A battery temperature monitoring method and device, electronic equipment and storage medium

Abstract

The application provides a battery temperature monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of temperature monitoring. The method comprises the following steps: obtaining a temperature prediction curve through a battery temperature prediction model; collecting a first temperature measured value through a target temperature sensor, and calculating a temperature difference value between the temperature prediction value; setting the target temperature sensor as a standard sampling period mode or a variable sampling period mode according to a preset condition; when the target temperature sensor is set as the variable sampling period mode, calculating a calculation temperature change curve of an adjacent temperature sensor; obtaining a second temperature measured value of each adjacent temperature sensor to form a measured temperature change curve; judging whether the target temperature sensor is faulty according to the calculation temperature change curve and the measured temperature change curve; when the target temperature sensor is not faulty, calculating a temperature fluctuation rate, and adjusting a sampling frequency according to the temperature fluctuation rate and the temperature difference value. The application solves the technical problem of how to reduce the energy consumption of the sensor while ensuring the reliability of temperature monitoring.

Description

Technical Field

[0001] This application relates to the field of temperature monitoring technology, specifically to a battery temperature monitoring method, device, electronic device, and storage medium. Background Technology

[0002] In modern battery systems such as electric vehicles, temperature monitoring is crucial for operational safety. To achieve comprehensive coverage, the system needs to deploy a large number of sensors, but this introduces an inherent trade-off between reliability and energy consumption: while high-frequency sampling ensures reliability, it consumes a significant amount of energy. Therefore, effectively reducing energy consumption while ensuring monitoring reliability is a core challenge in current battery management system design.

[0003] Therefore, existing technologies generally employ a dual-mode sampling scheme. In this scheme, the sensor typically operates at a fixed low sampling frequency to save energy. Simultaneously, a static absolute temperature threshold is preset within the system. Once a sensor measures a temperature exceeding this value, it is triggered to switch to a fixed high-frequency sampling mode for intensive monitoring.

[0004] However, this approach, which relies entirely on static absolute thresholds, has a fundamental flaw. Early battery failures are not characterized by absolute high temperatures, but rather by dynamic temperature anomalies, such as abnormal rates of temperature rise or abnormal temperature differences with adjacent cells. Existing solutions, with their fixed low-frequency sampling patterns, cannot effectively capture these dynamic characteristics due to inherent data sparsity. They only react after the fault worsens and the temperature rises significantly, resulting in a severe delay in warning timing. This mechanism lacks the ability to conduct "process-oriented" risk assessments, conflating normal operating conditions with potential early failure states and uniformly employing a low-power "ignore" strategy until the fault deteriorates to a level sufficient to trigger a high-temperature alarm. Consequently, the system makes an ineffective static compromise between safety and energy consumption, rather than a true dynamic balance. Summary of the Invention

[0005] To address the technical challenge of reducing sensor power consumption while ensuring reliable temperature monitoring, this application provides a battery temperature monitoring method, apparatus, electronic device, and storage medium.

[0006] In a first aspect, this application provides a battery temperature monitoring method, including:

[0007] The electrical operating parameters of the target battery are acquired in real time and input into the preset battery temperature prediction model to obtain the temperature prediction curve.

[0008] The first measured temperature value is collected by the target temperature sensor, and the temperature difference between the first measured temperature value and the temperature prediction value is calculated. The temperature prediction value is the temperature value on the temperature prediction curve that is the same as the first measured temperature value at that time.

[0009] When the preset conditions are met, the target temperature sensor is configured to a standard sampling period mode; when the preset conditions are not met, the target temperature sensor is configured to a variable sampling period mode. The preset conditions are that the number of times the temperature difference is less than the preset difference within a first preset time period is greater than a first preset number threshold.

[0010] When the target temperature sensor is in the variable sampling period mode, perform the following operations:

[0011] Based on the distance between the target temperature sensor and several adjacent temperature sensors and the first measured temperature value, heat conduction calculation is performed to obtain the calculated temperature change curve of each of the adjacent temperature sensors within a second preset time period. The adjacent temperature sensors are temperature sensors located within a preset range of the target temperature sensor.

[0012] Obtain the second measured temperature value of each of the adjacent temperature sensors within the second preset time period, and obtain the measured temperature change curve of each of the adjacent temperature sensors based on the second measured temperature value;

[0013] Determine whether the target temperature sensor is faulty based on the calculated temperature change curve and the measured temperature change curve.

[0014] When the target temperature sensor is fault-free, the temperature fluctuation rate is calculated based on several first temperature measurement values, and the sampling frequency is adjusted in real time based on the temperature fluctuation rate and the temperature difference. The temperature fluctuation rate represents the degree of drastic temperature change.

[0015] By adopting the above technical solution, this method first establishes a battery temperature prediction model to obtain a theoretical temperature prediction curve, which serves as a judgment benchmark. Then, by calculating the temperature difference between the measured and predicted values, the core monitoring indicator is transformed from a single absolute temperature to a "deviation degree" that better reflects abnormal states. Based on this, the solution can intelligently switch between a low-power standard mode and a high-alertness variable-cycle mode, achieving an initial balance between energy consumption and reliability. Crucially, upon entering the variable-cycle mode, the solution does not immediately and blindly increase the sampling frequency. Instead, it first performs a "cross-validation" fault diagnosis step: it uses the heat of the target sensor to calculate the theoretical temperature of surrounding sensors and compares it with the actual readings of the surrounding sensors. This design greatly improves the reliability of monitoring because it effectively eliminates false alarms caused by the target sensor's own malfunction. Only after confirming the reliability of the sensor data does the solution dynamically adjust the sampling frequency based on the temperature fluctuation rate and temperature difference. This closed-loop logic of diagnosis first, then adjustment ensures that the investment of monitoring resources is accurate and effective, thereby achieving refined management and optimization of sensor energy consumption while ensuring extremely high monitoring reliability.

[0016] Optionally, the step of determining whether the target temperature sensor is faulty based on the calculated temperature change curve and the measured temperature change curve specifically includes:

[0017] For each adjacent temperature sensor, the calculated temperature change curve is compared with the measured temperature change curve, and the curve deviation is calculated. The curve deviation represents the overall difference between the calculated temperature change curve and the measured temperature change curve within the second preset time period.

[0018] Based on the distance between each of the adjacent temperature sensors and the target temperature sensor, a first distance weight value is assigned to each curve deviation, and the weighted average of all the curve deviations is calculated to obtain a comprehensive weighted deviation, wherein the first distance weight value is inversely proportional to the distance between the adjacent temperature sensors and the target temperature sensor;

[0019] When the overall weighted deviation is greater than or equal to a preset deviation threshold, it is determined that the target temperature sensor is faulty.

[0020] When the overall weighted deviation is less than the preset deviation threshold, the target temperature sensor is determined to be fault-free.

[0021] By adopting the above technical solution, this method provides a specific and reliable quantitative standard for sensor fault diagnosis, significantly improving diagnostic accuracy. It simplifies the complex problem of curve comparison into numerical comparison by calculating the "curve deviation degree." More importantly, the solution introduces a "first distance weight value" closely integrated with physical reality; that is, the closer an adjacent temperature sensor is to the target sensor, the higher its data weight is in the comprehensive judgment. By calculating the "comprehensive weighted deviation degree" of the deviation degrees of all adjacent temperature sensors, this solution constructs a multi-point consensus decision-making mechanism based on physical laws. This effectively avoids misjudgments caused by jumps in data from a single adjacent temperature sensor, ensuring that the fault diagnosis result is a comprehensive evaluation of the local sensor network information, thus enabling a high degree of confidence in determining whether the target sensor is faulty.

[0022] Optionally, the step of calculating the temperature fluctuation rate based on several first measured temperature values ​​and adjusting the sampling frequency in real time based on the temperature fluctuation rate and the temperature difference when the target temperature sensor is fault-free specifically includes:

[0023] Multiple measured values ​​of the first temperature collected within the first adjustment period are obtained, and the average value and standard deviation are calculated. The standard deviation is then divided by the average value to obtain the temperature fluctuation rate.

