Intelligent control method and device for liquid-cooled energy storage system

Abstract

The application discloses a liquid-cooled energy storage system intelligent control method and device, and the method comprises the following steps: S1, acquiring system parameter information, collecting system real-time operation data, and calculating system actual demand load; S2, calculating an actuator operation demand parameter interval according to the system actual demand load; S3, acquiring actual working condition data, performing fuzzy processing on the actuator operation parameter optimal solution according to the actuator operation demand parameter interval and the actual working condition data, and controlling the actuator operation according to the actuator operation parameter optimal solution. The application has the beneficial effects of efficient fuzzy control, reduced optimal solution calculation amount by first calculating the actuator operation demand parameter interval, and avoided slow control response problem caused by a high time delay system; energy saving optimization, avoided traditional PID control system oscillation problem; and control optimization by using a genetic algorithm, and improved efficiency.

Description

Technical Field

[0001] This application relates to the field of liquid-cooled intelligent control, and in particular to intelligent control methods and devices for liquid-cooled energy storage systems. Background Technology

[0002] The cooling (heating) capacity of an energy storage system is designed to meet the maximum cooling and heating demand of the battery temperature control. However, in actual applications, the cooling and heating load consumed varies with environmental parameters and generally differs significantly from the maximum designed cooling and heating capacity. The system operates under non-full-load rated conditions for most of the time. Traditional energy storage cooling systems typically regulate water and airflow by adjusting valve or damper openings. This method not only consumes a large amount of energy but also struggles to achieve ideal regulation, resulting in fluctuating battery operating temperatures. Even existing energy storage cooling systems that use frequency converters to regulate pump or fan speeds to adjust water and airflow suffer from limitations due to the responsiveness of the compressor system load, the single operating mode of the control system, the simple control algorithm, and the inability to update control strategies in real time. This also fails to maximize energy savings and contributes to varying degrees of poor system controllability.

[0003] Chinese patent "A Method for Optimizing Energy Consumption of a Liquid Cooling System in a Battery Energy Storage System", publication number CN115084707A, publication date: September 20, 2022, specifically discloses a method including: acquiring liquid cooling cycle data and battery data in the battery energy storage system; comparing the cell temperature with the maximum and minimum allowable cell temperatures respectively; if the cell temperature is greater than or equal to the maximum allowable cell temperature, then the compressor and fan are run at full load; if the cell temperature is less than or equal to the minimum allowable cell temperature, then the compressor and fan are stopped and the electric heater is turned on; if the cell temperature is between the two, the battery status is obtained according to the charge / discharge rate, and the operating load of the compressor and fan is adjusted according to the battery status so that the liquid cooling cycle data and the battery data reach preset liquid cooling cycle temperature thresholds and battery temperature thresholds respectively. By setting the control logic between the compressor, fan, and external input parameters of the liquid cooling system, the equipment control requirements under different operating conditions are met. However, the impact of the actual working efficiency of the compressor and fan on the cooling or heating capacity is not considered, and the energy-saving effect is not ideal under long-term use.

[0004] Chinese patent "Liquid Cooling Control System, Method and Apparatus, Computer Equipment and Storage Medium", publication number: CN115133172A, publication date: September 30, 2022, specifically discloses a method including: determining the target flow rate of the main outlet pipeline of a first circulation system based on a preset correspondence between the load quantity and flow rate of the devices to be temperature controlled, where the target load quantity is the number of devices requiring temperature control; and controlling the first circulation system to adjust the flow rate to match the target flow rate. However, temperature control based on the relationship between the target load quantity and flow rate is prone to inaccuracies over long-term use due to equipment wear and tear. This not only affects the operation of the liquid cooling energy storage system but also easily leads to energy waste. Summary of the Invention

[0005] This application addresses the problems in existing technologies regarding temperature control of liquid-cooled energy storage systems, such as complex control methods and the inability to adjust parameters based on the actual operating status of the temperature control equipment, which leads to operational issues and energy waste. It provides an intelligent control method for liquid-cooled energy storage systems. First, it calculates the required parameter range for the actuator based on the actual load demand of the system, reducing the amount of data required for optimization calculations and improving computational efficiency. Then, it calculates the optimal solution for the actuator's operating parameters based on the actual operating data of the actuator. This yields the operating parameters that enable the actuator to achieve maximum energy efficiency under actual operating conditions, avoiding discrepancies between the actual output load and the calculated value due to operating losses. This ensures the normal operation of the liquid-cooled energy storage system while maximizing energy conservation.

