Flameproof mine energy storage battery management method, device, equipment and storage medium

Abstract

The present application relates to the technical field of energy storage battery management and control, in particular to a flameproof mine energy storage battery management and control method, device, equipment and computer storage medium. The present application collects real-time temperature data of the battery module to establish temperature field basic statistical characteristics; calculates the temperature rise rate and temperature rise acceleration based on the global average temperature to predict the future temperature; quantifies the spatial balance of the temperature field to obtain the spatial balance index; calculates the time dimension safety factor based on the predicted temperature, calculates the spatial dimension balance coefficient based on the spatial balance index, and dynamically adjusts the rated current using the two coefficients. The present application couples the time dimension thermal inertia prediction and the spatial dimension thermal balance evaluation, eliminates the influence of the flameproof cavity thermal hysteresis, solves the local hot spot problem, prolongs the battery life, and improves the work efficiency.

Description

Technical Field

[0001] This invention relates to the field of energy storage battery management technology, and in particular to a method, device, equipment, and computer storage medium for managing explosion-proof mining energy storage batteries. Background Technology

[0002] Explosion-proof, high-capacity energy storage batteries for mining serve as the core power source for intelligent auxiliary equipment in coal mines (such as explosion-proof electric flatbed transport vehicles and lifting vehicles). Their safety management directly impacts the continuity of underground operations and personnel safety. In related technologies, the battery system must be installed within an explosion-proof enclosure conforming to GB / T 3836 standards, achieving explosion-proof sealing through a heavy-duty cast steel or welded steel plate structure, and is monitored by a battery management system (BMS). Specifically, this management system covers the entire process from temperature acquisition and threshold triggering to power limiting, including key aspects such as surface temperature monitoring, fixed threshold protection, and global power control based on the highest single-cell temperature, to maintain battery operation within safe boundaries.

[0003] However, existing BMS control methods directly use instantaneous surface temperature as the sole criterion, which does not adequately adapt to the characteristics of the high heat capacity and low heat dissipation of the explosion-proof cavity's enclosed thermal environment. This may lead to serious thermal hysteresis protection problems, meaning that the actual cell temperature is significantly higher than the surface monitoring value, and heat accumulates rapidly during high-current discharge. The delayed response can easily cause irreversible thermal damage or even thermal runaway. At the same time, because the coupling relationship between temperature rise and charge / discharge power is not quantified, the fixed power limiting curve is prone to short-term over-temperature shutdown or excessive power limiting under complex operating conditions of frequent start-stop, affecting operational efficiency. In addition, the uneven temperature field caused by the distributed module layout, using only the highest temperature for global power limiting control, not only wastes the performance of normal modules but also accelerates battery uniform degradation, thereby affecting the overall system lifespan and utilization rate. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the problems in the prior art, such as the temperature response lag caused by the thermal hysteresis of the explosion-proof cavity, the low operating efficiency caused by the lack of quantification of the coupling relationship between temperature rise and power, and the battery uniform degradation caused by local hot spots in the decentralized layout.

[0005] To solve the above-mentioned technical problems, the present invention provides a method for controlling explosion-proof mining energy storage batteries, comprising:

[0006] Real-time temperature data of each battery module in the battery cluster is collected, and basic statistical features of the temperature field are established based on the real-time temperature data. The basic statistical features include at least the global average temperature. Based on the current global average temperature and the historical global average temperature, the temperature rise rate and temperature rise acceleration are calculated, and the temperature at future times are predicted using the temperature rise rate and temperature rise acceleration to obtain the predicted temperature. Based on the aforementioned basic statistical characteristics and the aforementioned real-time temperature data, the spatial uniformity of the temperature field is quantified to obtain a spatial uniformity index. The time dimension safety factor is calculated based on the predicted temperature, and the spatial dimension balance factor is calculated based on the spatial balance index. The rated maximum current of the battery cluster is dynamically adjusted using the time dimension safety factor and the spatial dimension balance factor to obtain the maximum allowable charge and discharge current.

[0007] Preferably, the basic statistical characteristics for establishing the temperature field include: The average temperature of each battery module is calculated based on the real-time temperature data to obtain the global average temperature. Based on the global average temperature and the real-time temperature data, the deviation of each module's temperature from the global average temperature is calculated, and the standard deviation is obtained. The range is obtained based on the difference between the highest and lowest temperatures in the real-time temperature data.

[0008] Preferably, the prediction of temperature at future times includes: Based on the difference between the current global average temperature and the global average temperature of the previous sampling period and the sampling interval, the instantaneous temperature rise rate, which characterizes the current rate of heat accumulation, is calculated. Based on the difference between the instantaneous temperature rise rate and the instantaneous temperature rise rate of the previous sampling period and the sampling interval, the temperature rise acceleration used to characterize the rate of change of the temperature rise trend is calculated. Based on the current global average temperature, the instantaneous temperature rise rate, the temperature rise acceleration, and the preset prediction step size, a prediction model is constructed using the Taylor expansion principle to calculate the predicted temperature at future times.

[0009] Preferably, the prediction step size is one response cycle of the control system, and the global average temperature at the historical moment includes the global average temperature of the previous sampling cycle and the global average temperature two sampling cycles ago.