[0024] The average value of all the temperature differences within the first adjustment period is obtained as the target temperature difference.

[0025] The temperature fluctuation rate is multiplied by a first conversion coefficient to obtain a first frequency adjustment value, and the target temperature difference is multiplied by a second conversion coefficient to obtain a second frequency adjustment value. The first conversion coefficient represents the proportional relationship between the temperature fluctuation rate and the first frequency adjustment value, and the second conversion coefficient represents the proportional relationship between the target temperature difference and the second frequency adjustment value.

[0026] The target sampling frequency is obtained by adding the first frequency adjustment value, the second frequency adjustment value, and the base sampling frequency.

[0027] The target sampling frequency is used as the sampling frequency of the target temperature sensor during the second adjustment period.

[0028] By employing the aforementioned technical solution, this method provides a refined, two-dimensional driving strategy for the dynamic adjustment of sampling frequency. The strategy's advancement lies in its simultaneous consideration of two indicators with different physical meanings: "temperature fluctuation rate" reflects the drastic nature of temperature changes, representing the immediate risk of thermal dynamics; while "target temperature difference" measures the degree of deviation from the theoretical model, representing the abnormal risk of the system state. The scheme transforms this risk information from both dimensions into specific frequency adjustment amounts through independent conversion coefficients, which are then superimposed on the base frequency. This multi-dimensional input mechanism enables frequency adjustment to respond accurately to different types of potential risks, ensuring that limited monitoring energy consumption is always efficiently allocated to the most needed moments, achieving a highly dynamic balance between safety redundancy and energy conservation.

[0029] Optionally, after the step of using the target sampling frequency as the sampling frequency of the target temperature sensor within the second adjustment period, the method further includes:

[0030] The duration of the second adjustment period is adjusted according to the target sampling frequency, and the duration is inversely proportional to the target sampling frequency.

[0031] By adopting the above technical solution, this method, based on frequency adjustment, introduces a method to adjust the evaluation cycle duration in reverse according to the target sampling frequency, constructing a higher-dimensional adaptive closed loop. This design enables the monitoring and evaluation cycle of the entire monitoring system to self-adjust according to the internal risk status. When the system determines a high risk and calculates a higher sampling frequency, it will simultaneously shorten the cycle of the next evaluation, making sampling frequency adjustments more frequent, thus enabling real-time tracking of rapidly changing states. Conversely, when the system is stable, the evaluation cycle is automatically extended, reducing unnecessary computational load. This allows for maximum efficiency in responding to risks at critical moments, while operating with minimal overhead during safe periods, achieving deep optimization of system resource efficiency.

[0032] Optionally, after the step of configuring the target temperature sensor to a standard sampling period mode when preset conditions are met, the method further includes:

[0033] Obtain the charge level of the target temperature sensor;

[0034] The sampling frequency is adjusted according to the preset mapping relationship between power and sampling frequency.

[0035] By adopting the above technical solution, this method introduces a refined energy management strategy for the temperature sensor itself, which is particularly suitable for wireless sensors with independent power supplies. This solution establishes an adaptive adjustment mechanism that directly correlates the sensor's sampling frequency with its own battery power. Specifically, when the monitored battery cell is in a stable, low-risk "standard sampling cycle mode," the system further acquires the sensor's own battery status. If the sensor's battery power is low, its sampling frequency is actively reduced according to a preset mapping relationship. By intelligently reducing energy consumption during non-critical periods, the effective working life of the sensor is greatly extended, the frequency and cost of maintenance or replacement due to battery depletion are reduced, and the long-term operational reliability and economy of the entire monitoring system are improved.

[0036] Optionally, after the step of determining whether the target temperature sensor is faulty based on the calculated temperature change curve and the measured temperature change curve, the method further includes:

[0037] When the target temperature sensor malfunctions, different second distance weight values ​​are assigned to different measured values ​​of the second temperature based on the distance between each of the adjacent temperature sensors and the target temperature sensor. The simulated temperature value of the target temperature sensor is calculated. The second distance weight value is inversely proportional to the distance between the adjacent temperature sensors and the target temperature sensor. The simulated temperature value is used to replace the first measured temperature value.

[0038] By adopting the above technical solution, this method provides an effective fault-tolerance mechanism for the monitoring system, significantly improving system stability and data integrity. When a target sensor is determined to be faulty, the system can avoid partial data loss due to single-point failure. Using data from functional sensors adjacent to the fault point, and weighting each data source according to the reciprocal of distance, a high-confidence temperature simulation value is generated. This temperature simulation value is used to replace the output of the failed sensor, compensating for data stream interruption. Even in the event of local sensor hardware failure, the system can still maintain comprehensive and continuous monitoring of the battery pack temperature field, ensuring that thermal safety management functions are not interrupted, thereby greatly enhancing the reliability and safety of the entire battery management system.

[0039] Optionally, the method further includes:

[0040] When the number of times the first measured temperature value exceeds a preset temperature threshold within a third preset time period is greater than or equal to a second preset number threshold, the sampling frequency of the target temperature sensor is adjusted to a preset sampling frequency.

[0041] By adopting the above technical solution, this method adds an independent monitoring logic based on an absolute temperature threshold, in addition to the model-based intelligent monitoring logic, forming a complementary two-layer safety monitoring system. This independent logic, by counting the number of times limits are exceeded within a preset time period, can effectively avoid misjudgments caused by instantaneous noise signals, thereby accurately identifying persistent high-temperature anomalies. Since its triggering does not rely on complex model calculations, once preset conditions are met, the sampling frequency can be forcibly adjusted to a high-frequency state to achieve rapid response to clear and potentially high-risk situations. This design combines the intelligent model's deep insight into early, hidden faults with the threshold mechanism's rapid intervention capability for emergency, overt thermal events, thereby comprehensively improving the overall safety and responsiveness of the monitoring scheme.

[0042] In a second aspect of this application, a battery temperature monitoring device is also provided, comprising:

[0043] The working parameter acquisition module is used to acquire the electrical working parameters of the target battery in real time and input them into the preset battery temperature prediction model to obtain the temperature prediction curve.

[0044] The temperature difference calculation module is used to collect the first measured temperature value through the target temperature sensor and calculate the temperature difference between the first measured temperature value and the temperature prediction value.

[0045] The working mode configuration module is used to configure the target temperature sensor to a standard sampling period mode when preset conditions are met, and to configure the target temperature sensor to a variable sampling period mode when preset conditions are not met.

[0046] The heat conduction calculation module is used to perform heat conduction calculation based on the distance between the target temperature sensor and several adjacent temperature sensors and the first measured temperature value, and to obtain the calculated temperature change curve of each of the adjacent temperature sensors within a second preset time period.

[0047] The temperature curve acquisition module is used to acquire the second measured temperature value of each of the adjacent temperature sensors within the second preset time period, and to obtain the measured temperature change curve of each of the adjacent temperature sensors based on the second measured temperature value.

[0048] The fault diagnosis module is used to determine whether the target temperature sensor is faulty based on the calculated temperature change curve and the measured temperature change curve.

[0049] The sampling frequency adjustment module is used to calculate the temperature fluctuation rate based on several measured values ​​of the first temperature when the target temperature sensor is fault-free, and to adjust the sampling frequency in real time based on the temperature fluctuation rate and the temperature difference.

[0050] In a third aspect of this application, an electronic device is also provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the program to implement the method steps of any of the above claims.

[0051] In a fourth aspect of this application, a computer-readable storage medium is also provided, which stores instructions that, when executed, perform the method steps of any of the above claims. Attached Figure Description

[0052] Figure 1 This is a flowchart of a battery temperature monitoring method provided in an embodiment of this application;

[0053] Figure 2 This is a structural block diagram of a battery temperature monitoring device provided in an embodiment of this application;

[0054] Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application.