[0006] To achieve the above technical objectives, this application provides a technical solution: an intelligent control method for a liquid-cooled energy storage system, comprising the following steps: S1: acquiring system parameter information, collecting real-time system operating data, and calculating the actual system load demand; S2: calculating the actuator operating parameter range based on the actual system load demand; S3: acquiring actual operating condition data, performing fuzzy processing on the actuator operating parameter range and actual operating condition data to calculate the optimal solution for actuator operating parameters, and controlling the actuator operation based on the optimal solution for actuator operating parameters.

[0007] Furthermore, the system parameter information includes at least the operating parameters of the actuators and the operating parameters of the energy storage mechanism.

[0008] Furthermore, the real-time operating data of the acquisition system includes: the supply and return water temperature, pressure, flow rate, and heating / cooling heat of the cooling water in the liquid-cooled energy storage system, as well as environmental parameters under the operating conditions of the liquid-cooled energy storage system.

[0009] Furthermore, the system parameter information also includes the energy consumption information of the actuators.

[0010] Furthermore, the intelligent control method for liquid-cooled energy storage systems also includes: S0: Selecting the control mode; if it is an automatic control mode, executing S1 to S3; if it is an autonomous control mode, executing S4; S4: Prompting for input of control parameters, collecting real-time system operating data, recording the control parameters and real-time system operating data, and controlling the operation of the actuators according to the control parameters.

[0011] Furthermore, the intelligent control method for the liquid-cooled energy storage system also includes: S51: Determine whether it is autonomous control. If yes, execute S52; if no, execute S4; S52: Calculate the actual operating condition data and the actual load demand of the system. Based on the actual operating condition data, determine whether the current operating parameters of the actuator meet the actual load demand of the system. If yes, control the operation of the actuator based on the current operating parameters of the actuator; if no, execute S2 to S3.

[0012] Furthermore, S61: Set a time threshold. When the system running time reaches the time threshold, execute S52.

[0013] Furthermore, S62: Set a temperature change threshold. When the ambient temperature change exceeds the temperature change threshold, execute S52.

[0014] Furthermore, feedback data on the optimal operation of the actuator based on the operating parameters is obtained, and a historical database is established based on historical operating parameters and feedback data.

[0015] Another technical solution provided in this application is an intelligent control device for a liquid-cooled energy storage system, used to control the operation of the actuators of the liquid-cooled energy storage system according to the intelligent control method for the liquid-cooled energy storage system described above. The device includes a data acquisition component for acquiring real-time system operation data; a fuzzy control component for receiving the real-time system operation data acquired by the data acquisition component, calculating the optimal solution for the operating parameters of the actuators, and controlling the operation of the actuators; and a host computer for receiving the real-time system operation data acquired by the data acquisition component and sending control commands to the fuzzy control component.

[0016] The beneficial effects of this application are: 1. High-efficiency fuzzy control reduces the computational load of optimal solution calculation by first calculating the range of parameters required for the operation of the actuator, thus avoiding the problem of slow control response caused by high time delay systems; 2. Optimized energy saving, avoiding the oscillation problem of traditional PID control systems; 3. Use genetic algorithms for control optimization to improve efficiency. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating one embodiment of the intelligent control method for the liquid-cooled energy storage system of this application.

[0018] Figure 2 This is a flowchart illustrating another embodiment of the intelligent control method for the liquid-cooled energy storage system of this application.

[0019] Figure 3 This is a schematic diagram of the fuzzy processing algorithm for the intelligent control method of the liquid-cooled energy storage system in this application.

