Adaptive thermal management method for liquid-cooled energy storage cabinet abnormal working conditions

Abstract

This invention discloses an adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets, relating to the field of secondary battery thermal management technology. The method includes the following steps: collecting and synchronously transmitting battery thermal characteristics, liquid circuit parameters, and electrical status data through multiple sensors; identifying five types of anomalies based on preset thresholds and trend analysis models, and defining them according to corresponding standards; dynamically adjusting thermal management parameters according to the anomaly type, severity, and operating status; controlling the actuator to perform adjustments within a set range and rate, and providing real-time feedback; continuously monitoring and optimizing strategies, strengthening them if the anomaly is not alleviated, and restoring them if the anomaly is resolved; and disconnecting connections and activating fire alarm linkage in case of extreme anomalies. This method achieves accurate anomaly identification through multi-dimensional data acquisition, dynamically adapts thermal management strategies to improve cooling efficiency, and balances energy consumption optimization and operational stability; the emergency protection mechanism strengthens safety protection, providing strong support for the stable and efficient operation of the liquid-cooled energy storage cabinet.

Description

Technical Field

[0001] This invention relates to the field of thermal management technology for secondary batteries, and in particular to an adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets. Background Technology

[0002] With the rapid development of the new energy industry, energy storage cabinets, as core equipment for energy storage and dispatch, have been widely used in photovoltaic power plants, wind power projects, and grid peak shaving. Liquid cooling technology, due to its high heat dissipation efficiency and good temperature uniformity, has become the mainstream heat dissipation solution for large-capacity energy storage cabinets. However, during long-term operation, energy storage cabinets are highly susceptible to various abnormal operating conditions due to factors such as changes in charge / discharge rate, ambient temperature fluctuations, battery aging, and wear and tear of cooling system components. These abnormal conditions can include localized overheating of individual battery cells, overall temperature exceeding limits, coolant leakage, cooling pump failure, and precursors to battery thermal runaway. If these abnormal conditions are not handled promptly and effectively, they can lead to battery capacity degradation and shortened cycle life, and may even cause heat dissipation, or even fires, explosions, and other safety accidents, seriously threatening the stable operation of the energy storage system and the safety of personnel and property.

[0003] Current mainstream liquid-cooled energy storage cabinet thermal management methods mostly employ fixed parameter control strategies, meaning the cooling system is started or stopped based on preset temperature thresholds. This lacks the ability to accurately identify and dynamically adapt to abnormal operating conditions. For example, when faced with localized overheating, traditional methods typically increase the overall coolant flow rate, leading to wasted cooling resources and difficulty in quickly alleviating localized heat accumulation. For severe anomalies such as cooling system failure, existing solutions often rely on simple shutdown protection, lacking transitional emergency cooling measures, which can easily cause sudden changes in battery state. Furthermore, traditional thermal management methods have a single data acquisition dimension, focusing only on battery temperature and neglecting the coordinated analysis of key parameters such as coolant flow rate, pressure, and battery SOC state, resulting in delayed identification of abnormal operating conditions and a high misjudgment rate. In addition, existing technologies often use a uniform distribution mode for coolant flow, failing to dynamically adjust according to the actual thermal load differences of the battery modules, resulting in insufficient targeted cooling and further reducing the effectiveness of thermal management under abnormal conditions.

[0004] As energy storage systems develop towards larger capacity and higher density, the thermal coupling effect within battery packs is becoming increasingly significant. Improper handling of a single abnormal condition can easily lead to a rapid escalation into a systemic failure. Current thermal management methods lack the ability to quantitatively assess the severity of abnormal conditions, making it difficult to match differentiated control strategy strengths. This results in either excessive handling of minor anomalies leading to energy waste, or insufficient handling of severe anomalies causing safety risks. Furthermore, existing solutions generally lack the ability to predict the thermal state of the battery, often reacting passively to anomalies that have already occurred, failing to intervene proactively for protection, further exacerbating the impact and difficulty of handling abnormal conditions. Therefore, developing an adaptive thermal management method that can accurately identify abnormal conditions, dynamically adapt thermal management strategies, and balance cooling efficiency with energy consumption optimization has become a critical technical challenge that urgently needs to be addressed in the field of liquid-cooled energy storage cabinets. Summary of the Invention

[0005] The present invention proposes an adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets, comprising the following steps: Multi-dimensional operational data of the liquid-cooled energy storage cabinet is collected. Through temperature, flow, pressure, voltage, current and liquid level sensors, the battery thermal characteristics, liquid circuit parameters and electrical status data are collected in real time. All collected data are synchronously linked by timestamp and transmitted to the thermal management control unit. Abnormal operating conditions are identified and classified. Based on preset thresholds and trend analysis models, abnormal types are identified, including local overheating, overall overheating, heat dissipation system failure, coolant leakage, and precursors to battery thermal runaway. Each abnormal type is defined according to the set temperature, flow rate, pressure, and liquid level judgment standards. Generate an adaptive thermal management strategy and dynamically adjust thermal management parameters based on the type and severity of the anomaly and the current operating status of the energy storage cabinet; Implement thermal management strategies and provide real-time feedback, control the actuator to issue adjustment commands, the liquid pump speed adjustment range is 50%-150% of the rated speed, the electromagnetic valve response time does not exceed 100 milliseconds, the auxiliary cooling fan starts in stages according to the coolant outlet temperature, and the charging and discharging power is adjusted in steps at a rate of 5% / minute. Continuous monitoring and dynamic optimization: The thermal management control unit receives operational data after execution in real time, analyzes temperature change trends, cooling efficiency and battery status response, compares the mitigation effect of abnormal conditions with the preset target, and further strengthens the strategy if the temperature does not drop back to the safe range within the specified time. If the abnormality is resolved, the normal thermal management parameters are gradually restored. When an emergency protection mechanism is activated and extreme anomalies such as battery thermal runaway or complete failure of the cooling system are detected, the connection between the energy storage cabinet and the external power grid and load is immediately cut off, the fire-fighting linkage device is activated, a special fire extinguishing agent is sprayed and the smoke exhaust channel is opened, and an alarm message is sent to the monitoring center.