[0055] Explanation of reference numerals in the attached diagram: 201-Working parameter acquisition module; 202-Temperature difference calculation module; 203-Working mode configuration module; 204-Heat conduction calculation module; 205-Temperature curve acquisition module; 206-Fault diagnosis module; 207-Sampling frequency adjustment module; 300-Electronic equipment; 301-Processor; 302-Communication bus; 303-User interface; 304-Network interface; 305-Memory. Detailed Implementation

[0056] This application provides a battery temperature monitoring method, referring to... Figure 1 , Figure 1 This is a flowchart of a battery temperature monitoring method provided in an embodiment of this application. In this method, temperature sensors are set on the surface of the battery cell according to different rules based on different cell models. The method includes:

[0057] Step S101: Real-time acquisition of the electrical operating parameters of the target battery and input into the preset battery temperature prediction model to obtain the temperature prediction curve;

[0058] Here, "target battery" refers to a specific temperature measurement point within a single cell, battery module, or battery pack designated by the monitoring system. "Electrical operating parameters" represent one or more sets of electrical physical quantities that characterize the current operating state of the battery; typical values ​​include, but are not limited to, real-time current, terminal voltage, state of charge (SOC), and DC internal resistance. "Battery temperature prediction model" refers to a pre-established mathematical algorithm or logical model embedded in the controller, which describes the intrinsic physical relationship between electrical operating parameters and battery thermal behavior. "Temperature prediction curve" represents the theoretical trajectory of the target battery's temperature change over time under current operating conditions, calculated by the model; this curve forms the benchmark for subsequent anomaly detection.

[0059] This step is continuously executed during battery system operation. Its function is to generate a theoretical temperature change benchmark based on the battery's real-time electrical state. This benchmark will serve as a reference standard for measuring whether the actual temperature is abnormal in subsequent steps. Specifically, the battery management unit (BMS) or dedicated controller within the system continuously collects real-time data from sensors such as current and voltage at a set frequency, forming the input of "electrical operating parameters." This input is passed to the deployed "battery temperature prediction model." The model, based on its internal established calculation rules (such as those based on thermodynamic formulas or data fitting relationships), calculates the dynamic balance between heat generation and dissipation of the battery under the current electrical excitation and outputs it in the form of a time series, forming a "temperature prediction curve." This curve provides the expected change in battery temperature over a future period starting from the current moment.

[0060] Step S102: Collect the first measured temperature value through the target temperature sensor, and calculate the temperature difference between the first measured temperature value and the predicted temperature value;

[0061] Here, "target temperature sensor" refers to the temperature sensor whose sampling frequency needs to be adjusted. "First measured temperature value" represents the temperature reading measured by the target sensor under any circumstances. "Temperature prediction value" refers to the corresponding theoretical temperature value extracted from the "temperature prediction curve" generated in step S101 at the same time point as the "first measured temperature value". "Temperature difference" refers to the algebraic difference between the "first measured temperature value" and the "temperature prediction value" at the same moment, which quantifies the degree of deviation between the actual battery temperature and the theoretically predicted temperature.

[0062] This step is triggered after each data acquisition by the temperature sensor. Its function is to quantitatively compare the model's prediction with the sensor's actual measurement, generating a difference signal characterizing the degree of deviation between the two. This signal is the direct input for subsequent decision-making. Specifically, after the "target temperature sensor" completes a temperature measurement according to its current sampling period, the system records the "first measured temperature value" and the corresponding sampling timestamp. Simultaneously, the system searches for or interpolates the "temperature prediction value" matching that timestamp from the data sequence of the "temperature prediction curve." Then, the processing unit performs a subtraction operation: Temperature difference = First measured temperature value - Temperature prediction value. The value, sign, and trend of this "temperature difference" constitute the core basis for determining whether the battery exhibits abnormal thermal behavior.

[0063] Step S103: When the preset conditions are met, the target temperature sensor is configured to a standard sampling period mode; when the preset conditions are not met, the target temperature sensor is configured to a variable sampling period mode.

[0064] Specifically, the "preset condition" refers to the number of times, within a "first preset duration," the "temperature difference" calculated in step S102 is less than the "preset difference," and greater than a "first preset frequency threshold." Here, the "first preset duration" represents a sliding time window used for statistical evaluation, such as 300 seconds. The "preset difference" represents a pre-set tolerance threshold used to define whether a single deviation is normal, such as 2°C. The "first preset frequency threshold" represents the minimum number of sampling points that the system must reach to determine as normal within this time window; it is an integer value. These conditions together constitute a statistically based stability judgment criterion.

[0065] This step is the core of the monitoring strategy's decision-making and execution. Its function is to dynamically adjust the allocation of sensor sampling resources based on a statistical assessment of the battery's thermal behavior stability over a period of time. This aims to improve the robustness of the decision-making and avoid pattern misjudgment caused by single-point, instantaneous measurement noise or disturbances. Specifically, the control system maintains a data buffer with a duration of "first preset duration" to store a series of recent "temperature difference" calculation results. In each decision cycle, the system iterates through the data points in this buffer and counts the number of data points whose absolute value is less than the "preset difference". Subsequently, the count is compared with a "first preset count threshold". If the count is greater than the threshold, the "preset condition" is met, and the system determines that the battery has consistently exhibited stable and expected thermal behavior in the near future. Therefore, the "target temperature sensor" is configured to or maintained in "standard sampling cycle mode" to save energy. Conversely, if the number of statistical counts is not greater than the threshold, the "preset condition" is not met, indicating that the frequency of abnormal deviations has been too high recently, and the battery may be in an unstable or faulty state. The system then instructs the sensor to switch to "variable sampling period mode" to perform high-density data acquisition and monitoring.

[0066] In some embodiments, the statistically-based mode switching logic in this step can be implemented in a variety of ways:

[0067] Optionally, a sliding window counting method based on a fixed number of samples can be used. First, parameters are set, such as a first preset duration corresponding to 10 sampling points (e.g., a standard period of 30 seconds, duration of 300 seconds), a preset difference value of 2.0℃, and a first preset threshold of 8 times. Second, the system maintains a first-in-first-out (FIFO) queue to store the absolute values ​​of the latest 10 "temperature differences". Third, whenever a new difference is calculated, it is enqueued, and the oldest data is dequeued. Then, the number of values ​​in the queue less than 2.0℃ is counted. Finally, if this number is greater than 8, the system is configured for standard mode; otherwise, it is configured for variable sampling mode.

[0068] It is understandable that other methods can be used to achieve the judgment and mode configuration of the preset conditions in this step, such as introducing weighted counting, that is, the closer the normal data point, the higher the weight, in order to increase the sensitivity of the decision. This is not limited here.

[0069] Step S104: Perform heat conduction calculation based on the distance between the target temperature sensor and several adjacent temperature sensors and the first measured temperature value to obtain the calculated temperature change curve of each adjacent temperature sensor within a second preset time period.

[0070] Here, "adjacent temperature sensors" refers to other temperature sensing units physically adjacent to the "target temperature sensor" within the battery system. "Distance" indicates the spatial geometric relationship between the sensors. "Heat conduction calculation" considers not only heat conduction within the medium but also the internal heat sources (heat generation) prevalent during battery operation and heat dissipation through the surface to the surrounding environment. This analysis, based on the principles of heat transfer and electrochemical thermodynamics, is used to estimate the temperature change at the location of "adjacent temperature sensors" under the influence of various factors. "Second preset duration" defines the prediction time span for this analysis. The "calculated temperature change curve" is the output of this step; it represents the physically consistent temperature evolution trajectory of each "adjacent temperature sensor," assuming accurate readings from the "target temperature sensor" and considering conduction, internal heat generation, and external heat dissipation.

[0071] This step is performed when a health status diagnosis of the "target temperature sensor" is required. Its function is to provide a reliable theoretical benchmark for subsequent cross-validation. This process no longer treats the "target temperature sensor" as a single, isolated heat source, but rather as a known and accurate temperature anchor point, embedded in a model describing the complete thermal behavior of a localized area of ​​the battery. Specifically, when the diagnostic procedure starts, the system not only acquires the "first measured temperature value" of the "target temperature sensor," but also simultaneously acquires the battery's real-time electrical operating parameters (such as current I) and ambient temperature during the diagnostic period (i.e., within the "second preset duration"). The system calls a built-in thermal balance model, which presets the "distance" between sensors, the battery's internal equivalent thermal properties (thermal conductivity, specific heat capacity, density), and surface heat dissipation coefficient. The model simulates the complete dynamic process of heat generation, transfer, and dissipation within a localized area of ​​the battery under real charge-discharge conditions, constrained by the "first measured temperature value," by solving partial differential equations containing heat conduction, internal heat source, and heat dissipation terms. The output of this simulation is the "calculated temperature change curve" for each "adjacent temperature sensor" location.

