Outdoor energy storage power supply heat management adjusting system for working conditions of mining area

Abstract

The invention, which relates to the technical field of energy storage power supply thermal management, discloses a mining area working condition-oriented outdoor energy storage power supply thermal management adjustment system comprising a data acquisition module, an energy reconstruction module, a mapping updating module, a prediction modeling module and an adjustment execution module. The method comprises the following steps: acquiring battery running state and environment state data, and reconstructing to obtain a temperature correction available energy state quantity; an environment action correction state is updated in combination with environment data mapping; and based on this, a thermal state prediction model is constructed, a predicted thermal management state is output, and a corresponding thermal management adjustment instruction is generated. According to the invention, a rainwater environment attenuation factor and an impedance dynamic increment are introduced to realize dynamic compensation for a severe environment; the four-order Runge-Kutta algorithm is adopted to predict the thermal trajectory of the battery in a high-precision manner, and the dew-point temperature is used as a safety boundary to carry out prospective regulation and control, so that the risk of overheating and dew formation of the battery under the working conditions of high humidity and rainfall in a mining area is effectively solved, and the thermal management precision and the system reliability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage power supply thermal management, and particularly relates to an outdoor energy storage power supply thermal management adjusting system for mine working conditions. BACKGROUND

[0002] In recent years, with the transformation of global energy structure and the promotion of green mine construction, outdoor energy storage power supply is increasingly widely applied in the fields of mine exploitation, remote monitoring and emergency power supply. Due to the particularity of the geographical location, the mine environment often presents the characteristics of large diurnal temperature difference, high humidity and severe load fluctuation, which poses a severe challenge to the thermal management system of energy storage batteries.

[0003] At present, the Chinese patent application with the application number CN202410759937.0 discloses a thermal management control method and system for mobile energy storage power supply, which includes a heat monitoring module, a heat analysis module, an analysis and judgment module, a heat dissipation management module and an abnormal alarm module. The system can realize real-time monitoring and analysis, quickly respond to temperature changes, realize efficient thermal management of mobile energy storage power supply, effectively prevent overheating, and reduce the risk of performance decline and safety accidents caused by overheating. However, in the related technology, the real-time correction influence of the dynamic environment of the mine (such as rainfall exposure and extreme humidity) on the heat generation item of the battery system cannot be taken into account, and the risk of dew point condensation triggered by temperature and humidity coupling in a high humidity environment is ignored, which leads to the inability to accurately define the numerical boundary of safe operation, and the inability to realize forward-looking fine adjustment through high-precision thermal state evolution trajectory prediction, resulting in poor thermal management efficiency and precision of mobile energy storage power supply under harsh working conditions. SUMMARY

[0004] The technical problem solved by the present application is that the prior art cannot take into account the real-time correction influence of the dynamic environment of the mine (such as rainfall exposure and extreme humidity) on the heat generation item of the battery system, and ignores the risk of dew point condensation triggered by temperature and humidity coupling in a high humidity environment, which leads to the inability to accurately define the numerical boundary of safe operation, and the inability to realize forward-looking fine adjustment through high-precision thermal state evolution trajectory prediction, resulting in poor thermal management efficiency and precision of mobile energy storage power supply under harsh working conditions.

[0005] To solve the above technical problems, the present application provides the following technical solutions: an outdoor energy storage power supply thermal management adjusting system for mine working conditions, comprising: a data acquisition module, an energy reconstruction module, a mapping update module, a prediction modeling module and an adjusting execution module; The data acquisition module is used to acquire the operating state data and environmental state data of the energy storage battery. The energy reconstruction module is used to reconstruct the available energy state of the energy storage battery based on the operating state data and the environmental state data, to obtain a temperature correction available energy state quantity ; The mapping update module is used to correct the available energy state quantity based on environmental state data. Perform a mapping update to obtain the environmental effect correction state; The predictive modeling module is used to construct a thermal state prediction model based on environmental effects to correct the state and output the predicted thermal management state. The predicted thermal management state includes risk-type predicted thermal management state and steady-state predicted thermal management state; The adjustment execution module is used to generate corresponding thermal management adjustment instructions based on the predicted thermal management status.

[0006] As a preferred embodiment of the outdoor energy storage power thermal management and regulation system for mining conditions described in this invention, the operating status data includes battery state of charge, terminal voltage, and current. The environmental status data includes ambient temperature, ambient humidity, and rainfall exposure status.

[0007] As a preferred embodiment of the outdoor energy storage power thermal management and regulation system for mining conditions described in this invention, the energy reconstruction module includes a state analysis unit, an environmental coupling unit, and an energy correction unit. The state parsing unit is used to parse and process the operating state data, constructing a basic energy state vector based on the battery state of charge, terminal voltage, and current. Its processing logic includes: Retrieve the battery state of charge, terminal voltage, and current from the operating status data, normalize the battery state of charge, and obtain the state of charge component. The dimensions of the terminal voltage and current are unified to obtain the voltage state component and the current state component, respectively. According to the preset energy state component arrangement order, the charge state component, voltage state component and current state component are vectorized and combined to obtain the basic energy state vector. The environmental coupling unit is used to introduce environmental state data into the basic energy state vector, perform environmental correlation calculations on the basic energy state vector, and obtain a temperature-related energy state characterization. Its processing logic includes: The ambient temperature is extracted from the environmental status data, and the ambient temperature is numerically standardized to obtain the temperature effect factor. Based on the component index of each energy state component in the basic energy state vector, the temperature action factor is matched with the corresponding energy state component at the component level. The temperature-dependent energy component is obtained by performing a weighted correlation calculation between the temperature factor and the corresponding energy state component. The calculation formula is as follows: ; in, For temperature-related energy components, The component number is the component sequence number. , For the preset component association weights, Temperature is the influencing factor. These are energy state components; By combining the temperature-related energy components, a temperature-related energy state characterization is obtained. The energy correction unit is used to correct the basic energy state vector based on the temperature-related energy state characterization to obtain the temperature-corrected usable energy state quantity. Its processing logic includes: Extract environmental humidity and rainfall exposure status from environmental status data to construct a rainwater environmental attenuation factor; The temperature-related energy state characterization is reduced and corrected using a rainwater environmental attenuation factor to obtain the temperature-corrected usable energy state quantity. The calculation formula is as follows: ; in, It is an energy state mapping function. This is for characterizing temperature-related energy states. This is the voltage cutoff correction factor. It is a rainwater environment attenuation factor.

[0008] As a preferred embodiment of the outdoor energy storage power supply thermal management and regulation system for mining conditions described in this invention, the system extracts environmental humidity and rainfall exposure status from environmental status data and constructs a rainwater environmental attenuation factor. The processing logic includes: The ambient humidity is retrieved through the communication interface, the relative humidity percentage value at the current moment is extracted, and the rainfall exposure status is analyzed. If it is determined that the current state is in a rainfall-triggered state, the duration of continuous exposure to the current rainfall process is accumulated and recorded through a timer. The relative humidity percentage value is biased against a preset humidity trigger threshold, and then substituted into a preset mapping function to calculate the instantaneous risk coefficient. The calculation formula is as follows: ; in, For instantaneous risk coefficient, This is a percentage value for relative humidity. The preset humidity trigger threshold, This is the preset humidity sensitivity coefficient; The cumulative exposure impact term is obtained by performing a time-weighted integral over the duration of continuous exposure using a preset time degradation operator. The calculation formula is as follows: ; in, For cumulative exposure impact, The environmental erosion constant, The degradation function of the protective layer over time. For continuous exposure time; By nonlinearly coupling the instantaneous risk coefficient term and the cumulative exposure impact term, a rainwater environmental attenuation factor is constructed, the calculation formula of which is as follows: ; in, It is a rainwater environment attenuation factor.

[0009] As a preferred embodiment of the outdoor energy storage power thermal management and regulation system for mining conditions described in this invention, the mapping update module includes an environmental effect mapping unit, a state update verification unit, and a parameter synchronization unit. The environmental effect mapping unit is used to analyze the rainwater environmental attenuation factor and, in conjunction with environmental temperature and humidity, obtains the dynamic impedance increment through a preset sensitivity matrix mapping. The calculation formula is as follows: ; in, For impedance dynamic increment, As the base for impedance influence, For activation energy operator, This refers to the absolute temperature value corresponding to the ambient temperature. As a rainwater environmental attenuation factor, The preset sensitivity matrix, The gas constant is The preset stability correction operator has a value range of [value range missing]. ; The state update verification unit is used to correct the available energy state quantity based on the temperature using impedance dynamic increments. The real-time compensation update process includes the following logic: The real-time voltage drop loss power of the terminal voltage is determined based on the dynamic impedance increment and the preset current value. The equivalent resistance of the battery is determined based on the ambient humidity and rainfall exposure status. The real-time voltage drop loss power of the terminal voltage and the battery equivalent resistance are integrated over time to obtain the corresponding environmental energy loss increment. Use of ambient energy loss increments to correct available energy state quantities for temperature The environmental impact correction state is obtained by performing numerical subtraction. The parameter synchronization unit is used to transmit the environmental action correction state to the prediction modeling module as an input variable for the thermal state prediction model.