[0010] Preferably, the spatial uniformity of the quantized temperature field includes: Based on the ratio of the standard deviation to the global average temperature in the aforementioned basic statistical characteristics, the coefficient of variation is calculated to eliminate the influence of absolute temperature values ​​and measure the relative uniformity of the temperature field. Based on the difference between the highest temperature in the real-time temperature data and the global average temperature, and the ratio of the global average temperature, a local hotspot deviation degree is calculated to measure the degree to which a local hotspot deviates from the overall level.

[0011] Preferably, the dynamic adjustment of the rated maximum current of the battery cluster includes: Based on the degree of closeness between the predicted temperature and the preset safe temperature threshold, and combined with the initial ambient temperature when the system starts up, a safety factor is calculated to characterize the thermal risk in the time dimension. Based on the combination of the coefficient of variation, which characterizes the degree of non-uniformity of the temperature field, and the degree of deviation of local hot spots, which characterizes the degree of deviation of local hot spots, the equilibrium coefficient, which characterizes the thermal equilibrium of the spatial dimension, is calculated. Using the time-dimensional safety factor and the spatial-dimensional balance factor as constraint factors, the rated maximum current of the battery cluster is multiplied together to obtain the maximum allowable charge and discharge current, which is simultaneously constrained by both future thermal state and current thermal distribution uniformity.

[0012] Preferably, the time dimension safety factor is used to characterize the closeness between the predicted temperature and the safe temperature threshold, and the spatial dimension uniformity factor is used to characterize the uniformity of the temperature field.

[0013] This invention provides an explosion-proof mining energy storage battery control device, comprising: The data acquisition module is used to collect real-time temperature data of each battery module in the battery cluster; The basic feature construction module is used to establish basic statistical features of the temperature field based on the real-time temperature data, wherein the basic statistical features include at least the global average temperature. The time prediction module is used to calculate the temperature rise rate and temperature rise acceleration based on the current global average temperature and the global average temperature at historical times, and to use the temperature rise rate and temperature rise acceleration to predict the temperature at future times, thereby obtaining the predicted temperature. The spatial quantization module is used to quantify the spatial uniformity of the temperature field based on the basic statistical characteristics and the real-time temperature data, and obtain a spatial uniformity index. The dynamic adjustment module is used to calculate the time dimension safety factor based on the predicted temperature and the spatial dimension balance factor based on the spatial balance index. It uses the time dimension safety factor and the spatial dimension balance factor to dynamically adjust the rated maximum current of the battery cluster and output the maximum allowable charging and discharging current.

[0014] The present invention also provides an explosion-proof mining energy storage battery management device, comprising: Memory, used to store computer programs; A processor is used to implement the steps of the above-described explosion-proof mining energy storage battery management method when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described explosion-proof mining energy storage battery management method.

[0016] The technical solution of the present invention has the following advantages over the prior art: The explosion-proof mining energy storage battery management method of this invention establishes the basic statistical characteristics of the temperature field by collecting real-time temperature data of each battery module in the battery cluster, and predicts future temperatures based on the temperature rise rate and temperature rise acceleration. Simultaneously, it quantifies the spatial uniformity of the temperature field, and then calculates the time-dimensional safety factor and spatial-dimensional uniformity factor to dynamically adjust the rated current. This effectively overcomes the temperature response lag problem caused by the high thermal inertia of the explosion-proof cavity, achieving early prediction and forward-looking compensation for thermal risks, and avoiding thermal runaway caused by temperature overshoot. Furthermore, by introducing a spatial uniformity index to quantitatively constrain local hotspots in a distributed layout, it avoids the waste of normal module performance caused by global current limiting based on a single highest temperature, extending the overall service life of the battery system. In addition, through dynamic adjustment coupled in both time and space dimensions, it achieves refined on-demand allocation of power boundaries, significantly improving the operating efficiency of mining equipment while ensuring safety. Attached Figure Description

[0017] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein: Figure 1 This is a flowchart illustrating the implementation of a control method for an explosion-proof mining energy storage battery provided by the present invention. Figure 2 This is a structural block diagram of an explosion-proof mining energy storage battery control device provided in an embodiment of the present invention. Detailed Implementation

[0018] The core of this invention is to provide a method, device, equipment, and computer storage medium for managing explosion-proof mining energy storage batteries. By coupling time-dimensional thermal inertia prediction with spatial-dimensional thermal uniformity assessment, it can effectively eliminate the thermal hysteresis effect of explosion-proof cavities, solve local hot spot problems in decentralized layouts, extend battery life, and improve the operating efficiency of mining equipment.