[0007] Furthermore, it also includes a step to quantify the severity of abnormal operating conditions. Anomaly levels are classified through multi-factor weighted calculations, providing a quantitative basis for the intensity of thermal management strategies. The calculation formula is: ,in This is a quantification value for the severity of abnormal operating conditions. As for the weight of temperature anomalies, This is the difference between the current highest temperature and the safe temperature threshold. For the duration of the abnormality, The baseline duration is set at 300 seconds. For abnormal traffic weighting, This represents the current coolant flow rate. Rated coolant flow rate, As battery state weights, This is the battery SOC correction factor. For abnormal pressure weighting, This is the difference between the current pressure and the rated pressure. This is the rated cooling circuit pressure.

[0008] Furthermore, it also includes a dynamic coolant flow distribution step, which adjusts the flow ratio of each branch based on the temperature distribution and heat load differences of the battery module. The calculation formula is as follows: ,in For the first Coolant distribution flow rate for each battery module This represents the total flow rate of the cooling circuit. For the first Real-time temperature of each battery module The average temperature of all battery modules. This represents the total number of battery modules. For the first The flow rate adjustment coefficient for each module is determined based on the module's heat capacity and heat dissipation area. Provide basic guaranteed traffic for each module.

[0009] Furthermore, it also includes a dynamic temperature zoning management step, which divides the inside of the energy storage cabinet into independent cooling zones. Each zone is equipped with a dedicated temperature sensor and solenoid valve. By analyzing the temperature differences between zones, the heat diffusion trend is determined, and cooling resources are allocated preferentially to locally overheated areas. Pre-cooling procedures are initiated in advance for adjacent areas.

[0010] Furthermore, it also includes steps to optimize emergency cooling start-up conditions: the emergency cooling start-up threshold is reduced by 20% when the ambient temperature is above 35°C, the start-up threshold is reduced by 30% when the initial battery temperature is above 40°C, the start-up threshold is reduced by 15% when the charge / discharge rate is greater than 1C, and the start-up threshold is reduced by 40% when the charge / discharge rate is greater than 2C, allowing for early intervention in cooling under high load and harsh environments.

[0011] Furthermore, it also includes thermal management energy consumption optimization steps. Under the premise of meeting cooling requirements, the liquid pump speed and fan start / stop status are allocated based on dynamic programming algorithm, and an energy consumption-cooling efficiency mapping model is established. When the liquid pump speed is in the range of 50%-80%, variable frequency speed control is adopted, and when it is in the range of 80%-150%, it is dynamically adjusted in combination with the load rate. The auxiliary cooling fan adopts a graded start / stop strategy: it is turned off when the coolant outlet temperature is below 30℃, runs at low speed when it is between 30℃ and 40℃, runs at medium speed when it is between 40℃ and 50℃, and runs at high speed when it is above 50℃.

[0012] Furthermore, it also includes a multi-mode cooling switching procedure, with four preset modes: normal cooling, enhanced cooling, emergency cooling, and energy-saving cooling. The normal cooling mode is suitable for normal operating conditions, using a combination of basic flow rate and natural heat dissipation. The enhanced cooling mode is suitable for mild abnormal operating conditions, increasing the flow rate and activating auxiliary heat dissipation. The emergency cooling mode is suitable for moderate abnormal operating conditions, switching to a backup circuit and limiting charging and discharging power. The energy-saving cooling mode is suitable for the recovery period after the abnormality is resolved, gradually reducing the cooling intensity and optimizing energy consumption.

[0013] Furthermore, it also includes a battery thermal state prediction step. Based on a long short-term memory network model, it takes historical temperature data, charge and discharge records, environmental parameters and current operating status as input to predict the battery temperature change trend in the next 15 minutes. If the temperature is predicted to exceed the safe range, a preheating management strategy is activated in advance to adjust cooling parameters and form predictive protection.

[0014] Furthermore, it also includes cooling system maintenance reminder steps. Based on the coolant flow rate decline trend, pressure fluctuation frequency, liquid level drop rate, and sensor detection data, it assesses the health status of cooling system components. When the pump speed is maintained at more than 130% of the rated value but still cannot reach the design flow rate, it reminds you to check the pump wear. When the cooling circuit pressure fluctuation frequency exceeds 5 times / hour, it reminds you to check the valve sealing. When the coolant level drops by more than 10% cumulatively, it reminds you to add coolant. When the number of abnormal sensor data exceeds 3 times / day, it reminds you to calibrate the sensor.

[0015] Furthermore, it also includes thermal management data storage and analysis steps, real-time storage of abnormal operating condition data, generation of statistical analysis reports by day, week, and month, optimization of preset thresholds, strategy parameters, and cooling mode switching conditions through big data analysis, data storage duration of no less than 1 year and support for traceability query.

[0016] Compared with existing technologies, the beneficial effects of this invention are: Regarding the accuracy of handling abnormal operating conditions, the method comprehensively acquires key parameters such as battery temperature, coolant flow rate, pressure, and SOC status through multi-dimensional data collection. Combined with preset thresholds and trend analysis models, it achieves rapid identification and accurate classification of various abnormal operating conditions, avoiding the misjudgment and lag problems caused by traditional single-parameter monitoring. The quantification step of the severity of abnormal operating conditions provides a scientific quantitative basis for thermal management strategies, enabling precise matching between strategy intensity and anomaly level. This ensures effective handling of severe anomalies while avoiding excessive intervention for minor anomalies, thus improving the rationality and pertinence of thermal management.

