Dynamic battery thermal management system and control method thereof

Abstract

The invention discloses a dynamic battery thermal management system and a control method thereof, and relates to the technical field of battery thermal management. The dynamic battery thermal management system comprises a thermoelectric array which is composed of a plurality of thermoelectric modules which are independently controlled; the thermoelectric module dynamically switches a refrigeration mode, a heating mode or a power generation mode based on the driving circuit; the phase change material heat dissipation layer is arranged at the hot end of the thermoelectric array; the temperature sensor array is distributed on the surface of the battery to collect temperature distribution data; the intelligent control module is connected with the temperature sensor array and the thermoelectric array; comprising a dynamic temperature field reconstruction unit, a multi-target fuzzy controller and a distributed collaborative decision-making unit. And the energy recovery circuit is connected with the thermoelectric module in the power generation mode and transmits power to the energy storage unit. Through a dynamic temperature field reconstruction algorithm and a multi-target fuzzy controller, the system can quickly respond to temperature changes and accurately predict the development trend of hot spots.

Description

Technical Field

[0001] This application relates to the field of battery thermal management technology, and in particular to a dynamic battery thermal management system and its control method. Background Technology

[0002] The efficiency, lifespan, and safety of lithium batteries are significantly affected by temperature. Existing thermal management solutions rely on passive heat dissipation or fixed heating systems, which suffer from problems such as slow response, high energy consumption, and low control precision.

[0003] In addition, traditional thermal power estimation methods are difficult to match dynamic operating conditions in real time; although thermoelectric materials can convert thermal energy into electrical energy through the Seebeck effect or Peltier effect, they have not yet effectively combined real-time thermal power prediction with thermoelectric dynamic regulation, resulting in insufficient temperature control efficiency and inability to adapt to changing discharge conditions and external environment. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a dynamic battery thermal management system. Through a dynamic temperature field reconstruction algorithm and a multi-objective fuzzy controller, the system can quickly respond to temperature changes and accurately predict the development trend of hot spots. Correspondingly, a control method is provided that can be applied to dynamic battery thermal management systems under different conditions.

[0005] The first technical solution adopted in this application is: providing a dynamic battery thermal management system, including:

[0006] A thermoelectric array, which is composed of multiple independently controlled thermoelectric modules; the thermoelectric modules dynamically switch between cooling, heating or power generation modes based on a drive circuit.

[0007] A phase change material heat dissipation layer is disposed at the hot end of the thermoelectric array, and the melting ratio of the phase change material is monitored based on a phase change state monitoring device.

[0008] A temperature sensor array is distributed on the battery surface to collect temperature distribution data;

[0009] The intelligent control module connects the temperature sensor array and the thermoelectric array; it includes:

[0010] The dynamic temperature field reconstruction unit is used to identify hot spots on the battery surface and predict the temperature rise trend of adjacent areas.

[0011] A multi-objective fuzzy controller generates current control commands based on temperature error, temperature rise rate, and energy recovery priority.

[0012] A distributed collaborative decision-making unit optimizes the power allocation and mode switching of each thermoelectric module;

[0013] The energy recovery circuit connects to the thermoelectric module in power generation mode and transmits power to the energy storage unit.

[0014] In an optional embodiment, the dynamic temperature field reconstruction unit is configured as follows:

[0015] Based on temperature distribution data, areas with temperatures higher than the average temperature plus a temperature threshold, and with a temperature rise rate higher than the temperature rise rate threshold, are designated as hotspot areas.

[0016] Predict the temperature rise trend in adjacent areas.

[0017] In an optional embodiment, the dynamic temperature field reconstruction unit achieves temperature field prediction through the following steps:

[0018] Temperature distribution characteristics were extracted based on historical temperature field databases;

[0019] Establish a mapping model between operating parameters and temperature field distribution;

[0020] Locate areas with abnormal temperatures.

[0021] In an optional embodiment, the output rule of the multi-objective fuzzy controller is:

[0022] When the temperature error is positive and the temperature rise rate exceeds the set threshold, an instruction to increase the drive current is output and the forced cooling mode is triggered.

[0023] In an optional embodiment, the objective function of the distributed collaborative decision-making unit achieves the minimization of total thermoelectric cooling power consumption and the maximization of battery temperature uniformity.

[0024] In an optional embodiment, the intelligent control module dynamically adjusts the operating current of the thermoelectric module based on feedback of the phase change material melting ratio.