[0019] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please refer to Figure 1. Figure 1 The flowchart illustrates the implementation of a control method for an explosion-proof mining energy storage battery provided by this invention; the specific operation steps are as follows: S101: Collect real-time temperature data of each battery module in the battery cluster, and establish basic statistical features of the temperature field based on the real-time temperature data. The basic statistical features include at least the global average temperature. In the aforementioned control method, the basic statistical characteristics of the battery cluster temperature field must first be established to obtain benchmark information reflecting the overall thermal state and the degree of dispersion in the distribution. Specifically, real-time temperature data of each battery module arranged within the explosion-proof enclosure is collected to form a dataset reflecting the overall temperature field. Based on this dataset, using basic statistical calculation methods, a global average temperature characterizing the overall thermal load level of the temperature field is calculated. This average temperature serves as the benchmark reference point for all subsequent analyses and decisions, and its trend directly reflects the accumulation and dissipation of heat within the explosion-proof cavity. Simultaneously, the root mean square (RMS) of the deviation of each module's temperature from this global average temperature, i.e., the standard deviation, is calculated. This standard deviation is an absolute indicator of the uniformity of temperature distribution and can reveal the degree of temperature dispersion caused by differences in module layout or heat dissipation conditions. Furthermore, the difference between the maximum and minimum values ​​in the temperature field, i.e., the range, is calculated. This range directly reflects the temperature difference between the hottest and coldest points and is an important parameter for assessing the uniformity of thermal stress distribution. By calculating the global average temperature, standard deviation, and range, the basic statistical characteristics of the temperature field are constructed, providing the necessary data foundation for subsequent time-series-based thermal inertia prediction and spatial equilibrium quantification. As one implementation method, the global average temperature can be calculated using the arithmetic mean formula, the standard deviation can be calculated using the sample standard deviation formula, and the range can be calculated by iteratively comparing the maximum and minimum values.

[0021] This step provides an objective and quantitative thermal state benchmark for the entire control method by constructing the basic statistical characteristics of the temperature field. This ensures that subsequent predictions of thermal inertia and assessments of spatial equilibrium are based on a unified and comparable reference system, thereby guaranteeing the scientific nature and accuracy of the entire control logic.

[0022] S102: Based on the current global average temperature and the historical global average temperature, calculate the temperature rise rate and temperature rise acceleration, and use the temperature rise rate and temperature rise acceleration to predict the temperature at future times to obtain the predicted temperature. Based on the current global average temperature, historical global average temperatures, and the total current of the battery cluster, a time-series thermal inertia prediction model is constructed. The core of this step lies in utilizing the discrete difference principle to capture the nonlinear characteristics of temperature change under the high heat capacity and low heat dissipation characteristics of the explosion-proof cavity by calculating the rate of temperature change over time and the trend of this rate of change, i.e., the temperature rise acceleration. Specifically, the instantaneous temperature rise rate is determined by the ratio of the difference between the current and previous sampling periods' global average temperatures to the sampling interval; this rate reflects the current rate of heat accumulation. Furthermore, the temperature rise acceleration is determined by the ratio of the difference between the current and previous temperature rise rates to the sampling interval; this acceleration characterizes the acceleration or deceleration of the temperature rise trend, thus allowing for early detection of dynamic changes in thermal inertia before the absolute temperature value has significantly increased. Based on the above temperature rise rate and temperature rise acceleration, combined with the current global average temperature, the first three terms of the Taylor expansion are extrapolated to construct a predicted temperature value for future times. This predicted value fully considers the linear trend and nonlinear correction of temperature changes, effectively compensating for temperature monitoring lag caused by the thermal resistance and heat capacity of the explosion-proof enclosure. As one implementation method, it can be based on the current global average temperature. Temperature rise rate and temperature rise acceleration According to the formula Calculate the predicted temperature, where This is the preset prediction step size.

[0023] This step, by introducing the second-order differential feature of temperature rise acceleration, enables advanced prediction of future thermal states, significantly improving the system's response speed to the thermal inertia of the explosion-proof cavity. It provides a precise time dimension basis for subsequent dynamic power adjustment, thereby effectively avoiding protection delays or excessive power limitation caused by temperature monitoring lag.

[0024] S103: Based on the aforementioned basic statistical characteristics and the aforementioned real-time temperature data, the spatial uniformity of the temperature field is quantified to obtain a spatial uniformity index. After acquiring the global average temperature, standard deviation, and real-time temperature data of each battery module, this step further constructs characteristic indicators to characterize the spatial uniformity of the temperature field in order to quantify the spatial distribution non-uniformity of the temperature field caused by the dispersed layout and different heat dissipation conditions of the battery clusters. Specifically, by introducing the coefficient of variation, which is used in statistics to measure relative dispersion, the standard deviation of the temperature field is correlated with the global average temperature, thereby eliminating the influence of the absolute value of temperature on the evaluation of dispersion, so that the uniformity of temperature distribution can be objectively reflected at different temperature levels. At the same time, in order to accurately identify and quantify the deviation of local hot spots from the overall temperature level, a local hot spot deviation index is also constructed. This index is based on the difference between the highest temperature in the real-time temperature of each module and the global average temperature, and is normalized in combination with the global average temperature, so as to highlight the deviation of the thermal state of the "worst case" module in the temperature field. Through the synergistic calculation of the above two indicators, the spatial uniformity of the temperature field can be comprehensively and quantitatively described from the two dimensions of overall uniformity and local anomalies. As one implementation method, the global average temperature can be used as the basis for the spatial uniformity of the temperature field. and standard deviation Calculate the coefficient of variation And based on the maximum value of the real-time temperature of each module. With global average temperature Calculate local hotspot deviation .

[0025] This step, by constructing the coefficient of variation and the deviation of local hot spots, enables a quantitative assessment of the spatial uniformity of the temperature field of the dispersed battery modules within the explosion-proof cavity. This provides a precise input for subsequent dynamic power decisions, reflecting the degree of temperature distribution non-uniformity and the risk of local hot spots, thereby effectively suppressing the risk of accelerated battery uniformity degradation and local thermal runaway caused by temperature field non-uniformity.