[0017] Regarding the adaptability of thermal management strategies, the adaptive thermal management strategy, generated based on the type and severity of anomalies, can dynamically adjust coolant flow rate, pump speed, fan start / stop status, and charging / discharging power to achieve differentiated handling of different abnormal operating conditions. The dynamic coolant flow allocation step precisely allocates cooling resources according to the differences in battery module thermal load, significantly improving cooling efficiency in localized overheating conditions and avoiding waste of cooling resources. Dynamic temperature zoning management, through regional control and heat diffusion prediction, effectively curbs the spread of abnormal operating conditions, ensuring the overall stable operation of the energy storage cabinet. Multi-mode cooling switching achieves a smooth transition between cooling modes under normal and abnormal operating conditions, balancing cooling effect and operational stability.

[0018] In terms of energy consumption optimization and safety improvement, the thermal management energy consumption optimization step uses a dynamic programming algorithm to rationally allocate cooling system operating parameters, reducing energy consumption while meeting cooling requirements and improving the overall energy efficiency of the energy storage system. The battery thermal state prediction step uses a big data model to predict battery temperature change trends, enabling early intervention and protection against abnormal operating conditions, transforming passive handling into proactive prevention, and significantly reducing safety risks caused by abnormal operating conditions. The emergency protection mechanism quickly cuts off the load and activates fire-fighting linkage in the event of extreme anomalies, providing multiple safety guarantees for the energy storage cabinet and minimizing accident losses.

[0019] Furthermore, the cooling system maintenance reminder step, by monitoring the operating status of cooling components and issuing timely maintenance warnings, extends the service life of the cooling system and reduces operation and maintenance costs. The thermal management data storage and analysis step, by accumulating operational data and handling experience, continuously optimizes control parameters and strategies, enabling iterative upgrades of the thermal management system. Overall, this method, through a comprehensive design encompassing precise identification, dynamic adaptation, predictive protection, and energy consumption optimization, significantly improves the thermal management performance and reliability of liquid-cooled energy storage cabinets under abnormal operating conditions. This provides strong support for the long-term stable operation of energy storage systems and promotes the development of energy storage cabinet thermal management technology towards intelligence, precision, and efficiency. Attached Figure Description

[0020] Figure 1 This is a schematic block diagram of the adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets proposed in this invention. Figure 2 A line graph comparing temperature change trends under local overheating conditions; Figure 3 This is a radar chart for the multi-dimensional performance evaluation of the method of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0024] Reference Figures 1 to 3 An adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets includes the following steps: The system collects multi-dimensional operational data of the liquid-cooled energy storage cabinet. It collects temperature data of individual cells, modules and PACK level batteries through distributed temperature sensors, collects real-time flow of coolant inlet and outlet through flow sensors, monitors cooling circuit pressure through pressure sensors, obtains battery SOC, charging and discharging current and individual cell voltage balance status through voltage and current sensors, and detects the remaining amount of coolant through liquid level sensors. All collected data are synchronously linked and transmitted to the thermal management control unit according to timestamps, with a sampling frequency of not less than 10Hz. Abnormal operating conditions are identified and classified. Based on preset thresholds and trend analysis models, abnormal types such as local overheating, overall overheating, heat dissipation system failure, coolant leakage, and precursors to battery thermal runaway are identified. Local overheating is defined as the temperature of a single battery cell exceeding the normal operating range by more than 5°C for more than 30 seconds. Overall overheating is defined as the average temperature of the module exceeding the normal operating range by more than 3°C. Heat dissipation system failure is defined as the coolant flow rate being less than 60% of the rated value or the abnormal fluctuation of the cooling circuit pressure exceeding ±20%. Coolant leakage is defined as the liquid level dropping by more than 5% within 1 hour. Precursors to battery thermal runaway are defined as the rate of temperature rise of a single battery cell exceeding 2°C / minute. Generate an adaptive thermal management strategy, dynamically adjust thermal management parameters according to the anomaly type, severity and current operating status of the energy storage cabinet. When local overheating occurs, increase the coolant flow rate in the corresponding area and open the directional heat dissipation channel. When overall overheating occurs, increase the liquid pump speed, turn on the auxiliary cooling fan and reduce the charging and discharging power. When the heat dissipation system fails, switch to the backup cooling circuit and start the emergency heat dissipation mode. When coolant leaks, close the circuit in the leaking area and trigger a liquid replenishment reminder. When there are signs of battery thermal runaway, start the emergency cooling program and cut off the charging and discharging circuit. The thermal management strategy is implemented and feedback is provided in real time. The control unit issues adjustment commands to actuators such as liquid pump, solenoid valve, cooling fan, and charge / discharge controller. The liquid pump speed adjustment range is 50%-150% of the rated speed. The response time of the solenoid valve does not exceed 100 milliseconds. The auxiliary cooling fan starts in stages according to the coolant outlet temperature. The charge / discharge power is adjusted in steps at a rate of 5% / minute. With continuous monitoring and dynamic optimization, the thermal management control unit receives operational data after execution in real time, analyzes temperature change trends, cooling efficiency and battery status response, compares the mitigation effect of abnormal conditions with preset targets, and further strengthens the strategy if the temperature does not drop back to the safe range within the specified time. If the abnormality is resolved, the normal thermal management parameters are gradually restored, forming a closed-loop control of "acquisition-identification-decision-execution-feedback-optimization". When an emergency protection mechanism is activated and extreme anomalies such as battery thermal runaway or complete failure of the cooling system are detected, the connection between the energy storage cabinet and the external power grid and load is immediately cut off, the fire-fighting linkage device is activated, a special fire extinguishing agent is sprayed and the smoke exhaust channel is opened, and an alarm message is sent to the monitoring center, including the type of anomaly, the location of the occurrence and real-time operating data.