[0072] To obtain the calculated temperature change curves of the "adjacent temperature sensors" within the "second preset time period," the system will perform a logically rigorous numerical simulation based on the finite difference method. This process predicts the temperature evolution at a specific location by accurately simulating the generation, transfer, and dissipation of heat inside the battery.

[0073] Phase 1: Construction and parameterization of the simulation environment;

[0074] The computation begins with the mathematical abstraction and discretization of the physical problem. First, the physical path from the "target temperature sensor" to the "adjacent temperature sensor" is discretized spatially into a one-dimensional computational domain consisting of N+1 equidistant grid nodes. Simultaneously, the "second preset duration" is discretized into M equal-length time steps Δt.

[0075] Subsequently, the system assigns a set of pre-calibrated key physical parameters to the computational domain, which will be directly used for subsequent calculations:

[0076] Equivalent thermal conductivity (k): This parameter characterizes the ability of heat to be conducted in a medium. In subsequent iterative calculations, the value of k will directly determine the intensity of heat exchange caused by temperature differences between nodes.

[0077] Equivalent density (ρ) and equivalent specific heat capacity (c): their product (ρc, i.e., volumetric heat capacity) represents the heat storage capacity per unit volume of medium, and can be regarded as a kind of "thermal inertia". In calculations, the amount of temperature change caused by all heat (whether it is transferred in, transferred out, or generated internally) will be inversely proportional to ρc. That is, the larger ρc is, the more gradual the temperature change.

[0078] Equivalent internal resistance (R_internal): This parameter is the root cause of Joule heat generation in the battery. In the calculation, the system uses R_internal and the measured real-time current I(t) to accurately calculate the heat generated by each node in each time step.

[0079] Convective heat transfer coefficient (h): This parameter quantifies the efficiency of heat exchange between the battery surface and the environment. In calculations, h is combined with the temperature difference between the node and the environment to determine the amount of heat lost from each node to the outside.

[0080] Second stage: Precise application of boundary value conditions;

[0081] The system sets the initial state for the simulation and the boundary rules throughout the simulation. Initially (t=0), the temperature of each node in the computational domain is determined by linear interpolation based on the measured values ​​of the "target" and "adjacent" sensors. Throughout the simulation (t>0), the temperature at the starting point (node ​​0) is forcibly set to be constant and equal to the "first measured temperature value," serving as an unshakeable calculation benchmark. The boundary at the ending point (node ​​N) is set to adiabatic, meaning no heat flows out.

[0082] Phase 3: Execution of core iterative computation;

[0083] This is the core of the entire simulation; all preset parameters work together according to the laws of physics at this stage. The system updates the temperature of each internal grid node (grid node 1 to N-1) within the computational domain in a time-step manner. At any time step j, the new temperature T of grid node i in the next time step j+1 is... (j+1) Due to its current temperature T j It is obtained by algebraically superimposing three independent temperature changes driven by physical processes:

[0084] T (j+1) =Tj +ΔT 传导 +ΔT 产热 -ΔT 散热 :

[0085] The derivation of these three temperature changes strictly follows the principles of physics and precisely reflects the role of the parameters defined in the first stage:

[0086] Conductive temperature change (ΔT) 传导 Derivation of )

[0087] This calculation follows Fourier's law of heat conduction. This law states that heat flux density (the amount of heat passing through a unit area per unit time) is proportional to the temperature gradient. In the discrete computational domain, the system evaluates the temperature gradient between grid node i and its two adjacent grid nodes (i-1 and i+1).

[0088] Quantification of the physical process: The equivalent thermal conductivity (k) serves as a proportionality constant, determining the intensity of heat conduction under a given temperature gradient. Based on this, the system calculates the total heat flowing from the hotter grid node to node i and the total heat flowing from grid node i to the colder grid node within a time step Δt. The algebraic sum of these two heat amounts represents the net increase in heat energy gained by grid node i due to conduction.

[0089] Conversion of heat energy to temperature: According to the fundamental thermodynamic relationship, the temperature change of an object is equal to the net heat energy it absorbs divided by its heat capacity. Therefore, the net heat energy increment calculated in the previous step is divided by the heat capacity of the control volume represented by the node (i.e., volumetric heat capacity (ρc) × V_cell) to obtain the final temperature change ΔT contributed by heat conduction. 传导 The physical significance of this step is that a medium with a larger heat capacity (i.e., a larger ρc value) will experience a more gradual temperature change when absorbing the same amount of heat.

[0090] Here, V_cell refers to the actual physical volume represented by the control volume. Each mesh node represents the physical properties of a small region surrounding it. This small region is called the "control volume" in computational fluid dynamics and heat transfer. All calculations (such as heat transfer, heat loss, and internal generation) are performed on these individual "control volumes".

[0091] Heat production temperature change (ΔT) 产热 Derivation of )

[0092] This calculation follows Joule's law. This law states that the heat generated by an electric current passing through a conductor is equal to the square of the current multiplied by the resistance.

[0093] Quantization of physical processes: The system acquires the real-time current I at this time step. j And using the preset equivalent internal resistance, the heat generation power (heat generated per unit time) of the adjacent temperature sensor location represented by the grid node is calculated.

[0094] The conversion of heat energy to temperature: Multiply the generated heat power by the time step Δt to obtain the total heat energy generated within that time step. Similar to the conduction term, this heat energy value also needs to be multiplied by the heat capacity of the control volume (ρc×V). cell Divide by ) to determine the temperature rise ΔT contributed by internal heat generation. 产热 This ensures that the heat generation effect is correctly translated into an actual increase in material temperature.

[0095] Heat dissipation temperature change (ΔT) 散热 Derivation of )

[0096] This calculation follows Newton's law of cooling. This law states that the heat dissipation of an object into its environment is proportional to the temperature difference between its surface and the environment.

[0097] Quantification of physical processes: The system first calculates the current temperature T of the grid nodes. j With ambient temperature T amb The difference in temperature is the driving force for heat dissipation. The convective heat transfer coefficient, as a key proportionality constant, is the product of the temperature difference and the heat dissipation surface area, which determines the heat dissipation power from the node to the environment.

[0098] The conversion of heat energy to temperature: Multiply this heat dissipation power by the time step Δt to obtain the total heat energy lost during that time step. Finally, divide this heat energy loss value by the heat capacity of the control volume (ρc×V). cell By doing so, we can obtain the temperature reduction ΔT contributed by external heat dissipation. 散热 .

[0099] Through the three rigorous derivation steps above, each preset parameter plays a clear role within the framework of its corresponding physical laws. The entire calculation process is no longer a simple arithmetic arrangement, but a logically rigorous numerical simulation of the dynamic thermal equilibrium process in the real physical world.

[0100] Fourth stage: Result extraction and final generation;

[0101] The system repeats the iterative calculations of the third stage until the simulation time covers the entire "second preset duration". After each iteration, the system accurately records the calculated temperature at the endpoint node N (i.e., the location of the "adjacent temperature sensor"). When all iterations are completed, these temperature values ​​recorded in chronological order constitute the final "calculated temperature change curve".

[0102] Step S105: Obtain the second measured temperature value of each of the adjacent temperature sensors within the second preset time period, and obtain the measured temperature change curve of each of the adjacent temperature sensors based on the second measured temperature value.

[0103] The “second measured temperature value” refers to the temperature value collected by the “adjacent temperature sensor” under any circumstances, to distinguish it from the “first measured temperature value” collected by the “target temperature sensor”. The “measured temperature change curve” refers to the trajectory that truly reflects the temperature change of each “adjacent temperature sensor” after arranging these “second measured temperature values” in the order of their collection time.