[0010] As a preferred embodiment of the outdoor energy storage power thermal management and regulation system for mining conditions described in this invention, the predictive modeling module includes a heat generation calculation unit, an environmental constraint extraction unit, and a thermal trajectory solving unit. The heat generation calculation unit is used to receive the environmental action correction state and the impedance dynamic increment, use the environmental action correction state as the starting state quantity for heat trajectory prediction, and introduce the impedance dynamic increment into the preset heat generation rate equation to determine the corrected system heat generation term. The environmental constraint extraction unit is used to analyze the ambient temperature and humidity in the environmental state data, determine the dew point temperature under the current working condition through the preset dew point mapping logic, and use the dew point temperature as the numerical boundary constraint of the predicted trajectory. The thermal trajectory solving unit is used to construct a thermal state prediction model based on the numerical boundary constraints of the corrected system heat generation term and the predicted trajectory. Through iterative solving of the thermal state prediction model, the predicted battery temperature trajectory within a future preset time window is output, and the predicted thermal management state is output according to the correlation between the predicted battery temperature trajectory and the numerical boundary constraints of the predicted trajectory.

[0011] As a preferred embodiment of the outdoor energy storage power thermal management and regulation system for mining conditions described in this invention, the system receives environmental action correction states and impedance dynamic increments, uses the environmental action correction states as the starting state quantity for thermal trajectory prediction, and introduces the impedance dynamic increments into a preset heat generation rate equation to determine the corrected system heat generation term. The processing logic includes: Extract the real-time state of charge and terminal voltage components from the environmental effect correction state, and combine them with the preset initial temperature vector to construct the iteration starting point of the thermal trajectory prediction model; Obtain the predicted current value within the prediction window, and incorporate the predicted current value and the dynamic impedance increment into the preset heat generation rate equation to determine the heat generation increment induced by rainwater conditions. The calculation formula is as follows: ; in, For the corrected system heat generation term, This is the predicted current value. The equivalent resistance of the battery. For impedance dynamic increment; The iteration start point of the thermal trajectory prediction model is synchronized with the corrected system heat generation term to the thermal trajectory solution unit.

[0012] As a preferred embodiment of the outdoor energy storage power supply thermal management and regulation system for mining conditions described in this invention, the system analyzes the ambient temperature and humidity from the environmental status data, determines the dew point temperature under the current operating conditions through a preset dew point mapping logic, and uses the dew point temperature as a numerical boundary constraint for the predicted trajectory. The processing logic includes: The ambient humidity is converted into a relative humidity percentage value using a linear proportional mapping algorithm. The ambient temperature and the relative humidity percentage value are then substituted into the Magnus formula to calculate the dew point temperature under the current operating conditions. The dew point temperature is used as the numerical boundary constraint of the predicted trajectory, and the numerical boundary constraint of the predicted trajectory is synchronized to the thermal trajectory solving unit as the lower limit range of the temperature component during the iterative solution process.

[0013] As a preferred embodiment of the outdoor energy storage power supply thermal management and regulation system for mining conditions described in this invention, a thermal state prediction model is constructed based on the numerical boundary constraints of the corrected system heat generation term and the predicted trajectory. The predicted battery temperature trajectory within a preset time window is output through iterative solving of the thermal state prediction model. Furthermore, the predicted thermal management state is output based on the correlation between the predicted battery temperature trajectory and the numerical boundary constraints of the predicted trajectory. The processing logic includes: Based on the corrected system heat production term and ambient temperature, a first-order differential equation is constructed, and its calculation formula is as follows: ; in, Predicting battery temperature, The heat conversion coefficient, This is the environmental heat dissipation correction factor. For the corrected system heat generation term, The ambient temperature; Divide the future preset time window into The first-order differential equation is iteratively calculated using a fourth-order Runge-Kutta algorithm within a discrete simulation step. Within each simulation step, numerical integration is performed based on the battery's predicted temperature from the previous moment and the environmental correction state to obtain the predicted battery temperature at the current sampling moment. The calculation formula is as follows: ; ; ; ; ; in, This is the predicted battery temperature at the current sampling time. Predict the battery temperature for the previous moment. The mapping function corresponding to the first-order differential equation. For time variables In the A numerical coordinate of the distance from the walking distance. The slope at the starting point of the discrete simulation step size. Based on The slope at the midpoint of the predicted discrete simulation step size. Based on The slope at the midpoint of the predicted discrete simulation step size. Based on The slope of the predicted discrete simulation step-size endpoint. This refers to the step size in discrete simulation. During the iterative solution process, the obtained battery temperature prediction value is compared with the numerical boundary constraints of the predicted trajectory in real time. The numerical boundary constraints of the predicted trajectory include the lower limit of the dew point temperature and the upper limit of the battery safety temperature. If any predicted battery temperature value is greater than or equal to the upper limit of the battery's safe temperature within a preset time window in the future, it will be determined as a trajectory state with overheating risk. If, within a preset time window in the future, any predicted battery temperature is less than or equal to the lower limit of the dew point temperature, then the trajectory is determined to be at risk of condensation. If all predicted battery temperatures are between the lower limit of the dew point temperature and the upper limit of the battery safe temperature within the preset time window in the future, it is determined to be a steady-state trajectory state. If a trajectory state is determined to have an overheating risk, the moment when the battery safety temperature limit is first reached and the predicted temperature exceeding the battery safety temperature limit are extracted to generate an overheating risk type predicted thermal management state. If the condition is determined to be a condensation risk trajectory state, the moment when the predicted battery temperature first reaches the dew point temperature and the extent to which the predicted temperature is lower than the lower limit of the dew point temperature are extracted to generate a condensation risk type predicted thermal management state. If the state is determined to be a steady-state trajectory, the extreme temperature values ​​within the future preset time window and the difference between the predicted battery temperature at the end of the future preset time window and the current battery temperature value are extracted to generate a steady-state predicted thermal management state.

[0014] As a preferred embodiment of the outdoor energy storage power supply thermal management regulation system for mining conditions described in this invention, the regulation execution module is used to generate corresponding thermal management regulation commands based on the predicted thermal management state, and its processing logic includes: If the predicted thermal management state is an overheat risk type predicted thermal management state, then an enhanced cooling regulation command will be executed; The enhanced cooling regulation command includes locking the cooling fan to maximum power operation and locking the liquid circulation pump to maximum power operation. If the predicted thermal management status is a condensation risk type predicted thermal management status, then execute the anti-condensation heating adjustment command; The anti-condensation heating adjustment command includes turning off the cooling fan, turning on the heater, and setting the heater power to the preset anti-condensation operating power; If the predicted thermal management state is a steady-state predicted thermal management state, then execute the power maintenance regulation command; The power maintenance and adjustment command includes maintaining the current operating status and power of the cooling fan, liquid circulation pump, and heater unchanged.

[0015] The beneficial effects of this invention are as follows: This invention innovatively utilizes the fourth-order Runge-Kutta algorithm to perform high-precision iterative solutions to the future thermal state evolution trajectory of the battery. By accurately locking the start time of the risk-based predicted thermal management state and generating corresponding thermal management adjustment commands, it effectively eliminates the response lag of thermal management, ensuring the adjustment efficiency and operational safety of outdoor energy storage power supplies for mining conditions under harsh environments; and it corrects the available energy state quantity based on environmental state data. By performing mapping updates and obtaining the environmental effect correction state, dynamic correction and compensation of the system's heat generation term are realized, significantly improving the accuracy of heat monitoring under complex working conditions in mining areas. The dynamic dew point temperature calculated based on environmental state data is used as the numerical boundary constraint of the prediction trajectory, realizing dual judgment of overheating prevention and condensation prevention for mining conditions, fundamentally eliminating the safety hazards of condensation and condensation of energy storage power supplies in high humidity environments. Attached Figure Description

[0016] Figure 1 This is a basic flowchart of an outdoor energy storage power thermal management and regulation system for mining conditions, provided as an embodiment of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] Example, refer to Figure 1 It provides an outdoor energy storage power thermal management and regulation system for mining conditions, including: a data acquisition module, an energy reconstruction module, a mapping update module, a predictive modeling module, and a regulation execution module; The data acquisition module is used to acquire the operating status data of the energy storage battery and the environmental status data; The energy reconstruction module is used to reconstruct the available energy state of the energy storage battery based on operating status data and environmental status data, and obtain the temperature-corrected available energy state quantity. ; The mapping update module is used to correct the available energy state quantities based on environmental state data to adjust the temperature. Perform a mapping update to obtain the environmental effect correction state; The predictive modeling module is used to construct a thermal state prediction model based on environmental effects to correct the state and output the predicted thermal management state. Predicted thermal management status includes risk-based predicted thermal management status and steady-state predicted thermal management status; The adjustment execution module is used to generate corresponding thermal management adjustment commands based on the predicted thermal management status.