[0025] This invention also includes a step for quantifying the severity of abnormal operating conditions. Through multi-factor weighted calculation, it achieves accurate classification of abnormal levels, providing a quantitative basis for the intensity of thermal management strategies. The calculation formula is as follows: ,in This is a quantification value for the severity of abnormal operating conditions, ranging from 0 to 10. The higher the value, the greater the severity. The weight for temperature anomalies is 0.4. This is the difference between the current highest temperature and the safe temperature threshold. For the duration of the abnormality, The baseline duration is set at 300 seconds. This is used as the weight for traffic anomalies, with a value of 0.3. This represents the current coolant flow rate. Rated coolant flow rate, This is the battery state weight, with a value of 0.2. This is a battery SOC correction factor; it applies when SOC ≥ 80% or SOC ≤ 20%. =1, other cases =0.5, The abnormal pressure weight is set to 0.1. This is the difference between the current pressure and the rated pressure. This is the rated cooling circuit pressure.

[0026] This invention also includes a dynamic coolant flow distribution step, which precisely adjusts the flow ratio of each branch based on the temperature distribution and heat load differences of the battery module, improving the cooling targeting for local overheating conditions. The calculation formula is as follows: ,in For the first Coolant distribution flow rate for each battery module This represents the total flow rate of the cooling circuit. For the first Real-time temperature of each battery module The average temperature of all battery modules. This represents the total number of battery modules. For the first The flow rate adjustment coefficient for each module is determined based on the module's heat capacity and heat dissipation area, and its value ranges from 0.8 to 1.2. The base guaranteed traffic for each module is set to 1 / n of the total traffic multiplied by 0.5.

[0027] This invention also includes a temperature zoning dynamic management step, which divides the interior of the energy storage cabinet into multiple independent cooling zones. Each zone is equipped with a dedicated temperature sensor and solenoid valve. The heat diffusion trend is determined by analyzing the temperature difference between zones. Cooling resources are allocated preferentially to locally overheated zones. Pre-cooling programs are started in adjacent zones in advance. When the temperature difference exceeds 2°C, the heat exchange channel between zones is opened to avoid heat diffusion leading to overall abnormalities. The temperature control accuracy of each zone is maintained within ±0.5°C.

[0028] This invention also includes an emergency cooling start-up condition optimization step, which dynamically adjusts the start-up threshold based on factors such as ambient temperature, initial battery temperature, and charge / discharge rate. When the ambient temperature is higher than 35°C, the emergency cooling start-up threshold is reduced by 20%; when the initial battery temperature is higher than 40°C, the start-up threshold is reduced by 30%; when the charge / discharge rate is greater than 1C, the start-up threshold is reduced by 15%; and when the charge / discharge rate is greater than 2C, the start-up threshold is reduced by 40%. This allows for early intervention in cooling under high load and harsh environments, preventing escalation of abnormal operating conditions.

[0029] This invention also includes a thermal management energy consumption optimization step. Under the premise of meeting cooling requirements, the liquid pump speed and fan start / stop status are allocated based on a dynamic programming algorithm to establish an energy consumption-cooling efficiency mapping model. When the liquid pump speed is in the 50%-80% range, variable frequency speed control is adopted. When it is in the 80%-150% range, dynamic adjustment is combined with the load rate. The auxiliary cooling fan adopts a graded start / stop strategy: it is turned off when the coolant outlet temperature is below 30°C, runs at low speed when it is between 30°C and 40°C, runs at medium speed when it is between 40°C and 50°C, and runs at high speed when it is above 50°C. After the abnormal operating conditions are relieved, the energy consumption is quickly reduced to a reasonable range.

[0030] This invention also includes a multi-mode cooling switching step, with four preset modes: conventional cooling, enhanced cooling, emergency cooling, and energy-saving cooling. The conventional cooling mode is suitable for normal operating conditions, using a combination of basic flow rate and natural heat dissipation. The enhanced cooling mode is suitable for mild abnormal operating conditions, increasing the flow rate and activating auxiliary heat dissipation. The emergency cooling mode is suitable for moderate abnormal operating conditions, switching to a backup circuit and limiting charging and discharging power. The energy-saving cooling mode is suitable for the recovery period after the abnormality is resolved, gradually reducing the cooling intensity and optimizing energy consumption. The mode switching process is smooth, with no temperature fluctuations exceeding 1°C.

[0031] This invention also includes a battery thermal state prediction step. Based on a long short-term memory network model, historical temperature data, charge and discharge records, environmental parameters and current operating status are input to predict the battery temperature change trend within the next 15 minutes. The prediction error is controlled within ±0.8℃. If the temperature is predicted to exceed the safe range, a preheating management strategy is activated in advance to adjust the cooling parameters to form a predictive protection and avoid abnormal operating conditions.

[0032] This invention also includes a cooling system maintenance reminder step. Based on the coolant flow rate decay trend, pressure fluctuation frequency, liquid level drop rate, and sensor detection data, the health status of cooling system components is assessed. When the pump speed is maintained at more than 130% of the rated value but still cannot reach the design flow rate, a reminder is given to check the pump wear. When the cooling circuit pressure fluctuation frequency exceeds 5 times / hour, a reminder is given to check the valve sealing. When the cumulative drop in coolant level exceeds 10%, a reminder is given to add coolant. When the number of abnormal sensor data exceeds 3 times / day, a reminder is given to calibrate the sensor.

