow. The variable thermal resistance material sheet is a functional material device, whose matrix is a high polymer, and inside it is doped with carbon nanotubes or graphene. The material sheet is made into a sheet with a thickness of 1 mm, which is inserted between the battery cell and the heat sink, or attached to the surface of the bus bar. Its core feature is that the thermal conductivity changes linearly with the direct current control voltage applied to its two electrodes. At 0 volts, the internal filler is dispersed, and the thermal conductivity is about 0.5 watts per meter kelvin, showing a high thermal insulation state; at 5 volts, the filler forms a heat-conducting chain under the action of the electric field, and the thermal conductivity can rise to 5 watts per meter kelvin, showing a high thermal conductivity state.

[0066] The array driving controller is a multi-channel motor servo driving and power amplification circuit board. After receiving the digitized control instruction sequence from the adaptive control decision module, the microcontroller inside the controller analyzes the sequence. For pulse width modulation control type actuators, the controller generates pulse width modulation waveforms of corresponding frequency and precision according to the duty cycle value in the instruction, and drives the motor through the power metal oxide semiconductor field effect transistor or semiconductor refrigeration sheet.

[0067] For angle control type actuators, the controller converts the target angle value into the number of pulses and direction signals required by the stepper motor to drive the stepper motor to accurately position. For voltage control type actuators, the controller outputs an analog voltage of 0 volts to 5 volts through a high-precision digital-to-analog converter, applies it to both ends of the variable thermal resistance material sheet, and monitors the voltage value in real time through the feedback circuit to ensure accurate control. The array driving controller ensures that all addressed actuators can start according to the required time synchronization or sequential asynchronous of the instruction, and work at the specified intensity, so as to construct a complex and dynamic control field in the internal space of the module. This control field may form a forced cooling air flow upstream of the predicted hot spot, or enhance the heat conduction path in the hot spot area, or accurately guide the cold air to the heat accumulation area through the guide vane.

[0068] The entire system runs in strict time sequence cycles. When the system is initialized, all necessary parameters are loaded from the non-volatile memory: three-dimensional geometric model, material database, variation function model set, reduced order simulation intrinsic mode database and trained multi-dimensional strategy query table. After entering the main loop, the thermal field dynamic perception module updates the digital twin model at a frequency of 50 Hz.

[0069] The heat flow path prediction module synchronously acquires data and completes the next prediction within 100 milliseconds. After receiving the prediction results, the adaptive control decision module completes the strategy query and instruction generation within 10 milliseconds. The execution delay of the actuator array coordination module is less than 50 milliseconds. Therefore, the entire closed-loop cycle from perception to execution is less than 200 milliseconds, and the control frequency is higher than 5 Hz. In actual implementation, it is usually set to 10 Hz, ensuring that the system can quickly respond to the rapidly changing thermal state during charging and discharging.

[0070] Through the synergistic work of the above specific embodiments, the system realizes panoramic perception, advanced prediction and precise regulation of the internal temperature field of the lithium battery module. Unlike the existing technology, which can only respond globally after the temperature at the fixed point exceeds the standard, the system can apply intervention in a specific location in advance before the hot spot is actually formed, thereby suppressing temperature fluctuations within a smaller and safer range, significantly improving the advance and safety of thermal management. The entire system is an adaptive intelligent agent, and its strategy lookup table can be updated incrementally based on the data accumulated during actual operation, thereby continuously optimizing the regulation effect and reducing overall energy consumption.

[0071] Embodiment 2: This embodiment provides a specific implementation variant based on the system architecture described in the above embodiments, mainly aiming at the differentiated design of the computational fluid dynamics simulation unit in the heat flow path prediction module and the heat flow guiding actuators in the actuator array coordination module, to adapt to a lithium battery module structure using a liquid cooling plate.

[0072] In this embodiment, the heat dissipation mode of the lithium battery module is liquid cooling, and the liquid cooling plate with internal flow channels is integrated at the bottom or side of the module. The cooling liquid circulates in the flow channels to carry away heat. Therefore, the deployment of the high-density distributed temperature sensing network in the thermal field dynamic perception module needs to be adjusted accordingly. In addition to deploying sensors at key parts such as the cell tab and busbar, a plurality of thin film pressure and temperature integrated sensors are also deployed in a matrix form at the contact interface between the liquid cooling plate and the cell, for monitoring the contact thermal resistance and interface temperature. At the same time, high-precision platinum resistance temperature sensors are deployed at the liquid inlet and outlet of the liquid cooling plate and the key flow channel turning points, for monitoring the temperature changes of the cooling liquid itself.

