Centralized energy storage equipment management system and method

Abstract

The invention relates to the field of energy storage management, and discloses a centralized energy storage equipment management system and method, and the system comprises a cabinet which serves as one site of an overall energy storage station and is used for protecting an energy storage box disposed in the cabinet; the temperature sensing module is used for monitoring the temperature of each battery unit in the energy storage box in real time; the temperature control module is used for adjusting the cooling power according to the temperature of each battery unit and the load condition of the energy storage equipment so as to ensure that the temperature of each battery unit is within a preset safety range; the data processing module is used for adjusting the cooling power according to the monitored real-time data and a preset algorithm; the temperature sensing module comprises a temperature sensor which is used for detecting temperature. Through combination of a centralized temperature control system and a real-time feedback mechanism, accurate management and control of energy storage equipment are realized, so that the temperatures of a plurality of battery units can be managed in a unified manner, the temperature control precision of each unit is ensured, and energy efficiency waste caused by insufficient local temperature control or excessive cooling is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage management, and in particular to a centralized energy storage equipment management system and method. Background Art

[0002] With the continuous development of energy storage technology, especially the increasing application of lithium batteries in large-scale energy storage systems, temperature control management of energy storage systems has become one of the important technologies to ensure battery safety, extend battery life and improve system energy efficiency. At present, traditional temperature control methods mainly rely on liquid cooling, air cooling and phase change material cooling. Liquid cooling systems have become a widely used solution due to their high thermal conductivity efficiency; air cooling systems are widely used in small and medium-sized energy storage systems due to their simple structure and low cost. In addition, phase change material cooling systems have also been used in some special scenarios by utilizing phase change materials to absorb and release heat. Although these methods can maintain the operating temperature of the battery pack to a certain extent, they usually have problems such as unstable cooling effect, slow response speed and low energy efficiency.

[0003] Most of the temperature control systems in the prior art rely on static cooling solutions and cannot be adjusted in real time according to the specific working status of the battery cells and environmental changes. This leads to problems such as insufficient temperature control accuracy and high energy consumption. The traditional distributed control system cannot uniformly and dynamically adjust the temperature of the battery cells, and it is easy for the temperature of some battery cells to be too high or too low, and the ideal temperature control effect cannot be achieved. In addition, in some existing solutions, there is a lack of effective feedback mechanism and adaptive ability, and it is impossible to respond quickly according to changes in battery pack load and temperature. This not only affects the temperature control accuracy, but also leads to a waste of energy efficiency in the cooling system. In terms of safety, most of the existing temperature control systems do not take into account the emergency response when the battery pack catches fire. Traditional cooling systems are often unable to extinguish the fire in time, thereby increasing the risk of battery fire. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a centralized energy storage device management system and method, which solves the problems of insufficient temperature control accuracy, low energy efficiency, slow response speed and lack of effective fire extinguishing mechanism in the existing energy storage device management system.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A centralized energy storage device management system, comprising: The cabinet, as one of the stations in the overall energy storage station, is used to protect the energy storage box installed inside; Temperature sensor module, used to monitor the temperature of each battery cell in the energy storage box in real time; A temperature control module, which is used to adjust the cooling power according to the temperature of each battery cell and the load condition of the energy storage device, so as to ensure that the temperature of the battery cell is within a predetermined safe range; A data processing module, which is used to adjust the cooling power according to the monitored real-time data and a preset algorithm; The temperature sensing module includes a temperature sensor, which is used to detect the temperature; The temperature control module includes a liquid cooling pump, which is arranged on one side inside the cabinet. A straight pipe is connected inside the liquid cooling pump, and a heat dissipation pipe passes through the straight pipe. The heat dissipation pipe is installed at the rear side of the energy storage box, and an air cooling is arranged at the rear side of the heat dissipation pipe; The data processing module includes a PLC module, which is arranged on the other side inside the cabinet; A fire protection component is arranged on the top of the energy storage box.

[0006] Preferably, the fire protection component includes a sprinkler head, which is arranged on the top inside the cabinet. An emergency strip is arranged at the bottom of the sprinkler head. A plurality of emergency strips are attached to the top wall of the energy storage box. Fire-fighting foam is arranged inside the emergency strip, and the fire-fighting foam is wrapped around by a melting layer.