[0033] This invention also includes a thermal management data storage and analysis step, which stores data such as the time, type, severity, handling strategy, execution effect, and battery status changes of abnormal operating conditions in real time, generates statistical analysis reports on a daily, weekly, and monthly basis, identifies high-frequency abnormal types, areas prone to abnormalities, and optimization space for thermal management strategies, and optimizes preset thresholds, strategy parameters, and cooling mode switching conditions through big data analysis to continuously improve the accuracy and effectiveness of adaptive thermal management. The data storage time is no less than one year and supports traceability query.

[0034] The following two examples further illustrate specific embodiments of the present invention: Example 1: Thermal Management Application of Liquid-Cooled Energy Storage Cabinet in Large Photovoltaic Power Plants This embodiment applies to a liquid-cooled energy storage cabinet in a 1000kWh large-scale photovoltaic power plant. The cabinet contains 20 battery modules, each consisting of 32 ternary lithium battery cells, using ethylene glycol aqueous solution as the coolant. The rated operating temperature range is 0℃-45℃. This energy storage cabinet undertakes photovoltaic energy storage and grid peak shaving tasks. During daily operation, it faces problems such as frequent changes in charge and discharge rates, excessively high ambient temperatures in summer, and aging of some battery cells. It is prone to abnormal operating conditions such as local overheating, overall overheating, and coolant flow rate reduction. This method is needed to achieve precise thermal management and safety protection.

[0035] I. Core Implementation Details Multi-dimensional operational data acquisition: A miniature temperature sensor is installed at the positive electrode tab of each battery cell; each module is equipped with two flow sensors to monitor the inlet and outlet coolant flow rates; three pressure sensors are installed at key nodes in the cooling circuit; voltage and current sensors and a SOC detection module are configured at the battery PACK level; and a liquid level sensor is installed at the bottom of the energy storage cabinet to monitor the total coolant volume. All sensors collect data at a sampling frequency of 10Hz and transmit it synchronously to the thermal management control unit via a CAN bus. The data is associated with timestamps and module numbers to ensure traceability. The collected data includes cell temperature, module average temperature, coolant inlet and outlet flow rates and pressures, battery SOC value, charge and discharge current, cell voltage equalization status, and coolant level. The control unit receives and stores the data in real time with a delay of no more than 50 milliseconds.

[0036] Abnormal Operating Condition Identification and Classification: Based on preset thresholds and trend analysis models, the control unit performs real-time analysis of the collected data. When the temperature of a single battery cell reaches 50℃ and remains above 30 seconds, it is determined to be localized overheating; when the average temperature of 20 modules exceeds 48℃, it is determined to be overall overheating; when the coolant flow rate is below 60% of the rated value or the pressure fluctuation exceeds ±20% and lasts for 10 seconds, it is determined to be a cooling system failure; when the coolant level drops by more than 5% within one hour, it is determined to be coolant leakage; when the temperature of a single battery cell rises by more than 2℃ within one minute, it is determined to be a precursor to battery thermal runaway. During the identification process, cross-validation is performed using the battery's SOC state and charge / discharge current to avoid misjudgments caused by instantaneous load changes.

[0037] Adaptive thermal management strategy generation and execution: When local overheating occurs, the control unit sends a command to the solenoid valve of the corresponding module to increase the coolant flow rate of that branch, and at the same time opens the directional heat dissipation channel on the side of the module to guide hot air to be discharged quickly; when overall overheating occurs, the liquid pump speed is increased from the rated value to 120%, four auxiliary cooling fans are started, and a command is sent to the charge and discharge controller to reduce the charge and discharge power at a rate of 5% / minute; when the heat dissipation system fails, the system immediately switches to the backup cooling circuit, starts the emergency cooling pump, maintains the basic coolant flow rate, and limits the charge and discharge power to 30% of the rated value; when coolant leaks, the solenoid valve corresponding to the leak area is closed, the leak circuit is cut off, a replenishment reminder is sent to the monitoring center, and normal cooling is maintained in other areas; when there are signs of battery thermal runaway, the emergency cooling program is initiated, the liquid pump speed is increased to 150%, all auxiliary heat dissipation equipment is turned on, and the charge and discharge circuit is cut off within 10 seconds.

[0038] Continuous monitoring and dynamic optimization: After the strategy is executed, the control unit continuously receives sensor data and analyzes temperature change trends. Under local overheating conditions, if the temperature of a single cell does not drop below 45°C within 5 minutes, the flow rate of the corresponding branch is further increased to 130% of the rated value, and the pre-cooling program of the adjacent module is activated. Under overall overheating conditions, if the average temperature does not drop below 45°C within 10 minutes, the charging and discharging power is further reduced to 50% of the rated value. After the anomaly is resolved, the liquid pump speed and charging and discharging power are gradually restored at a rate of 10% / minute to avoid parameter sudden changes that cause fluctuations in battery status. At the same time, through dynamic temperature zoning management, the energy storage cabinet is divided into 4 independent cooling zones, each equipped with a dedicated temperature sensor and solenoid valve. When the temperature difference between zones exceeds 2°C, the heat exchange channel is opened to prevent heat diffusion.