[0025] The second technical solution adopted in this application is: providing a dynamic battery thermal management control method, applied to the dynamic battery thermal management system as described in any of the preceding claims, comprising the following steps:

[0026] Real-time monitoring of battery surface temperature distribution and temperature rise rate;

[0027] When a hotspot area is detected, the corresponding thermoelectric module is switched to cooling mode.

[0028] The driving current of the thermoelectric module is dynamically adjusted based on fuzzy control rules.

[0029] The operating current is adjusted based on the feedback of the phase change material melting ratio;

[0030] Switch the thermoelectric modules in non-hotspot areas to power generation mode for energy recovery.

[0031] In an optional embodiment, it further includes:

[0032] Predict the overheating time window of the area adjacent to the hot spot, and set the corresponding thermoelectric module to standby mode before the overheating occurs.

[0033] The third technical solution adopted in this application is: providing an electronic device, the electronic device including: a memory and a processor coupled to each other, the processor being used to execute program instructions stored in the memory to realize the dynamic battery thermal management method as described above.

[0034] The fourth technical solution adopted in this application is: providing a computer-readable storage medium that stores program data, which can be executed by a processor to implement the dynamic battery thermal management method as described above.

[0035] Due to the adoption of the above technical solution, this application has at least one of the following beneficial effects compared with the prior art:

[0036] 1. By identifying hot spots on the battery surface through a dynamic temperature field reconstruction unit and predicting the temperature rise trend of adjacent areas, precise management of the temperature field can be achieved.

[0037] 2. The system can monitor the surface temperature distribution and temperature rise rate of the battery in real time, and quickly switch the corresponding thermoelectric module to cooling mode when a hot spot is identified, thus enhancing the safety of the system.

[0038] 3. In power generation mode, the thermoelectric modules in non-hotspot areas can switch to power generation mode to recover energy, and store the generated electrical energy in the energy storage unit through the energy recovery circuit to improve energy utilization.

[0039] 4. The multi-objective fuzzy controller generates current control commands based on temperature error, temperature rise rate, and energy recovery priority, thereby improving temperature control accuracy. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] in:

[0042] Figure 1 A schematic diagram of the framework of a dynamic battery thermal management system provided in an embodiment of this application;

[0043] Figure 2This is a schematic diagram of the structure of an H-bridge circuit provided in an embodiment of this application;

[0044] Figure 3 A schematic flowchart of a dynamic battery thermal management control method provided in an embodiment of this application;

[0045] Figure 4 This is a schematic diagram of the structure of a computer device according to an embodiment of this application;

[0046] Figure 5 This is a schematic diagram of the structure of an embodiment of the computer-readable storage medium of this application. Detailed Implementation

[0047] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It is understood that the specific embodiments described herein are only for explaining this application and not for limiting it. Furthermore, it should be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all structures. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0048] The terms "first," "second," etc., used in this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0049] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0050] Traditional battery thermal management systems often rely on passive cooling or fixed heating systems, which suffer from response lag and cannot quickly adapt to temperature changes. Furthermore, traditional systems have low control precision, making it difficult to accurately maintain the battery temperature within an ideal range. Moreover, existing thermoelectric temperature control systems lack dynamic prediction capabilities for the temperature field, failing to identify and handle impending overheating in advance, resulting in slow system response and a tendency for localized overheating. Therefore, this application provides a dynamic battery thermal management system that, through a dynamic temperature field reconstruction algorithm and a multi-objective fuzzy controller, enables the system to quickly respond to temperature changes and accurately predict the development trend of hot spots; for example... Figure 1 As shown, Figure 1 A schematic diagram of the framework of a dynamic battery thermal management system provided in an embodiment of this application includes a thermoelectric array, a phase change material heat dissipation layer, a temperature sensor array, an intelligent control module, and an energy recovery circuit.

[0051] The thermoelectric array consists of multiple independently controlled thermoelectric modules; the thermoelectric modules dynamically switch between cooling, heating, or power generation modes based on the drive circuit; the drive circuit includes a thermoelectric programmable circuit and an H-bridge circuit; the operating mode of the thermoelectric modules, i.e., cooling, heating, or power generation mode, is controlled based on the thermoelectric programmable circuit; the instructions issued by the intelligent control module precisely configure each independent thermoelectric module TEM to adapt to the temperature requirements of different areas.