[0020] Figure 4 This is a schematic diagram of the structure of one embodiment of the intelligent control device for the liquid-cooled energy storage system of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely one preferred embodiment of this application and are only used to explain this application. They do not limit the scope of protection of this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] like Figure 1 As shown, this application provides an intelligent control method for a liquid-cooled energy storage system, including the following steps: S1: Obtain system parameter information, collect real-time system operating data, and calculate the actual system load demand. S2: Calculate the range of operating parameters required by the actuators based on the actual load demand of the system; S3: Obtain actual operating condition data, perform fuzzy processing based on the operating parameter range of the actuator and the actual operating condition data to calculate the optimal solution of the actuator operating parameters, and control the operation of the actuator based on the optimal solution of the actuator operating parameters.

[0023] The acquisition of system parameter information includes: acquiring the operating parameters of the liquid-cooled energy storage system, which at least include the operating parameters of the actuators and the operating parameters of the energy storage mechanism. The operating parameters of the energy storage mechanism are the temperature parameters that ensure its normal operation.

[0024] Optionally, in a liquid-cooled energy storage system, the cooling or heating capacity of the liquid-cooled energy storage system is controlled by the parameter changes of the chilled water pump, pipe heater, regulating valve, and compressor. Thus, the operating parameters of the actuators are the operating parameters of the chilled water pump, pipe heater, regulating valve, and compressor.

[0025] Furthermore, the real-time operating data of the acquisition system includes: the supply and return water temperature, pressure, flow rate, and heating / cooling capacity of the liquid-cooled energy storage system.

[0026] The real-time operating data of the acquisition system also includes the acquisition of environmental parameters under the operating status of the liquid-cooled energy storage system, including at least the ambient temperature.

[0027] Optional environmental parameters also include ambient humidity.

[0028] As another embodiment of this application, the environmental parameters include internal and external environmental parameters, including the external environmental parameters of the liquid-cooled energy storage system and the energy storage mechanism environmental parameters of the liquid-cooled energy storage system.

[0029] Therefore, the actual system load demand is calculated based on real-time system operating data and system parameter information. Specifically, the required cooling or heating capacity is obtained based on the temperature and environmental parameters of the energy storage unit during normal operation, and this required cooling or heating capacity is taken as the actual system load demand. Then, the operating parameter range of the actuator is calculated based on the actuator's operating parameters and the actual system load demand. The operating condition data of the actuator under actual operating conditions is obtained, and fuzzy optimization processing is performed to calculate the optimal solution for the actuator's operating parameters. The actuator's operation is then controlled based on this optimal solution. By first calculating the actuator's operating parameter range that can maintain the normal operation of the energy storage unit, this range is used as a fuzzy constraint for the fuzzy optimization calculation. Considering that the actuator operates at a non-full load rated state for most of its normal operation, the actual operating load capacity of the actuator is obtained by acquiring its operating condition data under actual operating conditions. Combined with the fuzzy constraints, the optimal solution for the actuator's operating parameters is then calculated.

[0030] Optionally, actual operating data can be calculated based on real-time system operating data and system parameter information. That is, the output characteristics of each operating mechanism are calculated based on the operating parameters of the actuators. Thus, based on the actual cooling water supply and return water temperature, pressure, flow rate, and heating / cooling capacity, as well as the output characteristics of the operating mechanisms, the actual operating data of each operating mechanism can be calculated.

[0031] In another embodiment of this application, the system parameter information also includes the energy consumption information of the actuators. The energy conversion efficiency of each actuator is obtained through the energy consumption information. Fuzzy optimization calculations are then performed based on the actuator's required parameter range, actual operating data, and energy consumption information to obtain the optimal solution for the actuator's operating parameters that meet the normal operating temperature parameters of the energy storage device while minimizing energy consumption.

[0032] like Figure 2 As shown, considering the need for autonomous control in practical applications, the intelligent control method for liquid-cooled energy storage systems also includes: S0: Select the control mode. If it is automatic control mode, execute S1 to S3. If it is autonomous control mode, execute S4.