[0039] Additional implementation steps: The abnormal condition severity quantification step classifies abnormalities into three levels: mild, moderate, and severe, based on multi-factor weighted calculations. Mild abnormalities only require adjustments to local cooling parameters, moderate abnormalities trigger enhanced cooling, and severe abnormalities trigger emergency protection. The coolant flow dynamic allocation step allocates more cooling resources to modules with higher heat loads based on temperature differences between modules, ensuring temperature uniformity across modules. The thermal management energy consumption optimization step uses variable frequency speed control to regulate the pump speed under mild abnormal conditions, and shuts down some auxiliary cooling fans when the coolant outlet temperature is below 30°C, balancing cooling effect and energy consumption. The battery thermal state prediction step uses a long short-term memory network model, inputting historical temperature data and charge / discharge records to predict temperature changes in the next 15 minutes, adjusting cooling parameters in advance. The cooling system maintenance reminder step monitors the correlation between pump speed and flow rate in real time; when the pump speed reaches 130% of the rated value but still cannot meet the design flow rate, a reminder is issued to check pump wear. The thermal management data storage and analysis step generates daily, weekly, and monthly statistical reports, accumulating operational data for subsequent parameter optimization.

[0040] Table 1: Comparison of Abnormal Operating Condition Handling Effects of Liquid-Cooled Energy Storage Cabinets in Large Photovoltaic Power Plants

[0041] Table 1 clearly demonstrates the significant advantages of the method of this invention in handling abnormal operating conditions of energy storage cabinets in large photovoltaic power plants. Traditional thermal management methods suffer from slow response times, limited strategies, long processing times for both localized and overall overheating, direct shutdowns after cooling system failures, significant drops in cooling efficiency after coolant leaks, and the potential for thermal runaway precursors to escalate into serious accidents. The method of this invention, through precise identification of abnormal operating conditions, dynamic allocation of cooling resources, and execution of differentiated strategies, significantly shortens the temperature reduction time, allows for continued operation even when the cooling system fails, effectively isolates the impact of coolant leaks, and rapidly curbs the precursors of thermal runaway. These advantages stem from the comprehensiveness of multi-dimensional data acquisition, the accuracy of anomaly identification, and the dynamic adaptability of strategy execution, fully ensuring the stable and safe operation of large-scale energy storage systems.

[0042] Example 2: Thermal Management Application of Distributed Commercial Building Liquid-Cooled Energy Storage Cabinet This embodiment applies to a 500kWh distributed liquid-cooled energy storage cabinet in a commercial building, installed in the building's underground equipment layer. It comprises 12 lithium iron phosphate battery modules, each consisting of 24 individual cells. The coolant is a mixture of deionized water and additives, with a rated operating temperature range of -10℃ to 50℃. This energy storage cabinet is primarily used for peak-valley electricity price arbitrage and emergency power supply. Its operating environment is confined with limited ventilation and is susceptible to fluctuations in building air conditioning temperatures. Common abnormal operating conditions include overall overheating, coolant leakage, abnormal sensor data, and precursors to battery thermal runaway. This method is necessary to achieve efficient thermal management and safety protection.

[0043] I. Core Implementation Details Multi-dimensional operational data acquisition: Temperature sensors are attached to the surface of each battery cell, flow sensors are installed at the inlet and outlet of each module, pressure sensors are installed at the inlet and outlet of the cooling circuit and intermediate nodes, the battery pack is equipped with high-precision voltage and current sensors and a SOC detection module, and a liquid level sensor is installed at the bottom of the energy storage cabinet. All sensors collect data at a sampling frequency of 10Hz and transmit it to the local thermal management control unit via Ethernet, while simultaneously uploading it to the building energy management platform. The collected data includes cell temperature, module temperature, coolant flow rate, pressure, battery SOC, charge and discharge current, voltage equalization status, and coolant level. The data storage period is set to 1 year, supporting traceability and query.

[0044] Abnormal Operating Condition Identification and Classification: Based on the characteristics of distributed application scenarios, the abnormal judgment thresholds are optimized. Overall overheating is judged when the average temperature of 12 modules exceeds 48℃; localized overheating is judged when the temperature of a single battery cell exceeds 52℃ for 30 seconds; cooling system failure is judged when the coolant flow rate is less than 60% of the rated value or the pressure fluctuation exceeds ±20%; coolant leakage is judged when the coolant level drops by more than 5% within 1 hour; precursors to battery thermal runaway are judged when the temperature rise rate of a single cell exceeds 2℃ / minute; and sensor data anomalies are judged when a sensor collects data outside the normal range three times consecutively. During the identification process, adjustments are made based on ambient temperature and ventilation conditions to avoid misjudgments caused by environmental factors.

[0045] Adaptive thermal management strategy generation and execution: In case of overall overheating, increase the liquid pump speed to 130%, start 6 auxiliary cooling fans, open the ventilation skylight on the top of the energy storage cabinet, and reduce the charging and discharging power to 60% of the rated value; in case of coolant leakage, close the solenoid valve in the leakage area, send a liquid replenishment reminder to the building energy management platform, start the backup coolant replenishment device for emergency liquid replenishment, and maintain the basic function of the cooling system; when sensor data is abnormal, use adjacent sensor data interpolation to replace it, and mark the abnormal sensor and remind it to calibrate; when there are signs of battery thermal runaway, immediately increase the liquid pump speed to 150%, start all auxiliary cooling equipment, cut off the charging and discharging circuit, start the fire linkage device, and spray special fire extinguishing agent.

[0046] Continuous monitoring and dynamic optimization: After the strategy is executed, the control unit tracks temperature changes in real time. Under overall overheating conditions, if the average temperature does not drop below 45°C within 8 minutes, the charging and discharging power is further reduced to 40% of the rated value. Under coolant leakage conditions, if the coolant level continues to drop after replenishment, the cooling circuits of non-critical areas are gradually shut down to prioritize the cooling of the core power supply module. After the anomaly is resolved, the pump speed and charging and discharging power are restored at a rate of 15% / minute. At the same time, through dynamic temperature zoning management, the energy storage cabinet is divided into 3 independent cooling zones, each with dedicated control logic to improve the targeting of cooling.