[0019] In practice, the operating status data includes battery state of charge, terminal voltage, and current. Environmental status data includes ambient temperature, ambient humidity, and rainfall exposure status.

[0020] Specifically, operational status data is primarily acquired through the system's internal hardware sampling layer. The battery state of charge (SOC) is estimated by the battery management system (BMS) based on sampled terminal voltage and current values, combined with the ampere-hour integration method. Terminal voltage and current are obtained in real-time through high-precision voltage sensors and Hall effect current sensors integrated into the battery cluster bus, characterizing the real-time energy throughput of the energy storage battery. Environmental status data is acquired through sensor arrays deployed on the outer wall of the outdoor enclosure or at the air inlet. Ambient temperature and humidity are sampled in real-time by temperature and humidity transmitters; rainfall exposure status is acquired through rain-sensing plate trigger signals.

[0021] In open-pit mining conditions, rainfall events not only cause scalar changes in ambient humidity but also generate a significant cooling effect through the adhesion and evaporation of rainwater on equipment casings, altering the convective heat transfer coefficient at the heat dissipation interface. Simultaneously, prolonged rainfall exposure affects the pressure balance inside and outside the system and induces condensation risks; its impact on the system's heating model is fundamentally different from that of a simple high-humidity environment. By inputting the rainfall exposure state as an independent variable, the system can calculate the rainwater environment attenuation factor reflecting the shift in heat transfer characteristics, thereby correcting the available energy state quantity for temperature adjustment. Dynamic mapping correction is performed. This setting enables the thermal management system to accurately identify rainfall disturbances unique to the mining area, achieving a logical leap from passive temperature control to proactive risk warning, and ensuring the timeliness and safety of regulation command generation.

[0022] In practice, the energy reconfiguration module includes a state analysis unit, an environment coupling unit, and an energy correction unit. The state parsing unit is used to parse and process the operating state data, constructing a basic energy state vector based on the battery state of charge, terminal voltage, and current. Its processing logic includes: Retrieve the battery state of charge, terminal voltage, and current from the operating status data, normalize the battery state of charge, and obtain the state of charge component. The dimensions of the terminal voltage and current are unified to obtain the voltage state component and the current state component, respectively. According to the preset energy state component arrangement order, the charge state component, voltage state component and current state component are vectorized and combined to obtain the basic energy state vector. The environmental coupling unit is used to introduce environmental state data into the basic energy state vector, perform environmental correlation calculations on the basic energy state vector, and obtain a temperature-related energy state characterization. Its processing logic includes: The ambient temperature is extracted from the environmental status data, and the ambient temperature is numerically standardized to obtain the temperature effect factor. Based on the component index of each energy state component in the basic energy state vector, the temperature action factor is matched with the corresponding energy state component at the component level. The temperature-dependent energy component is obtained by performing a weighted correlation calculation between the temperature factor and the corresponding energy state component. The calculation formula is as follows: ; in, For temperature-related energy components, The component number is the component sequence number. , For the preset component association weights, Temperature is the influencing factor. These are energy state components; By combining the temperature-related energy components, a temperature-related energy state characterization is obtained. The energy correction unit is used to correct the basic energy state vector based on the temperature-dependent energy state characterization, so as to obtain the temperature-corrected usable energy state quantity. Its processing logic includes: Extract environmental humidity and rainfall exposure status from environmental status data to construct a rainwater environmental attenuation factor; The temperature-related energy state characterization is reduced and corrected using a rainwater environmental attenuation factor to obtain the temperature-corrected usable energy state quantity. The calculation formula is as follows: ; in, It is an energy state mapping function. This is for characterizing temperature-related energy states. This is the voltage cutoff correction factor. It is a rainwater environment attenuation factor.

[0023] In this embodiment, the voltage cutoff correction factor is set to 0.95, which can deduct the portion of the theoretical energy of the battery that cannot be effectively released under adverse operating conditions, thereby mapping the theoretical remaining energy to the actual releaseable energy. Adverse operating conditions include voltage drop caused by high current and low temperature. Temperature correction available energy state quantities The physical dimension of energy is defined as the joule, the unit of energy. The energy state mapping function maps the basic energy state vector, which includes current, terminal voltage, and charge state, to the theoretical remaining usable energy base value of the system at a specific temperature. The physical significance of introducing the voltage cutoff correction coefficient lies in the fact that under harsh open-pit mining conditions (such as high-current discharge or low-temperature environments), the polarization phenomenon caused by the battery's internal resistance can cause the battery terminal voltage to drop rapidly to the system's cutoff threshold, preventing the release of some of the remaining active material's charge. Therefore, the voltage cutoff correction coefficient acts as a "capacity cutoff correction," defining the upper limit of the macroscopically output capacity of the battery system under the current environment.

[0024] The preset energy state component arrangement order refers to the index positions of the state of charge (SOC), voltage, and current (VSO) components in memory or computation registers. For example, position 1 is always SOC, position 2 is terminal voltage, and position 3 is current. This order is set to ensure that subsequent vectorization operations and component-level matching have a definite physical orientation, avoiding the temperature effect factor from incorrectly acting on mismatched physical parameters. Specifically, the system allocates a fixed-length array and fills the normalized SOC, terminal voltage, and current into their corresponding index positions in sequence. The vectorization combination process encapsulates these three scalars using vector operators to construct a three-dimensional column vector, thereby transforming discrete electrical performance parameters into a unified state point for unified environmental coupling calculations in multi-dimensional space.

[0025] In the environmental coupling unit, the component order matching between the temperature action factor and the energy state component is achieved by verifying the index of the vector component. The system will achieve this based on... The value (1, 2, or 3) calls the corresponding preset component association weight, so that the effect of temperature on SOC, terminal voltage, and current is reflected differently. The combination of each temperature-related energy component is to fill the three new scalars after weighted association calculation into a new vector container according to the original index order to generate a temperature-related energy state representation.

[0026] The preset component correlation weights are set based on the sensitivity differences of battery electrochemical characteristics to the effects of temperature rise on different physical quantities. Specifically, since ambient temperature has the most direct impact on ohmic internal resistance, resulting in the largest fluctuation in heating in the current dimension, the current state component is assigned the maximum value in the weight sequence, preferably 0.3. The linearity of terminal voltage polarization affected by temperature is second, so the voltage state component is assigned an intermediate value, preferably 0.2. The state of charge component, as an energy reference, is least affected by instantaneous temperature fluctuations, so it is assigned the minimum value in the sequence, preferably 0.05. This is because ambient temperature has fundamentally different mechanisms affecting the diffusion rate of active materials, electrolyte ion mobility, and ohmic losses within the battery. This setting breaks away from the traditional approach of using a single coefficient for overall compensation, allowing for precise characterization of the differentiated effects of temperature on different dimensions of the battery, thus significantly improving the accuracy of state estimation. Secondly, this differentiated weight allocation gives the system clear physical interpretability; each weight corresponds to a specific electrochemical or physical mechanism, enhancing the system's reliability and maintainability. Through subsequent coupling with on-site environmental parameters in the mining area and online self-learning optimization during operation, this weight system can be dynamically adjusted, giving the system strong environmental adaptability and robustness, and ensuring that it can maintain high-precision state reconstruction capability under different harsh working conditions.