[0007] Preferably, the temperature control module adjusts the cooling power based on a non-linear heat transfer model and an optimal control algorithm, so that the system can keep the battery temperature within the target range while minimizing energy consumption.

[0008] Preferably, the temperature control module further includes: A temperature estimation module, which is used to use a Kalman filter to estimate the temperature of each battery cell in real time, so as to reduce measurement errors and provide more accurate temperature data for the optimal control algorithm.

[0009] Preferably, the temperature control module adjusts the cooling power according to the optimal control algorithm, so that the temperature of the battery cell approaches a predetermined target temperature and minimizes energy consumption.

[0010] Preferably, the data processing module processes data through the following steps: Receiving temperature sensor and load data and performing real-time calculation; Calculating the required cooling power according to the temperature deviation of each battery cell; Based on the optimal control theory, dynamically adjusting the power of the cooling system to ensure operation within a safe temperature range.

[0011] Preferably, the temperature control module derives the optimal cooling power based on the Pontryagin maximum principle, and the optimal cooling power is dynamically adjusted according to the following conditions: Among them, P * (t) is the optimal cooling power, and T i (t) is the temperature of the battery cell, and T target is the target temperature, and α is the adjustment coefficient.

[0012] A method for managing a centralized energy storage device includes the following steps: S1. Monitor the temperature of each battery cell in the energy storage box (2) in real time; S2. Model the thermal dynamics of the battery pack based on a preset non-linear heat transfer model; S3. Calculate the cooling power according to the battery load and temperature deviation; S4. Adjust the cooling power based on an optimal control algorithm to ensure that the temperature of the battery cell remains within the target range; S5. Use a Kalman filter to estimate the temperature of the battery cell in real time and adjust the cooling strategy according to the estimated value.

[0013] Preferably, in the step S4, the optimal control algorithm adjusts the cooling power through the following formula: Among them, J is the objective function, P(t) is the cooling power, and T i (t) is the battery cell temperature, and T target is the target temperature, and α and β are adjustment coefficients.

[0014] Preferably, in the step S3, the calculation includes calculating the required heat according to the charge and discharge state of each battery cell and the heat transfer relationship between the batteries, and further calculating the cooling power; In the step S5, the temperature estimation process includes: Eliminating noise through a Kalman filter according to the real-time monitored temperature data and the system model to provide an accurate estimate of the battery pack temperature; Adjusting the cooling power and temperature control strategy based on the estimated temperature value.

[0015] The present invention provides a centralized energy storage device management system and method. It has the following beneficial effects: 1. Through the combination of a centralized temperature control system and a real-time feedback mechanism, the present invention realizes precise control of the energy storage device. Compared with the decentralized control scheme in the prior art, the present invention can uniformly manage the temperatures of multiple battery cells, ensure the temperature control accuracy of each cell, and avoid energy efficiency waste caused by insufficient local temperature control or excessive cooling.

[0016] 2. The present invention adopts a solution that combines an optimal control algorithm with a non-linear heat transfer model, which can dynamically adjust the cooling power according to the changes in battery load and temperature, thereby maximizing the energy efficiency while ensuring the temperature control accuracy of the battery. Compared with the traditional technology, the present invention effectively reduces the consumption of cooling power, improves the overall energy efficiency of the system, and avoids the energy waste caused by overcooling.

[0017] 3. The present invention combines the Kalman filtering technology with a dynamic temperature control mechanism, which can accurately estimate the temperature of the battery cell and quickly adjust the cooling power. Compared with the single cooling method in the prior art, the present invention can dynamically adjust the cooling power according to real-time data and environmental changes to ensure that the battery cell is always within the safe temperature range, improving the adaptability and response speed of the system.