[0026] S104: Calculate the time dimension safety factor based on the predicted temperature, and calculate the spatial dimension balance coefficient based on the spatial balance index. Use the time dimension safety factor and the spatial dimension balance coefficient to dynamically adjust the rated maximum current of the battery cluster to obtain the maximum allowable charging and discharging current.

[0027] After obtaining parameters characterizing the spatiotemporal thermal state of the battery cluster, such as the predicted temperature, coefficient of variation, and local hotspot deviation, this step aims to transform these parameters into real-time constraints on the charging and discharging power of the battery cluster, thereby generating a dynamic and safe power boundary. Specifically, the method first constructs a safety factor to characterize the thermal risk in the time dimension based on the relationship between the predicted temperature and a preset safe temperature threshold. This safety factor reflects the degree to which the battery temperature approaches the safe threshold in the future; the closer the predicted temperature is to the safe threshold, the smaller the coefficient, thus achieving forward-looking compensation for thermal inertia. Simultaneously, based on the coefficient of variation and local hotspot deviation, an equilibrium coefficient to characterize the thermal uniformity in the spatial dimension is constructed. This equilibrium coefficient comprehensively reflects the uniformity of the current temperature field distribution and the severity of local hotspots; the higher the temperature field non-uniformity or the more prominent the local hotspots, the smaller the coefficient, thus achieving quantitative constraints on the spatial thermal distribution non-uniformity. Finally, the aforementioned time-dimensional safety factor and spatial-dimensional equilibrium coefficient are applied together to the rated maximum current of the battery cluster. Through multiplication or similar multi-factor constraints, the rated current is dynamically adjusted to calculate the maximum allowable charging and discharging current at the current moment. As one implementation method, the time dimension safety factor can be constructed based on the ratio of the difference between the predicted temperature and the safe temperature threshold to the reference temperature difference, and the spatial dimension equilibrium factor can be constructed based on the combination of the coefficient of variation and the hot spot deviation. Finally, the allowable current is the product of the rated current and the two coefficients.

[0028] This step couples the thermal inertia prediction in the time dimension with the thermal uniformity assessment in the spatial dimension, enabling dynamic and fine-grained adjustment of the battery cluster power boundary. This not only effectively prevents temperature overshoot caused by thermal hysteresis, but also automatically reduces power to protect hot spot modules when the temperature field distribution is uneven. Thus, while ensuring safety, it maximizes the available power and operating efficiency of the battery system.

[0029] Based on the above embodiments, in some embodiments, the fundamental statistical characteristics for establishing the temperature field include: The average temperature of each battery module is calculated based on the real-time temperature data to obtain the global average temperature. Based on the global average temperature and the real-time temperature data, the deviation of each module's temperature from the global average temperature is calculated, and the standard deviation is obtained. The range is obtained based on the difference between the highest and lowest temperatures in the real-time temperature data.

[0030] In this embodiment, step S101 specifically includes the following sub-steps. First, temperature data of N battery modules in the battery cluster are collected in real time by temperature sensors installed on each battery module, forming a real-time temperature set. ,in Let represent the measured temperature of the i-th module, and N be the total number of modules. This set of real-time temperatures serves as the foundational input for all subsequent statistical calculations. Then, based on this set of real-time temperatures, the formula is used... Calculate the global average temperature This global average temperature represents the overall thermal load level of the battery cluster. Within the explosion-proof cavity, its rate of rise directly reflects the heat accumulation inside the cavity and serves as a benchmark reference point for subsequent calculations of dispersion and prediction. Then, based on the calculated global average temperature... and real-time temperature collection Through formula Calculate the standard deviation of the temperature field This standard deviation is an absolute indicator of temperature distribution uniformity. In a distributed battery module layout, if... A large value indicates that some modules have extremely poor heat dissipation or localized overcurrent. Finally, based on real-time temperature data... Through formula Calculate the range of the temperature field The range directly reflects the temperature difference between the hottest and coldest points. Inside the explosion-proof enclosure, an excessively large R_T indicates uneven thermal stress distribution, which may lead to structural deformation or loosening of connectors. Through the above sub-steps, the global average temperature, standard deviation, and range are extracted sequentially from the real-time temperature data, which together constitute the basic statistical characteristics of the temperature field, providing reliable benchmark data for subsequent time series prediction and spatial equilibrium assessment.

[0031] This specific implementation method achieves accurate quantification of the basic statistical characteristics of the temperature field inside the explosion-proof cavity through a clear formula and data processing flow, providing a solid data foundation for subsequent prediction and decision-making. At the same time, its calculation process only involves basic operations, which can be easily implemented in a low-cost BMS, thus improving the engineering practicality of the method.

[0032] Based on the above embodiments, in some embodiments, the prediction of temperature at future times includes: Based on the difference between the current global average temperature and the global average temperature of the previous sampling period and the sampling interval, the instantaneous temperature rise rate, which characterizes the current rate of heat accumulation, is calculated. Based on the difference between the instantaneous temperature rise rate and the instantaneous temperature rise rate of the previous sampling period and the sampling interval, the temperature rise acceleration used to characterize the rate of change of the temperature rise trend is calculated. Based on the current global average temperature, the instantaneous temperature rise rate, the temperature rise acceleration, and the preset prediction step size, a prediction model is constructed using the Taylor expansion principle to calculate the predicted temperature at future times.