[0052] like Figure 2 As shown, Figure 2 The schematic diagram of an H-bridge circuit provided in an embodiment of this application includes a first switch Q1, a second switch Q2, a third switch Q3, and a fourth switch Q4; control signal A is connected to the control terminal of the first switch Q1, control signal B is connected to the control terminal of the second switch Q2, control signal C is connected to the control terminal of the third switch Q3, and control signal D is connected to the control terminal of the fourth switch Q4.

[0053] The first terminal of the first switch Q1 is connected to the first terminal of the second switch Q2; the third terminal of the first switch Q1 is connected to the first terminal of the third switch Q3; the third terminal of the second switch Q2 is connected to the first terminal of the fourth switch Q4; the third terminal of the third switch Q3 is grounded to GND; the first terminal of the third switch Q3 is connected to the first terminal of the fourth switch Q4; the third terminal of the fourth switch Q4 is connected to the negative terminal of the thermoelectric module; the first terminal of the fourth switch Q4 is connected to the positive terminal of the thermoelectric module.

[0054] The second switch Q2 is connected to VCC, and the relay K1 controls whether VCC flows into the H-bridge circuit. In this embodiment, VCC is a +12V voltage. In other embodiments, VCC can take other values, and there are no restrictions on this.

[0055] The first switch Q1 and the second switch Q2 are P-MOSFETs; the third switch Q3 and the fourth switch Q4 are N-MOSFETs; in other embodiments, the switches may be selected in other ways, and no limitation is made thereto.

[0056] The core function of the H-bridge circuit is to achieve different operating modes of the thermoelectric module (TEM) by changing the direction of the current. For example, when cooling is required, the current direction can be reversed to put the TEM into cooling mode; conversely, when heating is required, the current direction can be reversed to put the TEM into heating mode. In addition to controlling the current direction, the H-bridge circuit can also adjust the magnitude of the current flowing through the TEM, thereby achieving precise control of the output power. This is crucial for maintaining a constant temperature or responding quickly to temperature changes.

[0057] A phase change material heat dissipation layer is disposed at the hot end of the thermoelectric array, and the melting ratio of the phase change material is monitored based on the phase change state monitoring device. In this embodiment, a layer of paraffin-based PCM with a melting point of 42°C is installed at the hot end of the thermoelectric array as a heat dissipation layer. An infrared sensor or other type of phase change state monitoring device is provided to monitor the melting ratio of the PCM (phase change material) in real time.

[0058] When the battery pack is within its normal operating temperature range, the thermoelectric module is in power generation mode, converting waste heat into electrical energy and storing it in the energy storage unit; the PCM absorbs a small amount of heat, maintaining a solid or partially molten state to keep the battery pack's operating temperature stable.

[0059] If the battery temperature in a certain area rises rapidly above a threshold, the corresponding TEM in that area is switched to cooling mode to cool it down quickly. At the same time, the PCM begins to absorb a large amount of heat and undergo a phase change (from solid to liquid), which effectively slows down the further rise in temperature in that area and improves the overall heat dissipation efficiency.

[0060] The intelligent control module dynamically adjusts the operating current of the thermoelectric module based on the melting ratio feedback of the phase change material. For example, when the infrared sensor detects that the melting ratio of the PCM reaches 65%, the intelligent control module adjusts the operating current of the corresponding area TEM (such as reducing it by 10%) according to this information, thereby reducing the operating temperature difference of the cooling mode and improving the cooling efficiency. If the PCM is completely melted and the temperature continues to rise, the system will take further measures, such as increasing the cooling power or starting an additional cooling mechanism.

[0061] A temperature sensor array is distributed on the battery surface to collect temperature distribution data, including temperature and temperature rise rate. Multiple temperature sensors are evenly distributed on the surface of each cell in the battery pack, forming a dense temperature sensor array. The temperature sensors continuously collect temperature data at their respective locations and transmit the information to the intelligent control module.

[0062] Real-time data acquisition allows the system to respond quickly to any abnormal temperature changes, thereby promptly initiating necessary cooling measures and avoiding safety hazards caused by delays; for example, the corresponding cooling operation can be triggered the instant a hot spot is detected, shortening the response time.