[0104] This step is executed in parallel with step S104, and its function is to collect "ground truth" data for cross-validation. It records the actual temperature response of surrounding points under the actual influence of the heat source (the location of the target temperature sensor). Specifically, while performing heat conduction calculations in step S104, the control system sends instructions to all relevant "adjacent temperature sensors," requiring them to continuously collect temperatures at a high, fixed frequency during the following "second preset duration." The system collects and stores a series of "second measured temperature values" and their corresponding timestamps returned by each "adjacent temperature sensor." After the "second preset duration" ends, these collected discrete data points constitute the "measured temperature change curve" for each "adjacent temperature sensor."

[0105] Step S106: Determine whether the target temperature sensor is faulty based on the calculated temperature change curve and the measured temperature change curve;

[0106] The "calculated temperature change curve" refers to the theoretical value calculated in step S104, while the "measured temperature change curve" refers to the actual value collected in step S105. "Judgment" refers to the process of drawing conclusions about the health status of the "target temperature sensor" by quantitatively comparing and analyzing these two curves. "Fault" indicates that the readings of the "target temperature sensor" are unreliable, and may indicate problems such as drift, jamming, excessive noise, or complete failure.

[0107] This step is the decision-making stage in sensor fault diagnosis. Its function is to infer whether the "target temperature sensor," which serves as the source of the prediction, is functioning correctly by comparing the consistency between theoretical predictions and actual measurements. The basic logic is: if the "target temperature sensor" is accurate, then the heat conduction calculation based on its readings should closely match the actual readings of adjacent temperature sensors. Specifically, the system compares the "calculated temperature change curve" and the "measured temperature change curve" of each "adjacent temperature sensor." The comparison can be done by calculating the difference between the two curves at corresponding time points or by assessing the similarity of their overall shapes. If the two curves of all or most adjacent temperature sensors show significant and persistent deviations exceeding the preset tolerance range, the system determines that the root cause of the deviation lies in an error in the "first measured temperature value." Based on this, the system ultimately concludes that the "target temperature sensor" is "faulty."

[0108] Step S107: When the target temperature sensor is fault-free, calculate the temperature fluctuation rate based on several first temperature measured values, and adjust the sampling frequency in real time based on the temperature fluctuation rate and the temperature difference.

[0109] Here, "several measured values ​​of the first temperature" refers to a sequence of historical temperature data collected by the "target temperature sensor" within a time window. "Temperature fluctuation rate" is an indicator used to quantify the severity of temperature changes, typically obtained by calculating the standard deviation of the historical data sequence or the rate of temperature change over a period of time. "Temperature difference" is the value calculated in step S102 that reflects the deviation of the model's prediction. "Real-time adjustment of sampling frequency" indicates a more refined, continuous, or multi-level sampling frequency adjustment mechanism than the binary mode switching in S103.

[0110] This step, executed after confirming the sensor's reliability, introduces a more refined and comprehensive dynamic adjustment strategy for the sampling frequency to further optimize monitoring performance and power consumption. This strategy considers two different dimensions of information: model bias (temperature difference), reflecting unforeseen anomaly risks; and physical stability (temperature fluctuation rate), reflecting the battery's current thermal dynamic characteristics. Specifically, the system maintains a sliding window containing the most recent "several measured values ​​of the first temperature." In each adjustment cycle, the system calculates the "temperature fluctuation rate" based on the data within this window. Then, the system feeds this "temperature fluctuation rate" and the "temperature difference" from step S102 as two independent inputs into a decision logic. This logic calculates an optimal sampling frequency in real time based on the magnitude of these two input values. For example, even if the model bias is small, if the temperature itself fluctuates drastically, the sampling frequency should be increased to capture the dynamic process.

[0111] Optionally, steps S10601 to S10604 are more specific solutions for step S106 in the embodiments of this application.

[0112] Step S10601: For each of the adjacent temperature sensors, compare the calculated temperature change curve with the measured temperature change curve, and calculate the curve deviation.

[0113] The “curve deviation” is a quantitative indicator, a single value, used to accurately measure the degree of inconsistency between the “calculated” curve and the “measured” curve. For example, a deviation of 0 indicates that the two curves perfectly overlap, while a larger value indicates that the difference in shape or value between the two curves is more significant.

[0114] This step is executed after the system has completed the theoretical temperature simulation of all relevant "adjacent temperature sensors" and simultaneously acquired the actual temperature data of these sensors within the "second preset time period." It is the first step in the diagnostic logic, transitioning from data generation to data analysis and quantification. Specifically, for a given "adjacent temperature sensor," the system will extract its corresponding two temperature change curves. Both curves are defined within the same time domain ("second preset time period") and have the same time resolution (i.e., discrete time step Δt). The system will perform point-by-point comparisons along this time axis. At each discrete time point t, there exists a calculated temperature value T. calc and a measured temperature value T meas The system calculates the instantaneous deviation at each point in time, and then uses a preset mathematical algorithm to aggregate all these instantaneous, discrete deviation values ​​into a comprehensive value that can represent the overall level of difference, namely the "curve deviation degree".

[0115] In some embodiments, the deviation of the curve can be calculated in a variety of ways:

[0116] Optionally, the root mean square error algorithm can be used. The first step is to calculate the square of the difference between the calculated value and the measured value at each time point t, i.e., (T... calc -T meas The second step is to sum the squared differences calculated at all time points; the third step is to divide the sum by the total number of time points to obtain the mean squared difference, and finally take the square root of the mean, which is the "curve deviation".

[0117] Optionally, the integral absolute error algorithm can be used. The first step is to calculate the absolute value of the difference between the calculated value and the measured value at each time point t, i.e., |T|. calc -T measThe second step is to sum the absolute differences calculated at all time points. The third step is to multiply the sum by the time step Δt to obtain a numerical approximation of the area enclosed between the two curves. This area value is the "curve deviation".

[0118] It is understandable that other methods can be used to quantify the difference between two curves, and no specific method is specified here.

[0119] Step S10602: Based on the distance between each of the adjacent temperature sensors and the target temperature sensor, assign a first distance weight value to each curve deviation, and calculate the weighted average of all the curve deviations to obtain the comprehensive weighted deviation.

[0120] Among them, the physical straight-line distance from the center of the "target sensor" to the center of a certain "adjacent temperature sensor" is an inherent, static geometric parameter; the "first distance weight value" refers to a numerical coefficient allocated according to distance, used to represent the importance of the "curve deviation" of "adjacent temperature sensors" at different locations in the final comprehensive evaluation. Generally, the closer the distance, the larger the weight value; the "comprehensive weighted deviation" refers to the final single indicator that can summarize all deviations in the whole by calculating the weighted average method.

[0121] This step is executed after step S10601 is completed. It is the information aggregation and weighing stage in the diagnostic logic. Its core purpose is to integrate multiple scattered deviation information points into a single, more representative and valuable indicator based on the importance of their physical locations. Specifically, the closer an adjacent temperature sensor is to the "target temperature sensor," the more directly and significantly its temperature change is affected by the actual state of the "target temperature sensor." Therefore, its "curve deviation" has greater reference value for determining whether the target is faulty. Conversely, the temperature of a distant adjacent temperature sensor may be affected by other heat sources or more complex boundary conditions, and its deviation reference value should be correspondingly reduced. Therefore, the system iterates through all adjacent temperature sensors for which the "curve deviation" has been calculated, multiplies the "curve deviation" of each adjacent temperature sensor by its corresponding "first distance weight value," sums all these products, and finally divides by the sum of all "first distance weight values" to obtain the "comprehensive weighted deviation."

[0122] In some embodiments, the allocation of the first distance weight value and the calculation of the weighted average can be achieved in various ways:

[0123] Optionally, an inverse proportional function can be used for weight allocation. The first step is to obtain the distance d from each adjacent temperature sensor to the target sensor; the second step is to calculate its initial weight w. raw=1 / d (or 1 / d) 2 (To enhance the distance decay effect); the third step is to normalize all initial weights, calculate the final "first distance weight value", and then perform a weighted average calculation.