[0027] This application achieves a high-dimensional, dynamic characterization of the usable energy state of energy storage batteries through a three-level reconstruction logic of state analysis, environmental coupling, and energy correction. Traditional single-parameter monitoring often fails to cover the nonlinear impact of environmental fluctuations on battery performance. This application, however, constructs a basic energy state vector including SOC, terminal voltage, and current, and superimposes multiple environmental factor corrections such as temperature, humidity, and rainfall, resulting in a temperature-corrected usable energy state quantity. The system can accurately capture the battery's true heat generation capacity and energy boundary under extreme mining conditions, significantly improving the accuracy of dynamic correction of the system's heat generation term. The introduction of a rainwater environmental degradation factor enables the system to proactively respond to rainfall and high-humidity weather events in the mining area. By transforming rainfall exposure status and humidity abrupt changes into physically meaningful degradation operators, the system can accurately distinguish between "normal heat generation" and "system heat generation rate deviation caused by rainwater cooling shock," thus shifting the thermal management control logic from passive temperature rise feedback to proactive environmental risk warning. This deep perception and dynamic compensation of external environmental disturbances not only effectively eliminates the response lag in thermal management but also ensures safe system operation while avoiding battery cycle life reduction due to improper adjustment commands.

[0028] In practice, environmental humidity and rainfall exposure status are extracted from environmental status data to construct a rainwater environmental attenuation factor. The processing logic includes: The ambient humidity is retrieved through the communication interface, the relative humidity percentage value at the current moment is extracted, and the rainfall exposure status is analyzed. If it is determined that the current state is in a rainfall-triggered state, the duration of continuous exposure to the current rainfall process is accumulated and recorded through a timer. The relative humidity percentage value is biased against a preset humidity trigger threshold, and then substituted into a preset mapping function to calculate the instantaneous risk coefficient. The calculation formula is as follows: ; in, For instantaneous risk coefficient, This is a percentage value for relative humidity. The preset humidity trigger threshold, This is the preset humidity sensitivity coefficient; The cumulative exposure impact term is obtained by performing a time-weighted integral over the duration of continuous exposure using a preset time degradation operator. The calculation formula is as follows: ; in, For cumulative exposure impact, The environmental erosion constant, The degradation function of the protective layer over time. For continuous exposure time; By nonlinearly coupling the instantaneous risk coefficient term and the cumulative exposure impact term, a rainwater environmental attenuation factor is constructed, the calculation formula of which is as follows: ; in, It is a rainwater environment attenuation factor.

[0029] In this embodiment, the environmental erosion constant is set to 0.02, which can convert the rainfall exposure time in this application into a calculable cumulative performance degradation, thereby accurately distinguishing the different effects of short-term showers and long-term overcast and rainy weather on the thermal state of the system. Data retrieval via the communication interface refers to the thermal management controller interacting with an array of environmental sensors deployed outside the enclosure via a CAN bus. The controller periodically sends read commands to the integrated temperature and humidity transmitter. The sensors return messages conforming to Modbus-RTU or other standard protocols, from which the controller parses register values ​​representing relative humidity, such as floating-point numbers between 0 and 100. For rainfall exposure, the system determines the status by analyzing the switching signal or conductivity analog signal from the raindrop sensor. Once the signal exceeds the trigger level, the system interprets it as a rainfall-triggered state and starts an internal timer to accumulate and calculate the duration.

[0030] The impact of humidity on battery systems is not linear. When humidity exceeds a certain critical point, the risks of condensation and insulation degradation increase explosively. The exponential function can effectively simulate this physical characteristic of "the closer to saturation, the higher the risk." It uses the deviation between real-time humidity and a preset humidity threshold as the variable of the exponential term, amplifying the risk weight under high humidity conditions through exponential calculation, providing a sensitive trigger for energy reduction. The preset humidity trigger threshold is usually set at the critical humidity point where condensation easily occurs in mining areas. In this embodiment, it is set to [75%, 85%]. This value is set to establish a "starting point" for the system. Only when the relative humidity exceeds this value does the system consider that ambient moisture begins to pose a substantial threat to the thermal management of the equipment, thereby avoiding invalid calculations in low-humidity, dry environments.

[0031] The preset humidity sensitivity coefficient is a scaling operator used to adjust how quickly the system responds to changes in humidity. In this embodiment, it is set to 2 to 5. The larger the coefficient, the lower the system's tolerance to humidity fluctuations. Even if the humidity only increases slightly, the calculated instantaneous risk coefficient will increase significantly.

[0032] The preset time degradation operator aims to quantify the "cumulative effect" of rainfall in the mining area. Unlike instantaneous risks, continuous rainfall constantly erodes the equipment casing and seeps into gaps, leading to a continuous deterioration of heat transfer boundary conditions. By introducing an environmental erosion constant and a time-varying protective layer degradation function, the system performs a weighted integral on the rainfall duration, accurately capturing the available energy state quantity for temperature correction caused by "short-term showers" and "long-term continuous rain". The varying degrees of weakening caused by these factors result in the rainwater environmental attenuation factor exhibiting time-varying characteristics and environmental memory.

[0033] This application achieves a technological leap from "static parameter monitoring" to "dynamic process assessment" by constructing a nonlinear coupling model of instantaneous risk coefficient and cumulative exposure impact. Traditional environmental correction often only focuses on real-time humidity values, ignoring the time-varying damage to equipment protection capabilities and heat exchange characteristics caused by rainfall duration. In contrast, this application uses an integral operator to quantify the duration of continuous exposure, accurately distinguishing the different degrees of attenuation of battery system usable energy by "short-term high humidity" and "long-term rainfall erosion." This significantly enhances the physical reality of the rainwater environment attenuation factor, effectively avoids misadjustment caused by rainwater cooling shock, and achieves efficient matching of battery power output and heat dissipation intensity while ensuring the system remains within a safe operating range. This is of great significance for extending the service life of energy storage batteries in harsh mining environments.

[0034] In practice, the mapping update module includes an environment effect mapping unit, a state update verification unit, and a parameter synchronization unit. The environmental effect mapping unit is used to analyze the rainwater environmental attenuation factor and, in conjunction with environmental temperature and humidity, obtains the dynamic impedance increment through a preset sensitivity matrix mapping. The calculation formula is as follows: ; in, For impedance dynamic increment, As the base for impedance influence, For activation energy operator, This refers to the absolute temperature value corresponding to the ambient temperature. As a rainwater environmental attenuation factor, The preset sensitivity matrix, The gas constant is The preset stability correction operator has a value range of [value range missing]. ; The state update verification unit is used to correct the available energy state quantity for temperature using impedance dynamic increments. The real-time compensation update process includes the following logic: The real-time voltage drop loss power of the terminal voltage is determined based on the dynamic impedance increment and the preset current value. The equivalent resistance of the battery is determined based on the ambient humidity and rainfall exposure status. The real-time voltage drop loss power of the terminal voltage and the battery equivalent resistance are integrated over time to obtain the corresponding environmental energy loss increment. Use of ambient energy loss increments to correct available energy state quantities for temperature The environmental impact correction state is obtained by performing numerical subtraction. The parameter synchronization unit is used to transmit the environmental action correction state to the predictive modeling module as an input variable for the thermal state prediction model.

[0035] Specifically, in the processing logic of the mapping update module, the system needs to consider the impact of "heat loss" on the battery state during dynamic operation. To address the inconsistency between the dimensions of energy, voltage, and power, the state update verification unit does not directly correct the available energy state quantity based on temperature. Instead of adding or subtracting voltage or power values, the system uses a time-integration conversion method. Based on the voltage drop loss power determined by the dynamic impedance increment and the battery equivalent resistance triggered by ambient humidity, the system continuously integrates over the sampling time window to convert "power loss" into "ambient energy loss increment" with energy units. This increment is then used to correct the available energy state quantity for temperature. Numerical subtraction ensures the rigor of the calculation process in the energy dimension. This step is fundamentally different from the correction in claim 3: claim 3 deals with the upper limit of available capacity due to voltage constraints, while this step deals with the actual thermodynamic losses during system operation. Together, they constitute a precise definition of the corrected state for environmental effects.

[0036] The preset sensitivity matrix is ​​essentially concretized into a dynamic mapping model based on the Arrhenius equation, i.e. This setting is not a simple numerical lookup, but rather a profound revelation of how temperature modulates the impact of environmental severity on system impedance. The physical basis lies in the significant thermal activation characteristics of rainwater and high humidity on electrical structures and their interference with internal electrochemical processes: increased temperature significantly accelerates chemical reaction rates, causing the environmental erosion effect to be amplified exponentially. The activation energy operator in the function defines the energy threshold of the degradation process. Combining the gas constant with the absolute temperature value corresponding to the ambient temperature, the system calculates the current "sensitivity coefficient" in real time within the program submodule. This coefficient, as a multiplicative factor, is used in conjunction with a nonlinear term constructed from the rainwater environmental attenuation factor. The synergistic effect determines the final dynamic impedance increment, with the stability correction operator ensuring numerical robustness of the calculation process under extreme conditions.