[0063] The intelligent control module connects the temperature sensor array and the thermoelectric array; it includes:

[0064] The dynamic temperature field reconstruction unit is used to identify hot spots on the battery surface and predict the temperature rise trend of adjacent areas; the dynamic temperature field reconstruction unit is configured as follows:

[0065] Construct a battery surface temperature matrix T(x, y, t), where x and y represent the position coordinates of the sensor on the battery surface and t represents the time point; calculate the temperature rise rate dT / dt in real time by comparing the temperature difference between the current time and the previous time and dividing by the time interval.

[0066] Calculate the average temperature of the entire battery surface; based on the temperature distribution data, mark areas where the temperature is higher than the average temperature plus a temperature threshold and the temperature rise rate is higher than the temperature rise rate threshold as hotspot areas; in this embodiment, the temperature threshold is 3°C and the temperature rise rate threshold is 1°C / s; in other embodiments, the temperature threshold and the temperature rise rate threshold can be selected separately, and no limitation is made in this regard.

[0067] Predicting temperature rise trends in adjacent areas: By using dynamic temperature field reconstruction algorithms to predict temperature rise trends in adjacent areas, preventative measures can be taken before overheating occurs.

[0068] The dynamic temperature field reconstruction unit achieves temperature field prediction through the following steps:

[0069] Temperature distribution features are extracted based on a historical temperature field database; temperature field distribution data of the battery under different operating conditions are collected to construct a historical temperature field database, and spatial temperature distribution is stored using a gridding method.

[0070] Calculate the autocorrelation matrix of the temperature field:

[0071] Perform eigenvalue decomposition: [V,D]=eig(R).

[0072] Select the first k principal modal bases:

[0073] Optimize modal basis using K-means clustering: Where Φ is the optimal orthogonal basis matrix.

[0074] Projecting the historical temperature field into modal space: A = (Φ T Φ) -1Φ T T hist The reduced-order coefficient matrix is ​​obtained.

[0075] Establish a mapping model between operating parameters and temperature field distribution; normalize the operating parameters (current, voltage, ambient temperature, etc.) X. norm =mapminmax(X,0,1).

[0076] Establish an exact radial basis function network: net = newgrnn(X) train ,A,σ), where σ is the expansion coefficient, and in this embodiment, the expansion parameter is 0.1.

[0077] Input real-time operating parameters X real .

[0078] Predicted order reduction factor:

[0079] Reconstructing the temperature field:

[0080] Spatial interpolation:

[0081] T point =griddata(x grid ,y grid ,T pred ,x query ,y query ,'nearest').

[0082] Nearest neighbor interpolation is used to locate areas of abnormal temperature by analyzing hotspot areas.

[0083] The temperature field reconstruction algorithm will be described in detail below with reference to a specific embodiment:

[0084] 119 sets of historical temperature field data were pre-stored to build a grid database.

[0085] Extract the first 5 main modal bases.

[0086] Train an RBF (Radial Basis Function) neural network with a 16-dimensional vector of operating parameters as input.

[0087] During real-time monitoring, parameters such as current and voltage are normalized.

[0088] Predicting mode coefficients using neural networks Reconstructing the battery surface temperature field T pred .

[0089] Nearest neighbor interpolation is performed on hotspot areas to locate temperature anomalies.

[0090] By utilizing a large amount of historical data to build models, the changing patterns of temperature fields under different operating conditions can be captured more accurately, thereby improving the prediction accuracy of future temperature field distribution. The dynamic temperature field reconstruction unit can complete the prediction of the temperature field in a short time and quickly locate abnormal areas, shortening the response time.

[0091] The multi-objective fuzzy controller generates current control commands based on temperature error, temperature rise rate, and energy recovery priority; it dynamically adjusts the duty cycle of the H-bridge drive current according to the temperature error to achieve precise temperature control. The table below shows the input variables and their corresponding fuzzy set definitions.

[0092] Input variables Fuzzy set definition Temperature error (e) {Negative, Zero, Low Positive, Mid Positive, High Positive} Temperature rise rate (ec) {Negative, Zero, Small Positive Amount, Large Positive Amount} Energy recovery priority (P) {Low, Medium, High}

[0093] The output rules of the multi-objective fuzzy controller are as follows:

[0094] When the temperature error is positive and the temperature rise rate exceeds the set threshold, an instruction to increase the drive current is output and a forced cooling mode is triggered; that is, when the temperature error is "positive high" and the temperature rise rate is "positive large", an instruction to increase the drive current is output and a forced cooling mode is triggered; the duty cycle of the H-bridge drive current is adjusted based on different temperature errors, temperature rise rates and energy recovery priorities to achieve precise temperature control.