[0033] S4: Prompts for input of control parameters, collects real-time system operating data, records control parameters and real-time system operating data, and controls the operation of the actuator according to the control parameters.

[0034] As a further improvement of this application, when the liquid-cooled energy storage system switches from autonomous control to automatic control mode during operation, the operating parameters of the actuators need to be updated in a timely manner. The intelligent control method for the liquid-cooled energy storage system also includes: S51: Determine whether it is autonomous control. If yes, execute S52; otherwise, execute S4. S52: Calculate the actual operating data and the actual load demand of the system. Based on the actual operating data, determine whether the current operating parameters of the actuator meet the actual load demand of the system. If yes, control the operation of the actuator according to the current operating parameters of the actuator; otherwise, execute S2 to S3.

[0035] This ensures that the optimal operating parameters can be automatically matched when switching from autonomous control mode to automatic control mode.

[0036] As a further improved embodiment of this application, during the actual operation of the liquid-cooled energy storage system, due to changes in ambient temperature and the operating time of the actuators, it is necessary to update the operating parameters of the actuators in real time. Therefore, the intelligent control method for the liquid-cooled energy storage system further includes: S61: Set a time threshold. When the system running time reaches the time threshold, execute S52.

[0037] Optionally, obtain historical data on the correlation between the operating time and operating efficiency of the actuator, establish a linear regression analysis, obtain the relationship curve between the changes in the operating time and the changes in the operating efficiency of the actuator, and then obtain the time threshold based on the relationship curve between the changes in the operating time and the changes in the operating efficiency of the actuator.

[0038] Alternatively, the time when the operating efficiency decreases the most can be selected as the time threshold.

[0039] Since there are multiple actuators, the relationship curves between the changes in operating time and operating efficiency of each actuator may not be the same, and there are multiple time thresholds. Therefore, the median value of multiple time thresholds can be selected as the final time threshold.

[0040] Optionally, the relationship curves between the changes in running time and the changes in running efficiency of multiple actuators can be aggregated, and the time with the greatest decrease in overall running efficiency can be used as the time threshold.

[0041] In another feasible embodiment, the intelligent control method for liquid-cooled energy storage systems further includes: S62: Set a temperature change threshold. When the ambient temperature changes beyond the temperature change threshold, execute S52.

[0042] To ensure that when the operating efficiency of the actuator decreases over time or the ambient temperature changes too much and the operating parameters of the actuator cannot meet the temperature control requirements, the system can use actual operating data and the actual load demand of the system to calculate whether the current operating parameters of the actuator meet the actual load demand of the system. If not, the system will recalculate the optimal solution of the operating parameters of the actuator and control the operation of the actuator according to the optimal solution of the operating parameters of the actuator.

[0043] Optionally, a temperature-load change curve can be established based on historical ambient temperature data and actual system load demand data, and the temperature difference with the largest slope can be used as the temperature change threshold.

[0044] like Figure 3 As shown, the fuzzy optimization process employs genetic computation for optimization. It takes the actual system load, the required parameter range of the actuators, and the required accuracy as inputs, and sets the population size, crossover rate, mutation rate, γ, and maximum generations. The operating parameters are encoded to obtain population information, which is then decoded. Fitness is calculated, and it is determined whether the required generations have been reached. If not, population evolution is performed to obtain a new population, which is then decoded. If so, the operating parameters of the actuator corresponding to the current best individual are output, which is the optimal solution for the actuator operating parameters. Optimal solutions for the operating parameters of multiple actuators are obtained through population genetic computation.

[0045] In another feasible embodiment, feedback data of the actuator operating according to the optimal solution of operating parameters is obtained, and a historical database is established based on historical operating parameters and feedback data. After the actuator operates according to the optimal solution of operating parameters, it outputs feedback data, and the historical database is used to determine whether the feedback data matches the operating parameters. If they do not match, the feedback data is identified as erroneous, and the optimal solution of operating parameters for the actuator is recalculated.