[0037] The system utilizes Joule's law to determine the real-time voltage drop loss power at the terminal. When the system carries the predicted current, any impedance increment induced by the environment will result in an additional potential drop, which is converted into heat dissipation. The calculation formula is as follows: ;in, To reduce power loss due to voltage drop, For the preset current value, This is a dynamic increment of impedance; this approach enables the system to be predictive, allowing for a quantitative assessment of the additional power burden caused by environmental degradation during future operating cycles. Simultaneously, to address the battery's equivalent resistance caused by insulation degradation, the system models and simulates the accelerated degradation curve of insulation performance with humidity and rainfall duration, capturing the microscopic energy loss caused by moisture intrusion.

[0038] The two instantaneous power losses mentioned above are converted into incremental environmental energy losses through discretized numerical integration. Within each control step, the system accumulates and calculates the area under the power curve to obtain the precise energy loss value. Finally, this increment is used to correct the available energy state quantity for temperature. Performing numerical subtraction logically completes the real-time balance of the energy ledger. By performing subtraction to obtain the environmental impact correction state, the net loss cost caused by the environment is essentially removed from the theoretically available energy. This environmentally calibrated state quantity ensures that the thermal management decisions of the energy storage power source are always based on a true and real-time understanding of the state, thereby achieving predictive accuracy and forward-looking adjustment capabilities in the complex environment of the mining area.

[0039] This application achieves a technological leap from "passive temperature control" to "environmentally sensitive active compensation" through a mapping update module. Traditional solutions often overlook the nonlinear impact of extreme humidity and rainfall in mining areas on battery internal resistance and insulation characteristics, leading to significant prediction biases under severe weather conditions. This application, however, utilizes a dynamic mapping based on the Arrhenius equation to quantify macroscopic environmental stresses as a definite dynamic impedance increment. Combined with a preset current value and equivalent leakage loss, real-time power integration is performed, logically achieving real-time energy balance through numerical subtraction. This approach not only eliminates calculation errors in heat generation terms induced by rain chills or sudden humidity changes but also ensures that the state variables transmitted to the prediction module always closely match the battery's physical characteristics under real-world harsh conditions, effectively preventing thermal management lags or abnormal battery life reduction due to environmental disturbances.

[0040] In practice, the predictive modeling module includes a heat generation calculation unit, an environmental constraint extraction unit, and a thermal trajectory solution unit. The heat generation calculation unit is used to receive the environmental action correction state and impedance dynamic increment, use the environmental action correction state as the starting state quantity for heat trajectory prediction, and introduce the impedance dynamic increment into the preset heat generation rate equation to determine the corrected system heat generation term. The environmental constraint extraction unit is used to analyze the ambient temperature and humidity in the environmental state data, determine the dew point temperature under the current working condition through the preset dew point mapping logic, and use the dew point temperature as the numerical boundary constraint of the predicted trajectory. The thermal trajectory solving unit is used to construct a thermal state prediction model based on the numerical boundary constraints of the corrected system heat generation term and the predicted trajectory. Through iterative solving of the thermal state prediction model, it outputs the predicted battery temperature trajectory within a future preset time window, and outputs the predicted thermal management state according to the correlation between the predicted battery temperature trajectory and the numerical boundary constraints of the predicted trajectory.

[0041] Specifically, this invention sets the dew point temperature as the lower bound of the numerical boundary constraint for the predicted trajectory. Its core purpose is to eliminate the safety hazard of condensation under extreme high humidity conditions in mining areas. Although individual battery cells still possess energy throughput capacity in condensation conditions, the complex dust environment in mining areas combined with condensation significantly reduces the system's insulation resistance, inducing the risk of electrical short circuits. Therefore, this system considers the dew point temperature as the red line for safe dry-bulb temperature operation. When the predicted trajectory touches this lower bound, the system determines it to be in a risky predicted thermal management state, and actively adjusts the battery temperature to return above the dew point, achieving dual closed-loop control to prevent overheating and condensation.

[0042] This application achieves dynamic correction of the heat generation term from a physical level by substituting the environmental effect-corrected state and impedance dynamic increment into a pre-defined heat generation rate equation, ensuring the authenticity of the prediction starting point and heat generation benchmark. Simultaneously, the scheme innovatively extracts the dew point temperature as a numerical boundary constraint for the prediction trajectory, overcoming the limitation of traditional thermal management that only focuses on cooling and ignores condensation, ensuring that the battery's predicted temperature trajectory always operates within a safe range. This iterative solution logic based on physical constraints enables the system to anticipate overheating and condensation risks in advance, achieving a leap from "lagging regulation" to "proactive risk prevention and control." While ensuring safe operation in the high-humidity environment of the mining area, it effectively avoids the impact of regulation oscillations on battery life.

[0043] In specific implementation, the system receives the environmental effect correction state and impedance dynamic increment, uses the environmental effect correction state as the starting state quantity for thermal trajectory prediction, and introduces the impedance dynamic increment into the preset heat generation rate equation to determine the corrected system heat generation term. The processing logic includes: Extract the real-time state of charge and terminal voltage components from the environmental effect correction state, and combine them with the preset initial temperature vector to construct the iteration starting point of the thermal trajectory prediction model; Obtain the predicted current value within the prediction window, and incorporate the predicted current value and the dynamic impedance increment into the preset heat generation rate equation to determine the heat generation increment induced by rainwater conditions. The calculation formula is as follows: ; in, For the corrected system heat generation term, This is the predicted current value. The equivalent resistance of the battery. For impedance dynamic increment; The iteration start point of the thermal trajectory prediction model is synchronized with the corrected system heat generation term to the thermal trajectory solution unit.

[0044] Specifically, constructing the iterative starting point for the thermal trajectory prediction model is essentially a digital alignment process that integrates multi-dimensional state information into the initial input of the prediction algorithm. The system extracts its real-time state of charge (SOC) and terminal voltage components from the environmentally modified state. These two components together define the battery's electrical operating point at the current moment, i.e., clarifying the battery's energy level and potential plateau. Simultaneously, the system acquires the battery's current temperature data, directly measured by a temperature sensor and filtered, as the true starting point for the thermal state evolution. The core of constructing the iterative starting point lies in aligning the electrical operating point (SOC, V) with the thermal state starting point (…). These are logically linked in the algorithm to form a coordinated initial state vector, such as... This value is used as the initial value of the thermal trajectory prediction model at time zero, ensuring that the prediction path is derived from the most realistic physical state of the battery.

[0045] The preset initial temperature vector is a key technical parameter designed for the complex spatial thermal field of the mining area. In the refined thermal model, the temperature of the battery cluster is not a uniform single-point scalar, but rather exhibits a differentiated distribution at different locations such as the cell surface, busbar connections, and heat dissipation ducts. To characterize this spatial distribution, each component corresponds to real-time data from a physical temperature measurement point. This is because unilateral sunlight or localized rain in mining environments can easily lead to severe thermal inhomogeneity within the battery pack; using a single mean would mask the risk of localized overheating. In practice, each time a prediction is initiated, the system reads and formats data from all sensors located at key hotspots to provide the model with rich initial spatial information, thereby predicting a realistic temperature rise trajectory.

[0046] The pre-defined heat generation rate equation decomposes battery heat generation into two dimensions: "inherent heat generation" and "environmentally induced heat generation." The square term of the predicted current reflects the dominant role of Joule heating, the battery's equivalent resistance locks in the battery's internal resistance under basic operating conditions, and the dynamic impedance increment is the core increment quantifying the impact of environmental stress. The ingenuity of this design lies in its calculation not only of the battery's baseline heating during operation but also specifically addressing the additional heat generation burden induced by increased impedance due to rainwater and high humidity in the mining area. This dynamic response mechanism allows the heat generation term to be synchronously corrected as the environment deteriorates, accurately capturing the physical fact of increased thermal load on the battery under harsh conditions, providing a high-fidelity heat source input for the subsequent thermal trajectory calculation unit.