[0018] 4. Through the close cooperation between the data processing module and the temperature control module, the present invention achieves the technical effects of efficient cooling power regulation and real-time feedback. Compared with the single cooling method in the prior art, while ensuring the temperature control accuracy, the present invention can dynamically adjust according to real-time data to ensure that the battery cell operates within the safe temperature range, avoiding problems such as insufficient cooling or overcooling, and improving the adaptability and response speed of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a perspective view of the present invention; Figure 2 is a sectional view of the cabinet of the present invention; Figure 3 is Figure 1 the enlarged view at position A in Figure 4 is the internal structure distribution diagram of the emergency strip of the present invention; Figure 5 is the schematic diagram of the system module of the present invention; Figure 6 is the flowchart of the method steps of the present invention.

[0020] Among them, 1. Cabinet; 2. Energy storage box; 3. Liquid cooling pump; 4. PLC module; 5. Heat dissipation pipe; 6. Straight pipe; 7. Air cooling; 8. Sprinkler head; 9. Emergency strip; 10. Fire foam; 11. Melting layer; 12. Temperature sensor. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Example 1: Please refer to the appendix Figure 1 - appendix Figure 5 The embodiment of the present invention provides a centralized energy storage device management system, including: Cabinet 1, which serves as one of the sites of the overall energy storage station and is used to protect the energy storage box 2 installed inside; Temperature sensing module, which is used to monitor the temperature of each battery unit in the energy storage box 2 in real time; Temperature control module, which is used to adjust the cooling power according to the temperature of each battery unit and the load condition of the energy storage device to ensure that the temperature of the battery unit is within a predetermined safe range; Data processing module, which is used to adjust the cooling power according to the monitored real-time data and according to a preset algorithm; The temperature sensing module includes a temperature sensor 12, which is used to detect the temperature; The temperature control module includes a liquid cooling pump 3, which is arranged on one side inside the cabinet 1. A straight pipe 6 is connected inside the liquid cooling pump 3. A heat dissipation pipe 5 passes through the straight pipe 6. The heat dissipation pipe 5 is installed at the rear side of the energy storage box 2, and an air cooling 7 is arranged at the rear side of the heat dissipation pipe 5; The data processing module includes a PLC module 4, which is arranged on the other side inside the cabinet 1; A fire protection component is arranged on the top of the energy storage box 2; The fire protection component includes a sprinkler head 8, which is arranged on the top inside the cabinet 1. An emergency strip 9 is arranged at the bottom of the sprinkler head 8. The emergency strip 9 is attached to the top wall of the energy storage box 2 and there are multiple of them. A fire fighting foam 10 is arranged inside the emergency strip 9, and the fire fighting foam 10 is wrapped around by a melting layer 11; The temperature control module adjusts the cooling power based on a non-linear heat transfer model and an optimal control algorithm, so that the system can keep the battery temperature within the target range while minimizing energy consumption; The temperature control module further includes: A temperature estimation module, which is used to use a Kalman filter to estimate the temperature of each battery unit in real time, so as to reduce measurement errors and provide more accurate temperature data for the optimal control algorithm; The temperature control module adjusts the cooling power according to the optimal control algorithm to make the temperature of the battery unit approach the predetermined target temperature and minimize energy consumption; The data processing module processes data through the following steps: Receive temperature sensor and load data and perform real-time calculations; Calculate the required cooling power according to the temperature deviation of each battery unit; Based on the optimal control theory, dynamically adjust the power of the cooling system to ensure operation within a safe temperature range; The temperature control module derives the optimal cooling power based on the Pontryagin maximum principle, and the optimal cooling power is dynamically adjusted according to the following conditions: Among them, P * (t) is the optimal cooling power, T i (t) is the temperature of the battery cell, T target is the target temperature, and α is the adjustment coefficient.