[0046] Specifically, when the actuators are operating normally, the fluctuations in the feedback data output based on the optimal solution of the operating parameters are small and can usually be ignored. That is, at this time, the relationship between the operating parameters of each actuator and the feedback data can satisfy a unique correspondence, that is, each actuator's operating parameter uniquely corresponds to one set of feedback data. When the operating parameters and feedback data do not match, the operating state of that actuator is erroneous. At this time, by judging the erroneous feedback data based on the historical database, the erroneous actuator can be identified, and its working state can be adjusted to be turned off. The optimal solution of the operating parameters of the remaining actuators is calculated based on the actual load demand of the system, thereby adjusting the operating strategies of the actuators to meet the normal working environment of the energy storage system and preventing the erroneous operation of one actuator from affecting the normal operation of the entire liquid-cooled energy storage system.

[0047] Optionally, the feedback data includes the temperature, pressure, flow rate, and switching signals of the liquid-cooled energy storage system.

[0048] Optionally, an intelligent control library is formed based on historical operating parameters, system parameter information, and system operating data, and an intelligent control model is constructed. The system automatically outputs control strategies based on the current system operating data and system parameter information, and controls the operation of the actuators according to the control strategies.

[0049] Optionally, the actuator can be controlled to operate according to the control strategy, and feedback data can be collected. The control strategy can then be adjusted based on the feedback data and system parameter information.

[0050] like Figure 4 As shown, as another embodiment of this application, this application also provides an intelligent control device for a liquid-cooled energy storage system, used to control the operation of the actuators of the liquid-cooled energy storage system, including: Data acquisition component, used to collect real-time operating data of the system; The fuzzy control component is used to receive real-time system operation data collected by the data acquisition component, calculate the optimal solution of the operating parameters of the operating mechanism, and control the operation of the actuator. The host computer is used to receive real-time system operation data collected by the data acquisition component and send control commands to the fuzzy control component.

[0051] Specifically, the data acquisition components include temperature sensors, humidity sensors, pressure sensors, and flow meters. At least one combination of temperature sensors and humidity sensors is connected to the energy storage mechanism of the liquid-cooled energy storage system to receive the ambient temperature and humidity of the liquid-cooled energy storage system.

[0052] Optionally, the fuzzy control component includes a process parameter acquisition and filtering module, a coordinated analytical control module, an optimization algorithm module, a logic signal output module, a regulation signal output module, and a feedback signal acquisition and processing module. The process parameter acquisition and filtering module is connected to the data acquisition component to process the real-time system operation data acquired by the data acquisition component and send it to the coordinated analytical control module and the optimization algorithm module. The optimization algorithm module is connected to the logic signal output module and the regulation signal output module to receive the real-time system operation data, perform fuzzy optimization processing, and send control commands to the actuators of the liquid-cooled energy storage system through the logic signal output module and the regulation signal output module. The feedback signal acquisition and processing module is connected to the actuators of the liquid-cooled energy storage system to acquire the system's temperature, pressure, flow rate, and switching signals in real time and send them to the coordinated analytical control module. The coordinated analytical control module is connected to the process parameter acquisition and filtering module, the optimization algorithm module, the feedback signal acquisition and processing module, and the host computer to receive data and control commands, and send working instructions to the optimization algorithm module according to the control commands and forward the received data acquisition information.

[0053] In another feasible embodiment, the intelligent control device for the liquid-cooled energy storage system further includes a touch screen and a remote control computer. The touch screen is connected to the host computer to enable human-machine interaction control; the remote control computer is connected to the fuzzy control component to enable remote control of the actuator operation.

[0054] When the ambient temperature and the system's terminal load change, the supply and return water temperatures, temperature differences, and flow rates of each chilled and warm water source also change accordingly. Flow meters, differential pressure sensors, and temperature sensors send these detected parameters to the fuzzy control component. Based on the collected real-time data and historical operating data, the fuzzy control component calculates in real-time the cooling capacity required by the liquid-cooled energy storage system load, as well as the optimal values ​​for the supply and return water temperatures, temperature differences, pressure differences, and flow rates of each chilled and warm water source. It then adjusts the output frequency of each actuator, controls the speed of the chilled water pump, and changes its flow rate to ensure that the supply and return water temperatures, temperature differences, pressure differences, and flow rates of the liquid-cooled energy storage system operate at the optimal solution given by the fuzzy control component. This optimizes system energy consumption, simplifies operation, and adjusts the operating efficiency of each actuator in the system, achieving the goals of high efficiency, energy saving, and intelligent control.