[0047] This application constructs a multidimensional initial state variable including an initial temperature vector and introduces a heat generation rate equation corrected by dynamic impedance increments, achieving dynamic compensation of the heat generation term from a physical level, ensuring the authenticity of the prediction starting point and heat generation benchmark. This design overcomes the limitation of traditional schemes in quantifying the additional heat load induced by rainfall and high humidity conditions in mining areas. By combining real-time electrical components with the initial temperature vector characterizing the spatial thermal field inhomogeneity, a high-fidelity physical prediction origin is established. By synchronizing the dynamically corrected heat generation rate to the solution unit, the system can accurately capture the temperature rise trend deviation caused by environmental degradation, ensuring the safe operation of the energy storage system under extreme mining conditions while effectively avoiding the impact of regulation oscillations on battery service life.

[0048] In specific implementation, the ambient temperature and humidity in the environmental status data are analyzed, and the dew point temperature under the current operating conditions is determined through a preset dew point mapping logic. The dew point temperature is then used as the numerical boundary constraint for the predicted trajectory. The processing logic includes: The ambient humidity is converted into a relative humidity percentage value using a linear proportional mapping algorithm. The ambient temperature and the relative humidity percentage value are then substituted into the Magnus formula to calculate the dew point temperature under the current operating conditions. The dew point temperature is used as the numerical boundary constraint of the predicted trajectory, and the numerical boundary constraint of the predicted trajectory is synchronized to the thermal trajectory solving unit as the lower limit range of the temperature component during the iterative solution process.

[0049] Specifically, the pre-defined dew point mapping logic is an algorithmic process that dynamically and accurately converts real-time collected ambient temperature and humidity data into physical quantities characterizing the critical point of condensation risk. The system first standardizes the humidity sensor signal, converting it into a universal relative humidity percentage value through a linear scaling mapping. Then, it substitutes the current ambient temperature and this relative humidity percentage value into a Magnus-based formula. The model calculates the dew point temperature under the current air conditions by performing a series of algebraic operations involving logarithmic calculations. The fundamental reason for designing and pre-setting such logic is to proactively and accurately prevent the chain of safety risks caused by condensation, unique to the high-humidity environment of mining areas. The dew point temperature is not a fixed value but a dynamic boundary that fluctuates with ambient temperature and humidity. By calculating it in real time and setting it as an insurmountable lower limit constraint for battery temperature prediction, the system transforms a meteorological concept into a safety red line that can directly guide thermal management. This means that the entire prediction model no longer only focuses on how to prevent the battery from overheating, but must also ensure that the predicted temperature of the battery in the future is always above the critical point that will induce water vapor condensation. This achieves a fundamental improvement from simple temperature control to ensuring that the battery operates within a "safe temperature window", fundamentally eliminating the risk of electrical insulation degradation and short circuit caused by condensation, and completing a key leap from passive response to active safety defense in thermal management strategy.

[0050] In specific implementation, a thermal state prediction model is constructed based on the corrected system heat generation term and the numerical boundary constraints of the predicted trajectory. The predicted battery temperature trajectory within a preset time window is output through iterative solving of the thermal state prediction model. Furthermore, based on the correlation between the predicted battery temperature trajectory and the numerical boundary constraints of the predicted trajectory, the predicted thermal management state is output. The processing logic includes: Based on the corrected system heat production term and ambient temperature, a first-order differential equation is constructed, and its calculation formula is as follows: ; in, Predicting battery temperature, The heat conversion coefficient, This is the environmental heat dissipation correction factor. For the corrected system heat generation term, The ambient temperature; Divide the future preset time window into The first-order differential equation is iteratively calculated using a fourth-order Runge-Kutta algorithm within a discrete simulation step. Within each simulation step, numerical integration is performed based on the battery's predicted temperature from the previous moment and the environmental correction state to obtain the predicted battery temperature at the current sampling moment. The calculation formula is as follows: ; ; ; ; ; in, This is the predicted battery temperature at the current sampling time. Predict the battery temperature for the previous moment. The mapping function corresponding to the first-order differential equation. For time variables In the A numerical coordinate of the distance from the walking distance. The slope at the starting point of the discrete simulation step size. Based on The slope at the midpoint of the predicted discrete simulation step size. Based on The slope at the midpoint of the predicted discrete simulation step size. Based on The slope of the predicted discrete simulation step-size endpoint. This refers to the step size in discrete simulation. During the iterative solution process, the obtained battery temperature prediction value is compared with the numerical boundary constraints of the predicted trajectory in real time. The numerical boundary constraints for the predicted trajectory include the lower limit of the dew point temperature and the upper limit of the battery safety temperature. If any predicted battery temperature value is greater than or equal to the upper limit of the battery's safe temperature within a preset time window in the future, it will be determined as a trajectory state with overheating risk. If, within a preset time window in the future, any predicted battery temperature is less than or equal to the lower limit of the dew point temperature, then the trajectory is determined to be at risk of condensation. If all predicted battery temperatures are between the lower limit of the dew point temperature and the upper limit of the battery safe temperature within the preset time window in the future, it is determined to be a steady-state trajectory state. If a trajectory state is determined to have an overheating risk, the moment when the battery safety temperature limit is first reached and the predicted temperature exceeding the battery safety temperature limit are extracted to generate an overheating risk type predicted thermal management state. If the condition is determined to be a condensation risk trajectory state, the moment when the predicted battery temperature first reaches the dew point temperature and the extent to which the predicted temperature is lower than the lower limit of the dew point temperature are extracted to generate a condensation risk type predicted thermal management state. If the state is determined to be a steady-state trajectory, the extreme temperature values ​​within the future preset time window and the difference between the predicted battery temperature at the end of the future preset time window and the current battery temperature value are extracted to generate a steady-state predicted thermal management state.

[0051] Specifically, the first-order differential equation fully considers the dual regulatory effect of rainfall on the thermal balance of the battery system. On the one hand, rainfall and high humidity increase the corrected system heat generation term through the aforementioned dynamic impedance increment; on the other hand, the cooling effect and water evaporation caused by rainfall significantly alter the convective heat transfer characteristics of the heat dissipation interface. Therefore, the environmental heat dissipation correction coefficient in the formula is not a fixed value, but a dynamic function obtained by real-time mapping based on environmental state data (such as wind speed, rainfall exposure duration, and environmental humidity). In actual calculations, when the system detects a rainfall trigger signal, it adjusts the environmental heat dissipation correction coefficient in real time through a preset heat transfer gain operator to simulate the additional heat loss caused by water evaporation, i.e., the water cooling effect. This synchronous correction logic of increased heat generation and enhanced heat dissipation ensures the physical self-consistency of the thermal trajectory prediction model under extreme rainfall conditions and avoids the problem of overestimating the temperature due to neglecting the environmental cooling effect.

[0052] The environmental heat dissipation correction coefficient is constructed with reference to the Nusselt number correlation in fluid mechanics. The system maps real-time wind speed to Reynolds number and rainfall intensity to liquid phase heat conduction gain, thereby dynamically calculating the correction coefficient reflecting real-time heat dissipation capacity. This functional processing approach enables the thermal state prediction model to sensitively capture the impact of transient environmental changes such as gusts and sudden rainstorms in the mining area on battery temperature rise, fundamentally improving the prediction accuracy of the thermal trajectory solving unit.

[0053] Compared to traditional prediction methods that rely solely on historical temperature rise data for empirical extrapolation, this application achieves a technological leap in thermal management from "instant response" to "high-precision forward-looking prediction" by introducing a fourth-order Runge-Kutta algorithm and dynamic physical boundary discrimination logic. Traditional prediction models often suffer from severe trajectory drift due to truncation errors caused by low-order difference calculations when facing drastic load fluctuations in mining areas. This solution utilizes the fourth-order Runge-Kutta algorithm to perform four slope-weighted mappings within each simulation step, significantly improving the numerical accuracy of solving nonlinear differential equations and ensuring that the generated battery predicted temperature trajectory closely matches the actual physical evolution trend. Simultaneously, this application constructs a dynamic constraint space containing the lower limit of the dew point temperature and the upper limit of the safe temperature. This design completely changes the limitations of traditional thermal management's "one-way thermal protection." By extracting the starting time and risk magnitude of overheating and condensation risk trajectories, the system can gain crucial "strategic lead time" for control plans, accurately intercepting potential thermal runaway or insulation breakdown in high-humidity environments. The extraction of the extreme values ​​and endpoint trends of the steady-state trajectory provides precise data support for the optimized configuration of cooling power. This precise iterative logic based on physical constraints ensures that the battery system always operates within an absolutely safe "temperature corridor" while effectively avoiding power oscillations caused by adjustment lag, fundamentally balancing operational safety under complex mining conditions with the long-term reliability of energy storage equipment.