[0023] Specifically, when the temperature of the energy storage box 2 changes, the temperature sensor 12 will monitor it in real time. The monitoring results will be transmitted to the PLC module 4, and the PLC module 4 will make a judgment based on the transmitted data. The PLC module 4 will use the liquid cooling pump 3 and the heat dissipation pipe 5 to dissipate the temperature in the energy storage box 2, or use the air cooling 7 through the heat dissipation pipe 5 to dissipate the temperature in the energy storage box 2, or use a combination of both. In addition, the energy storage box 2 is set in an interval to better control the temperature of different positions of the energy storage box 2 and the power output required for cooling. At the same time, when a fire occurs in the corresponding compartment of the energy storage box 2, the corresponding temperature sensor 12 will transmit the corresponding real-time monitored temperature data to the PLC module 4. At this time, the PLC module 4 can immediately determine the compartment of the energy storage box 2 where the fire occurs, and then control the sprinkler head 8 to spray dry powder to extinguish the fire. And when the energy storage box 2 catches fire for the first time, in order to cope with the response delay of the equipment, the corresponding melting layer 11 will be melted due to the abnormal temperature caused by the fire, so that the fire-fighting foam 10 is no longer wrapped, and then it will fall on the energy storage box 2 in the corresponding compartment to take the first fire-fighting measures. The corresponding melting layer 11 is made of a heat-melting polymer matrix as the material, which includes polyethylene and polypropylene, and their melting points are 110°C - 130°C and 130°C - 160°C respectively, which can ensure sufficient mechanical strength and stability at normal working temperatures, and can quickly melt during a fire to release the internal fire-fighting foam 10. The fire-fighting foam 10 is made of fatty acid esters. When the melting layer 11 melts, the fire-fighting foam begins to flow out quickly, covering the battery surface and the fire source, and quickly suppressing the spread of the fire. The foam forms a barrier on the surface of the fire source through its physical properties, isolating oxygen and at the same time absorbing and taking away heat, thus achieving the fire extinguishing effect.

[0024] As part of the modular integration, in this embodiment, the temperature sensing module is an important part of the centralized energy storage device management system, mainly used to monitor the temperature of each battery cell in the energy storage box in real time. The temperature data will be transmitted to the data processing module in real time, providing accurate temperature information for the temperature control module. These temperature information will be used to calculate the required cooling power and determine the adjustment plan of the cooling system, so as to ensure that the battery pack always operates within a safe temperature range.

[0025] In this embodiment, the main function of the temperature sensing module is to use multiple temperature sensors (such as thermocouples or PT100 sensors, etc.) to collect the temperatures of each battery cell in the energy storage box in real time. According to different application requirements, the temperature sensors can be arranged at different positions of the battery cells to ensure comprehensive monitoring of the temperature changes of the battery pack. The temperature data is transmitted to the data processing module through an appropriate communication protocol (such as MODBUS, CAN protocol, etc.), thereby providing support for subsequent temperature estimation and control strategies.

[0026] Specifically, each temperature sensor monitors the battery cell in real time. When the temperature of the battery cell changes, the temperature sensor will detect this change and generate a corresponding temperature signal. This signal is transmitted to the data processing module in real time for use by the subsequent control system. The temperature signal is amplified and digitized through a signal conversion circuit to ensure that the temperature signal is not interfered with during transmission and can be accurately transmitted to the data processing module. This process uses high-precision analog-to-digital conversion technology to ensure that the system can accurately collect temperature change data, and the data processing module will analyze the received temperature data and calculate the current temperature values of each battery cell. If the temperature of a certain battery cell exceeds the safe range, the data processing module will issue an alarm or adjust the working state of the cooling system to ensure that the battery pack operates within the safe temperature range.

[0027] Generally, the main task of the temperature sensing module is to provide accurate real-time temperature data for the temperature control module. During the operation of the system, the data provided by the temperature sensing module will be used to calculate the temperature deviation and ultimately determine whether the cooling power needs to be adjusted. If the temperature is higher than the predetermined target temperature, the temperature control module will increase the cooling power; if the temperature is lower than the target temperature, the system may reduce the cooling power, thereby achieving precise temperature control.

[0028] In another possible implementation, the temperature sensing module can also transmit the temperature data to the data processing module through wireless communication. This method reduces the complexity of wiring and is particularly suitable for temperature monitoring between multiple energy storage boxes in a large-scale energy storage system. In this case, the temperature sensors transmit the data to the data processing module through a wireless communication module to ensure the efficiency of real-time monitoring and data transmission.

[0029] In this embodiment, the temperature control module adopts an optimal control algorithm, a non-linear heat transfer model, a temperature estimation module, etc. It calculates and adjusts the cooling power in real time to ensure precise control of the battery cell temperature.