[0047] Additional implementation steps: The severity quantification step for abnormal operating conditions matches differentiated strategies to different levels of abnormality: minor abnormalities only adjust cooling parameters, moderate abnormalities trigger enhanced cooling and power limiting, and severe abnormalities trigger emergency protection; the dynamic coolant flow distribution step precisely adjusts branch flow based on the thermal load differences of each module to ensure that the temperature difference between modules does not exceed 2℃; the emergency cooling start-up condition optimization step adjusts the start-up threshold based on ambient temperature and charge / discharge rate: the start-up threshold is reduced by 20% when the ambient temperature is higher than 35℃, and by 15% when the charge / discharge rate is greater than 1C; the battery thermal state prediction step predicts temperature changes in the next 15 minutes based on historical data and environmental parameters, and adjusts cooling parameters in advance; the thermal management energy consumption optimization step uses a dynamic programming algorithm, with variable frequency speed control of the liquid pump speed in the 50%-80% range, and auxiliary cooling fans start and stop in stages according to the coolant outlet temperature; the cooling system maintenance reminder step monitors the frequency of pressure fluctuations and the rate of liquid level drop, and promptly issues reminders for valve inspection, coolant replenishment, and sensor calibration; the thermal management data storage and analysis step identifies high-frequency abnormality types and easily abnormal areas, and continuously optimizes control thresholds and strategy parameters.

[0048] Table 2: Comparison of Abnormal Operating Condition Handling Effects of Distributed Commercial Building Liquid-Cooled Energy Storage Cabinets

[0049] Table 2 data highlights the advantages of the method of this invention in handling abnormal operating conditions of distributed commercial building energy storage cabinets. Traditional thermal management methods have long overall overheating handling times, direct failure and shutdown after coolant leakage, sensor malfunctions leading to strategy disorder, and early signs of thermal runaway easily causing safety accidents, and excessive energy consumption under abnormal operating conditions. The method of this invention significantly shortens the overall overheating handling time by optimizing the judgment threshold and implementing adaptive strategies. In the event of coolant leakage, operation can be maintained through emergency coolant replenishment. In the event of sensor malfunction, data interpolation is used to ensure normal strategy execution, and early signs of thermal runaway are quickly contained. At the same time, energy consumption optimization reduces operating costs. These advantages are fully adapted to the operating environment of distributed commercial buildings with limited space and ventilation, taking into account thermal management effectiveness, operational stability, and energy consumption optimization, providing a strong guarantee for the safe and efficient operation of building energy storage systems.

[0050] refer to Figure 2The line graph visually illustrates the difference in cooling effects between the two thermal management methods under localized overheating conditions. Initially, both methods reached a temperature of 50°C, constituting the localized overheating threshold. The traditional method relies on increasing overall flow rate, resulting in a slow cooling rate; after 2 minutes, the temperature only dropped to 49.2°C, and it took 15 minutes to reach the safe temperature of 45°C. The method of this invention, by accurately identifying the overheating area, dynamically allocating coolant flow, and opening directional heat dissipation channels, significantly improves cooling efficiency, dropping to 47.3°C in 2 minutes and reaching 44.8°C in 5 minutes, meeting safety requirements ahead of schedule, and stabilizing at 43.0°C after 15 minutes. This difference stems from the invention's precise location and differentiated strategy for abnormal operating conditions, avoiding waste of cooling resources, and achieving rapid mitigation of localized heat accumulation, fully demonstrating the targeted and efficient nature of adaptive thermal management.

[0051] refer to Figure 3 This radar chart comprehensively demonstrates the balanced and excellent performance of the method of this invention across core performance dimensions, with no obvious weaknesses. Anomaly identification speed scores 9.3, thanks to multi-dimensional data collaborative analysis and trend models, enabling rapid and accurate determination of abnormal operating conditions; cooling efficiency scores 9.5, stemming from differentiated thermal management strategies and dynamic flow allocation, significantly improving the cooling rate; energy consumption control scores 8.8, through optimization methods such as variable frequency speed regulation and staged start-stop, reducing energy consumption while ensuring cooling effect; operational stability scores 9.2, relying on closed-loop control and smooth parameter adjustment to avoid sudden changes in system state; safety protection capability scores 9.6, with emergency protection mechanisms and fire-fighting linkage design building a solid safety defense; and adaptability scores 9.4, capable of accurately responding to various abnormal operating conditions such as local overheating, leakage, and failure. The high scores across all dimensions demonstrate the systematic advantages of the method of this invention in identification, cooling, energy consumption, stability, safety, and adaptability, providing reliable support for handling abnormal operating conditions in all scenarios for energy storage cabinets.

[0052] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets, characterized in that, Includes the following steps: Multi-dimensional operational data of the liquid-cooled energy storage cabinet is collected. Through temperature, flow, pressure, voltage, current and liquid level sensors, the battery thermal characteristics, liquid circuit parameters and electrical status data are collected in real time. All collected data are synchronously linked by timestamp and transmitted to the thermal management control unit. Abnormal operating conditions are identified and classified. Based on preset thresholds and trend analysis models, abnormal types are identified, including local overheating, overall overheating, heat dissipation system failure, coolant leakage, and precursors to battery thermal runaway. Each abnormal type is defined according to the set temperature, flow rate, pressure, and liquid level judgment standards. Generate an adaptive thermal management strategy and dynamically adjust thermal management parameters based on the type and severity of the anomaly and the current operating status of the energy storage cabinet; Implement thermal management strategies and provide real-time feedback, control the actuator to issue adjustment commands, the liquid pump speed adjustment range is 50%-150% of the rated speed, the electromagnetic valve response time does not exceed 100 milliseconds, the auxiliary cooling fan starts in stages according to the coolant outlet temperature, and the charging and discharging power is adjusted in steps at a rate of 5% / minute. Continuous monitoring and dynamic optimization: The thermal management control unit receives operational data after execution in real time, analyzes temperature change trends, cooling efficiency and battery status response, compares the mitigation effect of abnormal conditions with the preset target, and further strengthens the strategy if the temperature does not drop back to the safe range within the specified time. If the abnormality is resolved, the normal thermal management parameters are gradually restored. When an emergency protection mechanism is activated and extreme anomalies such as battery thermal runaway or complete failure of the cooling system are detected, the connection between the energy storage cabinet and the external power grid and load is immediately cut off, the fire-fighting linkage device is activated, a special fire extinguishing agent is sprayed and the smoke exhaust channel is opened, and an alarm message is sent to the monitoring center.