[0055] The specific embodiments described above are preferred embodiments of the intelligent control method and device for liquid-cooled energy storage system of this application, and are not intended to limit the specific implementation scope of this application. The scope of this application includes but is not limited to the specific embodiments described above. All equivalent changes made in accordance with the shape and structure of this application are within the protection scope of this application.

Claims

1. An intelligent control method for a liquid-cooled energy storage system, characterized in that: Includes the following steps: S1: Obtain system parameter information, collect real-time system operating data, and calculate the actual system load demand. S2: Calculate the range of operating parameters required by the actuators based on the actual load demand of the system; S3: Obtain actual operating condition data, perform fuzzy processing based on the operating requirement parameter range of the actuator and the actual operating condition data to calculate the optimal solution of the actuator operating parameters, and control the operation of the actuator based on the optimal solution of the actuator operating parameters; Also includes: S0: Select the control mode. If it is automatic control mode, execute S1 to S3. If it is autonomous control mode, execute S4. S4: Prompts for input of control parameters, collects real-time system operating data, records control parameters and real-time system operating data, and controls the operation of the actuator according to the control parameters; S51: Determine whether it is autonomous control. If yes, execute S52; otherwise, execute S4. S52: Calculate the actual operating condition data and the actual load demand of the system. Based on the actual operating condition data, determine whether the current operating parameters of the actuator meet the actual load demand of the system. If yes, control the operation of the actuator according to the current operating parameters of the actuator; otherwise, execute S2 to S3. S61: Set a time threshold. When the system running time reaches the time threshold, execute S52. S62: Set a temperature change threshold. When the ambient temperature changes beyond the temperature change threshold, execute S52. Among them, the correlation data between the historical operating time and operating efficiency of the execution mechanism is obtained, a linear regression analysis is established, the relationship curve between the change in the operating time and the change in the operating efficiency of the execution mechanism is obtained, and the time when the operating efficiency decreases the most in the relationship curve between the change in the operating time and the change in the operating efficiency of the execution mechanism is used as the time threshold. A temperature-load change curve is established based on historical ambient temperature data and actual system load demand data, and the temperature difference with the largest slope is used as the temperature change threshold.

2. The intelligent control method for a liquid-cooled energy storage system as described in claim 1, characterized in that: The system parameter information includes at least the operating parameters of the actuators and the operating parameters of the energy storage mechanism.

3. The intelligent control method for a liquid-cooled energy storage system as described in claim 1, characterized in that: The real-time operating data collected by the system includes: the supply and return water temperature, pressure, flow rate, and heating / cooling heat of the cooling water in the liquid-cooled energy storage system, as well as environmental parameters under the operating conditions of the liquid-cooled energy storage system.

4. The intelligent control method for a liquid-cooled energy storage system as described in claim 2, characterized in that: The system parameter information also includes the energy consumption information of the actuators.

5. The intelligent control method for a liquid-cooled energy storage system as described in claim 1, characterized in that: Obtain feedback data from the actuator based on the optimal solution of the operating parameters, and establish a historical database based on historical operating parameters and feedback data.

6. An intelligent control device for a liquid-cooled energy storage system, used to control the operation of the actuator of the liquid-cooled energy storage system according to any one of claims 1 to 5, characterized in that: Data acquisition components are used to collect real-time operational data from the system. The fuzzy control component is used to receive real-time system operation data collected by the data acquisition component, calculate the optimal solution of the actuator operating parameters, and control the operation of the actuator. The host computer is used to receive real-time system operation data collected by the data acquisition component and send control commands to the fuzzy control component.

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