[0030] In the temperature control module, first, by receiving real-time data from the temperature sensing module, the system obtains the current temperature information of each battery cell. The data processing module calculates and analyzes these temperature data and transfers the calculation results to the temperature control module. The temperature control module determines the required cooling power based on the temperature of the battery cells and the load condition of the energy storage device through an internal control algorithm.

[0031] Specifically, the temperature control module achieves precise temperature control adjustment through the following two core technologies: Non-linear heat transfer model: This model describes the thermal dynamic behavior of each battery cell in the energy storage device and takes into account the thermal coupling between battery cells and the heat transfer relationship with the environment. Through this model, the system can predict the temperature change trend according to the discharge state, temperature change, and load condition of the battery cells and calculate the corresponding cooling requirements.

[0032] Optimal control algorithm: After obtaining the temperature data and load information of the battery cells, the temperature control module uses the optimal control algorithm to calculate the optimal cooling power. This algorithm aims to minimize the energy consumption of the system and ensure that the temperature of the battery cells is as close as possible to the target temperature. The core goal of the optimal control algorithm is to consider both the temperature control accuracy and energy efficiency optimization, specifically realized by adjusting the cooling power to dynamically maintain the battery temperature within a safe operating range.

[0033] In some embodiments, the optimal control algorithm is adjusted through the following objective function: where J is the objective function, representing the weighted sum of energy consumption and temperature deviation; P(t) is the cooling power, T i (t) is the temperature of battery cell i, T target is the target temperature, α is the adjustment coefficient, N is the number of battery cells, T is the time interval, and dt represents the tiny time step taken within the time interval [0, T]. The result optimized by this formula enables the temperature control system to ensure the temperature control accuracy of the battery pack on the premise of minimizing energy consumption.

[0034] To improve the accuracy and robustness of the temperature control system, the temperature control module also includes a temperature estimation module. This module uses a Kalman filter to estimate the temperature of the battery cells in real time and eliminate the noise in the sensor data. The role of the Kalman filter is to use the system model and real-time temperature measurement data to provide a more accurate temperature estimate value. This estimated value is used as the input of the optimal control algorithm to help the system better adjust the cooling power.

[0035] In some embodiments, the temperature control module realizes temperature estimation through the following state equation: wherein, is the estimated battery temperature state, P(t) is the cooling power, w(t) is the process noise, y(t) is the measured temperature, v(t) is the measurement noise, and A, B, are the state transition matrix and the measurement matrix. Through the combination of real-time temperature data and the Kalman filtering algorithm, the temperature estimation module can effectively improve the accuracy of temperature control.

[0036] In this embodiment, the working process of the temperature control module is as follows: Real-time temperature acquisition and data input: The temperature control module first receives the real-time temperature data of the battery cells sent by the temperature sensing module. These temperature data are transmitted to the data processing module for processing, and the cooling power is calculated according to the current temperature and load state of the battery.

[0037] Temperature estimation and processing: The temperature estimation module uses the Kalman filter to estimate the temperature of each battery cell in real time to eliminate sensor errors and noise. This module provides a more accurate temperature estimate value through filtering of real-time data.

[0038] Cooling power calculation: Through the optimal control algorithm, the temperature control module calculates the optimal cooling power according to the estimated temperature data. The adjustment of the cooling power will change dynamically according to the temperature deviation and load conditions. In the optimal control calculation, the system not only minimizes the consumption of cooling power, but also ensures that the temperature of each battery cell is as close as possible to the predetermined target temperature.

[0039] Cooling system regulation: According to the cooling power calculated by the optimal control algorithm, the temperature control module issues adjustment instructions to the cooling system. The cooling system automatically adjusts the cooling intensity according to these instructions to ensure that the temperature of the battery cells always remains within the target temperature range.