[0054] In practical implementation, the adjustment execution module is used to generate corresponding thermal management adjustment instructions based on the predicted thermal management status. Its processing logic includes: If the predicted thermal management state is an overheat risk type predicted thermal management state, then an enhanced cooling regulation command will be executed; The enhanced cooling regulation commands include locking the cooling fan and the liquid circulation pump to maximum power operation. If the predicted thermal management status is a condensation risk type predicted thermal management status, then execute the anti-condensation heating adjustment command; The anti-condensation heating adjustment command includes turning off the cooling fan, turning on the heater, and setting the heater power to the preset anti-condensation operating power; If the predicted thermal management state is a steady-state predicted thermal management state, then execute the power maintenance regulation command; The power maintenance control command includes maintaining the current operating status and power of the cooling fan, liquid circulation pump, and heater.

[0055] Specifically, the adjustment and execution module retrieves and executes differentiated control logic based on the three types of predicted thermal management states output in real time by the predictive modeling module. Specifically, when the predictive modeling module determines that the current state is an overheat risk-type predicted thermal management state, meaning the predicted trajectory indicates that the battery temperature will touch or exceed the safe temperature limit within a preset time window, the adjustment and execution module immediately generates an enhanced cooling adjustment command. This command uses a PWM control signal to drive the cooling fan to lock into maximum power operation, providing extreme air pressure for forced convection cooling. Simultaneously, the liquid circulation pump switches to maximum power operation, increasing the cooling medium circulation rate to maximize the heat exchange efficiency of the liquid cooling system, achieving dual enhancement of air cooling and liquid cooling, and ensuring that the battery temperature rise trend is instantly suppressed. If the system determines that the predicted thermal management state is one of condensation risk, meaning the predicted battery temperature will be lower than the lower limit of the ambient dew point, the adjustment execution module executes an anti-condensation heating adjustment command. First, it commands the cooling fan to shut down to stop cold air injection. Then, it turns on the heater and sets its power to a preset anti-condensation operating power. This specific low-efficiency compensation power actively raises the micro-area ambient temperature of the battery module, causing it to deviate from the dew point region, thus physically eliminating the conditions for condensation formation. When the predicted trajectory is in a steady-state predicted thermal management state, meaning the battery temperature remains within the safety corridor formed by the lower dew point limit and the upper safety limit within the future window, the system executes a power maintenance adjustment command to keep the current operating state and power of the cooling fan, liquid circulation pump, and heater unchanged. This locking strategy avoids adjustment oscillations.

[0056] The preset anti-condensation operating power ensures that the surface temperature of the battery module and its sensitive electrical components remains within a specific range above the ambient dew point temperature through continuous and controlled heat input. This specific power is chosen because the outdoor humidity in mining areas is extremely high and fluctuates drastically. Without intervention, if the battery surface temperature drops into the dew point range, water vapor in the air will rapidly condense into liquid water, leading to decreased insulation strength, leakage, or even short circuits. Conversely, using excessive heating power would result in wasted energy and unnecessary thermal stress. Therefore, by presetting a fixed or segmented power that provides a gentle temperature rise slope, the system's energy consumption and protection performance can be balanced to the maximum extent while ensuring intrinsic safety.

[0057] This application achieves a technological leap in thermal management from "feedback-based passive remediation" to "time-dimensional proactive hedging" by executing differentiated command combinations for three types of predicted trajectories: overheating, condensation, and steady-state conditions. First, for the two distinctly different extreme risks of overheating and condensation, this solution no longer employs a single temperature control logic. Instead, it addresses overheating risk by "locking maximum power air cooling" and condensation risk by "stopping the fan and linking it to specific power heating." This precise execution logic effectively resolves the technical contradiction of traditional solutions being unable to balance cooling efficiency and anti-condensation safety under complex mining conditions. Second, this solution utilizes predictive lead time to drive the actuators, completing full-power fan switching or heater pre-start before the risk actually occurs, offsetting the physical response lag of the actuators through "proactive compensation." Finally, by locking the steady-state trajectory, frequent start-stop cycles caused by minor sensor fluctuations within the safe temperature range are avoided. This not only significantly reduces the self-consumption rate of the energy storage system, but also effectively extends the service life of core components such as cooling fans and circulating pumps by reducing the mechanical stress impact on the actuators, ensuring the long-term reliable operation of energy storage equipment in the field environment of the mining area.

[0058] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. 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.

[0059] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. An outdoor energy storage power supply thermal management and regulation system for mining area operating conditions, characterized in that, include: The module comprises a data acquisition module, an energy reconstruction module, a mapping update module, a predictive modeling module, and a regulation execution module. The data acquisition module is used to acquire the operating status data and environmental status data of the energy storage battery; The energy reconstruction module is used to reconstruct the available energy state of the energy storage battery based on operating status data and environmental status data, and obtain the temperature-corrected available energy state quantity. ; The mapping update module is used to correct the available energy state quantity based on environmental state data. Perform a mapping update to obtain the environmental effect correction state; The predictive modeling module is used to construct a thermal state prediction model based on environmental effects to correct the state and output the predicted thermal management state. The predicted thermal management state includes risk-type predicted thermal management state and steady-state predicted thermal management state; The adjustment execution module is used to generate corresponding thermal management adjustment instructions based on the predicted thermal management status.

2. The outdoor energy storage power supply thermal management and regulation system for mining conditions as described in claim 1, characterized in that, The operating status data includes battery state of charge, terminal voltage, and current; The environmental status data includes ambient temperature, ambient humidity, and rainfall exposure status.

3. The outdoor energy storage power supply thermal management and regulation system for mining conditions as described in claim 2, characterized in that, The energy reconstruction module includes a state analysis unit, an environment coupling unit, and an energy correction unit; The state parsing unit is used to parse and process the operating state data, constructing a basic energy state vector based on the battery state of charge, terminal voltage, and current. Its processing logic includes: Retrieve the battery state of charge, terminal voltage, and current from the operating status data, normalize the battery state of charge, and obtain the state of charge component. The dimensions of the terminal voltage and current are unified to obtain the voltage state component and the current state component, respectively. According to the preset energy state component arrangement order, the charge state component, voltage state component and current state component are vectorized and combined to obtain the basic energy state vector. The environmental coupling unit is used to introduce environmental state data into the basic energy state vector, perform environmental correlation calculations on the basic energy state vector, and obtain a temperature-related energy state characterization. Its processing logic includes: The ambient temperature is extracted from the environmental status data, and the ambient temperature is numerically standardized to obtain the temperature effect factor. Based on the component index of each energy state component in the basic energy state vector, the temperature action factor is matched with the corresponding energy state component at the component level. The temperature-dependent energy component is obtained by performing a weighted correlation calculation between the temperature factor and the corresponding energy state component. The calculation formula is as follows: ； in, For temperature-related energy components, The component number is the component sequence number. , For the preset component association weights, Temperature is the influencing factor. These are energy state components; By combining the temperature-related energy components, a temperature-related energy state characterization is obtained. The energy correction unit is used to correct the basic energy state vector based on the temperature-related energy state characterization to obtain the temperature-corrected usable energy state quantity. Its processing logic includes: Extract environmental humidity and rainfall exposure status from environmental status data to construct a rainwater environmental attenuation factor; The temperature-related energy state characterization is reduced and corrected using a rainwater environmental attenuation factor to obtain the temperature-corrected usable energy state quantity. The calculation formula is as follows: ； in, It is an energy state mapping function. This is for characterizing temperature-related energy states. This is the voltage cutoff correction factor. It is a rainwater environment attenuation factor.

4. The outdoor energy storage power supply thermal management and regulation system for mining conditions as described in claim 3, characterized in that, The environmental humidity and rainfall exposure status are extracted from environmental status data to construct a rainwater environmental attenuation factor. The processing logic includes: The ambient humidity is retrieved through the communication interface, the relative humidity percentage value at the current moment is extracted, and the rainfall exposure status is analyzed. If it is determined that the current state is in a rainfall-triggered state, the duration of continuous exposure to the current rainfall process is accumulated and recorded through a timer. The relative humidity percentage value is biased against a preset humidity trigger threshold, and then substituted into a preset mapping function to calculate the instantaneous risk coefficient. The calculation formula is as follows: ； in, For instantaneous risk coefficient, This is a percentage value for relative humidity. The preset humidity trigger threshold, This is the preset humidity sensitivity coefficient; The cumulative exposure impact term is obtained by performing a time-weighted integral over the duration of continuous exposure using a preset time degradation operator. The calculation formula is as follows: ； in, For cumulative exposure impact, The environmental erosion constant, The degradation function of the protective layer over time. For continuous exposure time; By nonlinearly coupling the instantaneous risk coefficient term and the cumulative exposure impact term, a rainwater environmental attenuation factor is constructed, the calculation formula of which is as follows: ； in, It is a rainwater environment attenuation factor.