[0033] Furthermore, the prediction step size is one response cycle of the control system, and the global average temperature at the historical moment includes the global average temperature of the previous sampling cycle and the global average temperature two sampling cycles ago.

[0034] In this embodiment, step S102 specifically includes the following sub-steps. First, based on the current global average temperature... and the global average temperature of the previous sampling period The instantaneous temperature rise rate was calculated using the first-order difference formula. Its calculation formula is .in, Calculated from step S1, Historical data stored by the system The sampling period is fixed. The output of this step is the instantaneous temperature rise rate, which characterizes the rate of heat accumulation. Next, based on the calculated instantaneous temperature rise rate... Instantaneous temperature rise rate of the previous sampling period The temperature rise acceleration was calculated using the second-order difference formula. Its calculation formula is .in, This is the output of the previous sub-step. This is the global average temperature two sampling periods prior. The output of this step is the temperature rise acceleration, which characterizes the rate of change in the temperature rise trend. Finally, based on the current global average temperature... Temperature rise rate and temperature rise acceleration A prediction model is constructed using the first three terms of the Taylor expansion, and the formula is used to... Predicting the future The temperature after seconds is the predicted temperature. .in, The prediction step size is typically set to one response cycle of the control system, such as a fixed value between 5 and 10 seconds. The output of this step is a predicted future temperature that takes into account the thermal inertia of the explosion-proof cavity, providing a forward-looking time-dimensional basis for subsequent dynamic power decisions.

[0035] By using the above-mentioned second-order difference-based prediction method, this embodiment can capture the nonlinear trend of temperature change in advance by utilizing the temperature rise acceleration, effectively overcoming the temperature response lag problem caused by the high thermal inertia of the explosion-proof cavity, realizing accurate prediction of the future thermal state of the battery, and providing a reliable time window for early power intervention.

[0036] Based on the above embodiments, in some embodiments, the spatial uniformity of the quantized temperature field includes: Based on the ratio of the standard deviation to the global average temperature in the aforementioned basic statistical characteristics, the coefficient of variation is calculated to eliminate the influence of absolute temperature values ​​and measure the relative uniformity of the temperature field. Based on the difference between the highest temperature in the real-time temperature data and the global average temperature, and the ratio of the global average temperature, a local hotspot deviation degree is calculated to measure the degree to which a local hotspot deviates from the overall level.

[0037] In this embodiment, step S103 aims to quantify the spatial uniformity of the temperature field under the dispersed layout of battery modules, and its specific implementation process is as follows. First, based on the standard deviation of the temperature field calculated in step S101... and global average temperature Through formula Calculate the coefficient of variation of the temperature field Among them, standard deviation This reflects the absolute dispersion of the temperature of each module relative to the average temperature, while the global average temperature... This represents the overall heat load level of the battery cluster. Coefficient of variation As a dimensionless quantity, it eliminates the influence of the absolute value of temperature, allowing for a more accurate measurement of the relative uniformity of the temperature field. For example, a 5°C temperature difference in a low-temperature region (e.g., 20°C) and a 5°C temperature difference in a high-temperature region (e.g., 50°C) have different effects on battery uniformity, while the coefficient of variation... This coefficient of variation can effectively distinguish such differences. The larger the value, the more uneven the temperature field, and the higher the risk of localized hot spots. Next, based on the highest temperature among the real-time temperatures of each battery module... and global average temperature Through formula Calculate local hotspot deviation The formula uses the difference between the highest and average temperatures as the numerator and the average temperature as the denominator, specifically to measure the deviation of the "worst-case" scenario from the overall level. In a distributed layout, if a module is located in a heat dissipation dead zone, its temperature will be significantly higher than other modules; in this case, the deviation of local hotspots is considered significant. This will increase significantly. This index will be directly used in the calculation of the spatial dimension equilibrium coefficient in the subsequent step S104 to derated the power. Through the above two sub-steps, this embodiment realizes the quantification of the spatial equilibrium of the temperature field, providing a key spatial dimension input for subsequent dynamic power decisions.

[0038] Through the specific implementation methods described above, this embodiment can accurately detect temperature differences between modules located at different positions within the explosion-proof enclosure, particularly addressing localized hotspots. (Coefficient of variation) Deviation from local hotspots The introduction of this technology enables the system to accurately quantify the non-uniformity of the temperature field, providing a scientific basis for subsequent dynamic power limiting. This effectively avoids battery performance degradation and safety risks caused by local overheating, and extends the overall lifespan of the battery system.

[0039] Based on the above embodiments, in some embodiments, the dynamic adjustment of the rated maximum current of the battery cluster includes: Based on the degree of closeness between the predicted temperature and the preset safe temperature threshold, and combined with the initial ambient temperature when the system starts up, a safety factor is calculated to characterize the thermal risk in the time dimension. Based on the combination of the coefficient of variation, which characterizes the degree of non-uniformity of the temperature field, and the degree of deviation of local hot spots, which characterizes the degree of deviation of local hot spots, the equilibrium coefficient, which characterizes the thermal equilibrium of the spatial dimension, is calculated. Using the time-dimensional safety factor and the spatial-dimensional balance factor as constraint factors, the rated maximum current of the battery cluster is multiplied together to obtain the maximum allowable charge and discharge current, which is simultaneously constrained by both future thermal state and current thermal distribution uniformity.