[0040] Feedback and adjustment: The system continuously feeds back the real-time temperature data of the battery cells, and the temperature control module further adjusts the cooling power according to the feedback information. This process is dynamic to ensure that the battery cells always maintain the best temperature state under different loads and environmental conditions; In this embodiment, through the close cooperation with the temperature control module, the data processing module processes the real-time data using the optimal control algorithm. After receiving the data from the temperature sensing module, the data processing module first analyzes it to judge the temperature change trend of the battery cells. Then, according to the relationship between these temperature data and the battery load state, the corresponding cooling power is calculated. The cooling power calculation formula involves the square of the temperature deviation of the battery cells, and the weighted sum of the deviation value and time finally obtains the cooling power. This process enables the system to minimize energy consumption while ensuring that the temperature of the battery cells is within the best working range.

[0041] Specifically, the working process of the data processing module is as follows: Data acquisition and preprocessing: The data processing module receives temperature sensor data from each battery cell. After preprocessing, the sensor data is denoised and filtered to ensure that the errors of the sensors have the least impact on the final results.

[0042] Temperature deviation calculation: By calculating the deviation between the current temperature and the target temperature of the battery cell, the data processing module can determine the amplitude of the cooling power adjustment required. The specific formula is: ΔT i (t) = T i (t) - T target where, T i (t) is the current temperature of the i-th battery cell, T target is the target temperature, and ΔT i (t) is the temperature deviation of battery cell i at time t.

[0043] Cooling power calculation: Based on the temperature deviation and the battery load data, the data processing module obtains the required cooling power through the following calculation formula: P cool (t) = αΔT(t) + βL(t) where, P cool (t) is the cooling power, α and β are control coefficients, ΔT(t) is the temperature deviation, and L(t) is the load data of the energy storage device. This formula fully considers the influence of temperature deviation and load conditions on the cooling power requirement.

[0044] Application of the optimal control algorithm: The data processing module further applies the optimal control theory. Using the cooling power obtained by time integration calculation, while ensuring the stability of the battery cell temperature, it minimizes the energy consumption of the entire system. This process combines the cooling power calculation with the minimization of the objective function to achieve the optimization of system efficiency.

[0045] After calculating the required cooling power, the data processing module will transmit this information to the temperature control module, and the temperature control module adjusts the cooling power based on this information. During the control process, the data processing module will also continue to monitor and correct the adjustment of the cooling power according to the new real-time temperature data, so that the temperature of the battery cell always remains within a safe range.

[0046] Example 2: Please refer to the attached Figure 6 , a centralized energy storage device management method, including the following steps: S1. Real-time monitor the temperature of each battery cell in the energy storage box 2; S2. Model the thermal dynamics of the battery pack based on a preset non-linear heat transfer model; S3. Calculate the cooling power based on the battery load and temperature deviation; S4. Adjust the cooling power based on the optimal control algorithm to ensure that the temperature of the battery cells remains within the target range; S5. Use a Kalman filter to estimate the temperature of the battery cells in real time and adjust the cooling strategy according to the estimated value; In step S4, the optimal control algorithm adjusts the cooling power through the following formula: where J is the objective function, P(t) is the cooling power, T i (t) is the temperature of the battery cells, T target is the target temperature, and α and β are adjustment coefficients; In step S3, the calculation includes calculating the required heat according to the charge and discharge states of each battery cell and the heat transfer relationship between the batteries, and further calculating the cooling power; In step S5, the temperature estimation process includes: According to the real-time monitored temperature data and the system model, eliminate the noise through the Kalman filter to provide an accurate estimate of the battery pack temperature; Adjust the cooling power and temperature control strategy based on the estimated temperature value.

[0047] Specifically, when the system starts, first, the temperature sensing module monitors the temperature of each battery cell in the energy storage box in real time, and the data will be transmitted to the data processing module in a timely manner. The data processing module calculates the required cooling power according to the received temperature data and the current load condition of the energy storage device. Next, the temperature control module adjusts the cooling power according to the calculation result to control the operation of the cooling system to ensure that the temperature of the battery cells is maintained within a safe operating range. If the system detects that the temperature of the battery cells is too high and exceeds the preset safe range, the temperature control module will immediately adjust the cooling power to quickly reduce the temperature of the battery cells.