[0124] Optionally, a segmented mapping lookup table can be used for weight allocation. First, a pre-defined lookup table is used within the system, defining fixed weight values ​​for different distance ranges. For example, a distance of 0-5cm corresponds to a weight of 0.8, 5-10cm to 0.5, and over 10cm to 0.2. Second, based on the distance from adjacent temperature sensors to the target sensor, the "first distance weight value" is directly retrieved from this lookup table and assigned. Third, using these assigned weight values, a weighted average of all "curve deviations" is calculated.

[0125] It is understandable that other methods can be used to allocate weights, such as based on the Gaussian decay function or other nonlinear functions, as long as they can rigorously reflect the physical intuition that the closer the distance, the greater the importance. No restrictions are imposed here.

[0126] Step S10603: When the comprehensive weighted deviation is greater than or equal to the preset deviation threshold, it is determined that the target temperature sensor is faulty.

[0127] Among them, "preset deviation threshold" refers to a pre-set critical value used to determine whether there is a fault. It represents the maximum permissible deviation level between the theoretical model and the actual measurement that the system can tolerate. "Fault" refers to a judgment conclusion that indicates that the measurement function of the "target temperature sensor" is considered abnormal and its output data is unreliable.

[0128] Specifically, the system compares the calculated "comprehensive weighted deviation" with a "preset deviation threshold" stored in the system. This threshold is not arbitrarily set but calibrated through extensive experimental data, simulation analysis, and testing of battery thermal characteristics. It aims to distinguish between normal, minor deviations caused by model simplification or measurement noise, and significant and systematic deviations caused by sensor malfunctions (such as drift, failure, or hysteresis). When the "comprehensive weighted deviation" is greater than or equal to this threshold, it means that the overall difference between theoretical predictions and actual conditions has exceeded the range of normal fluctuations. The most reasonable explanation is that the readings of the "target temperature sensor," which serves as the benchmark for the entire theoretical calculation, were problematic from the outset, leading to a systematic inaccuracy in the predictions of the entire physical model. Therefore, the system determines that "the target temperature sensor is faulty."

[0129] Step S10604: When the overall weighted deviation is less than the preset deviation threshold, it is determined that the target temperature sensor is fault-free.

[0130] Specifically, the system compares the "comprehensive weighted deviation" with the "preset deviation threshold." When the comparison result shows that the "comprehensive weighted deviation" is significantly less than the "preset deviation threshold," it indicates that although there may be slight differences between the calculated curve and the measured curve (i.e., the comprehensive deviation is not zero), the overall level of these differences is within the tolerance range of the system design. These slight deviations are attributed to some unavoidable, non-fault-related factors, such as the simplification of the physical model, minor errors in the material's thermophysical parameters, and normal measurement noise of the sensor itself. Therefore, the final judgment is made that "the target temperature sensor is fault-free."

[0131] Optionally, S10701 to S10705 are more specific solutions to step S107 in the embodiments of this application:

[0132] Step S10701: Obtain multiple measured values ​​of the first temperature collected within the first adjustment period, calculate the average value and standard deviation, and divide the standard deviation by the average value to obtain the temperature fluctuation rate;

[0133] Among them, "first adjustment period" refers to the data acquisition time window that has just passed, which is used for the calculation of the sampling frequency adjustment; "first measured temperature value" refers to a series of discrete temperature readings that are actually measured and reported by the "target temperature sensor" hardware within this period; "average value" is used to represent the arithmetic mean of this series of temperature readings, which reflects the central trend or basic level of temperature within this period; "standard deviation" is used to represent the degree of deviation between this series of temperature readings and its average value, which quantifies the dispersion or fluctuation range of the temperature data itself; "temperature volatility" is a relative index obtained by normalizing the standard deviation, which is used to represent the degree of fluctuation of temperature relative to its own average level within this period.

[0134] This step is executed after a complete "first adjustment cycle," serving as the first computational step in initiating the adaptive sampling frequency adjustment process. Its core objective is to extract a characteristic quantity from the target sensor's own time-series data that characterizes its recent dynamic stability. Specifically, the system first caches and organizes all "first temperature measurements" collected during the "first adjustment cycle," forming a time-series array. Subsequently, the system performs two basic statistical operations on this array: calculating its arithmetic mean to determine the temperature baseline; and calculating its sample standard deviation to measure the amplitude of temperature oscillations around this baseline. Directly using the standard deviation may be affected by the absolute temperature (for example, a 2°C fluctuation at 80°C may have different physical meanings than a 2°C fluctuation at 20°C). Therefore, by dividing the standard deviation by the mean, this baseline dependence can be eliminated, yielding a dimensionless "temperature volatility." This ratio more purely reflects the inherent instability of the temperature signal; regardless of its absolute value, a higher volatility indicates more active or unstable temperature changes.

[0135] Step S10702: Obtain the average value of all the temperature differences within the first adjustment period, and use it as the target temperature difference.

[0136] Among them, "temperature difference" refers to the difference between the "first measured temperature value" and a theoretical "temperature prediction value" at the same moment within the first adjustment period. This prediction value is based on a mathematical or physical model to estimate the temperature that the "target temperature sensor" should have under the current operating conditions. "Target temperature difference" refers to a single value obtained by arithmetically averaging all these "temperature differences" within the "first adjustment period". It is used to represent the average systematic deviation between the actual behavior of the "target temperature sensor" and its theoretical expected behavior within this period.

[0137] This step is executed synchronously with S10701. Specifically, at each sampling moment within the "first adjustment cycle," in addition to recording the "first measured temperature value," the system also runs the battery temperature prediction model in parallel to calculate the "predicted temperature value" of the sensor at the current moment. Alternatively, the system can subsequently calculate the difference between the two, i.e., temperature difference = first measured temperature value - predicted temperature value. This difference (often called the "residual") reflects temperature changes that the model cannot explain. By averaging all "temperature differences" throughout the entire cycle, the system can filter out random, undirected measurement noise, thereby obtaining a "target temperature difference" that reflects a continuous, directional deviation. A significantly non-zero "target temperature difference" is a strong warning signal, suggesting the possible existence of physical phenomena not considered by the model (such as local short-circuit heat generation), sensor drift, or abnormal temperature changes. These are risks that need to be closely monitored by increasing the sampling frequency.

[0138] Step S10703: Multiply the temperature fluctuation rate by the first conversion coefficient to obtain the first frequency adjustment value, and multiply the target temperature difference by the second conversion coefficient to obtain the second frequency adjustment value;

[0139] Among them, the "first conversion coefficient" and the "second conversion coefficient" are two pre-set numerical parameters used to adjust the sensitivity of the system. They define how much sampling frequency increment should be converted from the unit "temperature fluctuation rate" and the unit "target temperature difference". The "first frequency adjustment value" and the "second frequency adjustment value" are two specific frequency increment values ​​obtained through conversion calculation, and the unit is usually Hertz (Hz).

[0140] This step is executed immediately after the calculation of the first two feature quantities. Its core function is to unify two indicators with different physical meanings and dimensions (one is a dimensionless ratio, and the other is temperature in degrees Celsius) into the dimension of sampling frequency, making it directly usable for subsequent frequency calculations. Specifically, the system multiplies the "temperature fluctuation rate" obtained in step S10701 with a preset "first conversion coefficient" to obtain the "first frequency adjustment value." This adjustment value reflects the increased sampling frequency required due to the inherent instability of the temperature. Simultaneously, the system multiplies the absolute value of the "target temperature difference" obtained in step S10702 with a preset "second conversion coefficient" to obtain the "second frequency adjustment value." This adjustment value reflects the increased sampling frequency required due to the difference between the predicted and measured temperatures. These two conversion coefficients are key calibration parameters of the system, determining the aggressiveness or conservatism of the frequency adjustment strategy.

[0141] Step S10704: Add the first frequency adjustment value, the second frequency adjustment value, and the basic sampling frequency to obtain the target sampling frequency;

[0142] Among them, the "basic sampling frequency" is a preset minimum sampling frequency that the system must maintain under any circumstances; the "target sampling frequency" is the final sampling frequency to be executed in the next cycle, obtained by adding the above three frequency values.