[0040] Furthermore, the time-dimensional safety factor is used to characterize the degree of closeness between the predicted temperature and the safe temperature threshold, and the spatial-dimensional equilibrium coefficient is used to characterize the uniformity of the temperature field.

[0041] In this embodiment, the specific implementation process of step S104 is as follows. First, based on the predicted temperature calculated in step S102... Safe temperature threshold of battery system and the initial ambient temperature at system startup Calculate the security factor in the time dimension. Specifically, this coefficient is calculated using the formula... ,in This is a preset fixed value, for example, it can be set to 55℃. The ambient temperature collected during the system's initial power-on or the lowest module temperature in the current battery cluster is used as the denominator of the reference temperature difference to prevent division by zero. The input source for this formula is the predicted temperature output from step S102. And preset and The processing involves quantifying the proximity between the predicted temperature and the safety threshold using subtraction and division operations, with the output being a dimensionless coefficient between 0 and 1. ,when The closer hour, The coefficient of variation approaches 0, and vice versa. Next, the coefficient of variation calculated in step S103 is... Deviation from local hotspots Calculate the spatial dimensional balance coefficient Specifically, this coefficient is calculated using the formula... Its input source is the output of step S103. and The processing involves converting the non-uniformity of the temperature field into a constraint factor between 0 and 1 through addition and reciprocal operations. The output result is... When the temperature field is uniform and there are no local hot spots, and All are close to 0. If the value is close to 1, then the denominator increases; otherwise, it decreases. Reduce. Finally, based on the time-dimensional security factor calculated above. Spatial Dimension Balance Coefficient The rated maximum current of the battery cluster Dynamic adjustments are made to obtain the maximum allowable charging and discharging current. Specifically, this adjustment uses a multiplication formula. Its input source is the time dimension safety factor. Spatial Dimensional Balance Coefficient and the preset rated maximum current The processing action involves multiplying the thermal inertia constraint in the time dimension with the uniformity constraint in the spatial dimension onto the rated current, and the output result is the final dynamic allowable current. This current value is simultaneously constrained by both "whether it will overheat in the future" and "whether it is uniform at present", thus enabling precise dynamic control of the battery cluster power.

[0042] This specific implementation introduces a safety factor based on predicted temperature and a balance factor based on the coefficient of variation and hot spot deviation, and dynamically adjusts the rated current in a multiplicative manner. This achieves dual compensation for thermal inertia and temperature field inhomogeneity within the explosion-proof cavity, effectively avoiding over-temperature protection triggering due to thermal hysteresis and excessive power limitation caused by local hot spots. Thus, it improves the operating efficiency of mining equipment and the service life of the battery system while ensuring safety.

[0043] Based on the above embodiments, this embodiment will describe in detail the specific implementation of a control method for explosion-proof mining energy storage batteries. This method collects real-time temperature data, voltage data, and total current of each battery module in the battery cluster. Based on a progressive logic of basic statistical feature construction, time series prediction, spatial equilibrium assessment, and dynamic power decision-making, it achieves accurate prediction of the battery's thermal state and calculation of dynamic power boundaries, ultimately outputting the maximum allowable charging and discharging current as a control command.

[0044] First, the system collects the real-time temperature data of N battery modules in the battery cluster. Where N is the total number of modules, and the temperature data of each module is collected by a temperature sensor installed on each battery module. Simultaneously, the system also collects the real-time voltage data of each battery module. and the total current of the battery cluster (Scalar, negative during charging, positive during discharging). In addition, the system stores historical temperature data, including the previous sampling period. average temperature before and the average temperature two sampling periods ago This provides a data foundation for subsequent time series forecasting.

[0045] In the basic statistical feature construction phase, the system first performs statistical analysis on the real-time temperature data of N modules to eliminate sensor noise and establish a temperature field benchmark. Specifically, this is achieved through the formula... Calculate the global average temperature This parameter represents the overall heat load level of the battery cluster, and its rate of increase directly reflects the heat accumulation inside the explosion-proof cavity. Next, using the formula... Calculate the standard deviation of the temperature field This parameter is an absolute indicator of the uniformity of temperature distribution. A larger value indicates that some modules have extremely poor heat dissipation or localized overcurrent. Finally, using the formula... Calculate temperature range This parameter directly reflects the temperature difference between the hottest and coldest points; an excessively large difference indicates a problem. This means that uneven distribution of thermal stress may lead to deformation of structural components or loosening of connectors.

[0046] In the time series prediction feature construction stage, the system addresses the temperature lag problem caused by the "large heat capacity and slow heat dissipation" of the explosion-proof cavity by using discrete difference to construct the temperature rise rate and temperature rise acceleration to predict the temperature at future times. First, based on the current global average temperature... and the global average temperature of the previous sampling period Through formula Calculate the instantaneous temperature rise rate This parameter reflects the rate of heat accumulation and increases rapidly during the initial stages of high-current discharge. Then, based on the instantaneous temperature rise rate... Instantaneous temperature rise rate of the previous sampling period Through formula Calculate the acceleration due to temperature rise This parameter can detect whether the temperature rise trend is "faster" or "slower," and when the current suddenly increases, It will react before temperature itself, manifesting as a positive increase, providing a window of opportunity for early intervention. Finally, using the first three terms of the Taylor expansion (position, velocity, acceleration), through the formula... Predicting the future The temperature after seconds is the predicted temperature. .in, To predict the step size, a response period of the control system is typically used, ranging from 5 to 10 seconds, which is a fixed value based on the system response time. This formula fully considers the thermal inertia of the explosion-proof cavity. When the predicted temperature is significantly higher than the linear predicted value, the power limit will be triggered in advance to prevent over-temperature protection caused by "overshoot".