[0048] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A centralized energy storage equipment management system, characterized in that: include: A cabinet (1), which is one of the stations of the overall energy storage station and is used to protect an energy storage box (2) arranged inside; A temperature sensing module, used for real-time monitoring of the temperature of each battery cell in the energy storage box (2); The temperature control module is used to adjust the cooling power according to the temperature of each battery cell and the load of the energy storage device to ensure that the temperature of the battery cell is within a predetermined safety range; A data processing module, used for adjusting cooling power according to real-time data monitored and a preset algorithm; The temperature sensing module comprises a temperature sensor (12) for detecting temperature; The temperature control module comprises a liquid cooling pump (3), which is arranged on one side of the cabinet (1); a straight pipe (6) is connected to the inside of the liquid cooling pump (3); a heat dissipation pipe (5) is passed through the straight pipe (6); the heat dissipation pipe (5) is installed on the rear side of the energy storage box (2); and an air cooler (7) is arranged on the rear side of the heat dissipation pipe (5); The data processing module comprises a PLC module (4), which is arranged on the other side of the cabinet (1); A fire-fighting component is arranged on the top of the energy storage box (2).

2. A centralized energy storage device management system according to claim 1, characterized in that: The firefighting assembly comprises a sprinkler head (8) which is arranged at the top of the cabinet (1); an emergency strip (9) is arranged at the bottom of the sprinkler head (8); a plurality of emergency strips (9) are attached to the top wall of the energy storage box (2); a firefighting foam (10) is arranged inside the emergency strip (9); and the firefighting foam (10) is wrapped around by a smelting layer (11).

3. A centralized energy storage device management system according to claim 1, characterized in that: The temperature control module adjusts the cooling power based on a nonlinear heat transfer model and an optimal control algorithm, so that the system can keep the battery temperature within a target range while minimizing energy consumption.

4. A centralized energy storage device management system according to claim 1, characterized in that: The temperature control module further comprises: The temperature estimation module is used to estimate the temperature of each battery cell in real time using a Kalman filter, thereby reducing measurement errors and providing more accurate temperature data for the optimal control algorithm.

5. A centralized energy storage device management system according to claim 1, characterized in that: The temperature control module adjusts the cooling power according to the optimal control algorithm to make the temperature of the battery unit close to the predetermined target temperature and minimize energy consumption.

6. A centralized energy storage device management system according to claim 1, characterized in that: The data processing module performs data processing by the following steps: Receive temperature sensor and load data and perform real-time calculations; Calculate the required cooling power based on the temperature deviation of each battery cell; Based on optimal control theory, the cooling system power is dynamically adjusted to ensure operation within a safe temperature range.

7. A centralized energy storage device management system according to claim 1, characterized in that: The temperature control module derives the optimal cooling power based on the Pontryagin maximum principle, and the optimal cooling power is dynamically adjusted according to the following conditions: Among them, P * (t) is the optimal cooling power, T i (t) is the temperature of the battery cell, T target is the target temperature, and α is the adjustment coefficient.

8. A centralized energy storage device management method, according to a centralized energy storage device management system according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1, real-time monitoring of the temperature of each battery cell in the energy storage box (2); S2. Modeling the thermal dynamics of the battery pack based on a preset nonlinear heat transfer model; S3, calculating cooling power according to battery load and temperature deviation; S4, adjusting the cooling power based on the optimal control algorithm to ensure that the temperature of the battery cell remains within the target range; S5. Use a Kalman filter to estimate the temperature of the battery cell in real time and adjust the cooling strategy based on the estimated value.

9. A centralized energy storage device management method according to claim 8, characterized in that: In step S4, the optimal control algorithm adjusts the cooling power by the following formula: Where J is the objective function, P(t) is the cooling power, T i (t) is the battery cell temperature, T target is the target temperature, α and β are the adjustment coefficients.

10. A centralized energy storage device management method according to claim 8, characterized in that: In the step S3, the calculation includes calculating the required heat according to the charge and discharge state of each battery cell and the heat transfer relationship between batteries, and further calculating the cooling power; In step S5, the temperature estimation process includes: Based on real-time monitored temperature data and system models, a Kalman filter eliminates noise and provides an accurate estimate of battery pack temperature. Adjust cooling power and temperature control strategy based on the estimated temperature value.

Images