2. The adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets according to claim 1, characterized in that, It also includes a step to quantify the severity of abnormal operating conditions. Anomaly levels are classified through multi-factor weighted calculations, providing a quantitative basis for the intensity of thermal management strategies. The calculation formula is: ,in This is a quantification value for the severity of abnormal operating conditions. As for the weight of temperature anomalies, This is the difference between the current highest temperature and the safe temperature threshold. For the duration of the abnormality, The baseline duration is set at 300 seconds. For abnormal traffic weighting, This represents the current coolant flow rate. Rated coolant flow rate, As battery state weights, This is the battery SOC correction factor. For abnormal pressure weighting, This is the difference between the current pressure and the rated pressure. This is the rated cooling circuit pressure.

3. The adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets according to claim 1, characterized in that, It also includes a dynamic coolant flow distribution step, which adjusts the flow ratio of each branch based on the temperature distribution and heat load differences of the battery module. The calculation formula is as follows: ,in For the first Coolant distribution flow rate for each battery module This represents the total flow rate of the cooling circuit. For the first Real-time temperature of each battery module The average temperature of all battery modules. This represents the total number of battery modules. For the first The flow rate adjustment coefficient for each module is determined based on the module's heat capacity and heat dissipation area. Provide basic guaranteed traffic for each module.

4. The adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets according to claim 1, characterized in that, It also includes a dynamic temperature zone management step, which divides the inside of the energy storage cabinet into independent cooling zones. Each zone is equipped with a dedicated temperature sensor and solenoid valve. By analyzing the temperature differences between zones, the heat diffusion trend is determined. Cooling resources are allocated to locally overheated areas first, and pre-cooling programs are started in adjacent areas in advance.

5. The adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets according to claim 1, characterized in that, It also includes steps to optimize emergency cooling start-up conditions: the emergency cooling start-up threshold is reduced by 20% when the ambient temperature is above 35°C, the start-up threshold is reduced by 30% when the initial battery temperature is above 40°C, the start-up threshold is reduced by 15% when the charge / discharge rate is greater than 1C, and the start-up threshold is reduced by 40% when the charge / discharge rate is greater than 2C.

6. The adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets according to claim 1, characterized in that, It also includes thermal management energy consumption optimization steps. Under the premise of meeting cooling requirements, the liquid pump speed and fan start / stop status are allocated based on dynamic programming algorithm, and an energy consumption-cooling efficiency mapping model is established. When the liquid pump speed is in the range of 50%-80%, variable frequency speed control is adopted. When it is in the range of 80%-150%, dynamic adjustment is combined with the load rate. The auxiliary cooling fan adopts a graded start / stop strategy: it is turned off when the coolant outlet temperature is below 30℃, runs at low speed when it is between 30℃ and 40℃, runs at medium speed when it is between 40℃ and 50℃, and runs at high speed when it is above 50℃.

7. The adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets according to claim 1, characterized in that, It also includes a multi-mode cooling switching procedure, with four preset modes: normal cooling, enhanced cooling, emergency cooling, and energy-saving cooling. The normal cooling mode is suitable for normal operating conditions, using a combination of basic flow rate and natural heat dissipation. The enhanced cooling mode is suitable for mild abnormal operating conditions, increasing the flow rate and activating auxiliary heat dissipation. The emergency cooling mode is suitable for moderate abnormal operating conditions, switching to the backup circuit and limiting the charging and discharging power. The energy-saving cooling mode is suitable for the recovery period after the abnormality is resolved, gradually reducing the cooling intensity and optimizing energy consumption.

8. The adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets according to claim 1, characterized in that, It also includes a battery thermal state prediction step. Based on a long short-term memory network model, it takes historical temperature data, charge and discharge records, environmental parameters and current operating status as input to predict the battery temperature change trend in the next 15 minutes. If the temperature is predicted to exceed the safe range, a preheating management strategy is activated in advance to adjust the cooling parameters and form a predictive protection.

9. The adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets according to claim 1, characterized in that, It also includes cooling system maintenance reminders. Based on the coolant flow rate decline trend, pressure fluctuation frequency, liquid level drop rate, and sensor detection data, it assesses the health status of cooling system components. When the pump speed is maintained at more than 130% of the rated value but still cannot reach the design flow rate, it reminds you to check the pump wear. When the cooling circuit pressure fluctuation frequency exceeds 5 times / hour, it reminds you to check the valve sealing. When the coolant level drops by more than 10% cumulatively, it reminds you to add coolant. When the number of abnormal sensor data exceeds 3 times / day, it reminds you to calibrate the sensor.

10. The adaptive thermal management method for abnormal operating conditions of liquid-cooled energy storage cabinets according to claim 1, characterized in that, It also includes thermal management data storage and analysis steps, real-time storage of abnormal operating condition data, generation of statistical analysis reports by day, week, and month, optimization of preset thresholds, strategy parameters, and cooling mode switching conditions through big data analysis, data storage duration of no less than 1 year and support for traceability query.

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