[0095] The distributed collaborative decision-making unit optimizes the power allocation and mode switching of each thermoelectric module; the distributed collaborative decision-making unit includes the following:

[0096] Objective function: To minimize the total power consumption of thermoelectric cooling and maximize the uniformity of battery temperature;

[0097] Each thermoelectric module unit exchanges temperature / mode status through local communication; each TEM unit communicates locally with other adjacent TEM units wirelessly or via wired means to share its own temperature information and current operating mode (cooling, heating, or power generation). For example, in an electric vehicle battery pack, each TEM unit sends updated data to its four nearest neighbor TEM units once per second.

[0098] The optimal power allocation is determined iteratively. All TEM units randomly select an operating mode and set an initial power. Using gradient descent or other optimization algorithms, the operating mode and power settings of each TEM unit are gradually adjusted according to the objective function to find the global optimum. For example, if the system detects that the temperature in a certain area is too high, the corresponding TEM unit will be allocated more power to enter the cooling mode, while TEM units in other non-hotspot areas may be adjusted to power generation mode to recover energy.

[0099] By optimizing power distribution, it is possible to minimize total energy consumption while meeting temperature control requirements. For example, high-power cooling mode can be activated only when necessary, while low-power or power generation mode can be used to maintain temperature stability under other circumstances, thereby reducing overall energy consumption. Maximizing temperature uniformity helps avoid extreme conditions of overheating or overcooling inside the battery, extending battery life and improving its performance stability.

[0100] The energy recovery circuit connects to the thermoelectric module in power generation mode and supplies power to the energy storage unit; the voltage generated by the TEM in power generation mode is regulated by the DC-DC converter in the energy recovery circuit; for example, if a TEM generates a voltage of 5V in power generation mode, and the operating voltage of the energy storage unit is 12V, the DC-DC boost converter will boost the voltage to 12V and store it in the energy storage unit.

[0101] The status of the energy storage unit (such as the power level) is monitored in real time and fed back to the intelligent control module. If the energy storage unit is close to full charge, the system may reduce the power generation of TEM in certain areas or completely shut down the power generation mode to avoid the risk of overcharging.

[0102] In summary, the dynamic battery thermal management system of this embodiment includes: a thermoelectric array, which consists of multiple independently controlled thermoelectric modules; the thermoelectric modules dynamically switch between cooling, heating, or power generation modes based on a drive circuit; a phase change material heat dissipation layer, which is disposed at the hot end of the thermoelectric array and monitors the melting ratio of the phase change material based on a phase change state monitoring device; a temperature sensor array, distributed on the battery surface to collect temperature distribution data; and an intelligent control module, connecting the temperature sensor array and the thermoelectric array; including: a dynamic temperature field reconstruction unit for identifying hot spot areas on the battery surface and predicting the temperature rise trend of adjacent areas; a multi-objective fuzzy controller that generates current regulation commands based on temperature error, temperature rise rate, and energy recovery priority; a distributed collaborative decision-making unit for optimizing the power allocation and mode switching of each thermoelectric module; and an energy recovery circuit that connects the thermoelectric modules in power generation mode and transmits power to the energy storage unit. Through the dynamic temperature field reconstruction algorithm and the multi-objective fuzzy controller, the system can quickly respond to temperature changes and accurately predict the development trend of hot spots.

[0103] This application also provides a dynamic battery thermal management control method, applied to the dynamic battery thermal management system described in the above embodiments; Figure 3 As shown, Figure 3 A flowchart illustrating a dynamic battery thermal management control method provided in an embodiment of this application includes:

[0104] Step S1: Monitor the surface temperature distribution and temperature rise rate of the battery in real time;

[0105] Step S2: When a hotspot area is detected, switch the corresponding thermoelectric module to cooling mode;

[0106] Step S3: Dynamically adjust the drive current of the thermoelectric module based on fuzzy control rules;

[0107] Step S4: Adjust the operating current based on the melting ratio of the phase change material;

[0108] Step S5: Switch the thermoelectric modules in non-hotspot areas to power generation mode for energy recovery.

[0109] The dynamic battery thermal management method also includes: predicting the over-temperature time window of the area adjacent to the hot spot, and setting the corresponding thermoelectric module to standby mode before the over-temperature occurs.