[0143] This step is executed after both independent frequency adjustment values ​​have been calculated, serving as the consolidation stage for the final sampling frequency decision. Its core objective is to combine adjustment requirements from different dimensions with a fixed safety baseline to generate a final execution frequency that ensures basic monitoring while dynamically adapting to changes in risk. Specifically, the system arithmetically sums the three values: the "basic sampling frequency," the "first frequency adjustment value," and the "second frequency adjustment value." The "basic sampling frequency" is crucial here, ensuring that even under ideal conditions of most stable temperature and most uniform space (where both adjustment values ​​might be zero), the target sensor will continue to operate at the lowest possible frequency, thus preventing complete monitoring interruption.

[0144] Step S10705: The target sampling frequency is used as the sampling frequency of the target temperature sensor in the second adjustment period;

[0145] This step is the final execution stage of the entire adaptive sampling frequency adjustment closed-loop control. The "target temperature sensor" will then strictly follow this new frequency for measurement and data reporting during the next "second adjustment cycle" until the end of the cycle, at which point the system will start a new round of evaluation and adjustment.

[0146] Optionally, after step S10705, this scheme may also execute step S10706.

[0147] Step S10706: Adjust the duration of the second adjustment period according to the target sampling frequency, wherein the duration is inversely proportional to the target sampling frequency;

[0148] The "second adjustment cycle" refers to the next data collection and frequency assessment window that follows the "first adjustment cycle".

[0149] Specifically, the underlying logic of this step is as follows: when the system calculates a high "target sampling frequency," it inherently implies that the system perceives a higher potential risk (whether it's drastic temperature fluctuations or significant deviations from the theoretical model). Under this high-risk state, the system must shorten the waiting time for the next comprehensive assessment to check the status more frequently and closely track the risk. Conversely, when the calculated sampling frequency is low, it indicates that the system is operating smoothly and the risk is low. In this case, extending the "duration of the second adjustment cycle" can effectively reduce the operational overhead and computational load of the entire diagnostic algorithm (including volatility and temperature difference calculations). This step makes the entire monitoring system adaptive not only at the data acquisition level but also at the macro-level of strategy evaluation.

[0150] Optionally, when the temperature sensor is a wireless sensor with an independent power supply, after the target temperature sensor is configured to the standard sampling period mode, this solution can also execute steps S108 and S109.

[0151] Step S108: Obtain the battery level of the target temperature sensor;

[0152] In this specific scenario, "the target temperature sensor" refers to a wireless temperature sensor with an independent battery or energy storage unit; "power" refers to the remaining energy reserves of the internal power supply of the wireless sensor at the current moment.

[0153] This step is performed when the system needs to evaluate and potentially adjust the power consumption strategy of the wireless sensor, such as after a regular adaptive frequency adjustment, or during a routine check at a fixed, long time interval (e.g., every few hours). Its core purpose is to obtain the sensor's own survivability information, providing a data basis for subsequent power management decisions. Specifically, a query command can be sent to the "target temperature sensor" via a wireless communication protocol, requesting it to report its current battery status to obtain the "power level".

[0154] Step S109: Adjust the sampling frequency according to the preset mapping relationship between power and sampling frequency;

[0155] Among them, "preset mapping relationship" refers to a set of rules or a data structure (such as a lookup table or function) that is predefined in the system software, which clearly maps different "power" ranges to specific "sampling frequency" values.

[0156] This step follows immediately after step S108. Specifically, the system matches the acquired "battery level" with the "preset mapping relationship." This mapping relationship typically manifests as follows: when the battery is sufficient, the sensor is allowed to operate at a higher or standard frequency to acquire high-quality data; when the battery level drops to a certain point, its sampling frequency is forcibly reduced; when the battery is severely insufficient, it may be switched to an extremely low-frequency "sleep-wake" mode, maintaining only the most basic vital sign monitoring. After matching the corresponding frequency, the main controller sends a new configuration command to the wireless sensor, instructing it to operate at this new, energy-saving frequency from that moment on.

[0157] In some embodiments, the adjustment logic for this step can be implemented in a variety of ways:

[0158] Optionally, a hierarchical lookup table mapping can be used. First, a power-frequency lookup table is embedded within the system, for example: power [100%-50%] → frequency 1Hz; power [50%-20%] → frequency 0.1Hz; power [20%-5%] → frequency 0.01Hz (once every 100 seconds); power [5%-0%] → frequency 0.001Hz (approximately once every 15 minutes). Second, the acquired power value is compared with this table to find the corresponding frequency. Third, this frequency value is used as a command parameter and transmitted wirelessly to the target sensor.

[0159] It is understandable that other methods can be used to achieve frequency adjustment based on power level, such as combining the importance level of temperature to allow highly important sensors to maintain a relatively high frequency at low power levels, while sacrificing the frequency of secondary sensors. This is not limited here.

[0160] Optionally, after step S106, step S110 can also be performed.

[0161] Step S110: When the target temperature sensor is faulty, different second distance weight values ​​are assigned to different measured values ​​of the second temperature according to the distance between each of the adjacent temperature sensors and the target temperature sensor, and the simulated temperature value of the target temperature sensor is calculated.

[0162] This step is executed after step S106 confirms that the "target temperature sensor" has malfunctioned. Specifically, the system first locks all adjacent temperature sensors of the malfunctioning sensor and obtains their current "second measured temperature values." Simultaneously, the system queries the preset configuration information to find the physical distance between each of the adjacent temperature sensors and the target sensor. Based on the principle that "the closer the distance, the stronger the correlation," the system assigns a "second distance weight value" (usually inversely proportional to the distance) to each adjacent temperature sensor. Finally, the system multiplies the temperature of each adjacent temperature sensor by its corresponding weight, sums all products, and divides by the sum of all weights to obtain the "simulated temperature value."

[0163] Optionally, this solution may also execute step S111.

[0164] Step S111: When the number of times the first measured temperature value is greater than or equal to the second preset number threshold within the third preset time period is greater than or equal to the second preset number threshold, the sampling frequency of the target temperature sensor is adjusted to the preset sampling frequency.

[0165] Among them, "third preset duration" is a specific sliding time window used for judging high-risk events, such as the most recent 30 seconds; "preset temperature threshold" is a critical temperature value representing danger or near danger, such as 65℃; "second preset number threshold" is a counting threshold used to confirm the persistence of an event, such as more than 5 over-temperature events within 30 seconds to filter out single false spikes caused by electromagnetic interference, etc.; "preset sampling frequency" is a predefined, fixed high-speed sampling frequency used in emergency situations, such as 20Hz.

[0166] This step is a parallel safety monitoring and emergency response mechanism independent of the regular adaptive adjustment logic. Its execution is continuous, with a judgment made at each sampling point. Specifically, the system maintains a buffer storing all readings from the "target temperature sensor" within the most recent "third preset time period." Whenever a new reading arrives, the system updates this buffer and recounts how many data points have values ​​greater than the "preset temperature threshold." If this count is greater than or equal to the "second preset count threshold," the system determines that a persistent high-temperature anomaly has occurred. At this point, the system immediately performs a forced overwrite operation, ignoring any other ongoing adaptive frequency calculations, and directly adjusts the sampling frequency of the "target temperature sensor" to a preset, high-intensity "preset sampling frequency." The purpose of this is to capture temperature changes with the highest temporal resolution during the potential thermal runaway development phase.

[0167] This application also provides a battery temperature monitoring device, such as... Figure 2 As shown, Figure 2 This is a structural block diagram of a battery temperature monitoring device provided in an embodiment of this application. The device includes:

[0168] The working parameter acquisition module 201 is used to acquire the electrical working parameters of the target battery in real time and input them into the preset battery temperature prediction model to obtain the temperature prediction curve.

[0169] The temperature difference calculation module 202 is used to collect the first measured temperature value through the target temperature sensor and calculate the temperature difference between the first measured temperature value and the temperature prediction value.

[0170] The working mode configuration module 203 is used to configure the target temperature sensor to a standard sampling period mode when the preset conditions are met, and to configure the target temperature sensor to a variable sampling period mode when the preset conditions are not met.