[0047] In the spatial equilibrium feature construction stage, the system addresses the temperature unevenness caused by the dispersed layout of battery modules by introducing the coefficient of variation and local hot spot deviation from statistics to quantify the relative dispersion. Firstly, based on the standard deviation... and global average temperature Through formula Calculate the coefficient of variation of the temperature field This parameter is a dimensionless quantity, eliminating the influence of the absolute value of the temperature. The larger the value, the more uneven the temperature field, and the higher the risk of localized hot spots. Then, based on the highest temperature among the real-time temperatures of each battery module... and global average temperature Through formula Calculate local hotspot deviation This parameter is specifically used to measure the deviation of the "worst-case" scenario relative to the overall level. In a distributed layout, if a module is located in a heat dissipation dead zone, It will increase significantly, and this indicator will be directly used in the calculation of power derating.

[0048] During the dynamic power decision-making phase, the system will predict the temperature over time. and the non-uniformity of spatial dimensions ( , Combined, calculate the maximum allowable charging and discharging current at the current moment. This scheme uses a multiplicative factor approach to represent a progressively increasing set of constraints. First, based on the predicted temperature... Safe temperature threshold of battery system and the initial ambient temperature at system startup Through formula Calculate the time dimension security factor .in, It can be set to 55℃. The reference temperature difference in the denominator is used to prevent division by zero. near At that time, the molecular weight approaches 0. Approaching 0, force a reduction in power; when far below hour, A value close to 1 allows for full power output. This coefficient utilizes predicted temperature to achieve "early derating," avoiding temperature overshoot. Then, based on the coefficient of variation... Deviation from local hotspots Through formula Calculate the spatial dimensional balance coefficient When the temperature field is very uniform, , ,but When the temperature field is non-uniform or there are local hot spots, the denominator increases. The formula, through division, naturally expresses the mathematical concept of the "barrel effect": the higher the non-uniformity, the lower the allowable overall power, thus forcing the system to reduce current and giving hotspot modules time to recover from heat dissipation. Finally, based on the time dimension safety factor... Spatial Dimension Balance Coefficient Through formula Rated maximum current for battery clusters Dynamic adjustments are made to obtain the maximum allowable charging and discharging current. The current is simultaneously constrained by both "whether it will overheat in the future" (time dimension) and "the current is not uniform" (spatial dimension), which serves as the final control command output.

[0049] This embodiment eliminates the thermal hysteresis effect of the explosion-proof cavity through the aforementioned progressive feature construction and calculation method, achieving "zero overshoot" temperature control, solving the problem of local hot spots in distributed layouts, extending battery life, and improving the operating efficiency of mining equipment, thus realizing "on-demand power allocation." The entire solution only involves basic operations such as addition, subtraction, multiplication, division, square root, and squaring, as well as basic statistical quantities such as mean and variance. It does not require complex matrix operations or large amounts of historical data training. The algorithm is lightweight and can be easily embedded directly into existing low-cost mining BMS microcontrollers, making it highly valuable for widespread application.

[0050] Please refer to Figure 2 , Figure 2 This invention provides a structural block diagram of an explosion-proof mining energy storage battery control device; the specific device may include: Data acquisition module 100 is used to collect real-time temperature data of each battery module in the battery cluster; The basic feature construction module 200 is used to establish basic statistical features of the temperature field based on the real-time temperature data, wherein the basic statistical features include at least the global average temperature. The time prediction module 300 is used to calculate the temperature rise rate and temperature rise acceleration based on the current global average temperature and the historical global average temperature, and to use the temperature rise rate and temperature rise acceleration to predict the temperature at future times, thereby obtaining the predicted temperature. The spatial quantization module 400 is used to quantify the spatial uniformity of the temperature field based on the basic statistical characteristics and the real-time temperature data, and obtain a spatial uniformity index. The dynamic adjustment module 500 is used to calculate the time dimension safety factor based on the predicted temperature and the spatial dimension balance factor based on the spatial balance index. It uses the time dimension safety factor and the spatial dimension balance factor to dynamically adjust the rated maximum current of the battery cluster and output the maximum allowable charging and discharging current.

[0051] The explosion-proof mining energy storage battery management device of this embodiment is used to implement the aforementioned explosion-proof mining energy storage battery management method. Therefore, the specific implementation of the explosion-proof mining energy storage battery management device can be found in the embodiment section of the explosion-proof mining energy storage battery management method above. For example, the data acquisition module 100, the basic feature construction module 200, the time prediction module 300, the spatial quantization module 400, and the dynamic adjustment module 500 are respectively used to implement steps S101-S104 in the above-mentioned explosion-proof mining energy storage battery management method. Therefore, its specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.