[0110] Regarding the above embodiments, this application provides a computer device; please refer to [link / reference]. Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device according to an embodiment of the present application. The computer device includes a memory and a processor, wherein the memory and the processor are coupled to each other. The memory stores program data, and the processor executes the program data to implement the steps of any embodiment of the dynamic battery thermal management control method described above.

[0111] In this embodiment, the processor may also be referred to as a CPU (Central Processing Unit). The processor may be an integrated circuit chip with signal processing capabilities. The processor may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0112] The methods described in the above embodiments can be implemented as computer programs; therefore, this application proposes a computer-readable storage medium. Please refer to [link to relevant documentation]. Figure 5 , Figure 5 This is a schematic diagram of the structure of an embodiment of the computer-readable storage medium of this application. The computer-readable storage medium stores program data that can be executed by a processor to implement the steps of any embodiment of the dynamic battery thermal management control method described above.

[0113] In this embodiment, the computer-readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or a medium that can store program data. Alternatively, it can be a server that stores the program data. The server can send the stored program data to other devices for execution, or it can run the stored program data itself.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0115] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0116] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0117] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A dynamic battery thermal management system, characterized in that, include: A thermoelectric array, which is composed of multiple independently controlled thermoelectric modules; the thermoelectric modules dynamically switch between cooling, heating or power generation modes based on a drive circuit. A phase change material heat dissipation layer is disposed at the hot end of the thermoelectric array, and the melting ratio of the phase change material is monitored based on a phase change state monitoring device. A temperature sensor array is distributed on the battery surface to collect temperature distribution data; The intelligent control module connects the temperature sensor array and the thermoelectric array; it includes: The dynamic temperature field reconstruction unit is used to identify hot spots on the battery surface and predict the temperature rise trend of adjacent areas. A multi-objective fuzzy controller generates current control commands based on temperature error, temperature rise rate, and energy recovery priority. A distributed collaborative decision-making unit optimizes the power allocation and mode switching of each thermoelectric module; The energy recovery circuit connects to the thermoelectric module in power generation mode and transmits power to the energy storage unit.

2. The dynamic battery thermal management system according to claim 1, characterized in that, The dynamic temperature field reconstruction unit is configured as follows: Based on temperature distribution data, areas with temperatures higher than the average temperature plus a temperature threshold, and with a temperature rise rate higher than the temperature rise rate threshold, are designated as hotspot areas. Predict the temperature rise trend in adjacent areas.

3. The dynamic battery thermal management system according to claim 2, characterized in that, The dynamic temperature field reconstruction unit achieves temperature field prediction through the following steps: Temperature distribution characteristics were extracted based on historical temperature field databases; Establish a mapping model between operating parameters and temperature field distribution; Locate areas with abnormal temperatures.

4. The dynamic battery thermal management system according to claim 1, characterized in that, The output rule of the multi-objective fuzzy controller is: When the temperature error is positive and the temperature rise rate exceeds the set threshold, an instruction to increase the drive current is output and the forced cooling mode is triggered.

5. The dynamic battery thermal management system according to claim 1, characterized in that, The objective function of the distributed collaborative decision-making unit aims to minimize the total power consumption of thermoelectric cooling and maximize the uniformity of battery temperature.

6. The dynamic battery thermal management system according to claim 1, characterized in that, The intelligent control module dynamically adjusts the operating current of the thermoelectric module based on the melting ratio feedback of the phase change material.

7. A dynamic battery thermal management control method, applied to the system described in any one of claims 1-6, characterized in that, Including the following steps: Real-time monitoring of battery surface temperature distribution and temperature rise rate; When a hotspot area is detected, the corresponding thermoelectric module is switched to cooling mode. The driving current of the thermoelectric module is dynamically adjusted based on fuzzy control rules. The operating current is adjusted based on the feedback of the phase change material melting ratio; Switch the thermoelectric modules in non-hotspot areas to power generation mode for energy recovery.

8. The dynamic battery thermal management method according to claim 7, characterized in that, Also includes: Predict the overheating time window of the area adjacent to the hot spot, and set the corresponding thermoelectric module to standby mode before the overheating occurs.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor coupled to each other, the processor being configured to execute program instructions stored in the memory to implement the dynamic battery thermal management method as described in claim 7 or claim 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program data that can be executed by a processor to implement the dynamic battery thermal management method as described in claim 7 or claim 8.

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