5. The outdoor energy storage power supply thermal management and regulation system for mining conditions as described in claim 1, characterized in that, The mapping update module includes an environment effect mapping unit, a state update verification unit, and a parameter synchronization unit; The environmental effect mapping unit is used to analyze the rainwater environmental attenuation factor and, in conjunction with environmental temperature and humidity, obtains the dynamic impedance increment through a preset sensitivity matrix mapping. The calculation formula is as follows: ； in, For impedance dynamic increment, As the base for impedance influence, For activation energy operator, This refers to the absolute temperature value corresponding to the ambient temperature. As a rainwater environmental attenuation factor, The preset sensitivity matrix, The gas constant is... The preset stability correction operator has a value range of [value range missing]. ; The state update verification unit is used to correct the available energy state quantity based on the temperature using impedance dynamic increments. The real-time compensation update process includes the following logic: The real-time voltage drop loss power of the terminal voltage is determined based on the dynamic impedance increment and the preset current value. The equivalent resistance of the battery is determined based on the ambient humidity and rainfall exposure status. The real-time voltage drop loss power of the terminal voltage and the battery equivalent resistance are integrated over time to obtain the corresponding environmental energy loss increment. Use of ambient energy loss increments to correct available energy state quantities for temperature Numerical subtraction is performed to obtain the environmental impact correction state; The parameter synchronization unit is used to transmit the environmental action correction state to the prediction modeling module as an input variable for the thermal state prediction model.

6. The outdoor energy storage power supply thermal management and regulation system for mining conditions as described in claim 1, characterized in that, The predictive modeling module includes a heat generation calculation unit, an environmental constraint extraction unit, and a thermal trajectory solution unit. The heat generation calculation unit is used to receive the environmental action correction state and the impedance dynamic increment, use the environmental action correction state as the starting state quantity for heat trajectory prediction, and introduce the impedance dynamic increment into the preset heat generation rate equation to determine the corrected system heat generation term. The environmental constraint extraction unit is used to analyze the ambient temperature and humidity in the environmental state data, determine the dew point temperature under the current working condition through the preset dew point mapping logic, and use the dew point temperature as the numerical boundary constraint of the predicted trajectory. The thermal trajectory solving unit is used to construct a thermal state prediction model based on the numerical boundary constraints of the corrected system heat generation term and the predicted trajectory. Through iterative solving of the thermal state prediction model, the predicted battery temperature trajectory within a future preset time window is output, and the predicted thermal management state is output according to the correlation between the predicted battery temperature trajectory and the numerical boundary constraints of the predicted trajectory.

7. The outdoor energy storage power supply thermal management and regulation system for mining conditions as described in claim 6, characterized in that, The system receives the environmental effect correction state and impedance dynamic increment, uses the environmental effect correction state as the starting state quantity for thermal trajectory prediction, and introduces the impedance dynamic increment into the preset heat generation rate equation to determine the corrected system heat generation term. The processing logic includes: Extract the real-time state of charge and terminal voltage components from the environmental effect correction state, and combine them with the preset initial temperature vector to construct the iteration starting point of the thermal trajectory prediction model; Obtain the predicted current value within the prediction window, and incorporate the predicted current value and the dynamic impedance increment into the preset heat generation rate equation to determine the heat generation increment induced by rainwater conditions. The calculation formula is as follows: ； in, For the corrected system heat generation term, This is the predicted current value. The equivalent resistance of the battery. For impedance dynamic increment; The iteration start point of the thermal trajectory prediction model is synchronized with the corrected system heat generation term to the thermal trajectory solution unit.

8. The outdoor energy storage power supply thermal management and regulation system for mining conditions as described in claim 6, characterized in that, The system analyzes the ambient temperature and humidity data, determines the dew point temperature under the current operating conditions through a pre-defined dew point mapping logic, and uses the dew point temperature as the numerical boundary constraint for the predicted trajectory. The processing logic includes: The ambient humidity is converted into a relative humidity percentage value using a linear proportional mapping algorithm. The ambient temperature and the relative humidity percentage value are then substituted into the Magnus formula to calculate the dew point temperature under the current operating conditions. The dew point temperature is used as the numerical boundary constraint of the predicted trajectory, and the numerical boundary constraint of the predicted trajectory is synchronized to the thermal trajectory solving unit as the lower limit range of the temperature component during the iterative solution process.

9. The outdoor energy storage power supply thermal management and regulation system for mining conditions as described in claim 6, characterized in that, A thermal state prediction model is constructed based on the corrected system heat generation term and the numerical boundary constraints of the predicted trajectory. The model is iteratively solved to output the predicted battery temperature trajectory within a preset time window. Based on the correlation between the predicted battery temperature trajectory and the numerical boundary constraints of the predicted trajectory, the predicted thermal management state is output. The processing logic includes: Based on the corrected system heat production term and ambient temperature, a first-order differential equation is constructed, and its calculation formula is as follows: ； in, Predicting battery temperature, The heat conversion coefficient, This is a correction factor for environmental heat dissipation. For the corrected system heat generation term, The ambient temperature; Divide the future preset time window into The first-order differential equation is iteratively calculated using a fourth-order Runge-Kutta algorithm within a discrete simulation step. Within each simulation step, numerical integration is performed based on the battery's predicted temperature from the previous moment and the environmental correction state to obtain the predicted battery temperature at the current sampling moment. The calculation formula is as follows: ； ； ； ； ； in, This is the predicted battery temperature at the current sampling time. Predict the battery temperature for the previous moment. The mapping function corresponding to the first-order differential equation. For time variables In the A numerical coordinate of the distance from the walking distance. The slope at the starting point of the discrete simulation step size. For based on The slope at the midpoint of the predicted discrete simulation step size. For based on The slope at the midpoint of the predicted discrete simulation step size. For based on The slope of the predicted discrete simulation step-size endpoint. This refers to the step size in discrete simulation. During the iterative solution process, the obtained battery temperature prediction value is compared with the numerical boundary constraints of the predicted trajectory in real time. The numerical boundary constraints of the predicted trajectory include the lower limit of the dew point temperature and the upper limit of the battery safety temperature. If any predicted battery temperature value is greater than or equal to the upper limit of the battery's safe temperature within a preset time window in the future, it will be determined as a trajectory state with overheating risk. If, within a preset time window in the future, any predicted battery temperature is less than or equal to the lower limit of the dew point temperature, then the trajectory is determined to be at risk of condensation. If all predicted battery temperatures are between the lower limit of the dew point temperature and the upper limit of the battery safe temperature within the preset time window in the future, it is determined to be a steady-state trajectory state. If a trajectory state is determined to have an overheating risk, the moment when the battery safety temperature limit is first reached and the predicted temperature exceeding the battery safety temperature limit are extracted to generate an overheating risk type predicted thermal management state. If the condition is determined to be a condensation risk trajectory state, the moment when the predicted battery temperature first reaches the dew point temperature and the extent to which the predicted temperature is lower than the lower limit of the dew point temperature are extracted to generate a condensation risk type predicted thermal management state. If the state is determined to be a steady-state trajectory, the extreme temperature values ​​within the future preset time window and the difference between the predicted battery temperature at the end of the future preset time window and the current battery temperature value are extracted to generate a steady-state predicted thermal management state.

10. The outdoor energy storage power supply thermal management and regulation system for mining conditions as described in claim 1, characterized in that, The adjustment execution module is used to generate corresponding thermal management adjustment commands based on the predicted thermal management status. Its processing logic includes: If the predicted thermal management state is an overheat risk type predicted thermal management state, then an enhanced cooling regulation command will be executed; The enhanced cooling regulation command includes locking the cooling fan to maximum power operation and locking the liquid circulation pump to maximum power operation. If the predicted thermal management status is a condensation risk type predicted thermal management status, then execute the anti-condensation heating adjustment command; The anti-condensation heating adjustment command includes turning off the cooling fan, turning on the heater, and setting the heater power to the preset anti-condensation operating power; If the predicted thermal management state is a steady-state predicted thermal management state, then execute the power maintenance regulation command; The power maintenance and adjustment command includes maintaining the current operating status and power of the cooling fan, liquid circulation pump, and heater unchanged.

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