[0052] A specific embodiment of the present invention also provides an explosion-proof mining energy storage battery management device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-described explosion-proof mining energy storage battery management method.

[0053] A specific embodiment of the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described explosion-proof mining energy storage battery management method.

[0054] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0056] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0057] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0058] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for managing explosion-proof mining energy storage batteries, characterized in that, Includes the following steps: Real-time temperature data of each battery module in the battery cluster is collected, and basic statistical features of the temperature field are established based on the real-time temperature data. The basic statistical features include at least the global average temperature. Based on the current global average temperature and the historical global average temperature, the temperature rise rate and temperature rise acceleration are calculated, and the temperature at future times are predicted using the temperature rise rate and temperature rise acceleration to obtain the predicted temperature. Based on the aforementioned basic statistical characteristics and the aforementioned real-time temperature data, the spatial uniformity of the temperature field is quantified to obtain a spatial uniformity index. The time dimension safety factor is calculated based on the predicted temperature, and the spatial dimension balance factor is calculated based on the spatial balance index. The rated maximum current of the battery cluster is dynamically adjusted using the time dimension safety factor and the spatial dimension balance factor to obtain the maximum allowable charge and discharge current.

2. The method according to claim 1, characterized in that, The basic statistical characteristics for establishing the temperature field include: The average temperature of each battery module is calculated based on the real-time temperature data to obtain the global average temperature. Based on the global average temperature and the real-time temperature data, the deviation of each module's temperature from the global average temperature is calculated, and the standard deviation is obtained. The range is obtained based on the difference between the highest and lowest temperatures in the real-time temperature data.

3. The method according to claim 1, characterized in that, The prediction of future temperatures includes: Based on the difference between the current global average temperature and the global average temperature of the previous sampling period and the sampling interval, the instantaneous temperature rise rate, which characterizes the current rate of heat accumulation, is calculated. Based on the difference between the instantaneous temperature rise rate and the instantaneous temperature rise rate of the previous sampling period and the sampling interval, the temperature rise acceleration used to characterize the rate of change of the temperature rise trend is calculated. Based on the current global average temperature, the instantaneous temperature rise rate, the temperature rise acceleration, and the preset prediction step size, a prediction model is constructed using the Taylor expansion principle to calculate the predicted temperature at future times.

4. The method according to claim 3, characterized in that, The prediction step size is one response cycle of the control system, and the global average temperature at the historical moment includes the global average temperature of the previous sampling cycle and the global average temperature two sampling cycles ago.

5. The method according to claim 1, characterized in that, The spatial uniformity of the quantized temperature field includes: Based on the ratio of the standard deviation to the global average temperature in the aforementioned basic statistical characteristics, the coefficient of variation is calculated to eliminate the influence of absolute temperature values ​​and measure the relative uniformity of the temperature field. Based on the difference between the highest temperature in the real-time temperature data and the global average temperature, and the ratio of the global average temperature, a local hotspot deviation degree is calculated to measure the degree to which a local hotspot deviates from the overall level.

6. The method according to claim 1, characterized in that, The dynamic adjustment of the rated maximum current of the battery cluster includes: Based on the degree of closeness between the predicted temperature and the preset safe temperature threshold, and combined with the initial ambient temperature when the system starts up, a safety factor is calculated to characterize the thermal risk in the time dimension. Based on the combination of the coefficient of variation, which characterizes the degree of non-uniformity of the temperature field, and the degree of deviation of local hot spots, which characterizes the degree of deviation of local hot spots, the equilibrium coefficient, which characterizes the thermal equilibrium of the spatial dimension, is calculated. Using the time-dimensional safety factor and the spatial-dimensional balance factor as constraint factors, the rated maximum current of the battery cluster is multiplied together to obtain the maximum allowable charge and discharge current, which is simultaneously constrained by both future thermal state and current thermal distribution uniformity.

7. The method according to claim 6, characterized in that, The time dimension safety factor is used to characterize the degree of closeness between the predicted temperature and the safe temperature threshold, and the spatial dimension uniformity factor is used to characterize the uniformity of the temperature field.

8. A control device for an explosion-proof mining energy storage battery, characterized in that, include: The data acquisition module is used to collect real-time temperature data of each battery module in the battery cluster; The basic feature construction module is used to establish basic statistical features of the temperature field based on the real-time temperature data, wherein the basic statistical features include at least the global average temperature. The time prediction module is used to calculate the temperature rise rate and temperature rise acceleration based on the current global average temperature and the global average temperature at historical times, and to use the temperature rise rate and temperature rise acceleration to predict the temperature at future times, thereby obtaining the predicted temperature. The spatial quantization module is used to quantify the spatial uniformity of the temperature field based on the basic statistical characteristics and the real-time temperature data, and obtain a spatial uniformity index. The dynamic adjustment module is used to calculate the time dimension safety factor based on the predicted temperature and the spatial dimension balance factor based on the spatial balance index. It uses the time dimension safety factor and the spatial dimension balance factor to dynamically adjust the rated maximum current of the battery cluster and output the maximum allowable charging and discharging current.

9. A control device for an explosion-proof mining energy storage battery, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the explosion-proof mining energy storage battery management 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 a computer program that, when executed by a processor, implements the steps of the explosion-proof mining energy storage battery management method as described in any one of claims 1 to 7.

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