[0171] The heat conduction calculation module 204 is used to perform heat conduction calculation based on the distance between the target temperature sensor and several adjacent temperature sensors and the first measured temperature value, and to obtain the calculated temperature change curve of each of the adjacent temperature sensors within a second preset time period.

[0172] The temperature curve acquisition module 205 is used to acquire the second measured temperature value of each of the adjacent temperature sensors within the second preset time period, and to obtain the measured temperature change curve of each of the adjacent temperature sensors based on the second measured temperature value.

[0173] The fault judgment module 206 is used to determine whether the target temperature sensor is faulty based on the calculated temperature change curve and the measured temperature change curve.

[0174] The sampling frequency adjustment module 207 is used to calculate the temperature fluctuation rate based on several first temperature measured values ​​when the target temperature sensor is fault-free, and to adjust the sampling frequency in real time based on the temperature fluctuation rate and the temperature difference.

[0175] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0176] This application also provides a computer-readable storage medium storing instructions that, when executed, perform the steps of any of the methods described above.

[0177] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0178] This application also discloses an electronic device. For example... Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, and a memory 305.

[0179] The communication bus 302 is used to enable communication between these components.

[0180] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0181] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0182] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the electronic device (such as a server) using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0183] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a battery temperature monitoring method.

[0184] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program of a battery temperature monitoring method stored in the memory 305. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0185] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0186] In the various embodiments provided in this application, it should be understood that the disclosed apparatus or system can be implemented in other ways. For example, the apparatus or system embodiments described above are merely illustrative; for instance, the division of 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. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0187] 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 according to actual needs.

[0188] 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.

[0189] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0190] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the disclosure herein.

[0191] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art that are not described in this disclosure.

Claims

1. A battery temperature monitoring method, characterized in that, Includes the following steps: The electrical operating parameters of the target battery are acquired in real time and input into the preset battery temperature prediction model to obtain the temperature prediction curve. The first measured temperature value is collected by the target temperature sensor, and the temperature difference between the first measured temperature value and the temperature prediction value is calculated. The temperature prediction value is the temperature value on the temperature prediction curve that is the same as the first measured temperature value at that time. When preset conditions are met, the target temperature sensor is configured to a standard sampling period mode; When the preset conditions are not met, the target temperature sensor is configured to a variable sampling period mode. The preset conditions are that the number of times the temperature difference is less than the preset difference within a first preset time period is greater than a first preset number threshold. When the target temperature sensor is in the variable sampling period mode, perform the following operations: Based on the distance between the target temperature sensor and several adjacent temperature sensors and the first measured temperature value, heat conduction calculation is performed to obtain the calculated temperature change curve of each of the adjacent temperature sensors within a second preset time period. The adjacent temperature sensors are temperature sensors located within a preset range of the target temperature sensor. Obtain the second measured temperature value of each of the adjacent temperature sensors within the second preset time period, and obtain the measured temperature change curve of each of the adjacent temperature sensors based on the second measured temperature value; Determine whether the target temperature sensor is faulty based on the calculated temperature change curve and the measured temperature change curve. When the target temperature sensor is fault-free, the temperature fluctuation rate is calculated based on several first temperature measurement values, and the sampling frequency is adjusted in real time based on the temperature fluctuation rate and the temperature difference. The temperature fluctuation rate represents the degree of drastic temperature change.

2. The method according to claim 1, characterized in that, The step of determining whether the target temperature sensor is faulty based on the calculated temperature change curve and the measured temperature change curve specifically includes: For each adjacent temperature sensor, the calculated temperature change curve is compared with the measured temperature change curve, and the curve deviation is calculated. The curve deviation represents the overall difference between the calculated temperature change curve and the measured temperature change curve within the second preset time period. Based on the distance between each of the adjacent temperature sensors and the target temperature sensor, a first distance weight value is assigned to each curve deviation, and the weighted average of all the curve deviations is calculated to obtain a comprehensive weighted deviation. The first distance weight value is inversely proportional to the distance between the adjacent temperature sensors and the target temperature sensor. When the overall weighted deviation is greater than or equal to a preset deviation threshold, it is determined that the target temperature sensor is faulty. When the overall weighted deviation is less than the preset deviation threshold, the target temperature sensor is determined to be fault-free.

3. The method according to claim 1, characterized in that, The step of calculating the temperature fluctuation rate based on several measured first temperature values ​​when the target temperature sensor is fault-free, and adjusting the sampling frequency in real time based on the temperature fluctuation rate and the temperature difference, specifically includes: Acquire multiple measured values ​​of the first temperature collected within the first adjustment period, calculate the average value and standard deviation, and divide the standard deviation by the average value to obtain the temperature fluctuation rate; The average value of all the temperature differences within the first adjustment period is obtained as the target temperature difference. The temperature fluctuation rate is multiplied by a first conversion coefficient to obtain a first frequency adjustment value, and the target temperature difference is multiplied by a second conversion coefficient to obtain a second frequency adjustment value. The first conversion coefficient represents the proportional relationship between the temperature fluctuation rate and the first frequency adjustment value, and the second conversion coefficient represents the proportional relationship between the target temperature difference and the second frequency adjustment value. The target sampling frequency is obtained by adding the first frequency adjustment value, the second frequency adjustment value, and the base sampling frequency. The target sampling frequency is used as the sampling frequency of the target temperature sensor during the second adjustment period.

4. The method according to claim 3, characterized in that, The method further includes the step of using the target sampling frequency as the sampling frequency of the target temperature sensor within the second adjustment period, followed by: The duration of the second adjustment period is adjusted according to the target sampling frequency, and the duration is inversely proportional to the target sampling frequency.

5. The method according to claim 1, characterized in that, After the step of configuring the target temperature sensor to a standard sampling period mode when the preset conditions are met, the method further includes: Obtain the charge level of the target temperature sensor; The sampling frequency is adjusted according to the preset mapping relationship between power and sampling frequency.

6. The method according to claim 1, characterized in that, After the step of determining whether the target temperature sensor is faulty based on the calculated temperature change curve and the measured temperature change curve, the method further includes: When the target temperature sensor malfunctions, different second distance weight values ​​are assigned to different measured values ​​of the second temperature based on the distance between each of the adjacent temperature sensors and the target temperature sensor. The simulated temperature value of the target temperature sensor is calculated. The second distance weight value is inversely proportional to the distance between the adjacent temperature sensors and the target temperature sensor. The simulated temperature value is used to replace the first measured temperature value.

7. The method according to claim 1, characterized in that, The method further includes: When the number of times the first measured temperature value exceeds a preset temperature threshold within a third preset time period is greater than or equal to a second preset number threshold, the sampling frequency of the target temperature sensor is adjusted to a preset sampling frequency.

8. A battery temperature monitoring device, characterized in that, include: The working parameter acquisition module is used to acquire the electrical working parameters of the target battery in real time and input them into the preset battery temperature prediction model to obtain the temperature prediction curve. The temperature difference calculation module is used to collect the first measured temperature value through the target temperature sensor and calculate the temperature difference between the first measured temperature value and the temperature prediction value. The working mode configuration module is used to configure the target temperature sensor to a standard sampling period mode when preset conditions are met, and to configure the target temperature sensor to a variable sampling period mode when preset conditions are not met. The heat conduction calculation module is used to perform heat conduction calculation based on the distance between the target temperature sensor and several adjacent temperature sensors and the first measured temperature value, and to obtain the calculated temperature change curve of each of the adjacent temperature sensors within a second preset time period. The temperature curve acquisition module is used to acquire the second measured temperature value of each of the adjacent temperature sensors within the second preset time period, and to obtain the measured temperature change curve of each of the adjacent temperature sensors based on the second measured temperature value. The fault diagnosis module is used to determine whether the target temperature sensor is faulty based on the calculated temperature change curve and the measured temperature change curve. The sampling frequency adjustment module is used to calculate the temperature fluctuation rate based on several measured values ​​of the first temperature when the target temperature sensor is fault-free, and to adjust the sampling frequency in real time based on the temperature fluctuation rate and the temperature difference.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 7.

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