[0073] The computational fluid dynamics simulation unit in the heat flow path prediction module changes its physical model from air convection dominance to solid heat conduction and liquid convection coupling dominance. The intrinsic mode database of its reduced order model is obtained by offline simulation of the three-dimensional temperature field of the liquid cooling system under different cooling liquid flow rates, different inlet temperatures, and different module heat generation powers. In online prediction, the calculation method of the volume heat source is the same as in the above embodiments. However, the control equation for simulation focuses more on the coupled solution of the heat conduction equation in the solid region and the convection heat transfer equation in the cooling liquid flow channel. The prediction module needs to obtain the current flow rate and inlet temperature of the cooling liquid circulation system in real time as the boundary conditions for simulation. In addition to the predicted temperature field of the module solid region and potential hot spots, the predicted cooling liquid outlet temperature and the temperature distribution on the surface of the liquid cooling plate are also included, which are used to evaluate the overall efficiency of the cooling system.

[0074] The multi-dimensional strategy lookup table of the adaptive control decision module needs to be retrained, and the action space changes significantly. Since the cooling medium is a liquid, the composition of the actuator array is different from the above-mentioned embodiments. The actuator array coordination module of this embodiment includes two types of new actuators:

[0075] The first type is a distributed micro piezoelectric pump and valve unit,

[0076] The second type is a variable thermal conductivity interface material.

[0077] The distributed micro piezoelectric pump and valve unit is integrated in the flow channel network of the liquid cooling plate. The main flow channel of the liquid cooling plate is designed as a tree branch structure, and a micro piezoelectric pump and a micro proportional valve are installed at the inlet of each branch flow channel. The piezoelectric pump can provide additional local driving force, and the proportional valve can accurately adjust the flow of the cooling liquid in the branch. The variable thermal conductivity interface material is filled in the assembly gap between the battery cell and the liquid cooling plate. Its function is similar to the variable thermal resistance material sheet in the above-mentioned embodiments, but it is in the form of paste or gasket, and its thermal conductivity changes by applying an electric field, thereby dynamically adjusting the thermal resistance of the battery cell to the liquid cooling plate.

Claims

1. A lithium battery module charge-discharge process temperature control system, characterized in that, The application relates to a thermal field dynamic perception module for constructing and updating a global temperature field digital twin model of a lithium battery module in real time. A thermal flow path prediction module is used for predicting the dynamic migration path of heat and potential hot spot positions in a future time window based on current and historical temperature field states. An adaptive regulation and decision module is used for generating targeted spatially differentiated thermal management regulation and control instructions according to the output of the thermal flow path prediction module. An actuator array coordination module is used for receiving and executing the regulation and control instructions issued by the adaptive regulation and decision module to implement precise spatial directional thermal management on the module. The thermal field dynamic perception module comprises a high-density distributed temperature sensing network and a data fusion and reconstruction unit. The adaptive regulation and decision module comprises a strategy mapping unit and an instruction generation unit. The strategy mapping unit is internally provided with a multi-dimensional strategy query table which takes the spatial coordinates of the predicted potential hot spot area, the temperature exceeding amplitude and the temperature rising rate as input indexes and maps out the corresponding optimal composite regulation and control strategy. The instruction generation unit generates a digital regulation and control instruction sequence comprising a specific actuator address code, a power control parameter and a time stamp according to the optimal composite regulation and control strategy output by the strategy mapping unit. The multi-dimensional strategy query table of the strategy mapping unit is generated through offline training of a deep reinforcement learning algorithm. The training process is carried out in a virtual environment comprising a high-fidelity thermal fluid simulation model and a battery electro-thermal coupling model. The reinforcement learning agent takes the predicted hot spot area state as the observation value, takes the action combination of the actuator array as the action space, and takes maintaining the module temperature uniformity, minimizing the regulation and control energy consumption and avoiding frequent actuator start-stop as the comprehensive reward function. Through scenario trial and error and strategy iteration, the query table capable of mapping the observation state to the optimal action strategy is finally obtained. The thermal flow path prediction module comprises a heat source calculation unit and a computational fluid dynamics simulation unit.

2. The temperature control system for charging and discharging process of lithium battery module according to claim 1, characterized in that, The heat source calculation unit receives the module total current, total voltage and voltage data of each battery cell provided by the battery management system in real time, and calculates the instantaneous heat generation rate of each calculation grid unit in the module according to the Joule law and the electrochemical heat generation model. The computational fluid dynamics simulation unit takes the current three-dimensional temperature field distribution map output by the thermal field dynamic perception module as the initial boundary condition, takes the instantaneous heat generation rate distribution output by the heat source calculation unit as the volume heat source, calls the preset fluid control equation and heat transfer equation for numerical solution, simulates the coupling process of air or cooling medium flow and heat conduction, convection in the module in the future preset time length, and thus predicts the evolution trend of the temperature field and identifies the area where the temperature gradient exceeds the preset safety threshold. The actuator array coordination module comprises an actuator array composed of a plurality of independently controllable micro actuators and an array driving controller.

3. The temperature control system for charging and discharging process of lithium battery module according to claim 2, characterized in that, The micro actuators in the actuator array include active heat dissipation actuators and thermal flow guiding actuators. The array driving controller analyzes and receives the digital regulation and control instruction sequence, and accurately controls the running state and power output of each addressed micro actuator through independent pulse width modulation channels or digital analog conversion channels. ​ 4. The temperature control system for charging and discharging process of lithium battery module according to claim 3, characterized in that, The high-density distributed temperature sensing network is composed of multiple miniature temperature sensor nodes deployed in key heat-sensitive areas inside the lithium battery module; The data fusion and reconstruction unit receives raw temperature data from all temperature sensor nodes in real time, and based on a preset three-dimensional geometric model of the lithium battery module and a material thermal physical property parameter database, adopts a Kriging spatial interpolation algorithm to perform spatial interpolation calculation on sparse sensor measurement point data, to generate a three-dimensional temperature field distribution map covering the entire module internal space and having a preset spatial resolution.

5. The temperature control system for charging and discharging process of lithium battery module according to claim 4, characterized in that, The Kriging spatial interpolation algorithm adopted by the data fusion and reconstruction unit has a variogram model that is adaptively selected according to the material composition of different areas inside the lithium battery module; For the cell body area, a Gaussian variogram model is adopted; For the metal connecting piece and busbar area, an exponential variogram model is adopted; Before each interpolation calculation, the data fusion and reconstruction unit dynamically adjusts the main parameters of the variogram according to the real-time calculation of the statistical characteristics of the temperature change in the region, and the main parameters include the range and the base value.

6. The temperature control system for charging and discharging process of lithium battery module according to claim 5, characterized in that, The computational fluid dynamics simulation unit runs in a simplified real-time simulation framework, which has performed model reduction processing on complete fluid control equations and heat transfer equations; the specific method of the model reduction processing is: Through high-fidelity offline simulation in advance, the eigenmodes of the flow field and temperature field of the module under different typical working conditions are obtained; in online prediction, the current temperature field and heat source distribution are projected onto the low-dimensional subspace formed by the preset eigenmodes for solving.

7. The temperature control system for charging and discharging process of lithium battery module according to claim 6, characterized in that, The multi-dimensional strategy query table is periodically updated by incremental learning according to actual operation data after the system is deployed.

8. A method for temperature control of the charging and discharging process of a lithium battery module applied to the system of any one of claims 1 to 7, characterized in that, The system automatically performs the following steps: Step 1, system initialization, load the three-dimensional model of the lithium battery module, material thermal physical property parameters, a set of preset variogram models, a database of reduced simulation eigenmodes, and a multi-dimensional strategy query table; Step 2, the thermal field dynamic perception module continuously collects temperature data of each monitoring point inside the module through the high-density distributed temperature sensing network, and uses the data fusion and reconstruction unit to construct and refresh the global temperature field digital twin model in real time based on the Kriging spatial interpolation algorithm and the adaptive variogram model; Step 3, the heat flow path prediction module synchronously obtains the current temperature field data and electrical parameters of the battery management system, calculates the instantaneous heat generation rate distribution through the heat source calculation unit, and performs rapid numerical simulation of the temperature field evolution in the preset future time window based on the reduced model through the computational fluid dynamics simulation unit, to predict the heat flow path and identify potential hot spot areas; Step 4, the strategy mapping unit of the adaptive regulation and control decision module queries the multi-dimensional strategy query table according to the predicted potential hot spot area information to obtain the corresponding optimal composite regulation and control strategy, and the instruction generation unit converts it into a specific digital regulation and control instruction sequence; Step 5, the array drive controller of the actuator array coordination module receives the regulation instruction sequence, parses and drives the corresponding micro-fan, semiconductor refrigeration sheet, guide vane and variable thermal resistance material sheet in the actuator array, and cooperates with the specified power, angle or thermal resistance value to generate a target regulation field in the module; Step 6, the system cycles steps 2 to 5 at a frequency of no less than 10 Hz.

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